activity
20192026
most citedE(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials

1.9k citations · 2.2k across the 19 of their papers we have counts for

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Showing cond-mat.mtrl-sciShow all

7 papers · 1 filter

cond-mat.mtrl-sci2025★ 3 cited

Coupled reaction and diffusion governing interface evolution in solid-state batteries

Jingxuan Ding, Laura Zichi, Matteo Carli +4

Understanding and controlling the atomistic-level reactions governing the formation of the solid-electrolyte interphase (SEI) is crucial for the viability of next-generation solid…

cond-mat.mtrl-sci2025★ 3 cited

Revealing the proton slingshot mechanism in solid acid electrolytes through machine learning molecular dynamics

Menghang Wang, Jingxuan Ding, Grace Xiong +8

In solid acid solid electrolytes CsHPO and CsHSO, mechanisms of fast proton conduction have long been debated and attributed to either local proton hopping or polyanion…

cond-mat.mtrl-sci2024★ 7 cited

Atomistic evolution of active sites in multi-component heterogeneous catalysts

Cameron J. Owen, Lorenzo Russotto, Christopher R. O'Connor +4

Multi-component metal nanoparticles (NPs) are of paramount importance in the chemical industry, as most processes therein employ heterogeneous catalysts. While these multi-componen…

cond-mat.mtrl-sci2024★ 4 cited

Unified Differentiable Learning of Electric Response

Stefano Falletta, Andrea Cepellotti, Anders Johansson +4

Predicting response of materials to external stimuli is a primary objective of computational materials science. However, current methods are limited to small-scale simulations due…

cond-mat.mtrl-sci2023★ 1 cited

Accurate Surface and Finite Temperature Bulk Properties of Lithium Metal at Large Scales using Machine Learning Interaction Potentials

Mgcini Keith Phuthi, Archie Mingze Yao, Simon Batzner +4

The properties of lithium metal are key parameters in the design of lithium ion and lithium metal batteries. They are difficult to probe experimentally due to the high reactivity a…

cond-mat.mtrl-sci2023★ 2 cited

Complexity of Many-Body Interactions in Transition Metals via Machine-Learned Force Fields from the TM23 Data Set

Cameron J. Owen, Steven B. Torrisi, Yu Xie +6

This work examines challenges associated with the accuracy of machine-learned force fields (MLFFs) for bulk solid and liquid phases of d-block elements. In exhaustive detail, we co…

cond-mat.mtrl-sci2019★ 33 cited

Unsupervised landmark analysis for jump detection in molecular dynamics simulations

Leonid Kahle, Albert Musaelian, Nicola Marzari +1

Molecular dynamics is a versatile and powerful method to study diffusion in solid-state ionic conductors, requiring minimal prior knowledge of equilibrium or transition states of t…