1、准备数据集：数据集为extxyz格式文件，可以查看example.xyz文件作为一个示例(example.xyz是此处部分用于训练的数据)
2、调整train.py中的参数后执行
python train.py
3、一个典型的输出参考 std.out 中
4、结果
训练涉及的结构
$ ls MACE_models/
mace_model_compiled.model mace_model_run-42_debug.log mace_model_run-42.log mace_model_run-42_train_Default_stage_one.png mace_model_stagetwo_compiled.model
mace_model_config.yml mace_model_run-42_epoch-192.pt mace_model_run-42.model mace_model_run-42_train_Default_stage_two.png mace_model_stagetwo.model
mace_model.model mace_model_run-42_epoch-498_swa.pt mace_model_run-42_stagetwo.model mace_model_run-42_train.txt
905 2026-06-28 15:30:33.024 INFO: Error-table on TRAIN and VALID:
906 +---------------+---------------------+------------------+-------------------+
907 | config_type | RMSE E / meV / atom | RMSE F / meV / A | relative F RMSE % |
908 +---------------+---------------------+------------------+-------------------+
909 | train_Default | 0.3 | 17.5 | 1.53 |
910 | valid_Default | 0.2 | 23.4 | 2.03 |
911 +---------------+---------------------+------------------+-------------------+
Batatia, I., Kovács, D. P., Simm, G. N. C., Ortner, C. & Csányi, G. MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields. Preprint at https://doi.org/10.48550/arXiv.2206.07697 (2023).