A deep learning package for many-body potential energy representation and molecular dynamics
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Updated
Sep 23, 2026 - Python
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
Interface for simulations and standalone Python script generation with universal machine-learning interatomic potentials (MACE, CHGNet, SevenNet, NequIP, ORB, Allegro, MatterSim, UPET, GRACE, UMA, ALIGNN-FF)
Genarris is a random molecular crystal structure generator.
Meta's UMA and Orbital Materials' Orb-v3/OrbMol interatomic potential models, running on Tenstorrent hardware.
Accelerating Metadynamics-Based Free-Energy Calculations with Adaptive Machine Learning Potentials
Endstate corrections from MM to QML potential
A lightweight Snakemake-based workflow that implements the DP-GEN scheme.
Collection of tools/codes/data used in the article D4DD00265B
Loads Crystallography Open Database structures, filters them for CHGNet, and streams live relaxations after substitution, vacancy or strain edits. 27.8% of COD qualifies.
Machine learning interatomic potentials and their application to lithium batteries (seminar talk in Spanish).
A minimal package for providing pretrained machine learning force fields (e.g. multi-fidelity M3GNet) for material simulations.
Lightweight tools for UMA-based structure relaxation, embedding extraction, and VASP input generation.
Companion code for Catal. Lett. 156(9) 2026: DFT → DeepMD → uncertainty-guided Bayesian optimization for SCR catalyst screening (9,726 DFT configurations, energy R² = 0.995, force R² = 0.963)
Physics bachelor's thesis project focused on testing the physical adequacy and physical foundations of MLIPs in the context of molecular simulations.
Reproducible CHGNet relaxation-energy pilot on Materials Project LLZO structures
AIMNet2 hands-on tutorial: nine notebooks from a single-point energy to chemical reactivity. AiMat Summer School 2026, Machine Learning for Molecules
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