Publications

^* Co-First Authors

AdsorbML: A Leap in Efficiency for Adsorption Energy Calculations using Generalizable Machine Learning Potentials

Janice Lan*, Aini Palizhati*, Muhammed Shuaibi*, Brandon M. Wood*, Brook Wander, Abhishek Das, Matt Uyttendaele, C. Lawrence Zitnick, Zachary W. Ulissi: "AdsorbML: Accelerating Adsorption Energy Calculations with Machine Learning", 2023; npj Computational Materials

Spherical Channels for Modeling Atomic Interactions

C. Lawrence Zitnick, Abhishek Das, Adeesh Kolluru, Janice Lan, Muhammed Shuaibi, Anuroop Sriram, Zachary Ulissi, Brandon Wood: "Spherical Channels for Modeling Atomic Interactions", 2022; NeurIPS 2022

The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalysis

Richard Tran*, Janice Lan*, Muhammed Shuaibi*, Brandon M. Wood*, Siddharth Goyal*, Abhishek Das, Javier Heras-Domingo, Adeesh Kolluru, Ammar Rizvi, Nima Shoghi, Anuroop Sriram, Zachary Ulissi, and C. Lawrence Zitnick: "The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalysis, 2023; ACS Catalysis"

Open Challenges in Developing Generalizable Large Scale Machine Learning Models for Catalyst Discovery

Adeesh Kolluru*, Muhammed Shuaibi*, Aini Palizhati, Nima Shoghi, Abhishek Das, Brandon Wood, C. Lawrence Zitnick, John R Kitchin, and Zachary W Ulissi: "Open Challenges in Developing Generalizable Large Scale Machine Learning Models for Catalyst Discovery" arXiv:2206.02005 2022

Transfer Learning using Attentions across Atomic Systems with Graph Neural Networks (TAAG)

Adeesh Kolluru, Nima Shoghi, Muhammed Shuaibi, Siddharth Goyal, Abhishek Das, C. Lawrence Zitnick, and Zachary Ulissi: "Transfer Learning using Attentions across Atomic Systems with Graph Neural Networks (TAAG)" J. Chem Phys. 2022

How Do Graph Networks Generalize to Large and Diverse Molecular Systems?

Johannes Gasteiger, Muhammed Shuaibi, Anuroop Sriram, Stephan Günnemann, Zachary Ulissi, C. Lawrence Zitnick, Abhishek Das: "How Do Graph Networks Generalize to Large and Diverse Molecular Systems?, 2022; TMLR"

Rotation Invariant Graph Neural Networks using Spin Convolutions

Muhammed Shuaibi, Adeesh Kolluru, Abhishek Das, Aditya Grover, Anuroop Sriram, Zachary Ulissi, and C. Lawrence Zitnick: "Rotation Invariant Graph Neural Networks using Spin Convolutions, 2021; arXiv:2106.09575"

The Open Catalyst 2020 (OC20) Dataset and Community Challenges

Lowik Chanussot*, Abhishek Das*, Siddharth Goyal*, Thibaut Lavril*, Muhammed Shuaibi*, Morgane Riviere, Kevin Tran, Javier Heras-Domingo, Caleb Ho, Weihua Hu, Aini Palizhati, Anuroop Sriram, Brandon Wood, Junwoong Yoon, Devi Parikh, C. Lawrence Zitnick, Zachary Ulissi: “The Open Catalyst 2020 (OC20) Dataset and Community Challenges”, ACS Catalysis 2021

ForceNet: A Graph Neural Network for Large-Scale Quantum Calculations

Weihua Hu, Muhammed Shuaibi, Abhishek Das, Siddharth Goyal, Anuroop Sriram, Jure Leskovec, Devi Parikh, C. Lawrence Zitnick: “ForceNet: A Graph Neural Network for Large-Scale Quantum Calculations”, 2021; ICLR 2021 workshop: Deep Learning for Simulation (contributed talk).

An Introduction to Electrocatalyst Design using Machine Learning for Renewable Energy Storage

C. Lawrence Zitnick, Lowik Chanussot, Abhishek Das, Siddharth Goyal, Javier Heras-Domingo, Caleb Ho, Weihua Hu, Thibaut Lavril, Aini Palizhati, Morgane Riviere, Muhammed Shuaibi, Anuroop Sriram, Kevin Tran, Brandon Wood, Junwoong Yoon, Devi Parikh, Zachary Ulissi: “An Introduction to Electrocatalyst Design using Machine Learning for Renewable Energy Storage”, 2020; arXiv:2010.09435.

Enabling robust offline active learning for machine learning potentials using simple physics-based priors

Muhammed Shuaibi, Saurabh Sivakumar, Rui Qi Chen, and Zachary W. Ulissi. Enabling robust offline active learning for machine learning potentials using simple physics-based priors. MLST 2020