450 citations · 480 across the 7 of their papers we have counts for
7 papers
Lang2MLIP: End-to-End Language-to-Machine Learning Interatomic Potential Development with Autonomous Agentic Workflows
Wenwen Li, Yuki Orimo, Nontawat Charoenphakdee
Developing machine learning interatomic potentials (MLIPs) for complex materials systems remains challenging because it requires expertise in atomistic simulations, machine learnin…
LightPFP: A Lightweight Route to Ab Initio Accuracy at Scale
Wenwen Li, Nontawat Charoenphakdee, Yong-Bin Zhuang +5
Atomistic simulation methods have evolved through successive computational levels, each building upon more fundamental approaches: from quantum mechanics to density functional theo…
P-DRUM: Post-hoc Descriptor-based Residual Uncertainty Modeling for Machine Learning Potentials
Shih-Peng Huang, Nontawat Charoenphakdee, Yuta Tsuboi +2
Ensemble method is considered the gold standard for uncertainty quantification (UQ) in machine learning interatomic potentials (MLIPs). However, their high computational cost can l…
Dielectric Tensor Prediction for Inorganic Materials Using Latent Information from Preferred Potential
Zetian Mao, Wenwen Li, Jethro Tan
Dielectrics are crucial for technologies like flash memory, CPUs, photovoltaics, and capacitors, but public data on these materials are scarce, restricting research and development…
Towards Universal Neural Network Potential for Material Discovery Applicable to Arbitrary Combination of 45 Elements
So Takamoto, Chikashi Shinagawa, Daisuke Motoki +19
Computational material discovery is under intense study owing to its ability to explore the vast space of chemical systems. Neural network potentials (NNPs) have been shown to be p…
Phase stability of Au-Li binary systems studied using neural network potential
Koji Shimizu, Elvis F. Arguelles, Wenwen Li +3
The miscibility of Au and Li exhibits a potential application as an adhesion layer and electrode material in secondary batteries. Here, to explore alloying properties, we construct…
Construction of accurate machine learning force fields for copper and silicon dioxide
Wenwen Li, Yasunobu Ando
Recently, the machine learning force field has emerged as a powerful atomic simulation approach for its high accuracy and low computational cost. However, its applications in the m…