Similar success in atomic property prediction has been limited due to the challenges of training effective models across multiple chemical domains.
We propose the Spherical Channel Network (SCN) to model atomic energies and forces.
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.
We introduce a novel approach to modeling angular information between sets of neighboring atoms in a graph neural network.
Ranked #3 on Initial Structure to Relaxed Energy (IS2RE) on OC20