MatSciML: A Broad, Multi-Task Benchmark for Solid-State Materials Modeling

1 code implementation • 12 Sep 2023 • Kin Long Kelvin Lee, Carmelo Gonzales, Marcel Nassar, Matthew Spellings, Mikhail Galkin, Santiago Miret

We propose MatSci ML, a novel benchmark for modeling MATerials SCIence using Machine Learning (MatSci ML) methods focused on solid-state materials with periodic crystal structures.

Atomic Forces Multi-Task Learning

The Open MatSci ML Toolkit: A Flexible Framework for Machine Learning in Materials Science

1 code implementation • 31 Oct 2022 • Santiago Miret, Kin Long Kelvin Lee, Carmelo Gonzales, Marcel Nassar, Matthew Spellings

We present the Open MatSci ML Toolkit: a flexible, self-contained, and scalable Python-based framework to apply deep learning models and methods on scientific data with a specific focus on materials science and the OpenCatalyst Dataset.

Geometric Algebra Attention Networks for Small Point Clouds

1 code implementation • 5 Oct 2021 • Matthew Spellings

Much of the success of deep learning is drawn from building architectures that properly respect underlying symmetry and structure in the data on which they operate - a set of considerations that have been united under the banner of geometric deep learning.

Classify 3D Point Clouds Generating 3D Point Clouds +1

Geometric Attention Networks for Small Point Clouds

no code implementations • NeurIPS 2021 • Matthew Spellings

Much of the success of deep learning is drawn from building architectures that properly respect underlying symmetry and structure in the data on which they operate—a set of considerations that have been united under the banner of geometric deep learning.

Agglomerative Attention

1 code implementation • 15 Jul 2019 • Matthew Spellings

Neural networks using transformer-based architectures have recently demonstrated great power and flexibility in modeling sequences of many types.

Language Modelling