59 citations · 158 across the 14 of their papers we have counts for
6 papers · 1 filter
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…
Language Models for Controllable DNA Sequence Design
Xingyu Su, Xiner Li, Yuchao Lin +3
We consider controllable DNA sequence design, where sequences are generated by conditioning on specific biological properties. While language models (LMs) such as GPT and BERT have…
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…
Augmenting Molecular Graphs with Geometries via Machine Learning Interatomic Potentials
Cong Fu, Yuchao Lin, Zachary Krueger +6
Accurate molecular property predictions require 3D geometries, which are typically obtained using expensive methods such as density functional theory (DFT). Here, we attempt to obt…
Efficient Prediction of SO(3)-Equivariant Hamiltonian Matrices via SO(2) Local Frames
Haiyang Yu, Yuchao Lin, Xuan Zhang +2
We consider the task of predicting Hamiltonian matrices to accelerate electronic structure calculations, which plays an important role in physics, chemistry, and materials science.…
A Two-Phase Deep Learning Framework for Adaptive Time-Stepping in High-Speed Flow Modeling
Jacob Helwig, Sai Sreeharsha Adavi, Xuan Zhang +11
We consider the problem of modeling high-speed flows using machine learning methods. While most prior studies focus on low-speed fluid flows in which uniform time-stepping is pract…