7 papers
MatterSim-MT: A multi-task foundation model for in silico materials characterization
Han Yang, Xixian Liu, Chenxi Hu +25
Accurate property characterization is a major bottleneck in materials design. While first-principles methods and task-specific machine-learning models have driven important progres…
Generative structure search for efficient and diverse discovery of molecular and crystal structures
Yifang Qin, Yu Shi, Junfu Tan +3
Predicting stable and metastable structures is central to molecular and materials discovery, but remains limited by the cost of searching high-dimensional energy landscapes. Deep g…
Designing the Haystack: Programmable Chemical Space for Generative Molecular Discovery
Yuchen Zhu, Donghai Zhao, Yangyang Zhang +10
Chemical space exploration underlies drug discovery, yet most generative models treat chemical space as a fixed, implicitly learned distribution, focusing on sampling molecules rat…
E2Former: An Efficient and Equivariant Transformer with Linear-Scaling Tensor Products
Yunyang Li, Lin Huang, Zhihao Ding +10
Equivariant Graph Neural Networks (EGNNs) have demonstrated significant success in modeling microscale systems, including those in chemistry, biology and materials science. However…
Potential Score Matching: Debiasing Molecular Structure Sampling with Potential Energy Guidance
Liya Guo, Zun Wang, Chang Liu +3
The ensemble average of physical properties of molecules is closely related to the distribution of molecular conformations, and sampling such distributions is a fundamental challen…
Efficient and Scalable Density Functional Theory Hamiltonian Prediction through Adaptive Sparsity
Erpai Luo, Xinran Wei, Lin Huang +7
Hamiltonian matrix prediction is pivotal in computational chemistry, serving as the foundation for determining a wide range of molecular properties. While SE(3) equivariant graph n…