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Found 5 papers, 5 papers with code
AdsorbDiff: Adsorbate Placement via Conditional Denoising Diffusion
Given the novelty of this framework and application, we provide insights into the impact of pre-training, model architectures, and conduct extensive experiments to underscore the significance of this approach.
From Molecules to Materials: Pre-training Large Generalizable Models for Atomic Property Prediction
Similar success in atomic property prediction has been limited due to the challenges of training effective models across multiple chemical domains.
Spherical Channels for Modeling Atomic Interactions
We propose the Spherical Channel Network (SCN) to model atomic energies and forces.
The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalysts
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.
Rotation Invariant Graph Neural Networks using Spin Convolutions
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
