activity
20222026
most citedMatterSim: A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures

108 citations · 138 across the 9 of their papers we have counts for

collaborators

9 papers

cs.CL2026

Accurate, Interdisciplinary and Transparent Structure-property Understanding with Deep Native Structural Reasoning

Chen Tang, Yizhou Wang, Jianyu Wu +26

Structure-property relationships are foundational to biology, chemistry and materials science, where function, reactivity and physical response emerge from spatial, chemical and pe…

cond-mat.mtrl-sci2026

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…

cs.CL2025★ 1 cited

A Survey of Scientific Large Language Models: From Data Foundations to Agent Frontiers

Ming Hu, Chenglong Ma, Wei Li +117

Scientific Large Language Models (Sci-LLMs) are transforming how knowledge is represented, integrated, and applied in scientific research, yet their progress is shaped by the compl…

cond-mat.mtrl-sci2025

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…

cs.AI2025★ 1 cited

Nature Language Model: Deciphering the Language of Nature for Scientific Discovery

Yingce Xia, Peiran Jin, Shufang Xie +43

Foundation models have revolutionized natural language processing and artificial intelligence, significantly enhancing how machines comprehend and generate human languages. Inspire…

cond-mat.mtrl-sci2024★ 108 cited

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…

cond-mat.mtrl-sci2023★ 3 cited

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…

physics.comp-ph2022★ 1 cited

Complex-valued K-means clustering of interpolative separable density fitting algorithm for large-scale hybrid functional enabled \textit{ab initio} molecular dynamics simulations within plane waves

Shizhe Jiao, Jielan Li, Xinming Qin +3

K-means clustering, as a classic unsupervised machine learning algorithm, is the key step to select the interpolation sampling points in interpolative separable density fitting (IS…

physics.comp-ph2022★ 24 cited

KSSOLV 2.0: An efficient MATLAB toolbox for solving the Kohn-Sham equations with plane-wave basis set

Shizhe Jiao, Zhenlin Zhang, Kai Wu +12

KSSOLV (Kohn-Sham Solver) is a MATLAB toolbox for performing Kohn-Sham density functional theory (DFT) calculations with a plane-wave basis set. KSSOLV 2.0 preserves the design fea…