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cond-mat.mtrl-sci2025
Benchmarking Universal Machine Learning Interatomic Potentials for Elastic Property Prediction
Pengfei Gao, Haidi Wang
Universal machine learning interatomic potentials have emerged as efficient tools for materials simulation, yet their reliability for elastic property prediction remains unclear. H…
cond-mat.mtrl-sci2025
Uni2D: A Universal Machine Learning Interatomic Potential for Two-Dimensional Materials
Haidi Wang, Yufan Yao, Haonan Song +7
Accurate interatomic potentials (IAPs) are essential for modeling the potential energy surfaces (PES) that govern atomic interactions in materials. However, most existing IAPs are…
cond-mat.mes-hall2025
High-throughput calculations of two-dimensional auxetic with magnetism, electrocatalysis, and alkali metal battery applications
Haidi Wang, Wei Lin, Weiduo Zhu +3
Two-dimensional (2D) materials with multifunctional properties, such as negative Poisson's ratio (NPR), magnetism, catalysis, and energy storage capabilities, are of significant in…