Muhammed Shuaibi

Tata Institute of Fundamental Research — India

Materials Science · Physical Sciences

15h-index1.5kcitations32works11.22yr avg
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Research Topics

Machine Learning in Materials Science(27), Topic Modeling(8), Electrocatalysts for Energy Conversion(6), Computational Drug Discovery Methods(6), Catalytic Processes in Materials Science(5)

Publications32 total

Open Molecular Crystals 2025 (OMC25) dataset and models
Scientific Data·2026Open Access
The Open Polymers 2026 (OPoly26) Dataset and Evaluations
arXiv (Cornell University)·2025Open Access
The Open Polymers 2026 (OPoly26) Dataset and Evaluations
ArXiv.org·2025Open Access
The Open Catalyst 2025 (OC25) Dataset and Models for Solid-Liquid Interfaces
ArXiv.org·2025· 3 citedOpen Access
Open Molecular Crystals 2025 (OMC25) Dataset and Models
ArXiv.org·2025· 1 citedOpen Access
FastCSP: Accelerated Molecular Crystal Structure Prediction with Universal Model for Atoms
arXiv (Cornell University)·2025· 1 citedOpen Access
UMA: A Family of Universal Models for Atoms
ArXiv.org·2025· 14 citedOpen Access
CatTSunami: Accelerating Transition State Energy Calculations with Pretrained Graph Neural Networks
ACS Catalysis·2025· 24 cited
Practical Application of Machine Learning in Catalysis
2024
Open Catalyst Experiments 2024 (OCx24): Bridging Experiments and Computational Models
arXiv (Cornell University)·2024· 15 citedOpen Access
Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models
arXiv (Cornell University)·2024· 74 citedOpen Access
CatTSunami: Accelerating Transition State Energy Calculations with Pre-trained Graph Neural Networks
arXiv (Cornell University)·2024· 7 citedOpen Access
Investigating the error imbalance of large-scale machine learning potentials in catalysis
Catalysis Science & Technology·2024· 6 citedOpen Access
Chemical Properties from Graph Neural Network-Predicted Electron Densities
The Journal of Physical Chemistry C·2023· 20 citedOpen Access
AdsorbML: a leap in efficiency for adsorption energy calculations using generalizable machine learning potentials
npj Computational Materials·2023· 82 citedOpen Access
Chemical Properties from Graph Neural Network-Predicted Electron Densities
arXiv (Cornell University)·2023Open Access
AmpTorch: A Python package for scalablefingerprint-based neural network training on multi-element systems withintegrated uncertainty quantification
The Journal of Open Source Software·2023· 4 citedOpen Access
AdsorbML: A Leap in Efficiency for Adsorption Energy Calculations using Generalizable Machine Learning Potentials
Research Square (Research Square)·2023· 3 citedOpen Access
The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalysts
ACS Catalysis·2023· 253 cited
Open Challenges in Developing Generalizable Large-Scale Machine-Learning Models for Catalyst Discovery
ACS Catalysis·2022· 60 cited
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Frequent Co-authors

Zachary W. Ulissi(27), C. Lawrence Zitnick(22), Abhishek Das(17), Anuroop Sriram(15), Brandon M. Wood(14), Siddharth Goyal(9), John R. Kitchin(9), Aini Palizhati(8), Adeesh Kolluru(8), Javier Heras‐Domingo(6), Weihua Hu(6), Devi Parikh(6), Janice Lan(6), Daniel S. Levine(6), Junwoong Yoon(5), Ammar Rizvi(5), Brook Wander(5), Matt Uyttendaele(5), Vahe Gharakhanyan(5), Thibaut Lavril(4)