1 citations · 1 across the 4 of their papers we have counts for
Showing cond-mat.mtrl-sciShow all
3 papers · 1 filter
cond-mat.mtrl-sci2026
What drives performance in molecular MPNNs? An operator-level factorial benchmark
Panyu Jiao, Shuizhou Chen, Yiheng Shen +3
Message-passing neural networks (MPNNs) are widely used for molecular property prediction, but their deployment as monolithic architectures makes it difficult to identify how speci…
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…
cond-mat.mtrl-sci2024
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…