180 citations · 406 across the 23 of their papers we have counts for
23 papers
Atomistic Language Models Understand and Generate Materials
Sathya Edamadaka, Krithik Ramesh, Ju Li +1
Atomistic structure and natural language have long been modeled separately, with language models either calling atomistic models as tools or being fine-tuned on lossy textual encod…
Few-step Cofolding with All-Atom Flow Maps
Gianluca Scarpellini, Ron Shprints, Peter Holderrieth +7
All-atom generative modeling of 3D biomolecular complexes has emerged as the dominant paradigm for predicting the structure of proteins and protein-ligand systems. Generating struc…
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…
MetaDNS: Enhancing Exploration in Discrete Neural Samplers via Well-Tempered Metadynamics
Xiaochen Du, Juno Nam, Jaemoo Choi +7
Sampling from discrete distributions with multiple modes and energy barriers is fundamental to machine learning and computational physics. Recent discrete neural samplers like MDNS…
Transferable Learning of Reaction Pathways from Geometric Priors
Juno Nam, Miguel Steiner, Max Misterka +3
Identifying minimum-energy paths (MEPs) is crucial for understanding chemical reaction mechanisms but remains computationally demanding. We introduce MEPIN, a scalable machine-lear…
High-Throughput Transition-State Searches in Zeolite Nanopores
Pau Ferri-Vicedo, Alexander J. Hoffman, Avni Singhal +1
Zeolites are important for industrial catalytic processes involving organic molecules. Understanding molecular reaction mechanisms within the confined nanoporous environment can gu…
Accelerating and enhancing thermodynamic simulations of electrochemical interfaces
Xiaochen Du, Mengren Liu, Jiayu Peng +6
Electrochemical interfaces are crucial in catalysis, energy storage, and corrosion, where their stability and reactivity depend on complex interactions between the electrode, adsor…
Efficient Generation of Molecular Clusters with Dual-Scale Equivariant Flow Matching
Akshay Subramanian, Shuhui Qu, Cheol Woo Park +3
Amorphous molecular solids offer a promising alternative to inorganic semiconductors, owing to their mechanical flexibility and solution processability. The packing structure of th…
Think While You Generate: Discrete Diffusion with Planned Denoising
Sulin Liu, Juno Nam, Andrew Campbell +4
Discrete diffusion has achieved state-of-the-art performance, outperforming or approaching autoregressive models on standard benchmarks. In this work, we introduce Discrete Diffusi…
Flow Matching for Accelerated Simulation of Atomic Transport in Crystalline Materials
Juno Nam, Sulin Liu, Gavin Winter +3
Atomic transport underpins the performance of materials in technologies such as energy storage and electronics, yet its simulation remains computationally demanding. In particular,…
Learning Ordering in Crystalline Materials with Symmetry-Aware Graph Neural Networks
Jiayu Peng, James Damewood, Jessica Karaguesian +2
Graph convolutional neural networks (GCNNs) have become a machine learning workhorse for screening the chemical space of crystalline materials in fields such as catalysis and energ…
Interpolation and differentiation of alchemical degrees of freedom in machine learning interatomic potentials
Juno Nam, Jiayu Peng, Rafael Gómez-Bombarelli
Machine learning interatomic potentials (MLIPs) have become a workhorse of modern atomistic simulations, and recently published universal MLIPs, pre-trained on large datasets, have…
Data-Driven, Physics-Informed Descriptors of Cation Ordering in Multicomponent Oxides
Jiayu Peng, James Damewood, Rafael Gómez-Bombarelli
The structural tunability and compositional diversity of multicomponent perovskite oxides have enabled their various applications, including catalysis and electronics. The cation o…
Single-model uncertainty quantification in neural network potentials does not consistently outperform model ensembles
Aik Rui Tan, Shingo Urata, Samuel Goldman +2
Neural networks (NNs) often assign high confidence to their predictions, even for points far out-of-distribution, making uncertainty quantification (UQ) a challenge. When they are…
Chemically Transferable Generative Backmapping of Coarse-Grained Proteins
Soojung Yang, Rafael Gómez-Bombarelli
Coarse-graining (CG) accelerates molecular simulations of protein dynamics by simulating sets of atoms as singular beads. Backmapping is the opposite operation of bringing lost ato…
Automated patent extraction powers generative modeling in focused chemical spaces
Akshay Subramanian, Kevin P. Greenman, Alexis Gervaix +2
Deep generative models have emerged as an exciting avenue for inverse molecular design, with progress coming from the interplay between training algorithms and molecular representa…
Representations of Materials for Machine Learning
James Damewood, Jessica Karaguesian, Jaclyn R. Lunger +4
High-throughput data generation methods and machine learning (ML) algorithms have given rise to a new era of computational materials science by learning relationships among composi…
Learning Pair Potentials using Differentiable Simulations
Wujie Wang, Zhenghao Wu, Rafael Gómez-Bombarelli
Learning pair interactions from experimental or simulation data is of great interest for molecular simulations. We propose a general stochastic method for learning pair interaction…
Generative Coarse-Graining of Molecular Conformations
Wujie Wang, Minkai Xu, Chen Cai +5
Coarse-graining (CG) of molecular simulations simplifies the particle representation by grouping selected atoms into pseudo-beads and drastically accelerates simulation. However, s…
Graph theory-based structural analysis on density anomaly of silica glass
Aik Rui Tan, Shingo Urata, Masatsugu Yamada +1
Analyzing the atomic structure of glassy materials is a tremendous challenge both experimentally and computationally, and the lack of direct, detailed insights into glass structure…
GLAMOUR: Graph Learning over Macromolecule Representations
Somesh Mohapatra, Joyce An, Rafael Gómez-Bombarelli
The near-infinite chemical diversity of natural and artificial macromolecules arises from the vast range of possible component monomers, linkages, and polymers topologies. This eno…
Differentiable Molecular Simulations for Control and Learning
Wujie Wang, Simon Axelrod, Rafael Gómez-Bombarelli
Molecular dynamics simulations use statistical mechanics at the atomistic scale to enable both the elucidation of fundamental mechanisms and the engineering of matter for desired t…
Coarse-Graining Auto-Encoders for Molecular Dynamics
Wujie Wang, Rafael Gómez-Bombarelli
Molecular dynamics simulations provide theoretical insight into the microscopic behavior of materials in condensed phase and, as a predictive tool, enable computational design of n…