108 citations · 342 across the 19 of their papers we have counts for
10 papers · 1 filter
Understanding compression-extension error asymmetry in universal machine-learning interatomic potentials
Hongwei Du, Dingyang Lv, Boyan Zhang +11
Universal machine-learning interatomic potentials are increasingly used across diverse materials, yet their reliability under directional deformation remains unclear. We compare co…
Origins of Universal Machine Learning Force-Field Errors in Multicomponent Materials
Hongwei Du, Dingyang Lv, Baole Wei +11
Universal machine learning force-field generalization to multicomponent environments generated by compositional design remains insufficiently assessed. We construct a benchmark of…
Train for Accuracy, Execute at Scale: Architecture-Preserving Inference for Equivariant Atomistic Foundation Models
Lei Fu, Zihui Feng, Yongheng Li +8
Equivariant atomistic foundation models provide broadly transferable interatomic potentials trained against quantum-mechanical reference data, but their repeated execution at simul…
A Hierarchical Synergistic Deep Learning Framework Integrating Composition, Structure, and Ionic Transport for Solid-State Electrolyte Discovery
Hongwei Du, Dingyang Lv, Baole Wei +5
Inorganic solid-state electrolytes must combine high room-temperature ionic conductivity, a wide electrochemical window, excellent electronic insulation, and favorable mechanical c…
MatterSim-MT: A multi-task foundation model for in silico materials characterization
Han Yang, Xixian Liu, Chenxi Hu +25
Accurate property characterization is a major bottleneck in materials design. While first-principles methods and task-specific machine-learning models have driven important progres…
Probing the Limit of Heat Transfer in Inorganic Crystals with Deep Learning
Jielan Li, Zekun Chen, Qian Wang +21
Heat transfer is a fundamental property of matter. Research spanning decades has attempted to discover materials with exceptional thermal conductivity, yet the upper limit remains…
MatterSim: A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures
Han Yang, Chenxi Hu, Yichi Zhou +19
Accurate and fast prediction of materials properties is central to the digital transformation of materials design. However, the vast design space and diverse operating conditions p…
AI-guided inverse design and discovery of recyclable vitrimeric polymers
Yiwen Zheng, Prakash Thakolkaran, Agni K. Biswal +6
Vitrimer is a new, exciting class of sustainable polymers with the ability to heal due to their dynamic covalent adaptive network that can go through associative rearrangement reac…
Overcoming the Size Limit of First Principles Molecular Dynamics Simulations with an In-Distribution Substructure Embedding Active Learner
Lingyu Kong, Jielan Li, Lixin Sun +7
Large-scale first principles molecular dynamics are crucial for simulating complex processes in chemical, biomedical, and materials sciences. However, the unfavorable time complexi…
Accelerating Cathode Material Discovery through Ab Initio Random Structure Searching
Bonan Zhu, Ziheng Lu, Chris J. Pickard +1
The choice of cathode material in Li-ion batteries (LIBs) underpins their overall performance. Discovering new cathode materials is a slow process, and all major commercial cathode…