132 citations · 232 across the 4 of their papers we have counts for
5 papers
LunaTP: Enabling Efficient and Scalable Tensor Product for Equivariant MLIPs
Yuanchang Zhou, Mingzhen Li, Chen Wang +3
Equivariant machine learning interatomic potentials (MLIPs) promise atomistic simulations with accuracy approaching that of quantum-mechanical calculations. However, the high compu…
Breaking the Training Barrier of Billion-Parameter Universal Machine Learning Interatomic Potentials
Yuanchang Zhou, Hongyu Wang, Yiming Du +12
Universal Machine Learning Interatomic Potentials (uMLIPs), pre-trained on massively diverse datasets encompassing inorganic materials and organic molecules across the entire perio…
MatRIS: Toward Reliable and Efficient Pretrained Machine Learning Interatomic Potentials
Yuanchang Zhou, Siyu Hu, Xiangyu Zhang +3
Foundation MLIPs demonstrate broad applicability across diverse material systems and have emerged as a powerful and transformative paradigm in chemical and computational materials…
DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials
Jinzhe Zeng, Duo Zhang, Anyang Peng +44
In recent years, machine learning potentials (MLPs) have become indispensable tools in physics, chemistry, and materials science, driving the development of software packages for m…
DPA-2: a large atomic model as a multi-task learner
Duo Zhang, Xinzijian Liu, Xiangyu Zhang +40
The rapid advancements in artificial intelligence (AI) are catalyzing transformative changes in atomic modeling, simulation, and design. AI-driven potential energy models have demo…