This project is to reduce the number of samples required for training a large atomic model (LAM) developed based on the cloud-native workflow framework dflow. So far, DeePMD, OCP and Nequip are supported.
Install this project by
pip install .
Configure workflow server required by dflow before submit a workflow by command line
dpclean submit input.json
Some example input files can be found in examples.
In Parallel Mode, training sets are ramdomly splitted into several groups based on the appointed ratios; In Active Learning Mode, training sets are initially splitted into a subset (0.001 by default), after training on the subset, dp test will be conducted to the remaining systems, among which with the largest error will be added to training set for the next iteration of training.
Please note that numb_steps and decay_steps should vary according to the frames, so that the epochs of every loop are controlled to be the same. For instance, if a training system contains 10000 frames and the expected training steps for full-size system is 1000000, the parameter of numb_steps should set as "math.ceil(100*n)", n represents the frame number of every iteration, the coefficient 100 represents full-size training step / full-size frame number.