Tips and tricks¶

Active Learning¶

Fake MD candidates¶

DFT stage collects configurations in dir 01_md/md_candidate/*.extxyz. These are the candidates selected by the active learning algorithm.

1. You can totally "fake" these candidates by: adding some configurations, removing some configurations, or even replacing all candidates with your own configurations,... just by place your .extxyz files in the dir 01_md/md_candidate/. The active learning algorithm will just read the configurations in this dir and use them for the next DFT labeling stage. This is useful when you want have more custom configurations to be labeled by DFT.

Note: avoid adding duplicated custom configurations, do as follow:

• Run below cell to collect extxyz files in DFT label directories. Then, add the collected extxyz file back to dataset for training.
• Delete all DFT task directories
• Add custom MD candidate structures to 01_md/md_candidate/*.extxyz
• Modify _alff.iter to be n-1 2 step,
  • Relaunch alff_train step to recollect interation-data for checking duplicates. (remember to quit alff before graph building)
• Then, modify _alff.iter to be n 1 step, and relaunch alff to DFT label the new candidate structures.

2. You can also ignore completely the MD runs, and just place your own configurations in the dir 01_md/md_candidate/ for DFT labeling. Then, modify the ALFF iterlog _alff.iter to skip the MD stage and directly go to DFT stage (for example ignoring MD stage at iter 12, modify corresponding lines to 12 1). This is useful when you have some specific configurations that you want to be labeled by DFT, but you don't want to run the MD simulations.

Effective sampling¶

Rules of thumb:

• Explicitly index init_struct_paths for each structure for easier control sampling (can get them from 01_md/init_paths.yml), i.e., avoid using wildcards like init_structs/*.
• Exclude enough sampled structures from the sampling space to save computational resources (MD run).
• Add empty sampling spaces if want to ignore some iterations of sampling

md:
  sampling_spaces:
    - {} # ignore some iterations
    - {}