5 papers
FRIGID: Scaling Diffusion-Based Molecular Generation from Mass Spectra at Training and Inference Time
Montgomery Bohde, Hongxuan Liu, Mrunali Manjrekar +4
Tandem mass spectrometry is prominent in scientific discovery workflows for identifying unknown small molecules, yet high-throughput structural elucidation remains challenging. Whi…
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
Xuan Zhang, Limei Wang, Jacob Helwig +60
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences. Today, AI has started to advance natural sciences by improving, accelerating…
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…
DiffMS: Diffusion Generation of Molecules Conditioned on Mass Spectra
Montgomery Bohde, Mrunali Manjrekar, Runzhong Wang +2
Mass spectrometry plays a fundamental role in elucidating the structures of unknown molecules and subsequent scientific discoveries. One formulation of the structure elucidation ta…
Equivariant Graph Network Approximations of High-Degree Polynomials for Force Field Prediction
Zhao Xu, Haiyang Yu, Montgomery Bohde +1
Recent advancements in equivariant deep models have shown promise in accurately predicting atomic potentials and force fields in molecular dynamics simulations. Using spherical har…