4 papers
A Benchmark for Quantum Chemistry Relaxations via Machine Learning Interatomic Potentials
Cong Fu, Yuchao Lin, Zachary Krueger +8
Computational quantum chemistry plays a critical role in drug discovery, chemical synthesis, and materials science. While first-principles methods, such as density functional theor…
Tensor Decomposition Networks for Fast Machine Learning Interatomic Potential Computations
Yuchao Lin, Cong Fu, Zachary Krueger +6
-equivariant networks are the dominant models for machine learning interatomic potentials (MLIPs). The key operation of such networks is the Clebsch-Gordan (CG) tensor…
A Materials Foundation Model via Hybrid Invariant-Equivariant Architectures
Keqiang Yan, Montgomery Bohde, Andrii Kryvenko +10
Machine learning interatomic potentials (MLIPs) can predict energy, force, and stress of materials and enable a wide range of downstream discovery tasks. A key design choice in MLI…
Invariant Tokenization of Crystalline Materials for Language Model Enabled Generation
Keqiang Yan, Xiner Li, Hongyi Ling +8
We consider the problem of crystal materials generation using language models (LMs). A key step is to convert 3D crystal structures into 1D sequences to be processed by LMs. Prior…