A deep learning package for many-body potential energy representation and molecular dynamics

Graphics Processing Units Molecular Dynamics

AI-enhanced computational chemistry

GPU Monte Carlo Simulation Code with a taste of RASPA

Accelerating Metadynamics-Based Free-Energy Calculations with Adaptive Machine Learning Potentials

Genarris is a random molecular crystal structure generator.

Endstate corrections from MM to QML potential

GUI for running simulations with universal machine learning interatomic potentials (MACE, CHGNet, SevenNet, Nequix, ORB, MatterSim))

A lightweight Snakemake-based workflow that implements the DP-GEN scheme.

Collection of tools/codes/data used in the article D4DD00265B

A minimal package for providing pretrained machine learning force fields (e.g. multi-fidelity M3GNet) for material simulations.

Physics bachelor's thesis project based on the study and applicability of Machine Learning Potentials in the context of biophysics.

This is the GitHub repo to support the manuscript "Machine Learning Approaches for Developing Potential Surfaces: Applications to OH−(H2O)n (n = 1 − 3) Complexes"

Machine learning interatomic potentials and their application to lithium batteries (seminar talk in Spanish).

Code for term project of Molecular Data Science & Informatics (CH5650) course taken at IIT Madras during Jan-May 2022

Evaluate the ensemble model deviation in the same fashion as DeepMD, integrate with ai2kit workflow for MACE