Muhammed Shuaibi

Muhammed Shuaibi

Carnegie Mellon University

H-index: 12

North America-United States

About Muhammed Shuaibi

Muhammed Shuaibi, With an exceptional h-index of 12 and a recent h-index of 12 (since 2020), a distinguished researcher at Carnegie Mellon University, specializes in the field of Computational Catalysis, Machine Learning.

His recent articles reflect a diverse array of research interests and contributions to the field:

Chemical Properties from Graph Neural Network-Predicted Electron Densities

AdsorbML: a leap in efficiency for adsorption energy calculations using generalizable machine learning potentials

AmpTorch: A Python package for scalable fingerprint-based neural network training on multi-element systems with integrated uncertainty quantification

The Open Catalyst 2022 (OC22) dataset and challenges for oxide electrocatalysts

Spherical channels for modeling atomic interactions

Adsorbml: Accelerating adsorption energy calculations with machine learning

Open challenges in developing generalizable large-scale machine-learning models for catalyst discovery

Transfer learning using attentions across atomic systems with graph neural networks (TAAG)

Muhammed Shuaibi Information

University

Position

PhD Student

Citations(all)

713

Citations(since 2020)

712

Cited By

13

hIndex(all)

12

hIndex(since 2020)

12

i10Index(all)

12

i10Index(since 2020)

12

Email

University Profile Page

Google Scholar

Muhammed Shuaibi Skills & Research Interests

Computational Catalysis

Machine Learning

Top articles of Muhammed Shuaibi

Chemical Properties from Graph Neural Network-Predicted Electron Densities

The Journal of Physical Chemistry C

2023/11/27

Muhammed Shuaibi
Muhammed Shuaibi

H-Index: 3

AdsorbML: a leap in efficiency for adsorption energy calculations using generalizable machine learning potentials

npj Computational Materials

2023/9/22

AmpTorch: A Python package for scalable fingerprint-based neural network training on multi-element systems with integrated uncertainty quantification

Journal of Open Source Software

2023/7/26

The Open Catalyst 2022 (OC22) dataset and challenges for oxide electrocatalysts

ACS Catalysis

2023/2/16

Spherical channels for modeling atomic interactions

Advances in Neural Information Processing Systems

2022/12/6

Adsorbml: Accelerating adsorption energy calculations with machine learning

arXiv e-prints

2022/11

Open challenges in developing generalizable large-scale machine-learning models for catalyst discovery

ACS Catalysis

2022/7/5

Transfer learning using attentions across atomic systems with graph neural networks (TAAG)

The Journal of Chemical Physics

2022/5/14

Gemnet-oc: developing graph neural networks for large and diverse molecular simulation datasets

Transactions on Machine Learning Research

2022/9

Generalizable Machine Learning Models for Electrocatalyst Discovery

2022

Muhammed Shuaibi
Muhammed Shuaibi

H-Index: 3

How do graph networks generalize to large and diverse molecular systems

arXiv preprint arXiv

2022

Forcenet: A graph neural network for large-scale quantum calculations

arXiv preprint arXiv:2103.01436

2021/3/2

The Open Catalyst Challenge 2021: Competition Report.

2021

Rotation invariant graph neural networks using spin convolutions

arXiv preprint arXiv:2106.09575

2021/6/17

Muhammed Shuaibi
Muhammed Shuaibi

H-Index: 3

Abhishek Das
Abhishek Das

H-Index: 4

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

Machine Learning: Science and Technology

2020/12/29

Muhammed Shuaibi
Muhammed Shuaibi

H-Index: 3

Saurabh Sivakumar
Saurabh Sivakumar

H-Index: 1

An introduction to electrocatalyst design using machine learning for renewable energy storage

arXiv preprint arXiv:2010.09435

2020/10/14

See List of Professors in Muhammed Shuaibi University(Carnegie Mellon University)

Co-Authors

academic-engine