Dual Adaptive Sampling for Machine Learning Interatomic potential.

Related tags

Machine Learningdas
Overview

DAS

Dual Adaptive Sampling for Machine Learning Interatomic potential.

How to cite

If you use this code in your research, please cite this using: Hongliang Yang, Yifan Zhu, Erting Dong, Yabei Wu, Jiong Yang, and Wenqing Zhang. Dual adaptive sampling and machine learning interatomic potentials for modeling materials with chemical bond hierarchy. Phys. Rev. B 104, 094310 (2021).

Install

Install pymtp

You should first install the python interface for mtp: https://github.com/hlyang1992/pymtp

Install das

You can download the code by

git clone https://github.com/hlyang1992/das
cd das
cp -r <path-to-mlip-2>/untrained_mtps/*.mtp das/utils/untrained_mtps

Then remove the redundant settings from each mtp file. Only the following settings can be retained for each mtp file:

radial_funcs_count = 
alpha_moments_count = 
alpha_index_basic_count = 
alpha_index_basic = 
alpha_index_times_count = 
alpha_index_times = 
alpha_scalar_moments = 
alpha_moment_mapping =

Install das by

cd <path-to-das>
pip install -r requirements.txt
pip install .

Usage

das  config_dir  job_name

Configuration

The configuration directory config_dir must contain the configuration file conf.yaml, which controls all sampling processes. The conf.yaml file should look like the following:

"global_settings":

"machine_settings":

"selector_settings": {} 

"labeler_settings":

"trainer_settings":

"sampler_settings":

"init_conf_setting":

"iter_params_template":

"iter_params":
  • global_settings:
"global_settings":
  # The elements in the system, the order of the elements does not matter, the program automatically numbers the 
  # atomic types according to their atomic number from smallest to largest.
  "unique_elements": [ "Co", "Sb" ]
  # path to VASP Pseudopotential Database, see detail at https://wiki.fysik.dtu.dk/ase/ase/calculators/vasp.html#vasp
  "vasp_pp_path": "path_to_directory" 
  • machine_settings:

All time-consuming computational tasks such as sampling, labeling, and training can be dispatched to designated machines via ssh. Currently only LSF is supported and migration to other job management systems is very easy.

"machine_settings":
  "machine_1":
    # The supported machine types are now: `machine_lsf`, `machine_shell`
    "machine_type": "machine_lsf"
    "host": "ip address"
    "user": "username"
    "password": "password"
    # Exclude these nodes when submitting tasks.
    "bad_nodes": [ ] # #BSUB -R "hname!={{node}}"
    "port": 22
    # number of cores for each task
    "n_cores": 40 # #BSUB -n {{ncores}}
    "n_tasks": 40 # The maximum number of tasks to run simultaneously.
    "q_name": "short" # #BSUB -q {{q_name}}
    "env_source_file": "env.sh" # env.sh is in the config_dir
    "run_dir": "path-to-run-directory-in-target"
    "extra_params":
      "vasp_cmd": "mpiexec.hydra -machinefile $LSB_DJOB_HOSTFILE -np $NP vasp"
      "lmp_cmd": "mpiexec.hydra -machinefile $LSB_DJOB_HOSTFILE -np $NP lmp_mlp"
      "mlip_cmd": "mpiexec.hydra -machinefile $LSB_DJOB_HOSTFILE -np $NP mlp train"
      "python_cmd": "absolute path to python path"
  "machine_2":
    # setting for machchine_2
    "machine_type": "machine_lsf"
    # ...

You should prepare a file to set the environment variables. The program will source this file to set the environment variables after connecting to the machine via ssh. For technical reasons please see: The remote shell environment doesn’t match interactive shells

  • sampler_settings
"scale_1":
  "kind": "scale_box"
  "scale_factors": [0.998, 0.9985, 0.999]
"scale_2":
  "kind": "scale_box"
  "scale_factors": [[0.998, 0.9985, 0.999, 0.997], # a
                    [1.002, 1.003, 1.004, 1.005],  # b
                    [0.997, 0.995, 0.999, 0.996]] # c
"nvt_0": 
  "kind": "lmp_model_sampler"
  "max_number_confs": 5
  "min_number_confs": 0
  "machine": "machine_1"
  "lmp_vars":
    "temp": [ 100, 150 ]
    "steps": [ 10000 ]
    "nevery": [ 20 ]
    "prev_steps": [ 0 ]
 
"npt_0": 
  "kind": "lmp_model_sampler"
  "max_number_confs": 5
  "min_number_confs": 0
  "machine": "machine_2"
  "lmp_vars":
    "temp": [ 100, 150 ]
    "steps": [ 10000 ]
    "nevery": [ 20 ]
    "press": [100, 200] # bar
    "prev_steps": [ 0 ]
  • "labeler_settings"

We use ase to generate input files (INCAR, POTCAR, KPOINTS) for VASP calculation. Please see detail at Ase vasp calculator

"labeler_settings":
  "vasp":
    "kind": "vasp"
    "machine": "ty_label"
    "vasp_parms":
      "xc": "pbe"
      "prec": "A"
      # other setting for vasp calculations
  • "trainer_settings"
"trainer_settings":
  "train_5_model":
    "kind": "mtp_trainer"
    "machine": "ty_train" 
    "model_index": 18 
    "min_dist": 1.39 
    "max_dist": 5.0
    "n_models": 5 
    "train_from_prev_model": true 
  • init_conf_setting:
"init_conf_setting":
  "-1": [ "init_MD.cfg" ]
  "-2": [ "init_1.vasp" ]
  "-3": [ "init_2.vasp" ]
  • iter_params_template:
"iter_params_template":
  "0":
    "init_conf": [ -1 ]
    "sampler": [ ]
    "selector": [ ]
    "labeler": [ ]
    "trainer": [ "train_5_model" ]
  "10":
    "init_conf": [ -2 ]
    "sampler": [ "scale_0", "nvt_0" ]
    "selector": [ ]
    "labeler": [ "vasp" ]
    "trainer": [ "train_5_model" ]
  "20":
    "init_conf": [ -3 ]
    "sampler": [ "npt_0"]
    "selector": [ ]
    "labeler": [ "vasp" ]
    "trainer": [ "train_5_model" ]
  "30":
    "init_conf": [ -2,-3 ]
    "sampler": [ "npt_0"]
    "selector": [ ]
    "labeler": [ "vasp" ]
    "trainer": [ "train_5_model" ]
  • iter_params:
"iter_params":
  [
    [ "0" ],
    # If the last one is LOOP, repeat all the previous ones until convergence.
    ["10", "LOOP"], 
    ["30", "LOOP"],
    ["10", "10"]  
    ["20"],
  ]
Scikit-Garden or skgarden is a garden for Scikit-Learn compatible decision trees and forests.

Scikit-Garden or skgarden (pronounced as skarden) is a garden for Scikit-Learn compatible decision trees and forests.

260 Dec 21, 2022
Deploy AutoML as a service using Flask

AutoML Service Deploy automated machine learning (AutoML) as a service using Flask, for both pipeline training and pipeline serving. The framework imp

Chris Rawles 221 Nov 04, 2022
Built various Machine Learning algorithms (Logistic Regression, Random Forest, KNN, Gradient Boosting and XGBoost. etc)

Built various Machine Learning algorithms (Logistic Regression, Random Forest, KNN, Gradient Boosting and XGBoost. etc). Structured a custom ensemble model and a neural network. Found a outperformed

Chris Yuan 1 Feb 06, 2022
Decision tree is the most powerful and popular tool for classification and prediction

Diabetes Prediction Using Decision Tree Introduction Decision tree is the most powerful and popular tool for classification and prediction. A Decision

Arjun U 1 Jan 23, 2022
This is a curated list of medical data for machine learning

Medical Data for Machine Learning This is a curated list of medical data for machine learning. This list is provided for informational purposes only,

Andrew L. Beam 5.4k Dec 26, 2022
MLOps pipeline project using Amazon SageMaker Pipelines

This project shows steps to build an end to end MLOps architecture that covers data prep, model training, realtime and batch inference, build model registry, track lineage of artifacts and model drif

AWS Samples 3 Sep 16, 2022
A webpage that utilizes machine learning to extract sentiments from tweets.

Tweets_Classification_Webpage The goal of this project is to be able to predict what rating customers on social media platforms would give to products

Ayaz Nakhuda 1 Dec 30, 2021
Skforecast is a python library that eases using scikit-learn regressors as multi-step forecasters

Skforecast is a python library that eases using scikit-learn regressors as multi-step forecasters. It also works with any regressor compatible with the scikit-learn API (pipelines, CatBoost, LightGBM

Joaquín Amat Rodrigo 297 Jan 09, 2023
Auto updating website that tracks closed & open issues/PRs on scikit-learn/scikit-learn.

Repository Status for Scikit-learn Live webpage Auto updating website that tracks closed & open issues/PRs on scikit-learn/scikit-learn. Running local

Thomas J. Fan 6 Dec 27, 2022
MLBox is a powerful Automated Machine Learning python library.

MLBox is a powerful Automated Machine Learning python library. It provides the following features: Fast reading and distributed data preprocessing/cle

Axel 1.4k Jan 06, 2023
Data Version Control or DVC is an open-source tool for data science and machine learning projects

Continuous Machine Learning project integration with DVC Data Version Control or DVC is an open-source tool for data science and machine learning proj

Azaria Gebremichael 2 Jul 29, 2021
Tangram makes it easy for programmers to train, deploy, and monitor machine learning models.

Tangram Website | Discord Tangram makes it easy for programmers to train, deploy, and monitor machine learning models. Run tangram train to train a mo

Tangram 1.4k Jan 05, 2023
Lingtrain Alignment Studio is an ML based app for texts alignment on different languages.

Lingtrain Alignment Studio Intro Lingtrain Alignment Studio is the ML based app for accurate texts alignment on different languages. Extracts parallel

Sergei Averkiev 186 Jan 03, 2023
Iris-Heroku - Putting a Machine Learning Model into Production with Flask and Heroku

Puesta en Producción de un modelo de aprendizaje automático con Flask y Heroku L

Jesùs Guillen 1 Jun 03, 2022
ZenML 🙏: MLOps framework to create reproducible ML pipelines for production machine learning.

ZenML is an extensible, open-source MLOps framework to create production-ready machine learning pipelines. It has a simple, flexible syntax, is cloud and tool agnostic, and has interfaces/abstraction

ZenML 2.6k Jan 08, 2023
Retrieve annotated intron sequences and classify them as minor (U12-type) or major (U2-type)

(intron I nterrogator and C lassifier) intronIC is a program that can be used to classify intron sequences as minor (U12-type) or major (U2-type), usi

Graham Larue 4 Jul 26, 2022
Esse é o meu primeiro repo tratando de fim a fim, uma pipeline de dados abertos do governo brasileiro relacionado a compras de contrato e cronogramas anuais com spark, em pyspark e SQL!

Olá! Esse é o meu primeiro repo tratando de fim a fim, uma pipeline de dados abertos do governo brasileiro relacionado a compras de contrato e cronogr

Henrique de Paula 10 Apr 04, 2022
Sequence learning toolkit for Python

seqlearn seqlearn is a sequence classification toolkit for Python. It is designed to extend scikit-learn and offer as similar as possible an API. Comp

Lars 653 Dec 27, 2022
Implementation of K-Nearest Neighbors Algorithm Using PySpark

KNN With Spark Implementation of KNN using PySpark. The KNN was used on two separate datasets (https://archive.ics.uci.edu/ml/datasets/iris and https:

Zachary Petroff 4 Dec 30, 2022
Timeseries analysis for neuroscience data

=================================================== Nitime: timeseries analysis for neuroscience data ===============================================

NIPY developers 212 Dec 09, 2022