9 citations · 27 across the 11 of their papers we have counts for
13 papers
Universal Thermodynamic Interatomic Potentials for Crystalline Materials
Juno Nam, Bowen Deng, Xiaochen Du +3
Free energies govern solid-state phase stability, yet computational materials discovery still relies largely on ground-state energies because free energy calculations require ensem…
XRDiff: Crystal Structure Prediction from Powder X-Ray Diffraction Data Using Diffusion Models
Nofit Segal, Mingda Li, Benjamin Kurt Miller +1
Determining the crystal structure of a material from its powder X-ray diffraction (PXRD) pattern is a central challenge in materials science. PXRD is an accessible and widely used…
Offline Materials Optimization with CliqueFlowmer
Jakub Grudzien Kuba, Benjamin Kurt Miller, Sergey Levine +1
Recent advances in deep learning inspired neural network-based approaches to computational materials discovery (CMD). A plethora of problems in this field involve finding materials…
Hermes: Large DEL Datasets Train Generalizable Protein-Ligand Binding Prediction Models
Maxwell Kleinsasser, Brayden J. Halverson, Edward Kraft +6
The quality and consistency of training data remain critical bottlenecks for protein-ligand binding prediction. Public affinity datasets, aggregated from thousands of labs and assa…
The Loss Landscape of Powder X-Ray Diffraction-Based Structure Optimization Is Too Rough for Gradient Descent
Nofit Segal, Akshay Subramanian, Mingda Li +2
Solving crystal structures from powder X-ray diffraction (XRD) is a central challenge in materials characterization. In this work, we study the powder XRD-to-structure mapping usin…
Enhancing Diffusion-Based Sampling with Molecular Collective Variables
Juno Nam, Bálint Máté, Artur P. Toshev +6
Diffusion-based samplers learn to sample complex, high-dimensional distributions using energies or log densities alone, without training data. Yet, they remain impractical for mole…
Learning from the electronic structure of molecules across the periodic table
Manasa Kaniselvan, Benjamin Kurt Miller, Meng Gao +2
Machine-Learned Interatomic Potentials (MLIPs) require vast amounts of atomic structure data to learn forces and energies, and their performance continues to improve with training…
Space Group Conditional Flow Matching
Omri Puny, Yaron Lipman, Benjamin Kurt Miller
Inorganic crystals are periodic, highly-symmetric arrangements of atoms in three-dimensional space. Their structures are constrained by the symmetry operations of a crystallographi…
Adjoint Schrödinger Bridge Sampler
Guan-Horng Liu, Jaemoo Choi, Yongxin Chen +2
Computational methods for learning to sample from the Boltzmann distribution -- where the target distribution is known only up to an unnormalized energy function -- have advanced s…
Adjoint Sampling: Highly Scalable Diffusion Samplers via Adjoint Matching
Aaron Havens, Benjamin Kurt Miller, Bing Yan +10
We introduce Adjoint Sampling, a highly scalable and efficient algorithm for learning diffusion processes that sample from unnormalized densities, or energy functions. It is the fi…
All-atom Diffusion Transformers: Unified generative modelling of molecules and materials
Chaitanya K. Joshi, Xiang Fu, Yi-Lun Liao +4
Diffusion models are the standard toolkit for generative modelling of 3D atomic systems. However, for different types of atomic systems -- such as molecules and materials -- the ge…
FlowLLM: Flow Matching for Material Generation with Large Language Models as Base Distributions
Anuroop Sriram, Benjamin Kurt Miller, Ricky T. Q. Chen +1
Material discovery is a critical area of research with the potential to revolutionize various fields, including carbon capture, renewable energy, and electronics. However, the imme…
FlowMM: Generating Materials with Riemannian Flow Matching
Benjamin Kurt Miller, Ricky T. Q. Chen, Anuroop Sriram +1
Crystalline materials are a fundamental component in next-generation technologies, yet modeling their distribution presents unique computational challenges. Of the plausible arrang…