249 citations · 392 across the 11 of their papers we have counts for
5 papers · 1 filter
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…
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-…
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…