6 papers
Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles
MikoÅaj J. Gawkowski, Nongnuch Artrith, Silvia Bonfanti +14
Foundation machine learning interatomic potentials (MLIPs) are increasingly being used as drop-in replacements for first-principles calculations, enabling simulations of materials…
A foundation model for atomistic materials chemistry
Ilyes Batatia, Philipp Benner, Yuan Chiang +85
Atomistic simulations of matter, especially those that leverage first-principles (ab initio) electronic structure theory, provide a microscopic view of the world, underpinning much…
TorchSim: An efficient atomistic simulation engine in PyTorch
Orion Cohen, Janosh Riebesell, Rhys Goodall +6
We introduce TorchSim, an open-source atomistic simulation engine tailored for the Machine Learned Interatomic Potential (MLIP) era. By rewriting core atomistic simulation primitiv…
A Foundational Potential Energy Surface Dataset for Materials
Aaron D. Kaplan, Runze Liu, Ji Qi +6
Accurate potential energy surface (PES) descriptions are essential for atomistic simulations of materials. Universal machine learning interatomic potentials (UMLIPs) offer…
Matbench Discovery -- A framework to evaluate machine learning crystal stability predictions
Janosh Riebesell, Rhys E. A. Goodall, Philipp Benner +7
The rapid adoption of machine learning (ML) in domain sciences necessitates best practices and standardized benchmarking for performance evaluation. We present Matbench Discovery,…
LLaMP: Large Language Model Made Powerful for High-fidelity Materials Knowledge Retrieval and Distillation
Yuan Chiang, Elvis Hsieh, Chia-Hong Chou +1
Reducing hallucination of Large Language Models (LLMs) is imperative for use in the sciences, where reliability and reproducibility are crucial. However, LLMs inherently lack long-…