249 citations · 392 across the 11 of their papers we have counts for
7 papers · 1 filter
Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles
Mikołaj 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 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…
Overcoming systematic softening in universal machine learning interatomic potentials by fine-tuning
Bowen Deng, Yunyeong Choi, Peichen Zhong +6
Machine learning interatomic potentials (MLIPs) have introduced a new paradigm for atomic simulations. Recent advancements have seen the emergence of universal MLIPs (uMLIPs) that…
Developments and applications of the OPTIMADE API for materials discovery, design, and data exchange
Matthew L. Evans, Johan Bergsma, Andrius Merkys +56
The Open Databases Integration for Materials Design (OPTIMADE) application programming interface (API) empowers users with holistic access to a growing federation of databases, enh…
Pushing the Pareto front of band gap and permittivity: ML-guided search for dielectric materials
Janosh Riebesell, T. Wesley Surta, Rhys Goodall +2
Materials with high-dielectric constant easily polarize under external electric fields, allowing them to perform essential functions in many modern electronic devices. Their practi…
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,…