Brandon M. Wood

According to our database1, Brandon M. Wood authored at least 16 papers between 2020 and 2025.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Links

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Bibliography

2025
UMA: A Family of Universal Models for Atoms.
CoRR, June, 2025

Adjoint Sampling: Highly Scalable Diffusion Samplers via Adjoint Matching.
CoRR, April, 2025

A practical guide to machine learning interatomic potentials - Status and future.
CoRR, March, 2025

Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction.
CoRR, February, 2025

2024
Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models.
CoRR, 2024

FlowLLM: Flow Matching for Material Generation with Large Language Models as Base Distributions.
Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

FlowMM: Generating Materials with Riemannian Flow Matching.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

From Molecules to Materials: Pre-training Large Generalizable Models for Atomic Property Prediction.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

2022
AdsorbML: Accelerating Adsorption Energy Calculations with Machine Learning.
CoRR, 2022

The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalysis.
CoRR, 2022

Spherical Channels for Modeling Atomic Interactions.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Towards Training Billion Parameter Graph Neural Networks for Atomic Simulations.
Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

2020
The Open Catalyst 2020 (OC20) Dataset and Community Challenges.
CoRR, 2020

An Introduction to Electrocatalyst Design using Machine Learning for Renewable Energy Storage.
CoRR, 2020


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