An Introduction to Electrocatalyst Design using Machine Learning for Renewable Energy Storage. - C. Lawrence Zitnick; Lowik Chanussot; Abhishek Das; Siddharth Goyal; Javier Heras-Domingo; Caleb Ho; Weihua Hu; Thibaut Lavril; Aini Palizhati; Morgane Riviere; Muhammed Shuaibi; Anuroop Sriram; et al. (CoRR, 2020)

The Open Catalyst 2020 (OC20) Dataset and Community Challenges. - Lowik Chanussot; Abhishek Das; Siddharth Goyal; Thibaut Lavril; Muhammed Shuaibi; Morgane Riviere; Kevin Tran; Javier Heras-Domingo; Caleb Ho; Weihua Hu; Aini Palizhati; Anuroop Sriram; et al. (CoRR, 2020)

The Open Catalyst Challenge 2021: Competition Report. - Abhishek Das; Muhammed Shuaibi; Aini Palizhati; Siddharth Goyal; Aditya Grover; Adeesh Kolluru; Janice Lan; Ammar Rizvi; Anuroop Sriram; Brandon M. Wood; Devi Parikh; Zachary W. Ulissi; et al. (NeurIPS (Competition and Demos), 2021)

Towards Training Billion Parameter Graph Neural Networks for Atomic Simulations. - Anuroop Sriram; Abhishek Das; Brandon M. Wood; Siddharth Goyal; C. Lawrence Zitnick (ICLR, 2022)

Spherical Channels for Modeling Atomic Interactions. - Larry Zitnick; Abhishek Das; Adeesh Kolluru; Janice Lan; Muhammed Shuaibi; Anuroop Sriram; Zachary W. Ulissi; Brandon M. Wood (NeurIPS, 2022)

The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalysis. - Richard Tran; Janice Lan; Muhammed Shuaibi; Siddharth Goyal; Brandon M. Wood; Abhishek Das; Javier Heras-Domingo; Adeesh Kolluru; Ammar Rizvi; Nima Shoghi; Anuroop Sriram; Zachary W. Ulissi; et al. (CoRR, 2022)

AdsorbML: Accelerating Adsorption Energy Calculations with Machine Learning. - Janice Lan; Aini Palizhati; Muhammed Shuaibi; Brandon M. Wood; Brook Wander; Abhishek Das; Matt Uyttendaele; C. Lawrence Zitnick; Zachary W. Ulissi (CoRR, 2022)

EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations. - Yi-Lun Liao; Brandon M. Wood; Abhishek Das; Tess E. Smidt (ICLR, 2024)

From Molecules to Materials: Pre-training Large Generalizable Models for Atomic Property Prediction. - Nima Shoghi; Adeesh Kolluru; John R. Kitchin; Zachary W. Ulissi; C. Lawrence Zitnick; Brandon M. Wood (ICLR, 2024)

FlowMM: Generating Materials with Riemannian Flow Matching. - Benjamin Kurt Miller; Ricky T. Q. Chen; Anuroop Sriram; Brandon M. Wood (ICML, 2024)

FlowLLM: Flow Matching for Material Generation with Large Language Models as Base Distributions. - Anuroop Sriram; Benjamin Kurt Miller; Ricky T. Q. Chen; Brandon M. Wood (NeurIPS, 2024)

Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models. - Luis Barroso-Luque; Muhammed Shuaibi; Xiang Fu 005; Brandon M. Wood; Misko Dzamba; Meng Gao; Ammar Rizvi; C. Lawrence Zitnick; Zachary W. Ulissi (CoRR, 2024)

Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction. - Xiang Fu 005; Brandon M. Wood; Luis Barroso-Luque; Daniel S. Levine 003; Meng Gao; Misko Dzamba; C. Lawrence Zitnick (CoRR, 2025)

A practical guide to machine learning interatomic potentials - Status and future. - Ryan Jacobs; Dane Morgan; Siamak Attarian; Jun Meng; Chen Shen; Zhenghao Wu; Clare Yijia Xie; Julia H. Yang; Nongnuch Artrith; Ben Blaiszik; Gerbrand Ceder; Kamal Choudhary; et al. (CoRR, 2025)

Adjoint Sampling: Highly Scalable Diffusion Samplers via Adjoint Matching. - Aaron J. Havens; Benjamin Kurt Miller; Bing Yan; Carles Domingo-Enrich; Anuroop Sriram; Brandon M. Wood; Daniel Levine; Bin Hu; Brandon Amos; Brian Karrer; Xiang Fu 005; Guan-Horng Liu; et al. (CoRR, 2025)

UMA: A Family of Universal Models for Atoms. - Brandon M. Wood; Misko Dzamba; Xiang Fu 005; Meng Gao; Muhammed Shuaibi; Luis Barroso-Luque; Kareem Abdelmaqsoud; Vahe Gharakhanyan; John R. Kitchin; Daniel S. Levine 003; Kyle Michel; Anuroop Sriram; et al. (CoRR, 2025)

Enhancing Diffusion-Based Sampling with Molecular Collective Variables. - Juno Nam; Bálint Máté; Artur P. Toshev; Manasa Kaniselvan; Rafael Gómez-Bombarelli; Ricky T. Q. Chen; Brandon M. Wood; Guan-Horng Liu; Benjamin Kurt Miller (CoRR, 2025)