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Module for Ab Initio Structure Evolution (MAISE) features
* neural network-based description of interatomic interactions
* evolutionary optimization
* structure analysis
1. General info
2. Download and Installation
3. Input
4. Examples
5. Setup input tag description
MAISE has been developed by
Alexey Kolmogorov kolmogorov@binghamton.edu
Samad Hajinazar hajinazar@binghamton.edu
Ernesto Sandoval esandov1@binghamton.edu
Current version 2.2 works on Linux platforms and combines 3 modules for modeling, optimizing, and analyzing atomic structures.
1 The neural network (NN) module builds, tests, and uses NN models to describe interatomic interactions with near-ab initio accuracy at a low computational cost compared to density functional theory calculations.
With the primary goal of using NN models to accelerate structure search, the main function of the module is to relax given structures. To simplify the NN application and comparison, we closely matched the input and output file formats with those used in the VASP software. Previously parameterized NN models available in the 'models/' directory have been generated and extensively tested for crystalline and/or nanostructured materials. First practical applications of NNs include the prediction of new synthesizable Mg-Ca alloys [1] and identification of more stable Cu-Pd-Ag nanoparticles [2].
Users can create their own NN models with MAISE which are typically trained on density functional theory (DFT) total energy and atomic force data for relatively small structures. The generation of relevant and diverse configurations is done separately with an 'evolutionary sampling' protocol detailed in our published work [3]. The code introduces a unique feature, 'stratified training', of how to build robust NNs for chemical systems with several elements [3]. NN models are developed in a hierarchical fashion, first for elements, then for binaries, and so on, which enables generation of reusable libraries for extended blocks in the periodic table.
2 The implemented evolutionary algorithm (EA) enables an efficient identification of ground state configurations at a given chemical composition. Our studies have shown that the EA is particularly advantageous in dealing with large structures when no experimental structural input is available [3,4].
The searches can be performed for 3D bulk crystals, 2D films, and 0D nanoparticles. Population of structures can be generated either randomly or predefined based on prior information. Essential operations are 'crossover', when a new configuration is created based on two parent structures in the previous generation, and 'mutation', when a parent structure is randomly distorted. For 0D nanoparticles we have introduced a multitribe evolutionary algorithm that allows an efficient simultaneous optimization of clusters in a specified size range [2].
3 The analysis functions include the comparison of structures based on
the radial distribution function (RDF), the determination of the space
group and the Wyckoff positions with an external SPGLIB package,
etc. In particular, the RDF-based structure dot product is essential
for eliminating duplicate structures in EA searches and selecting
different configurations in the pool of found low-energy structures.
[1] https://pubs.rsc.org/en/content/articlelanding/2018/cp/c8cp05314f#!divAbstract
[2] https://pubs.rsc.org/en/content/articlelanding/2019/cp/c9cp00837c#!divAbstract
[3] https://journals.aps.org/prb/abstract/10.1103/PhysRevB.95.014114
[4] https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.109.075501
[5] https://journals.aps.org/prb/abstract/10.1103/PhysRevB.98.085131