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

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

31 October 2022

Kin Long Kelvin Lee

Carmelo Gonzales

Matthew Spellings

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

15 / 15 papers shown

Title
SymmCD: Symmetry-Preserving Crystal Generation with Diffusion Models Daniel Levy Siba Smarak Panigrahi Sékou-Oumar Kaba Qiang Zhu Kin Long Kelvin Lee Mikhail Galkin Santiago Miret Siamak Ravanbakhsh 99 11 0 05 Feb 2025
Deconstructing equivariant representations in molecular systems Kin Long Kelvin Lee Mikhail Galkin Santiago Miret 19 1 0 10 Oct 2024
MatText: Do Language Models Need More than Text & Scale for Materials Modeling? Nawaf Alampara Santiago Miret K. Jablonka 40 8 0 25 Jun 2024
On the importance of catalyst-adsorbate 3D interactions for relaxed energy predictions Alvaro Carbonero Alexandre Duval Victor Schmidt Santiago Miret Alex Hernandez-Garcia Yoshua Bengio David Rolnick 22 0 0 10 Oct 2023
EGraFFBench: Evaluation of Equivariant Graph Neural Network Force Fields for Atomistic Simulations Vaibhav Bihani Utkarsh Pratiush Sajid Mannan Tao Du Zhimin Chen Santiago Miret Matthieu Micoulaut M. Smedskjaer Sayan Ranu N. M. A. Krishnan 19 19 0 03 Oct 2023
MatSciML: A Broad, Multi-Task Benchmark for Solid-State Materials Modeling Kin Long Kelvin Lee Carmelo Gonzales Marcel Nassar Matthew Spellings Mikhail Galkin Santiago Miret 19 15 0 12 Sep 2023
Using Multiple Vector Channels Improves E(n)-Equivariant Graph Neural Networks Daniel Levy Sekouba Kaba Carmelo Gonzales Santiago Miret Siamak Ravanbakhsh 23 5 0 06 Sep 2023
FAENet: Frame Averaging Equivariant GNN for Materials Modeling Alexandre Duval Victor Schmidt A. Garcia Santiago Miret Fragkiskos D. Malliaros Yoshua Bengio David Rolnick 20 51 0 28 Apr 2023
PhAST: Physics-Aware, Scalable, and Task-specific GNNs for Accelerated Catalyst Design Alexandre Duval Victor Schmidt Santiago Miret Yoshua Bengio Alex Hernández-García David Rolnick 14 7 0 22 Nov 2022
Sequential Aggregation and Rematerialization: Distributed Full-batch Training of Graph Neural Networks on Large Graphs Hesham Mostafa GNN 40 21 0 11 Nov 2021
Geometric Algebra Attention Networks for Small Point Clouds Matthew Spellings 3DPC 45 12 0 05 Oct 2021
Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges M. Bronstein Joan Bruna Taco S. Cohen Petar Velivcković GNN 163 1,095 0 27 Apr 2021
E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials Simon L. Batzner Albert Musaelian Lixin Sun Mario Geiger J. Mailoa M. Kornbluth N. Molinari Tess E. Smidt Boris Kozinsky 188 1,218 0 08 Jan 2021
The Open Catalyst 2020 (OC20) Dataset and Community Challenges L. Chanussot Abhishek Das Siddharth Goyal Thibaut Lavril Muhammed Shuaibi ... Brandon M. Wood Junwoong Yoon Devi Parikh C. L. Zitnick Zachary W. Ulissi 207 370 0 20 Oct 2020
Geometric deep learning: going beyond Euclidean data M. Bronstein Joan Bruna Yann LeCun Arthur Szlam P. Vandergheynst GNN 231 3,202 0 24 Nov 2016