5 papers
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…
UniMatSim: A High-Throughput Materials Simulation Automation Framework Based on Universal Machine Learning Potentials
Yanjin Xiang, Yihan Nie, Yunzhi Gao +2
Universal machine learning interatomic potentials (UMLIPs) offer accuracy close to first-principles calculations at a fraction of the cost, showing significant potential for large-…
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…
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…
High-throughput Screening of Ferrimagnetic Semiconductors With Ultrahigh Nel Temperature
Haidi Wang, Qingqing Feng, Shuo Li +6
Ferrimagnetic semiconductors, integrated with net magnetization, antiferromagnetic coupling and semi-conductivity, have constructed an ideal platform for spintronics. For practical…