From 195500025ebad6f65114ed03118b1c291ce6551d Mon Sep 17 00:00:00 2001 From: Simon Gravelle Date: Sun, 29 Dec 2024 16:22:36 +0100 Subject: [PATCH 1/2] deal with warning in tutorial 4 --- files/tutorial4/parameters.inc | 4 +- files/tutorial4/solution/minimize.data | 7398 ++++++++++++------------ files/tutorial4/solution/minimize.lmp | 15 +- 3 files changed, 3707 insertions(+), 3710 deletions(-) diff --git a/files/tutorial4/parameters.inc b/files/tutorial4/parameters.inc index f81b0d6..b0c8d5e 100644 --- a/files/tutorial4/parameters.inc +++ b/files/tutorial4/parameters.inc @@ -16,5 +16,5 @@ pair_coeff Cl- Cl- 0.1500 4.04470 pair_coeff WALL WALL 11.697 2.574 pair_coeff O WALL 0.4 2.86645 -bond_coeff O-H 0 0.9572 -angle_coeff H-O-H 0 104.52 +bond_coeff O-H 5000 0.9572 +angle_coeff H-O-H 400 104.52 diff --git a/files/tutorial4/solution/minimize.data b/files/tutorial4/solution/minimize.data index 36fb0f3..4bbf91f 100644 --- a/files/tutorial4/solution/minimize.data +++ b/files/tutorial4/solution/minimize.data @@ -253,1254 +253,1254 @@ Atoms # full 214 0 5 0 6.0600000000000005 10.1 -12.120000000000001 0 0 0 215 0 5 0 8.08 8.08 -12.120000000000001 0 0 0 216 0 5 0 10.1 10.1 -12.120000000000001 0 0 0 -583 51 1 -1.1128 -7.263465256717666 -7.712974785261128 -8.599265966002143 0 0 0 -584 51 2 0.5564 -7.255766504286833 -8.209700376403491 -7.781075113120325 0 0 0 -585 51 2 0.5564 -8.051280678785472 -7.172094674856585 -8.544244036583827 0 0 0 -1396 322 1 -1.1128 -7.6606694971901605 -9.258836768605386 -6.219896693738146 0 0 0 -1397 322 2 0.5564 -7.736919833141835 -10.1183139092019 -6.634283961885137 0 0 0 -1398 322 2 0.5564 -7.5633806862033035 -9.449964485370304 -5.287031776261537 0 0 0 -644 71 2 0.5564 -7.758680716347811 -4.399033695965297 -7.491874282404706 1 0 0 -643 71 1 -1.1128 -8.155581733514907 -4.9189803198080675 -6.793048686456252 1 0 0 -650 73 2 0.5564 -9.94238570394878 -6.786919238562363 -4.552407346846183 0 0 0 -649 73 1 -1.1128 -10.806973868307313 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1 2330 2329 2331 +396 1 2237 2236 2238 +397 1 1907 1906 1908 +398 1 2102 2101 2103 +399 1 2039 2038 2040 +400 1 1595 1594 1596 +401 1 2336 2335 2337 +402 1 2270 2269 2271 +403 1 2060 2059 2061 +404 1 1655 1654 1656 +405 1 2150 2149 2151 +406 1 2087 2086 2088 diff --git a/files/tutorial4/solution/minimize.lmp b/files/tutorial4/solution/minimize.lmp index 7e10f12..ca39cdb 100644 --- a/files/tutorial4/solution/minimize.lmp +++ b/files/tutorial4/solution/minimize.lmp @@ -17,10 +17,9 @@ read_data create.data include parameters.inc include groups.inc -fix mynve fluid nve/limit 0.1 +fix nvelim fluid nve/limit 0.1 fix myber fluid temp/berendsen 1 1 100 -fix myshk H2O shake 1.0e-5 200 0 b O-H a H-O-H -timestep 0.5 +timestep 0.25 dump mydmp all image 200 myimage-*.ppm type type & shiny 0.1 box no 0.01 view 90 0 zoom 1.8 @@ -35,16 +34,14 @@ compute Tfluid fluid temp thermo_modify temp Tfluid thermo 200 -run 4000 +run 8000 +unfix nvelim +fix mynve fluid nve +fix myshk H2O shake 1.0e-5 200 0 b O-H a H-O-H fix myber fluid temp/berendsen 300 300 100 timestep 1.0 run 4000 -unfix mynve -fix mynve fluid nve - -run 4000 - write_data minimize.data nocoeff From c43f6c7c7fb682c5cde789f125140c139dab65a2 Mon Sep 17 00:00:00 2001 From: Simon Gravelle Date: Mon, 30 Dec 2024 11:20:22 +0100 Subject: [PATCH 2/2] simplified minimization tutorial 4 --- figures/NANOSHEAR-equilibration.png | Bin 163280 -> 151094 bytes files/tutorial4/equilibrate.lmp | 2 +- files/tutorial4/minimize.lmp | 18 - files/tutorial4/parameters.inc | 4 +- files/tutorial4/solution/create.log | 13 +- files/tutorial4/solution/equilibrate.data | 9138 ++++++++++---------- files/tutorial4/solution/equilibrate.ipynb | 46 +- files/tutorial4/solution/equilibrate.lmp | 24 +- files/tutorial4/solution/equilibrate.log | 354 +- files/tutorial4/solution/minimize.data | 4626 ---------- files/tutorial4/solution/minimize.ipynb | 161 - files/tutorial4/solution/minimize.lmp | 47 - files/tutorial4/solution/minimize.log | 290 - 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" ] }, "metadata": {}, @@ -103,7 +94,7 @@ "\n", "# Initialise figure\n", "myplt = PltTools()\n", - "myplt.prepare_figure(fig_size = (12,5.6), dark_mode = False,\n", + "myplt.prepare_figure(fig_size = (12,5.8), dark_mode = False,\n", " transparency = False, use_serif=False, n_colone=2)\n", "\n", "# Panel a\n", @@ -118,7 +109,7 @@ " xlabel = r'$t~\\mathrm{(ps)}$',\n", " xpad = 10, legend=True, handlelength_legend=1)\n", "myplt.set_boundaries(x_ticks=np.arange(0, 31, 10), y_ticks=np.arange(-20, -10, 2),\n", - " x_boundaries=(-1, 31), y_boundaries=(-20, -12))\n", + " x_boundaries=(-5, 35), y_boundaries=(-20, -12))\n", "\n", "# Panel b\n", "myplt.add_panel()\n", @@ -132,7 +123,7 @@ " xlabel = r'$t~\\mathrm{(ps)}$',\n", " xpad = 10, legend=True, handlelength_legend=1)\n", "myplt.set_boundaries(x_ticks=np.arange(0, 31, 10), y_ticks=np.arange(2.6, 3.3, 0.2),\n", - " x_boundaries=(-1, 31), y_boundaries=(2.5, 3.3))\n", + " x_boundaries=(-5, 35), y_boundaries=(2.5, 3.3))\n", "\n", "# Print figure\n", "myplt.add_subplotlabels()\n", @@ -142,7 +133,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3.10.6 64-bit", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -157,11 +148,6 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.3" - }, - "vscode": { - "interpreter": { - "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1" - } } }, "nbformat": 4, diff --git a/files/tutorial4/solution/equilibrate.lmp b/files/tutorial4/solution/equilibrate.lmp index 7f70c91..6653e2a 100644 --- a/files/tutorial4/solution/equilibrate.lmp +++ b/files/tutorial4/solution/equilibrate.lmp @@ -12,16 +12,34 @@ pair_style lj/cut/tip4p/long O H O-H H-O-H 0.1546 12.0 kspace_style pppm/tip4p 1.0e-5 kspace_modify slab 3.0 -read_data minimize.data +read_data create.data include parameters.inc include groups.inc +fix myshk H2O shake 1.0e-5 200 0 b O-H a H-O-H kbond 2000 + +dump mydmp all image 1 myimage-*.ppm type type & + shiny 0.1 box no 0.01 view 90 0 zoom 1.8 +dump_modify mydmp backcolor white & + acolor O red adiam O 2 & + acolor H white adiam H 1 & + acolor Na+ blue adiam Na+ 2.5 & + acolor Cl- cyan adiam Cl- 3 & + acolor WALL gray adiam WALL 3 + +thermo 1 +thermo_style custom step temp etotal press + +minimize 1.0e-6 1.0e-6 1000 1000 +reset_timestep 0 + fix mynvt all nvt temp 300 300 100 fix myshk H2O shake 1.0e-5 200 0 b O-H a H-O-H fix myrct all recenter NULL NULL 0 timestep 1.0 +undump mydmp dump mydmp all image 250 myimage-*.ppm type type & shiny 0.1 box no 0.01 view 90 0 zoom 1.8 dump_modify mydmp backcolor white & @@ -41,3 +59,7 @@ thermo_style custom step temp etotal press v_deltaz run 30000 write_data equilibrate.data nocoeff + + + + diff --git a/files/tutorial4/solution/equilibrate.log b/files/tutorial4/solution/equilibrate.log index 40a1b86..0dbfb69 100644 --- a/files/tutorial4/solution/equilibrate.log +++ b/files/tutorial4/solution/equilibrate.log @@ -28,15 +28,15 @@ Finding 1-2 1-3 1-4 neighbors ... 1 = max # of 1-4 neighbors 2 = max # of special neighbors special bonds CPU = 0.001 seconds - read_data CPU = 0.037 seconds + read_data CPU = 0.035 seconds 1218 atoms in group H2O 15 atoms in group Na 15 atoms in group Cl 30 atoms in group ions 1248 atoms in group fluid 432 atoms in group wall -847 atoms in group top -833 atoms in group bot +833 atoms in group top +847 atoms in group bot 216 atoms in group walltop 216 atoms in group wallbot Finding SHAKE clusters ... @@ -59,6 +59,8 @@ WARNING: Communication cutoff 0 is shorter than a bond length based estimate of WARNING: Increasing communication cutoff to 15.1118 for TIP4P pair style (src/KSPACE/pair_lj_cut_tip4p_long.cpp:497) Generated 9 of 10 mixed pair_coeff terms from geometric mixing rule Last active /omp style is kspace_style pppm/tip4p/omp +WARNING: Using fix shake with minimization. + Substituting constraints with harmonic restraint forces using kbond=2000 (src/RIGID/fix_shake.cpp:386) Neighbor list info ... update: every = 1 steps, delay = 0 steps, check = yes max neighbors/atom: 2000, page size: 100000 @@ -71,161 +73,233 @@ Neighbor list info ... pair build: half/bin/newton/omp stencil: half/bin/3d bin: standard +Setting up cg style minimization ... + Unit style : real + Current step : 0 +Per MPI rank memory allocation (min/avg/max) = 21.09 | 21.09 | 21.09 Mbytes + Step Temp TotEng Press + 0 0 501415.67 6161955.9 + 1 0 221649.28 2927467.2 + 2 0 86222.163 1360519.3 + 3 0 25421.043 654148.66 + 4 0 3961.6221 401043.83 + 5 0 -8324.4768 251712.84 + 6 0 -14973.799 171285.71 + 7 0 -19198.3 128519.05 + 8 0 -22077.096 102083.34 + 9 0 -24119.414 80619.468 + 10 0 -25467.05 71699.976 + 11 0 36930631 -38296594 +Loop time of 1.14705 on 8 procs for 11 steps with 1680 atoms + +705.0% CPU use with 1 MPI tasks x 8 OpenMP threads + +Minimization stats: + Stopping criterion = linesearch alpha is zero + Energy initial, next-to-last, final = + 501415.673478378 -25467.0496354816 36930631.1434843 + Force two-norm initial, final = 2825414.5 950914.82 + Force max component initial, final = 1248444.9 233177.98 + Final line search alpha, max atom move = 1.0612513e-15 2.4746043e-10 + Iterations, force evaluations = 11 49 + +MPI task timing breakdown: +Section | min time | avg time | max time |%varavg| %total +--------------------------------------------------------------- +Pair | 0.3975 | 0.3975 | 0.3975 | 0.0 | 34.65 +Bond | 0.00086941 | 0.00086941 | 0.00086941 | 0.0 | 0.08 +Kspace | 0.25441 | 0.25441 | 0.25441 | 0.0 | 22.18 +Neigh | 0.0056257 | 0.0056257 | 0.0056257 | 0.0 | 0.49 +Comm | 0.0027688 | 0.0027688 | 0.0027688 | 0.0 | 0.24 +Output | 0.42917 | 0.42917 | 0.42917 | 0.0 | 37.41 +Modify | 0.0062557 | 0.0062557 | 0.0062557 | 0.0 | 0.55 +Other | | 0.05045 | | | 4.40 + +Nlocal: 1680 ave 1680 max 1680 min +Histogram: 1 0 0 0 0 0 0 0 0 0 +Nghost: 6825 ave 6825 max 6825 min +Histogram: 1 0 0 0 0 0 0 0 0 0 +Neighs: 828884 ave 828884 max 828884 min +Histogram: 1 0 0 0 0 0 0 0 0 0 + +Total # of neighbors = 828884 +Ave neighs/atom = 493.38333 +Ave special neighs/atom = 1.45 +Neighbor list builds = 1 +Dangerous builds = 0 +Finding SHAKE clusters ... + 0 = # of size 2 clusters + 0 = # of size 3 clusters + 0 = # of size 4 clusters + 406 = # of frozen angles + find clusters CPU = 0.000 seconds +PPPM initialization ... + extracting TIP4P info from pair style + using 12-bit tables for long-range coulomb (src/kspace.cpp:342) + G vector (1/distance) = 0.25237451 + grid = 18 18 60 + stencil order = 5 + estimated absolute RMS force accuracy = 0.0034985124 + estimated relative force accuracy = 1.0535666e-05 + using double precision KISS FFT + 3d grid and FFT values/proc = 39375 19440 +Generated 9 of 10 mixed pair_coeff terms from geometric mixing rule +Last active /omp style is kspace_style pppm/tip4p/omp Setting up Verlet run ... Unit style : real Current step : 0 Time step : 1 Per MPI rank memory allocation (min/avg/max) = 19.97 | 19.97 | 19.97 Mbytes Step Temp TotEng Press v_deltaz - 0 192.6604 -37825.452 -21587.787 28.28 - 250 233.65204 -37461.198 -13499.482 28.241015 - 500 276.93363 -37002.505 -19347.424 28.19986 - 750 311.19222 -36707.189 -13219.974 28.325673 - 1000 307.45859 -36707.432 -17897.685 28.552075 - 1250 287.03456 -36738.377 -15641.296 28.796166 - 1500 303.44062 -36611.091 -16680.691 29.090952 - 1750 294.08574 -36651.598 -17479.041 29.431539 - 2000 305.89056 -36603.305 -16923.643 29.719773 - 2250 313.99329 -36601.647 -18014.996 29.927213 - 2500 299.72893 -36616.25 -15690.295 30.150093 - 2750 305.16239 -36524.16 -18430.169 30.394721 - 3000 296.98737 -36574.474 -16620.679 30.610133 - 3250 310.72973 -36529.748 -19365.387 30.743005 - 3500 300.16164 -36563.501 -16786.229 30.862625 - 3750 297.51551 -36658.166 -17744.234 30.888025 - 4000 310.26326 -36545.664 -18039.25 30.847872 - 4250 299.04123 -36566.176 -16010.669 30.726459 - 4500 307.97427 -36525.869 -18174.262 30.52352 - 4750 288.57069 -36601.815 -16277.379 30.366085 - 5000 308.73681 -36539.823 -17675.116 30.194477 - 5250 306.30648 -36625.29 -15818.712 29.980416 - 5500 296.20877 -36634.159 -17759.933 29.798247 - 5750 303.14769 -36602.927 -16399.789 29.570819 - 6000 293.20726 -36690.366 -15921.857 29.379941 - 6250 289.56471 -36736.695 -16294.771 29.249595 - 6500 300.52643 -36673.286 -16532.492 29.070261 - 6750 289.38644 -36746.231 -17013.378 28.961643 - 7000 311.23764 -36677.622 -15830.944 28.836859 - 7250 304.95141 -36744.564 -16909.991 28.743943 - 7500 304.11331 -36711.246 -15544.277 28.672672 - 7750 294.69339 -36766.524 -16881.777 28.697561 - 8000 296.18629 -36717.793 -15964.95 28.778158 - 8250 300.34584 -36725.853 -16174.063 28.917343 - 8500 298.11752 -36819.012 -17467.573 29.050876 - 8750 294.62956 -36743.751 -15743.564 29.104511 - 9000 290.60441 -36693.627 -16301.448 29.095449 - 9250 310.00428 -36703.779 -16735.081 29.095088 - 9500 290.6813 -36762.969 -17314.627 29.056711 - 9750 298.83133 -36670.294 -15974.776 29.046424 - 10000 302.19144 -36713.836 -16284.747 29.040908 - 10250 282.69912 -36821.72 -17139.188 29.010764 - 10500 303.33515 -36740.33 -16424.283 28.84803 - 10750 289.32171 -36759.464 -16382.312 28.711544 - 11000 303.48515 -36704.975 -16332.729 28.547719 - 11250 297.68252 -36772.649 -14536.893 28.429589 - 11500 308.40738 -36748.585 -17124.858 28.302165 - 11750 296.03638 -36838.929 -13803.079 28.201278 - 12000 307.82877 -36791.602 -16423.877 28.192521 - 12250 289.07827 -36847.135 -16336.699 28.303794 - 12500 304.66612 -36765.026 -15934.579 28.361284 - 12750 304.1178 -36770.551 -15557.516 28.451699 - 13000 297.75249 -36767.65 -16056.845 28.569184 - 13250 296.34109 -36813.857 -16387.211 28.661767 - 13500 308.95057 -36739.765 -14975.371 28.725826 - 13750 312.77277 -36780.049 -16813.831 28.876901 - 14000 295.48716 -36793.893 -15354.886 29.007706 - 14250 308.22919 -36720.609 -17388.209 29.135949 - 14500 288.43862 -36807.144 -15684.86 29.203047 - 14750 281.9242 -36861.038 -15801.283 29.131354 - 15000 296.73311 -36761.783 -16505.495 29.014509 - 15250 306.18268 -36779.213 -15181.17 28.964879 - 15500 293.96138 -36806.474 -15588.568 28.869004 - 15750 305.57403 -36761.099 -15840.911 28.877284 - 16000 294.60033 -36833.489 -16384.72 28.889517 - 16250 304.95299 -36761.2 -16422.398 28.829318 - 16500 293.81866 -36833.295 -15641.629 28.767085 - 16750 299.92095 -36814.562 -15993.939 28.675859 - 17000 297.04879 -36814 -16107.013 28.526727 - 17250 303.72914 -36826.248 -14557.115 28.368698 - 17500 318.99409 -36790.75 -15158.741 28.325408 - 17750 301.96421 -36877.958 -15423.553 28.347744 - 18000 304.21773 -36848.606 -15768.194 28.382644 - 18250 296.15677 -36829.203 -15278.444 28.378086 - 18500 300.04729 -36876.558 -15844.955 28.400263 - 18750 309.10111 -36792.182 -15841.73 28.401154 - 19000 300.35977 -36854.659 -16081.007 28.356092 - 19250 287.88306 -36927.404 -15814.667 28.364987 - 19500 297.36102 -36864.889 -15727.661 28.392048 - 19750 310.42489 -36821.567 -15286.508 28.430867 - 20000 289.03796 -36896.438 -16445.86 28.491455 - 20250 306.73999 -36849.255 -14765.455 28.495928 - 20500 297.03333 -36875.729 -16936.901 28.483632 - 20750 293.84407 -36909.495 -16520.497 28.407597 - 21000 286.98616 -36916.138 -14676.983 28.313239 - 21250 310.49259 -36839.638 -17507.903 28.090442 - 21500 291.91952 -36900.914 -12601.698 27.813542 - 21750 314.14606 -36808.006 -16624.251 27.668962 - 22000 302.71892 -36904.497 -13414.527 27.576163 - 22250 305.65515 -36885.644 -15059.728 27.566774 - 22500 295.02716 -36979.853 -16168.765 27.600099 - 22750 314.32543 -36893.251 -13834.686 27.754975 - 23000 288.70599 -37010.936 -18014.068 27.939717 - 23250 290.34448 -36911.568 -13797.773 28.032198 - 23500 293.15776 -36870.421 -16231.367 28.132508 - 23750 287.1523 -36958.389 -15988.44 28.218781 - 24000 301.48565 -36870.516 -14881.035 28.247988 - 24250 292.17255 -36906.877 -17388.438 28.218548 - 24500 309.20887 -36917.54 -13753.418 28.132625 - 24750 290.66224 -36913.234 -16124.542 28.130674 - 25000 303.12083 -36903.917 -16052.417 28.071992 - 25250 297.71124 -36866.151 -16010.407 28.018962 - 25500 297.82205 -36980.695 -15278.279 27.953726 - 25750 298.33959 -36893.136 -14953.975 27.910032 - 26000 294.7495 -36909.075 -15702.347 27.902489 - 26250 291.37815 -36871.714 -15292.209 27.937158 - 26500 302.0335 -36877.817 -15838.003 27.977794 - 26750 295.62802 -36900.771 -15548.762 28.033623 - 27000 316.02151 -36829.503 -15591.545 28.078614 - 27250 283.86389 -36940.655 -14689.892 28.121335 - 27500 311.08335 -36865.054 -16621.127 28.146179 - 27750 307.01663 -36892.028 -15287.064 28.042745 - 28000 288.97274 -36964.162 -15515.86 27.98028 - 28250 306.31357 -36875.214 -15211.466 27.990386 - 28500 308.38821 -36967.832 -15297.61 28.012721 - 28750 305.24977 -36974.13 -15140.362 28.024367 - 29000 299.12508 -36927.037 -15440.218 28.029731 - 29250 300.87946 -36933.365 -14818.84 28.090526 - 29500 301.50763 -36887.959 -16016.911 28.201906 - 29750 291.49404 -36891.564 -15428.951 28.242765 - 30000 294.43081 -36968.549 -15512.199 28.256058 -Loop time of 260.785 on 8 procs for 30000 steps with 1680 atoms + 0 0 -26222.184 68202.077 28.279691 + 250 584.39722 -34232.776 -8388.6343 28.760428 + 500 299.58594 -36286.8 -19167.126 29.699613 + 750 278.77216 -36713.003 -16293.899 30.280079 + 1000 308.06188 -36470.485 -19530.149 30.760616 + 1250 297.59758 -36521.224 -16499.686 31.162351 + 1500 295.3665 -36533.697 -19338.545 31.407331 + 1750 312.13728 -36402.676 -17971.445 31.500329 + 2000 291.92393 -36502.459 -18500.896 31.477754 + 2250 295.63084 -36564.853 -17048.542 31.360765 + 2500 313.66265 -36528.479 -17230.986 31.17012 + 2750 294.00039 -36599.522 -17239.628 30.934484 + 3000 305.21333 -36570.728 -17141.127 30.698291 + 3250 285.98315 -36657.609 -16778.592 30.422242 + 3500 304.31528 -36620.307 -17443.862 30.111406 + 3750 308.13142 -36631.584 -16088.752 29.736823 + 4000 285.72748 -36778.376 -15532.16 29.47705 + 4250 320.14328 -36619.293 -15630.93 29.312531 + 4500 287.26618 -36784.723 -17688.444 29.24987 + 4750 299.08903 -36673.849 -15760.633 29.128535 + 5000 301.38115 -36700.488 -17230.746 29.003619 + 5250 289.88057 -36798.105 -15766.751 28.94962 + 5500 310.64179 -36627.408 -17900.724 28.907346 + 5750 305.82993 -36691.958 -15389.646 28.812196 + 6000 291.45547 -36821.709 -15633.501 28.717326 + 6250 300.57472 -36697.778 -16022.949 28.714386 + 6500 294.94985 -36778.347 -15826.402 28.704572 + 6750 302.1701 -36766.229 -16086.288 28.693671 + 7000 302.55762 -36709.776 -15829.036 28.686863 + 7250 300.80932 -36790.754 -17197.299 28.669686 + 7500 290.22629 -36831.917 -15878.702 28.710207 + 7750 304.61718 -36743.459 -17326.208 28.813173 + 8000 300.44248 -36726.238 -16308.488 28.863855 + 8250 311.04195 -36743.198 -16528.602 28.885219 + 8500 301.58266 -36749.445 -16449.299 28.863825 + 8750 309.57633 -36716.48 -17309.703 28.850207 + 9000 296.9074 -36773.142 -15869.959 28.802893 + 9250 300.19051 -36725.212 -16376.302 28.701116 + 9500 296.5616 -36773.212 -15228.275 28.59576 + 9750 294.16314 -36868.714 -16341.589 28.581707 + 10000 305.04918 -36746.294 -15915.7 28.541215 + 10250 300.65848 -36786.401 -15479.167 28.521732 + 10500 292.03988 -36828.956 -15693.143 28.548343 + 10750 302.98483 -36745.679 -15882.768 28.549254 + 11000 299.35022 -36808.07 -15790.344 28.539511 + 11250 307.32653 -36812.848 -16358.531 28.537355 + 11500 296.93944 -36765.796 -15370.264 28.58919 + 11750 305.93844 -36731.073 -17237.903 28.660549 + 12000 288.68693 -36831.079 -14992.763 28.650025 + 12250 313.00234 -36759.16 -16818.459 28.656831 + 12500 296.30078 -36810.525 -16836.68 28.701735 + 12750 313.35681 -36717.507 -15055.131 28.734798 + 13000 296.5639 -36853.187 -17245.183 28.711969 + 13250 303.3509 -36778.336 -14831.842 28.680457 + 13500 296.9637 -36773.107 -16373.483 28.691137 + 13750 293.76158 -36859.183 -16245.967 28.663328 + 14000 311.64448 -36768.988 -15302.343 28.694451 + 14250 307.19026 -36745.649 -16302.803 28.766771 + 14500 295.99382 -36775.524 -16184.047 28.828887 + 14750 308.31551 -36763.424 -15740.235 28.848748 + 15000 309.72851 -36800.881 -16713.692 28.847667 + 15250 297.0599 -36862.962 -15559.325 28.816712 + 15500 300.83134 -36852.906 -16800.623 28.806678 + 15750 306.35167 -36777.532 -14514.424 28.730182 + 16000 281.50395 -36823.378 -17213.992 28.708412 + 16250 300.73028 -36848.15 -14932.423 28.675659 + 16500 304.21244 -36786.783 -16254.96 28.653366 + 16750 290.14643 -36854.844 -15452.705 28.602524 + 17000 312.44087 -36759.404 -15930.418 28.526325 + 17250 304.61228 -36846.599 -16700.834 28.423804 + 17500 298.86409 -36831.375 -15594.261 28.339346 + 17750 295.68524 -36897.233 -17325.916 28.245795 + 18000 306.52664 -36866.264 -15280.007 28.15135 + 18250 302.19234 -36863.415 -15666.238 28.016388 + 18500 291.96283 -36898.414 -15397.853 27.890577 + 18750 307.12422 -36877.533 -13960.168 27.803221 + 19000 294.15103 -36917.086 -16920.702 27.743978 + 19250 307.49951 -36830.703 -13890.3 27.72032 + 19500 295.71924 -36868.963 -15748.946 27.767926 + 19750 309.30964 -36849.896 -16287.457 27.824207 + 20000 302.22889 -36934.877 -14678.576 27.81618 + 20250 301.82955 -36869.662 -16840.558 27.85391 + 20500 296.59269 -36901.536 -14508.008 27.842447 + 20750 305.05462 -36917.003 -16489.595 27.871256 + 21000 303.35604 -36851.714 -14712.483 27.91986 + 21250 302.47991 -36895.087 -15224.073 27.942175 + 21500 298.26981 -36928.937 -15698.021 27.949536 + 21750 310.66597 -36828.097 -15165.778 28.009399 + 22000 301.05034 -36925.531 -16846.866 28.086298 + 22250 303.17407 -36897.653 -15017.148 28.184937 + 22500 305.96274 -36842.016 -17014.192 28.268956 + 22750 293.50126 -36917.479 -15222.913 28.236956 + 23000 302.85371 -36893.935 -15577.355 28.249673 + 23250 298.90867 -36947.716 -15541.265 28.267006 + 23500 302.54506 -36873.222 -15900.027 28.189052 + 23750 293.70496 -36911.241 -16367.597 28.057692 + 24000 300.88931 -36947.138 -13865.222 27.919589 + 24250 282.86025 -37010.486 -15784.133 27.781536 + 24500 299.50718 -36889.736 -14475.996 27.692315 + 24750 309.44946 -36885.412 -15358.27 27.662891 + 25000 297.26633 -36928.218 -16640.01 27.612962 + 25250 305.8489 -36902.974 -14943.178 27.542515 + 25500 296.30849 -36941.692 -14757.049 27.508457 + 25750 311.60299 -36926.416 -16473.063 27.504788 + 26000 293.00181 -36958.278 -15333.052 27.44296 + 26250 297.07531 -37000.732 -14736.331 27.449949 + 26500 292.37709 -37025.259 -15890.13 27.518646 + 26750 298.85839 -36951.146 -15400.925 27.590081 + 27000 303.03023 -36968.504 -15868.915 27.638514 + 27250 294.23752 -36928.642 -15288.327 27.768923 + 27500 309.73961 -36925.531 -15428.899 27.868821 + 27750 304.75557 -36808.521 -15888.93 27.980977 + 28000 292.50299 -36887.379 -15799.009 28.092303 + 28250 304.01856 -36875.885 -14754.637 28.132802 + 28500 301.38099 -36961.398 -16256.937 28.127082 + 28750 310.13837 -36876.596 -13570.862 28.173235 + 29000 299.30307 -36906.975 -17411.139 28.260354 + 29250 287.70558 -36993.781 -14904.406 28.227257 + 29500 298.66344 -36906.201 -15691.959 28.239372 + 29750 302.78867 -36883.874 -14803.756 28.214151 + 30000 313.05912 -36853.576 -14797.115 28.114691 +Loop time of 258.107 on 8 procs for 30000 steps with 1680 atoms -Performance: 9.939 ns/day, 2.415 hours/ns, 115.037 timesteps/s, 193.263 katom-step/s -813.4% CPU use with 1 MPI tasks x 8 OpenMP threads +Performance: 10.042 ns/day, 2.390 hours/ns, 116.231 timesteps/s, 195.268 katom-step/s +814.7% CPU use with 1 MPI tasks x 8 OpenMP threads MPI task timing breakdown: Section | min time | avg time | max time |%varavg| %total --------------------------------------------------------------- -Pair | 115.33 | 115.33 | 115.33 | 0.0 | 44.22 -Bond | 0.23978 | 0.23978 | 0.23978 | 0.0 | 0.09 -Kspace | 123.92 | 123.92 | 123.92 | 0.0 | 47.52 -Neigh | 8.0193 | 8.0193 | 8.0193 | 0.0 | 3.08 -Comm | 1.4269 | 1.4269 | 1.4269 | 0.0 | 0.55 -Output | 5.139 | 5.139 | 5.139 | 0.0 | 1.97 -Modify | 6.5191 | 6.5191 | 6.5191 | 0.0 | 2.50 -Other | | 0.1854 | | | 0.07 +Pair | 117.55 | 117.55 | 117.55 | 0.0 | 45.54 +Bond | 0.19706 | 0.19706 | 0.19706 | 0.0 | 0.08 +Kspace | 119.29 | 119.29 | 119.29 | 0.0 | 46.22 +Neigh | 8.331 | 8.331 | 8.331 | 0.0 | 3.23 +Comm | 1.3636 | 1.3636 | 1.3636 | 0.0 | 0.53 +Output | 4.9195 | 4.9195 | 4.9195 | 0.0 | 1.91 +Modify | 6.2799 | 6.2799 | 6.2799 | 0.0 | 2.43 +Other | | 0.1771 | | | 0.07 Nlocal: 1680 ave 1680 max 1680 min Histogram: 1 0 0 0 0 0 0 0 0 0 -Nghost: 6789 ave 6789 max 6789 min +Nghost: 6788 ave 6788 max 6788 min Histogram: 1 0 0 0 0 0 0 0 0 0 -Neighs: 787934 ave 787934 max 787934 min +Neighs: 792514 ave 792514 max 792514 min Histogram: 1 0 0 0 0 0 0 0 0 0 -Total # of neighbors = 787934 -Ave neighs/atom = 469.00833 +Total # of neighbors = 792514 +Ave neighs/atom = 471.73452 Ave special neighs/atom = 1.45 -Neighbor list builds = 1443 +Neighbor list builds = 1463 Dangerous builds = 0 System init for write_data ... PPPM initialization ... diff --git a/files/tutorial4/solution/minimize.data b/files/tutorial4/solution/minimize.data deleted file mode 100644 index 4bbf91f..0000000 --- a/files/tutorial4/solution/minimize.data +++ /dev/null @@ -1,4626 +0,0 @@ -LAMMPS data file via write_data, version 29 Aug 2024, timestep = 12000, units = real - -1680 atoms -5 atom types -812 bonds -1 bond types -406 angles -1 angle types - --12.120000000000001 12.120000000000001 xlo xhi --12.120000000000001 12.120000000000001 ylo yhi --20.2 20.2 zlo zhi - -Atom Type Labels - -1 O -2 H -3 Na+ -4 Cl- -5 WALL - -Bond Type Labels - -1 O-H - -Angle Type Labels - -1 H-O-H - -Masses - -1 15.9994 -2 1.008 -3 22.99 -4 35.453 -5 26.9815 - -Atoms # full - -1 0 5 0 -12.120000000000001 -12.120000000000001 -16.16 0 0 0 -2 0 5 0 -10.1 -10.1 -16.16 0 0 0 -3 0 5 0 -10.1 -12.120000000000001 -14.14 0 0 0 -4 0 5 0 -12.120000000000001 -10.1 -14.14 0 0 0 -5 0 5 0 -8.08 -12.120000000000001 -16.16 0 0 0 -6 0 5 0 -6.0600000000000005 -10.1 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1 2087 2086 2088 diff --git a/files/tutorial4/solution/minimize.ipynb b/files/tutorial4/solution/minimize.ipynb deleted file mode 100644 index bb58247..0000000 --- a/files/tutorial4/solution/minimize.ipynb +++ /dev/null @@ -1,161 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "7c8c9669", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import sys, os, git, lammps_logfile\n", - "import warnings\n", - "warnings.filterwarnings(\"ignore\")" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "a28c02aa", - "metadata": {}, - "outputs": [], - "source": [ - "current_path = os.getcwd()\n", - "git_repo = git.Repo(current_path, search_parent_directories=True)\n", - "git_path = git_repo.git.rev_parse(\"--show-toplevel\")\n", - "path_in_folder = current_path[len(git_path)+1:]\n", - "sys.path.append(git_path + \"/files/shared-pyplot\")\n", - "from plttools import PltTools\n", - "path_figures = git_path + \"/figures/\"" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "2f5b9386", - "metadata": {}, - "outputs": [], - "source": [ - "color1 = np.array([1, 0.682, 0.286])\n", - "color2 = np.array([0.008, 0.294, 0.478])\n", - "color3 = np.array([0.267, 0.647, 0.761])\n", - "color4 = np.array([0.5, 0.5, 0.5])" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "5f6dbb52", - "metadata": {}, - "outputs": [], - "source": [ - "log = lammps_logfile.File(\"minimize.log\")\n", - "\n", - "timestep = 0.5 # fs\n", - "time_0 = log.get(\"Step\", run_num=0)*timestep/1000 # ps\n", - "TotEng_0 = log.get(\"TotEng\", run_num=0)/1000 # MJ/mol\n", - "temp_0 = log.get(\"Temp\", run_num=0) # K\n", - "\n", - "timestep = 1.0 # fs\n", - "time_1 = log.get(\"Step\", run_num=1)*timestep/1000-2 # ps\n", - "TotEng_1 = log.get(\"TotEng\", run_num=1)/1000 # MJ/mol\n", - "temp_1 = log.get(\"Temp\", run_num=1) # K\n", - "\n", - "time_2 = log.get(\"Step\", run_num=2)*timestep/1000-2 # ps\n", - "TotEng_2 = log.get(\"TotEng\", run_num=2)/1000 # MJ/mol\n", - "temp_2 = log.get(\"Temp\", run_num=2) # K" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "d4f04489", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "filename = \"NANOSHEAR-minimization\"\n", - "\n", - "# Initialise figure\n", - "myplt = PltTools()\n", - "myplt.prepare_figure(fig_size = (12,5.6), dark_mode = False,\n", - " transparency = False, use_serif=False, n_colone=2)\n", - "# Panel a\n", - "myplt.add_panel()\n", - "myplt.add_plot(x = time_0, y = temp_0, type = \"plot\", linewidth_data = 3,\n", - " marker = \"-\", data_color = color2, markersize = 12)\n", - "myplt.add_plot(x = time_1, y = temp_1, type = \"plot\", linewidth_data = 3,\n", - " marker = \"-\", data_color = color2, markersize = 12)\n", - "myplt.add_plot(x = time_2, y = temp_2, type = \"plot\", linewidth_data = 3,\n", - " marker = \"-\", data_color = color2, markersize = 12)\n", - "x = np.linspace(-2, 15)\n", - "myplt.add_plot(x = x, y = x*0+300, type = \"plot\", linewidth_data = 1.5,\n", - " marker = \"--\", data_color = color4, markersize = 12)\n", - "x = np.linspace(-1000, 1000)\n", - "myplt.add_plot(x = x*0+2, y = x, type = \"plot\", linewidth_data = 1.5,\n", - " marker = \":\", data_color = color4, markersize = 12)\n", - "myplt.add_plot(x = x*0+6, y = x, type = \"plot\", linewidth_data = 1.5,\n", - " marker = \":\", data_color = color4, markersize = 12)\n", - "myplt.complete_panel(ylabel = r'$T ~ \\mathrm{(K)}$',\n", - " xlabel = r'$t~\\mathrm{(ps)}$',\n", - " xpad = 10, legend=True, handlelength_legend=1)\n", - "myplt.set_boundaries(x_ticks=np.arange(0, 11, 2), y_ticks=np.arange(0, 360, 100),\n", - " x_boundaries=(-1.5, 11), y_boundaries=(-20, 370))\n", - "\n", - "# Panel a\n", - "myplt.add_panel()\n", - "myplt.add_plot(x = time_0, y = TotEng_0, type = \"plot\", linewidth_data = 3,\n", - " marker = \"-\", data_color = color2, markersize = 12)\n", - "myplt.add_plot(x = time_1, y = TotEng_1, type = \"plot\", linewidth_data = 3,\n", - " marker = \"-\", data_color = color2, markersize = 12)\n", - "myplt.add_plot(x = time_2, y = TotEng_2, type = \"plot\", linewidth_data = 3,\n", - " marker = \"-\", data_color = color2, markersize = 12)\n", - "x = np.linspace(-1000, 1000)\n", - "myplt.add_plot(x = x*0+2, y = x, type = \"plot\", linewidth_data = 1.5,\n", - " marker = \":\", data_color = color4, markersize = 12)\n", - "myplt.add_plot(x = x*0+6, y = x, type = \"plot\", linewidth_data = 1.5,\n", - " marker = \":\", data_color = color4, markersize = 12)\n", - "myplt.complete_panel(ylabel = r'$E_\\mathrm{tot} ~ \\mathrm{(MJ/mol)}$',\n", - " xlabel = r'$t~\\mathrm{(ps)}$',\n", - " xpad = 10, legend=True, handlelength_legend=1)\n", - "myplt.set_boundaries(x_ticks=np.arange(0, 11, 2), y_ticks=np.arange(-40, -19, 5),\n", - " x_boundaries=(-1.5, 11), y_boundaries=(-42, -18))\n", - "\n", - "# Print figure\n", - "myplt.add_subplotlabels()\n", - "myplt.save_figure(filename = filename, saving_path = path_figures)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/files/tutorial4/solution/minimize.lmp b/files/tutorial4/solution/minimize.lmp deleted file mode 100644 index ca39cdb..0000000 --- a/files/tutorial4/solution/minimize.lmp +++ /dev/null @@ -1,47 +0,0 @@ -# Python script (License CC BY 4.0) -# By Simon Gravelle, Jacob R. Gissinger, and Axel Kohlmeyer -# The DOI will be added upon publication -# Find more on GitHub: https://github.com/lammpstutorials - -boundary p p f -units real -atom_style full -bond_style harmonic -angle_style harmonic -pair_style lj/cut/tip4p/long O H O-H H-O-H 0.1546 12.0 -kspace_style pppm/tip4p 1.0e-5 -kspace_modify slab 3.0 - -read_data create.data - -include parameters.inc -include groups.inc - -fix nvelim fluid nve/limit 0.1 -fix myber fluid temp/berendsen 1 1 100 -timestep 0.25 - -dump mydmp all image 200 myimage-*.ppm type type & - shiny 0.1 box no 0.01 view 90 0 zoom 1.8 -dump_modify mydmp backcolor white & - acolor O red adiam O 2 & - acolor H white adiam H 1 & - acolor Na+ blue adiam Na+ 2.5 & - acolor Cl- cyan adiam Cl- 3 & - acolor WALL gray adiam WALL 3 - -compute Tfluid fluid temp -thermo_modify temp Tfluid -thermo 200 - -run 8000 - -unfix nvelim -fix mynve fluid nve -fix myshk H2O shake 1.0e-5 200 0 b O-H a H-O-H -fix myber fluid temp/berendsen 300 300 100 -timestep 1.0 - -run 4000 - -write_data minimize.data nocoeff diff --git a/files/tutorial4/solution/minimize.log b/files/tutorial4/solution/minimize.log deleted file mode 100644 index 678d469..0000000 --- a/files/tutorial4/solution/minimize.log +++ /dev/null @@ -1,290 +0,0 @@ - using 8 OpenMP thread(s) per MPI task -using multi-threaded neighbor list subroutines -Reading data file ... - orthogonal box = (-12.12 -12.12 -20.2) to (12.12 12.12 20.2) - 1 by 1 by 1 MPI processor grid - reading atom labelmap ... - reading bond labelmap ... - reading angle labelmap ... - reading atoms ... - 1680 atoms - reading velocities ... - 1680 velocities - scanning bonds ... - 2 = max bonds/atom - scanning angles ... - 1 = max angles/atom - orthogonal box = (-12.12 -12.12 -20.2) to (12.12 12.12 20.2) - 1 by 1 by 1 MPI processor grid - reading bonds ... - 812 bonds - reading angles ... - 406 angles -Finding 1-2 1-3 1-4 neighbors ... - special bond factors lj: 0 0 0 - special bond factors coul: 0 0 0 - 2 = max # of 1-2 neighbors - 1 = max # of 1-3 neighbors - 1 = max # of 1-4 neighbors - 2 = max # of special neighbors - special bonds CPU = 0.002 seconds - read_data CPU = 0.079 seconds -1218 atoms in group H2O -15 atoms in group Na -15 atoms in group Cl -30 atoms in group ions -1248 atoms in group fluid -432 atoms in group wall -833 atoms in group top -847 atoms in group bot -216 atoms in group walltop -216 atoms in group wallbot -Finding SHAKE clusters ... - 0 = # of size 2 clusters - 0 = # of size 3 clusters - 0 = # of size 4 clusters - 406 = # of frozen angles - find clusters CPU = 0.001 seconds -WARNING: Temperature for thermo pressure is not for group all (src/thermo.cpp:533) - -CITE-CITE-CITE-CITE-CITE-CITE-CITE-CITE-CITE-CITE-CITE-CITE-CITE - -Your simulation uses code contributions which should be cited: -- Type Label Framework: https://doi.org/10.1021/acs.jpcb.3c08419 -The log file lists these citations in BibTeX format. - -CITE-CITE-CITE-CITE-CITE-CITE-CITE-CITE-CITE-CITE-CITE-CITE-CITE - -PPPM initialization ... - extracting TIP4P info from pair style - using 12-bit tables for long-range coulomb (src/kspace.cpp:342) - G vector (1/distance) = 0.25237451 - grid = 18 18 60 - stencil order = 5 - estimated absolute RMS force accuracy = 0.0034985124 - estimated relative force accuracy = 1.0535666e-05 - using double precision KISS FFT - 3d grid and FFT values/proc = 39375 19440 -WARNING: Communication cutoff 0 is shorter than a bond length based estimate of 3.4358. This may lead to errors. (src/comm.cpp:731) -WARNING: Increasing communication cutoff to 15.1118 for TIP4P pair style (src/KSPACE/pair_lj_cut_tip4p_long.cpp:497) -Generated 9 of 10 mixed pair_coeff terms from geometric mixing rule -Last active /omp style is kspace_style pppm/tip4p/omp -WARNING: Should not use fix nve/limit with fix shake or fix rattle (src/fix_nve_limit.cpp:76) -Neighbor list info ... - update: every = 1 steps, delay = 0 steps, check = yes - max neighbors/atom: 2000, page size: 100000 - master list distance cutoff = 14.3092 - ghost atom cutoff = 15.1118 - binsize = 7.1546, bins = 4 4 6 - 1 neighbor lists, perpetual/occasional/extra = 1 0 0 - (1) pair lj/cut/tip4p/long/omp, perpetual - attributes: half, newton on, omp - pair build: half/bin/newton/omp - stencil: half/bin/3d - bin: standard -Setting up Verlet run ... - Unit style : real - Current step : 0 - Time step : 0.5 -Per MPI rank memory allocation (min/avg/max) = 19.97 | 19.97 | 19.97 Mbytes - Step Temp E_pair E_mol TotEng Press - 0 0 501411.03 0 501411.03 -8.4785925e+232 - 200 8114.0354 -31133.813 0 -10793.052 59851.453 - 400 3590.6404 -33466.786 0 -24465.548 18773.485 - 600 1755.2211 -35216.965 0 -30816.87 -880.18648 - 800 928.86374 -36385.427 0 -34056.895 -13007.859 - 1000 543.68605 -37187.34 0 -35824.394 -18511.743 - 1200 350.25006 -37783.893 0 -36905.865 -21588.394 - 1400 241.93459 -38237.458 0 -37630.962 -22612.682 - 1600 173.17665 -38575.03 0 -38140.9 -23006.59 - 1800 119.16026 -38798.595 0 -38499.877 -23454.883 - 2000 84.127403 -38962.696 0 -38751.8 -23255.015 - 2200 59.63002 -39078.804 0 -38929.32 -23738.58 - 2400 37.217824 -39140.621 0 -39047.321 -23799.649 - 2600 26.563649 -39192.302 0 -39125.71 -23701.176 - 2800 18.902099 -39227.026 0 -39179.641 -23630.411 - 3000 12.575103 -39247.776 0 -39216.252 -23744.401 - 3200 8.808055 -39261.99 0 -39239.909 -23776.354 - 3400 7.104077 -39275.568 0 -39257.759 -23770.886 - 3600 5.7048514 -39285.227 0 -39270.926 -23761.582 - 3800 4.6159083 -39293.041 0 -39281.47 -23801.952 - 4000 4.4702043 -39301.337 0 -39290.131 -23731.575 -Loop time of 114.447 on 8 procs for 4000 steps with 1680 atoms - -Performance: 1.510 ns/day, 15.895 hours/ns, 34.951 timesteps/s, 58.717 katom-step/s -803.3% CPU use with 1 MPI tasks x 8 OpenMP threads - -MPI task timing breakdown: -Section | min time | avg time | max time |%varavg| %total ---------------------------------------------------------------- -Pair | 44.915 | 44.915 | 44.915 | 0.0 | 39.25 -Bond | 0.16829 | 0.16829 | 0.16829 | 0.0 | 0.15 -Kspace | 61.021 | 61.021 | 61.021 | 0.0 | 53.32 -Neigh | 1.4782 | 1.4782 | 1.4782 | 0.0 | 1.29 -Comm | 0.67717 | 0.67717 | 0.67717 | 0.0 | 0.59 -Output | 3.4631 | 3.4631 | 3.4631 | 0.0 | 3.03 -Modify | 2.6361 | 2.6361 | 2.6361 | 0.0 | 2.30 -Other | | 0.0888 | | | 0.08 - -Nlocal: 1680 ave 1680 max 1680 min -Histogram: 1 0 0 0 0 0 0 0 0 0 -Nghost: 6827 ave 6827 max 6827 min -Histogram: 1 0 0 0 0 0 0 0 0 0 -Neighs: 799510 ave 799510 max 799510 min -Histogram: 1 0 0 0 0 0 0 0 0 0 - -Total # of neighbors = 799510 -Ave neighs/atom = 475.89881 -Ave special neighs/atom = 1.45 -Neighbor list builds = 89 -Dangerous builds = 0 -PPPM initialization ... - extracting TIP4P info from pair style - using 12-bit tables for long-range coulomb (src/kspace.cpp:342) - G vector (1/distance) = 0.25237451 - grid = 18 18 60 - stencil order = 5 - estimated absolute RMS force accuracy = 0.0034985124 - estimated relative force accuracy = 1.0535666e-05 - using double precision KISS FFT - 3d grid and FFT values/proc = 39375 19440 -Generated 9 of 10 mixed pair_coeff terms from geometric mixing rule -Last active /omp style is kspace_style pppm/tip4p/omp -WARNING: Should not use fix nve/limit with fix shake or fix rattle (src/fix_nve_limit.cpp:76) -Setting up Verlet run ... - Unit style : real - Current step : 4000 - Time step : 1 -Per MPI rank memory allocation (min/avg/max) = 20.61 | 20.61 | 20.61 Mbytes - Step Temp E_pair E_mol TotEng Press - 4000 4.4702043 -39301.337 0 -39290.131 -23731.597 - 4200 191.645 -38912.853 0 -38432.425 -21675.822 - 4400 257.01369 -38687.676 0 -38043.378 -19235.502 - 4600 277.23807 -38564.791 0 -37869.793 -19891.506 - 4800 291.49728 -38518.15 0 -37787.407 -20246.427 - 5000 305.7816 -38519.271 0 -37752.719 -19791.799 - 5200 305.90247 -38516.53 0 -37749.675 -20388.436 - 5400 298.12343 -38476.844 0 -37729.49 -20796.867 - 5600 278.71135 -38411.741 0 -37713.05 -20799.021 - 5800 303.65592 -38459.264 0 -37698.04 -21433.875 - 6000 301.5322 -38445.981 0 -37690.082 -21326.428 - 6200 294.84716 -38437.756 0 -37698.615 -20567.152 - 6400 302.42026 -38461.445 0 -37703.32 -21074.266 - 6600 303.16517 -38482.995 0 -37723.002 -21303.465 - 6800 296.72 -38475.146 0 -37731.31 -20605.26 - 7000 306.24872 -38517.41 0 -37749.687 -21263.543 - 7200 307.22322 -38529.13 0 -37758.964 -21283.391 - 7400 288.44067 -38462.007 0 -37738.926 -21033.652 - 7600 302.44448 -38490.652 0 -37732.466 -21405.431 - 7800 299.06547 -38501.781 0 -37752.065 -20877.487 - 8000 302.39723 -38510.526 0 -37752.458 -21544.229 -Loop time of 121.666 on 8 procs for 4000 steps with 1680 atoms - -Performance: 2.841 ns/day, 8.449 hours/ns, 32.877 timesteps/s, 55.233 katom-step/s -802.9% CPU use with 1 MPI tasks x 8 OpenMP threads - -MPI task timing breakdown: -Section | min time | avg time | max time |%varavg| %total ---------------------------------------------------------------- -Pair | 46.745 | 46.745 | 46.745 | 0.0 | 38.42 -Bond | 0.52542 | 0.52542 | 0.52542 | 0.0 | 0.43 -Kspace | 64.783 | 64.783 | 64.783 | 0.0 | 53.25 -Neigh | 2.572 | 2.572 | 2.572 | 0.0 | 2.11 -Comm | 0.70311 | 0.70311 | 0.70311 | 0.0 | 0.58 -Output | 3.4031 | 3.4031 | 3.4031 | 0.0 | 2.80 -Modify | 2.8415 | 2.8415 | 2.8415 | 0.0 | 2.34 -Other | | 0.09329 | | | 0.08 - -Nlocal: 1680 ave 1680 max 1680 min -Histogram: 1 0 0 0 0 0 0 0 0 0 -Nghost: 6795 ave 6795 max 6795 min -Histogram: 1 0 0 0 0 0 0 0 0 0 -Neighs: 791675 ave 791675 max 791675 min -Histogram: 1 0 0 0 0 0 0 0 0 0 - -Total # of neighbors = 791675 -Ave neighs/atom = 471.23512 -Ave special neighs/atom = 1.45 -Neighbor list builds = 157 -Dangerous builds = 0 -PPPM initialization ... - extracting TIP4P info from pair style - using 12-bit tables for long-range coulomb (src/kspace.cpp:342) - G vector (1/distance) = 0.25237451 - grid = 18 18 60 - stencil order = 5 - estimated absolute RMS force accuracy = 0.0034985124 - estimated relative force accuracy = 1.0535666e-05 - using double precision KISS FFT - 3d grid and FFT values/proc = 39375 19440 -Generated 9 of 10 mixed pair_coeff terms from geometric mixing rule -Last active /omp style is kspace_style pppm/tip4p/omp -Setting up Verlet run ... - Unit style : real - Current step : 8000 - Time step : 1 -Per MPI rank memory allocation (min/avg/max) = 20.61 | 20.61 | 20.61 Mbytes - Step Temp E_pair E_mol TotEng Press - 8000 302.39723 -38510.526 0 -37752.458 -21544.221 - 8200 299.88588 -38503.173 0 -37751.401 -21847.246 - 8400 302.06786 -38509.911 0 -37752.669 -20895.036 - 8600 301.47847 -38500.585 0 -37744.821 -21054.4 - 8800 309 -38535.579 0 -37760.959 -20914.715 - 9000 305.85178 -38541.071 0 -37774.343 -20654.618 - 9200 284.78763 -38472.23 0 -37758.306 -21301.889 - 9400 294.46275 -38503.368 0 -37765.191 -20859.95 - 9600 305.63709 -38527.402 0 -37761.212 -21224.058 - 9800 298.27467 -38506.15 0 -37758.417 -20773.785 - 10000 304.0294 -38532.209 0 -37770.049 -21310.685 - 10200 295.42575 -38515.067 0 -37774.476 -20681.042 - 10400 295.35638 -38514.917 0 -37774.5 -20362.707 - 10600 297.85218 -38531.368 0 -37784.694 -21452.535 - 10800 308.22613 -38549.72 0 -37777.04 -21556.422 - 11000 305.29696 -38532.738 0 -37767.401 -21318.995 - 11200 308.52202 -38546.808 0 -37773.386 -21488.908 - 11400 304.55053 -38532.204 0 -37768.738 -21743.911 - 11600 296.23116 -38491.871 0 -37749.26 -21175.551 - 11800 306.81101 -38530.947 0 -37761.814 -21293.436 - 12000 295.18001 -38500.612 0 -37760.636 -21257.709 -Loop time of 117.337 on 8 procs for 4000 steps with 1680 atoms - -Performance: 2.945 ns/day, 8.148 hours/ns, 34.090 timesteps/s, 57.271 katom-step/s -805.2% CPU use with 1 MPI tasks x 8 OpenMP threads - -MPI task timing breakdown: -Section | min time | avg time | max time |%varavg| %total ---------------------------------------------------------------- -Pair | 43.901 | 43.901 | 43.901 | 0.0 | 37.41 -Bond | 0.29219 | 0.29219 | 0.29219 | 0.0 | 0.25 -Kspace | 63.866 | 63.866 | 63.866 | 0.0 | 54.43 -Neigh | 2.5105 | 2.5105 | 2.5105 | 0.0 | 2.14 -Comm | 0.74761 | 0.74761 | 0.74761 | 0.0 | 0.64 -Output | 3.3361 | 3.3361 | 3.3361 | 0.0 | 2.84 -Modify | 2.5719 | 2.5719 | 2.5719 | 0.0 | 2.19 -Other | | 0.111 | | | 0.09 - -Nlocal: 1680 ave 1680 max 1680 min -Histogram: 1 0 0 0 0 0 0 0 0 0 -Nghost: 6804 ave 6804 max 6804 min -Histogram: 1 0 0 0 0 0 0 0 0 0 -Neighs: 790161 ave 790161 max 790161 min -Histogram: 1 0 0 0 0 0 0 0 0 0 - -Total # of neighbors = 790161 -Ave neighs/atom = 470.33393 -Ave special neighs/atom = 1.45 -Neighbor list builds = 158 -Dangerous builds = 0 -System init for write_data ... -PPPM initialization ... - extracting TIP4P info from pair style - using 12-bit tables for long-range coulomb (src/kspace.cpp:342) - G vector (1/distance) = 0.25237451 - grid = 18 18 60 - stencil order = 5 - estimated absolute RMS force accuracy = 0.0034985124 - estimated relative force accuracy = 1.0535666e-05 - using double precision KISS FFT - 3d grid and FFT values/proc = 39375 19440 -Generated 9 of 10 mixed pair_coeff terms from geometric mixing rule -Last active /omp style is kspace_style pppm/tip4p/omp diff --git a/lammps-tutorials.tex b/lammps-tutorials.tex index dd9d904..874b861 100644 --- a/lammps-tutorials.tex +++ b/lammps-tutorials.tex @@ -2429,13 +2429,15 @@ \subsubsection{System preparation} \paragraph{Energy minimization} Let us move the atoms and place them in more energetically favorable positions -before starting the actual molecular dynamics simulation. Although we refer to this step as -\emph{energy minimization}, it is not a conventional minimization -like that performed in the first tutorial; \hyperref[lennard-jones-label]{Lennard-Jones fluid}. -Instead, we will conduct a molecular dynamics simulation, employing certain techniques -to prevent the system from exploding due to overlapping atoms. - -Open the \flecmd{minimize.lmp} file that was downloaded alongside +before starting the actual molecular dynamics simulation. +% SG: removed the nve/limit part for compatibility with shake +% Although we refer to this step as +% \emph{energy minimization}, it is not a conventional minimization +% like that performed in the first tutorial; \hyperref[lennard-jones-label]{Lennard-Jones fluid}. +% Instead, we will conduct a molecular dynamics simulation, employing certain techniques +% to prevent the system from exploding due to overlapping atoms. + +Open the \flecmd{equilibrate.lmp} file that was downloaded alongside \flecmd{create.lmp} during the tutorial setup. It contains the following lines: \begin{lstlisting} boundary p p f @@ -2455,30 +2457,30 @@ \subsubsection{System preparation} The only difference from the previous input is that, instead of creating a new box and new atoms, we open the previously created \flecmd{create.data} file. -Now, let us create a first simulation step using a relatively small -timestep ($0.5\,\text{fs}$) and a low temperature of $T = 1\,\text{K}$: -\begin{lstlisting} -fix mynve fluid nve/limit 0.1 -fix myber fluid temp/berendsen 1 1 100 -fix myshk H2O shake 1.0e-5 200 0 b O-H a H-O-H -timestep 0.5 -\end{lstlisting} -Similar to the \lmpcmd{fix nve} command, the \lmpcmd{fix nve/limit} command performs constant -NVE integration to update the positions and velocities of the atoms at each -timestep. The difference is that \lmpcmd{fix nve/limit} also limits the maximum -distance atoms can travel during each timestep, with a chosen maximum distance -of $0.1~\text{\AA{}}$. Since the \lmpcmd{fix nve/limit} is applied to the -group \lmpcmd{fluid}, only the water molecules and ions will move. -The \lmpcmd{fix temp/berendsen} command rescales the velocities of the atoms -to ensure the system reaches the desired temperature of $1~\text{K}$. -The SHAKE algorithm is used in order to maintain -the shape of the water molecules~\cite{ryckaert1977numerical, andersen1983rattle}. +Now, let us use the SHAKE algorithm to maintain the shape of the +water molecules~\cite{ryckaert1977numerical, andersen1983rattle}. +\begin{lstlisting} +fix myshk H2O shake 1.0e-5 200 0 b O-H a H-O-H kbond 2000 +\end{lstlisting} +Here the SHAKE algorithm applies to the \lmpcmd{O-H} bond and the \lmpcmd{H-O-H} angle +of the water molecules. The \lmpcmd{kbond} keyword specifies the force constant that will be +used to apply a restraint force when used during minimization. This last keyword is important +here, because the spring constants of the rigid water molecules were set +to 0 (see the \flecmd{parameter.inc} file). +% Similar to the \lmpcmd{fix nve} command, the \lmpcmd{fix nve/limit} command performs constant +% NVE integration to update the positions and velocities of the atoms at each +% timestep. The difference is that \lmpcmd{fix nve/limit} also limits the maximum +% distance atoms can travel during each timestep, with a chosen maximum distance +% of $0.1~\text{\AA{}}$. Since the \lmpcmd{fix nve/limit} is applied to the +% group \lmpcmd{fluid}, only the water molecules and ions will move. +% The \lmpcmd{fix temp/berendsen} command rescales the velocities of the atoms +% to ensure the system reaches the desired temperature of $1~\text{K}$. Let us also create images of the system and control the printing of thermodynamic outputs by adding the following lines -to \flecmd{minimize.lmp}: +to \flecmd{equilibrate.lmp}: \begin{lstlisting} -dump mydmp all image 200 myimage-*.ppm type type & + dump mydmp all image 1 myimage-*.ppm type type & shiny 0.1 box no 0.01 view 90 0 zoom 1.8 dump_modify mydmp backcolor white & acolor O red adiam O 2 & @@ -2487,83 +2489,66 @@ \subsubsection{System preparation} acolor Cl- cyan adiam Cl- 3 & acolor WALL gray adiam WALL 3 -compute Tfluid fluid temp -thermo 200 -thermo_modify temp Tfluid +thermo 1 +thermo_style custom step temp etotal press \end{lstlisting} -The \lmpcmd{thermo\_modify} ensures that the fluid temperature \lmpcmd{Tfluid} -is printed, and not the temperature of the entire system (which is less relevant -with frozen walls). +% The \lmpcmd{thermo\_modify} ensures that the fluid temperature \lmpcmd{Tfluid} +% is printed, and not the temperature of the entire system (which is less relevant +% with frozen walls). -Let us run for 4000 steps by adding the following lines into \flecmd{minimize.lmp}: +Let us perform an energy minization by adding the following lines to \flecmd{equilibrate.lmp}: \begin{lstlisting} -run 4000 -\end{lstlisting} -When running the \flecmd{minimize.lmp} file with LAMMPS, you should observe that the -total energy of the system is initially very high but rapidly decreases, -eventually reaching a plateau (Fig.~\ref{fig:NANOSHEAR-minimization}). From the generated -images of the system, you will notice that some of the atoms are moving, particularly -those that were initially in problematic positions. - -To better equilibrate the system, we will perform two additional steps -with a larger timestep and a higher imposed temperature. Add the following lines -to \flecmd{minimize.lmp}: -\begin{lstlisting} -fix myber fluid temp/berendsen 300 300 100 -timestep 1.0 - -run 4000 - -unfix mynve -fix mynve fluid nve - -run 4000 - -write_data minimize.data nocoeff +minimize 1.0e-6 1.0e-6 1000 1000 +reset_timestep 0 \end{lstlisting} -For the last step, since the system is reaching a more -equilibrated step, it is no longer necessary to use the \lmpcmd{nve/limit} command. -Instead, we use the classic \lmpcmd{fix nve} command. - -When running the \flecmd{minimize.lmp} file with LAMMPS, you should observe that the -the temperature reaches the desired value of $300\,\text{K}$, and that the total -energy reaches a plateau (Fig.~\ref{fig:NANOSHEAR-minimization}). The generated file -\flecmd{minimize.data} contains the final state of the system. - -\begin{figure} -\centering -\includegraphics[width=\linewidth]{NANOSHEAR-minimization} -\caption{a) Temperature of the fluid as a function of time $t$ during the -minimization step of \hyperref[sheared-confined-label]{Tutorial 4}. -The horizontal dashed line is the target temperature, $T = 300$\,K, and the -vertical dotted lines are the transitions between the different steps -of the minimization. b) Total -energy $E_\text{tot}$ of the system as a function of the time $t$.} -\label{fig:NANOSHEAR-minimization} -\end{figure} +When running the \flecmd{equilibrate.lmp} file with LAMMPS, you should observe that the +total energy of the system is initially very high but rapidly decreases. From the generated +images of the system, you will notice that the atoms and molecules are moving to adopt more favorable positions. + +% To better equilibrate the system, we will perform two additional steps +% with a larger timestep and a higher imposed temperature. Add the following lines +% to \flecmd{minimize.lmp}: +% \begin{lstlisting} +% fix myber fluid temp/berendsen 300 300 100 +% timestep 1.0 + +% run 4000 + +% unfix mynve +% fix mynve fluid nve + +% run 4000 + +% write_data minimize.data nocoeff +% \end{lstlisting} +% For the last step, since the system is reaching a more +% equilibrated step, it is no longer necessary to use the \lmpcmd{nve/limit} command. +% Instead, we use the classic \lmpcmd{fix nve} command. + +% When running the \flecmd{minimize.lmp} file with LAMMPS, you should observe that the +% the temperature reaches the desired value of $300\,\text{K}$, and that the total +% energy reaches a plateau (Fig.~\ref{fig:NANOSHEAR-minimization}). The generated file +% \flecmd{minimize.data} contains the final state of the system. + +% \begin{figure} +% \centering +% \includegraphics[width=\linewidth]{NANOSHEAR-minimization} +% \caption{a) Temperature of the fluid as a function of time $t$ during the +% minimization step of \hyperref[sheared-confined-label]{Tutorial 4}. +% The horizontal dashed line is the target temperature, $T = 300$\,K, and the +% vertical dotted lines are the transitions between the different steps +% of the minimization. b) Total +% energy $E_\text{tot}$ of the system as a function of the time $t$.} +% \label{fig:NANOSHEAR-minimization} +% \end{figure} \paragraph{System equilibration} Let us equilibrate further the entire system by letting both fluid and piston -relax at ambient temperature. Open the \flecmd{equilibrate.lmp} file that was -downloaded during the tutorial setup. It contains the following lines: -\begin{lstlisting} -boundary p p f -units real -atom_style full -bond_style harmonic -angle_style harmonic -pair_style lj/cut/tip4p/long O H O-H H-O-H 0.1546 12.0 -kspace_style pppm/tip4p 1.0e-5 -kspace_modify slab 3.0 - -read_data minimize.data - -include parameters.inc -include groups.inc -\end{lstlisting} +relax at ambient temperature. Here, the commands are written within the same +\flecmd{equilibrate.lmp} file, right after the \lmpcmd{reset\_timestep} command. -Let us update the positions of \emph{all} the atoms and use a Nosé-Hoover +Let us update the positions of all the atoms and use a Nosé-Hoover thermostat. Add the following lines to \flecmd{equilibrate.lmp}: \begin{lstlisting} fix mynvt all nvt temp 300 300 100 @@ -2576,6 +2561,7 @@ \subsubsection{System preparation} visualization easier. Then, add the following lines into \flecmd{equilibrate.lmp} for the trajectory visualization: \begin{lstlisting} +undump mydmp dump mydmp all image 250 myimage-*.ppm type type & shiny 0.1 box no 0.01 view 90 0 zoom 1.8 dump_modify mydmp backcolor white & @@ -2585,6 +2571,8 @@ \subsubsection{System preparation} acolor Cl- cyan adiam Cl- 3 & acolor WALL gray adiam WALL 3 \end{lstlisting} +The \lmpcmd{undump} command is used to cancel the previous \lmpcmd{dump} command. +Then, a new \lmpcmd{dump} command with a larger dumping period is used. To monitor the system equilibration, let us print the distance between the two walls. Add the following lines to \flecmd{equilibrate.lmp}: @@ -2608,7 +2596,7 @@ \subsubsection{System preparation} write_data equilibrate.data nocoeff \end{lstlisting} -Run the \flecmd{input.lmp} file using LAMMPS. Both the pressure and the distance +Run the \flecmd{equilibrate.lmp} file using LAMMPS. Both the pressure and the distance between the two walls show oscillations at the start of the simulation but eventually stabilize at their equilibrium values toward the end of the simulation (Fig.~\ref{fig:NANOSHEAR-equilibration}). @@ -4235,8 +4223,7 @@ \subsubsection{Simulating the reaction} \end{lstlisting} With the \lmpcmd{stabilization} keyword, the \lmpcmd{bond/react} command will stabilize the atoms involved in the reaction using the \lmpcmd{nve/limit} -command with a maximum displacement of $0.03\,\mathrm{\AA{}}$ (a command that was -used, for instance, in \hyperref[sheared-confined-label]{Tutorial 4}). By default, +command with a maximum displacement of $0.03\,\mathrm{\AA{}}$. By default, each reaction is stabilized for 60 time steps. Each \lmpcmd{react} keyword corresponds to a reaction, e.g.,~a transformation of \lmpcmd{mol1} into \lmpcmd{mol2} based on the atom map \lmpcmd{M-M.rxnmap}. Implementation details about each reaction,