324 citations · 438 across the 9 of their papers we have counts for
9 papers
MATRIX: A Multimodal Benchmark and Post-Training Framework for Materials Science
Delia McGrath, Curtis Chong, Rohil Kulkarni +2
Scientific reasoning in materials science requires integrating multimodal experimental evidence with underlying physical theory. Existing benchmarks make it difficult to assess whe…
TorchSim: An efficient atomistic simulation engine in PyTorch
Orion Cohen, Janosh Riebesell, Rhys Goodall +6
We introduce TorchSim, an open-source atomistic simulation engine tailored for the Machine Learned Interatomic Potential (MLIP) era. By rewriting core atomistic simulation primitiv…
Beyond Force Metrics: Pre-Training MLFFs for Stable MD Simulations
Shagun Maheshwari, Zhengxian Tang, Janghoon Ock +3
Machine-learning force fields (MLFFs) have emerged as a promising solution for speeding up ab initio molecular dynamics (MD) simulations, where accurate force predictions are criti…
AdsorbDiff: Adsorbate Placement via Conditional Denoising Diffusion
Adeesh Kolluru, John R Kitchin
Determining the optimal configuration of adsorbates on a slab (adslab) is pivotal in the exploration of novel catalysts across diverse applications. Traditionally, the quest for th…
From Molecules to Materials: Pre-training Large Generalizable Models for Atomic Property Prediction
Nima Shoghi, Adeesh Kolluru, John R. Kitchin +3
Foundation models have been transformational in machine learning fields such as natural language processing and computer vision. Similar success in atomic property prediction has b…
Open Challenges in Developing Generalizable Large Scale Machine Learning Models for Catalyst Discovery
Adeesh Kolluru, Muhammed Shuaibi, Aini Palizhati +6
The development of machine learned potentials for catalyst discovery has predominantly been focused on very specific chemistries and material compositions. While effective in inter…
Spherical Channels for Modeling Atomic Interactions
C. Lawrence Zitnick, Abhishek Das, Adeesh Kolluru +5
Modeling the energy and forces of atomic systems is a fundamental problem in computational chemistry with the potential to help address many of the world's most pressing problems,…
The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalysts
Richard Tran, Janice Lan, Muhammed Shuaibi +14
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. One class of materials…
Rotation Invariant Graph Neural Networks using Spin Convolutions
Muhammed Shuaibi, Adeesh Kolluru, Abhishek Das +4
Progress towards the energy breakthroughs needed to combat climate change can be significantly accelerated through the efficient simulation of atomic systems. Simulation techniques…