3 citations · 5 across the 5 of their papers we have counts for
5 papers
Breaking Bottlenecks in Solid Electrolyte Discovery with Large Artificial Intelligence Models
Eric Jianfeng Cheng, Min Hong, Zhiquan Zeng +22
Solid electrolytes (SEs) are central to next-generation metal batteries, yet their discovery remains constrained by fragmented data, limited transferability of simulations, and slo…
Interpolation and differentiation of alchemical degrees of freedom in machine learning interatomic potentials
Juno Nam, Jiayu Peng, Rafael Gómez-Bombarelli
Machine learning interatomic potentials (MLIPs) have become a workhorse of modern atomistic simulations, and recently published universal MLIPs, pre-trained on large datasets, have…
Atom-by-atom design of metal oxide catalysts for the oxygen evolution reaction with machine learning
Jaclyn R. Lunger, Jessica Karaguesian, Hoje Chun +6
Green hydrogen production is crucial for a sustainable future, but current catalysts for the oxygen evolution reaction (OER) suffer from slow kinetics, despite many efforts to prod…
Data-Driven, Physics-Informed Descriptors of Cation Ordering in Multicomponent Oxides
Jiayu Peng, James Damewood, Rafael Gómez-Bombarelli
The structural tunability and compositional diversity of multicomponent perovskite oxides have enabled their various applications, including catalysis and electronics. The cation o…
Representations of Materials for Machine Learning
James Damewood, Jessica Karaguesian, Jaclyn R. Lunger +4
High-throughput data generation methods and machine learning (ML) algorithms have given rise to a new era of computational materials science by learning relationships among composi…