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

no code implementations • 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

Spherical Channels for Modeling Atomic Interactions

2 code implementations • 29 Jun 2022 • C. Lawrence Zitnick, Abhishek Das, Adeesh Kolluru, Janice Lan, Muhammed Shuaibi, Anuroop Sriram, Zachary Ulissi, Brandon Wood

We propose the Spherical Channel Network (SCN) to model atomic energies and forces.

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

2 code implementations • 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 +1

Rotation Invariant Graph Neural Networks using Spin Convolutions

1 code implementation • 17 Jun 2021 • Muhammed Shuaibi, Adeesh Kolluru, Abhishek Das, Aditya Grover, Anuroop Sriram, Zachary Ulissi, C. Lawrence Zitnick

We introduce a novel approach to modeling angular information between sets of neighboring atoms in a graph neural network.

Initial Structure to Relaxed Energy (IS2RE)