10 citations · 26 across the 8 of their papers we have counts for
8 papers
OXtal: An All-Atom Diffusion Model for Organic Crystal Structure Prediction
Emily Jin, Andrei Cristian Nica, Mikhail Galkin +8
Accurately predicting experimentally realizable 3D molecular crystal structures from their 2D chemical graphs is a long-standing open challenge in computational chemistry called cr…
Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials
Santiago Miret, Kin Long Kelvin Lee, Carmelo Gonzales +2
Universal Machine Learning Interactomic Potentials (MLIPs) enable accelerated simulations for materials discovery. However, current research efforts fail to impactfully utilize MLI…
Stiefel Flow Matching for Moment-Constrained Structure Elucidation
Austin Cheng, Alston Lo, Kin Long Kelvin Lee +2
Molecular structure elucidation is a fundamental step in understanding chemical phenomena, with applications in identifying molecules in natural products, lab syntheses, forensic s…
Deconstructing equivariant representations in molecular systems
Kin Long Kelvin Lee, Mikhail Galkin, Santiago Miret
Recent equivariant models have shown significant progress in not just chemical property prediction, but as surrogates for dynamical simulations of molecules and materials. Many of…
-Diffusion: A diffusion-based density estimation framework for computational physics
Maxwell X. Cai, Kin Long Kelvin Lee
In physics, density is a fundamentally important scalar function to model, since it describes a scalar field or a probability density function that governs a physical pr…
Towards Foundation Models for Materials Science: The Open MatSci ML Toolkit
Kin Long Kelvin Lee, Carmelo Gonzales, Matthew Spellings +3
Artificial intelligence and machine learning have shown great promise in their ability to accelerate novel materials discovery. As researchers and domain scientists seek to unify a…
MatSciML: A Broad, Multi-Task Benchmark for Solid-State Materials Modeling
Kin Long Kelvin Lee, Carmelo Gonzales, Marcel Nassar +3
We propose MatSci ML, a novel benchmark for modeling MATerials SCIence using Machine Learning (MatSci ML) methods focused on solid-state materials with periodic crystal structures.…
The Open MatSci ML Toolkit: A Flexible Framework for Machine Learning in Materials Science
Santiago Miret, Kin Long Kelvin Lee, Carmelo Gonzales +2
We present the Open MatSci ML Toolkit: a flexible, self-contained, and scalable Python-based framework to apply deep learning models and methods on scientific data with a specific…