1.6k citations · 6.3k across the 48 of their papers we have counts for
57 papers
Quantum-accurate atomistic modeling of enzyme catalysis using a machine learned potential
Meng Gao, Armin Shayesteh Zadeh, Aniruddha Seal +10
Electronic rearrangements associated with bond forming/breaking in catalytic enzymes require quantum mechanical (QM) treatment beyond classical molecular mechanics (MM). Hybrid QM/…
The Open Polymers 2026 (OPoly26) Dataset and Evaluations
Daniel S. Levine, Nicholas Liesen, Lauren Chua +12
Polymers-macromolecular systems composed of repeating chemical units-constitute the molecular foundation of living organisms, while their synthetic counterparts drive transformativ…
The Open DAC 2025 Dataset for Sorbent Discovery in Direct Air Capture
Anuroop Sriram, Logan M. Brabson, Xiaohan Yu +12
Identifying useful sorbent materials for direct air capture (DAC) from humid air remains a challenge. We present the Open DAC 2025 (ODAC25) dataset, a significant expansion and imp…
Insights into CO dimerization at electrified Cu interfaces from large-scale machine learning simulations
Sushree Jagriti Sahoo, Mikael Maraschin, Joel B Varley +7
Catalysis at solid-liquid interfaces underpins many energy technologies, yet ab initio simulations that capture interfacial dynamics remain prohibitively expensive. Here we introdu…
Open Molecular Crystals 2025 (OMC25) Dataset and Models
Vahe Gharakhanyan, Luis Barroso-Luque, Yi Yang +16
The development of accurate and efficient machine learning models for predicting the structure and properties of molecular crystals has been hindered by the scarcity of publicly av…
FastCSP: Accelerated Molecular Crystal Structure Prediction with Universal Model for Atoms
Vahe Gharakhanyan, Yi Yang, Luis Barroso-Luque +24
Molecular crystal structure prediction (CSP) is essential for applications in pharmaceuticals and organic electronics. However, CSP remains challenging and computationally intensiv…
UMA: A Family of Universal Models for Atoms
Brandon M. Wood, Misko Dzamba, Xiang Fu +15
The ability to quickly and accurately compute properties from atomic simulations is critical for advancing a large number of applications in chemistry and materials science includi…
The Open Molecules 2025 (OMol25) Dataset, Evaluations, and Models
Daniel S. Levine, Muhammed Shuaibi, Evan Walter Clark Spotte-Smith +20
Machine learning (ML) models hold the promise of transforming atomic simulations by delivering quantum chemical accuracy at a fraction of the computational cost. Realization of thi…
Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction
Xiang Fu, Brandon M. Wood, Luis Barroso-Luque +4
Machine learning interatomic potentials (MLIPs) have become increasingly effective at approximating quantum mechanical calculations at a fraction of the computational cost. However…
Open Catalyst Experiments 2024 (OCx24): Bridging Experiments and Computational Models
Jehad Abed, Jiheon Kim, Muhammed Shuaibi +17
The search for low-cost, durable, and effective catalysts is essential for green hydrogen production and carbon dioxide upcycling to help in the mitigation of climate change. Disco…
Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models
Luis Barroso-Luque, Muhammed Shuaibi, Xiang Fu +6
The ability to discover new materials with desirable properties is critical for numerous applications from helping mitigate climate change to advances in next generation computing…
CatTSunami: Accelerating Transition State Energy Calculations with Pre-trained Graph Neural Networks
Brook Wander, Muhammed Shuaibi, John R. Kitchin +2
Direct access to transition state energies at low computational cost unlocks the possibility of accelerating catalyst discovery. We show that the top performing graph neural networ…
Fine-Tuned Language Models Generate Stable Inorganic Materials as Text
Nate Gruver, Anuroop Sriram, Andrea Madotto +3
We propose fine-tuning large language models for generation of stable materials. While unorthodox, fine-tuning large language models on text-encoded atomistic data is simple to imp…
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…
Reducing SO(3) Convolutions to SO(2) for Efficient Equivariant GNNs
Saro Passaro, C. Lawrence Zitnick
Graph neural networks that model 3D data, such as point clouds or atoms, are typically desired to be equivariant, i.e., equivariant to 3D rotations. Unfortunately equivaria…
AdsorbML: A Leap in Efficiency for Adsorption Energy Calculations using Generalizable Machine Learning Potentials
Janice Lan, Aini Palizhati, Muhammed Shuaibi +6
Computational catalysis is playing an increasingly significant role in the design of catalysts across a wide range of applications. A common task for many computational methods is…
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…
GemNet-OC: Developing Graph Neural Networks for Large and Diverse Molecular Simulation Datasets
Johannes Gasteiger, Muhammed Shuaibi, Anuroop Sriram +4
Recent years have seen the advent of molecular simulation datasets that are orders of magnitude larger and more diverse. These new datasets differ substantially in four aspects of…
Towards Training Billion Parameter Graph Neural Networks for Atomic Simulations
Anuroop Sriram, Abhishek Das, Brandon M. Wood +2
Recent progress in Graph Neural Networks (GNNs) for modeling atomic simulations has the potential to revolutionize catalyst discovery, which is a key step in making progress toward…
Compositional Transformers for Scene Generation
Drew A. Hudson, C. Lawrence Zitnick
We introduce the GANformer2 model, an iterative object-oriented transformer, explored for the task of generative modeling. The network incorporates strong and explicit structural p…
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…
ForceNet: A Graph Neural Network for Large-Scale Quantum Calculations
Weihua Hu, Muhammed Shuaibi, Abhishek Das +5
With massive amounts of atomic simulation data available, there is a huge opportunity to develop fast and accurate machine learning models to approximate expensive physics-based ca…