A practical guide to machine learning interatomic potentials -- Status and future

no code implementations • 12 Mar 2025 • 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, Gabor Csanyi, Ekin Dogus Cubuk, Bowen Deng, Ralf Drautz, Xiang Fu, Jonathan Godwin, Vasant Honavar, Olexandr Isayev, Anders Johansson, Boris Kozinsky, Stefano Martiniani, Shyue Ping Ong, Igor Poltavsky, KJ Schmidt, So Takamoto, Aidan Thompson, Julia Westermayr, Brandon M. Wood

The rapid development and large body of literature on machine learning interatomic potentials (MLIPs) can make it difficult to know how to proceed for researchers who are not experts but wish to use these tools.

Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction

no code implementations • 17 Feb 2025 • Xiang Fu, Brandon M. Wood, Luis Barroso-Luque, Daniel S. Levine, Meng Gao, Misko Dzamba, C. Lawrence Zitnick

Machine learning interatomic potentials (MLIPs) have become increasingly effective at approximating quantum mechanical calculations at a fraction of the computational cost.

Prediction Property Prediction

FlowLLM: Flow Matching for Material Generation with Large Language Models as Base Distributions

1 code implementation • 30 Oct 2024 • Anuroop Sriram, Benjamin Kurt Miller, Ricky T. Q. Chen, Brandon M. Wood

Material discovery is a critical area of research with the potential to revolutionize various fields, including carbon capture, renewable energy, and electronics.

Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models

1 code implementation • 16 Oct 2024 • Luis Barroso-Luque, Muhammed Shuaibi, Xiang Fu, Brandon M. Wood, Misko Dzamba, Meng Gao, Ammar Rizvi, C. Lawrence Zitnick, Zachary W. Ulissi

The ability to discover new materials with desirable properties is critical for numerous applications from helping mitigate climate change to advances in next generation computing hardware.

Denoising

From Molecules to Materials: Pre-training Large Generalizable Models for Atomic Property Prediction

1 code implementation • 25 Oct 2023 • Nima Shoghi, Adeesh Kolluru, John R. Kitchin, Zachary W. Ulissi, C. Lawrence Zitnick, Brandon M. Wood

Similar success in atomic property prediction has been limited due to the challenges of training effective models across multiple chemical domains.

Property Prediction

AdsorbML: A Leap in Efficiency for Adsorption Energy Calculations using Generalizable Machine Learning Potentials

1 code implementation • 29 Nov 2022 • Janice Lan, Aini Palizhati, Muhammed Shuaibi, Brandon M. Wood, Brook Wander, Abhishek Das, Matt Uyttendaele, C. Lawrence Zitnick, Zachary W. Ulissi

Computational catalysis is playing an increasingly significant role in the design of catalysts across a wide range of applications.

Benchmarking

The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalysts

1 code implementation • 17 Jun 2022 • Richard Tran, Janice Lan, Muhammed Shuaibi, Brandon M. Wood, Siddharth Goyal, Abhishek Das, Javier Heras-Domingo, Adeesh Kolluru, Ammar Rizvi, Nima Shoghi, Anuroop Sriram, Felix Therrien, Jehad Abed, Oleksandr Voznyy, Edward H. Sargent, Zachary Ulissi, C. Lawrence Zitnick

The development of machine learning models for electrocatalysts requires a broad set of training data to enable their use across a wide variety of materials.

BIG-bench Machine Learning Property Prediction

Towards Training Billion Parameter Graph Neural Networks for Atomic Simulations

1 code implementation • ICLR 2022 • Anuroop Sriram, Abhishek Das, Brandon M. Wood, Siddharth Goyal, C. Lawrence Zitnick

Recent progress in Graph Neural Networks (GNNs) for modeling atomic simulations has the potential to revolutionize catalyst discovery, which is a key step in making progress towards the energy breakthroughs needed to combat climate change.

Initial Structure to Relaxed Energy (IS2RE)