Gerbrand Ceder

Harnessing X-ray Absorption Spectroscopy Data through Multimodal Mining of Battery Literature

Tanjin He, Aikaterini Vriza, Logan Ward +7

X-ray absorption spectroscopy (XAS) is central to understanding the local electronic and atomic structure of materials, yet most published spectra remain inaccessible to data-drive…

Crystallographic Challenges in Microscopy of Multidomain Spinel Materials

Ninon Scherz, Shashwat Anand, Colin Ophus +4

Electron microscopy techniques are instrumental in the characterization of energy storage materials, with atomic resolution images providing the detailed structural features that a…

Thermodynamic accessibility of Li-Mn-Ti-O cation disordered rock-salt phases

Ronald L. Kam, Shilong Wang, Gerbrand Ceder

Disordered rock-salt with Li-excess (DRX) cathode phases within the Li-Mn-Ti-O (LMTO) composition space have recently been extensively studied, as they promise to deliver exception…

cs.LG2026★ 1 cited

MATRIX: A Multimodal Benchmark and Post-Training Framework for Materials Science

Delia McGrath, Curtis Chong, Rohil Kulkarni +2

Scientific reasoning in materials science requires integrating multimodal experimental evidence with underlying physical theory. Existing benchmarks make it difficult to assess whe…

cond-mat.mtrl-sci2025★ 4 cited

Mechanisms of alkali ionic transport in amorphous oxyhalides solid state conductors

Luca Binci, KyuJung Jun, Bowen Deng +1

Amorphous oxyhalides have attracted significant attention due to their relatively high ionic conductivity ( $>$ 1 mS cm $^{- 1}$ ), excellent chemical stability, mechanical softness, an…

Generative Models for Crystalline Materials

Houssam Metni, Laura Ruple, Lauren N. Walters +13

Understanding structure-property relationships in materials is fundamental in condensed matter physics and materials science. Over the past few years, machine learning (ML) has eme…

Hierarchical high-throughput screening of alkaline-stable lithium-ion conductors combining machine learning and first-principles calculations

Zhuohan Li, KyuJung Jun, Bowen Deng +1

Solid-state batteries require lithium-ion conductors that combine high ionic conductivity with stability under harsh electrochemical and chemical conditions. Here, we investigate t…

Li+/H+ exchange in solid-state oxide Li-ion conductors

Zhuohan Li, Benjamin X. Lam, Shilong Wang +1

Understanding the moisture stability of oxide Li-ion conductors is important for their practical applications in solid-state batteries. Unlike sulfide or halide conductors, oxide c…

cond-mat.mtrl-sci2025★ 3 cited

Investigating the Degradation of LATP Solid Electrolyte in High Alkaline Li- $O_{2}$ Batteries

Tara P Mishra, Zhuohan Li, Meghan Shen +8

In this study, we address the challenge of electrolyte degradation in all-solid-state humidified Li- $O_{2}$ batteries, which offer high theoretical energy density and potential cost…

physics.comp-ph2025

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…

Modeling phase transformations in Mn-rich disordered rocksalt cathodes with machine learning interatomic potentials

Peichen Zhong, Bowen Deng, Shashwat Anand +2

Mn-rich disordered rocksalt (DRX) cathode materials exhibit a phase transformation from a disordered to a partially disordered spinel-like structure ( $δ$ -phase) during electrochemi…

cs.DC2025★ 3 cited

DistMLIP: A Distributed Inference Platform for Machine Learning Interatomic Potentials

Kevin Han, Bowen Deng, Amir Barati Farimani +1

Large-scale atomistic simulations are essential to bridge computational materials and chemistry to realistic materials and drug discovery applications. In the past few years, rapid…

cond-mat.mtrl-sci2025★ 6 cited

Cross-functional transferability in universal machine learning interatomic potentials

Xu Huang, Bowen Deng, Peichen Zhong +3

The rapid development of universal machine learning interatomic potentials (uMLIPs) has demonstrated the possibility for generalizable learning of the universal potential energy su…

Crystal structure prediction with host-guided inpainting generation and foundation potentials

Peichen Zhong, Xinzhe Dai, Bowen Deng +2

Unconditional crystal structure generation with diffusion models faces challenges in identifying symmetric crystals as the unit cell size increases. We present the Crystal Host-Gui…

cond-mat.mtrl-sci2025★ 14 cited

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) $^{1 - 3}$ offer…

Materials Graph Library (MatGL), an open-source graph deep learning library for materials science and chemistry

Tsz Wai Ko, Bowen Deng, Marcel Nassar +7

Graph deep learning models, which incorporate a natural inductive bias for a collection of atoms, are of immense interest in materials science and chemistry. Here, we introduce the…

Origin of Enhanced Performance when Mn-Rich Rocksalt Cathodes transform to $δ$ -DRX

Shashwat Anand, Tara P. Mishra, Peichen Zhong +4

Most Mn-rich cathodes are known to undergo phase transformation into structures resembling spinel-like ordering upon electrochemical cycling. Recently, the irreversible transformat…

Ion correlations explain kinetic selectivity in diffusion-limited solid state synthesis reactions

Vir Karan, Max C. Gallant, Yuxing Fei +2

Establishing viable solid-state synthesis pathways for novel inorganic materials remains a major challenge in materials science. Previous pathway design methods using pair-wise rea…