Ilyes Batatia

University of Cambridge — United Kingdom

Materials Science · Physical Sciences

11h-index1.1kcitations26works17.92yr avg
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Research Topics

Machine Learning in Materials Science(20), Protein Structure and Dynamics(6), Computational Drug Discovery Methods(5), Advanced Chemical Physics Studies(2), Neural Networks and Applications(2)

Publications26 total

A foundation model for atomistic materials chemistry
The Journal of Chemical Physics·2025· 65 citedOpen Access
Optimizing Data Distribution and Kernel Performance for Efficient Training of Chemistry Foundation Models: A Case Study with MACE
2025
Zero shot molecular generation via similarity kernels
Nature Communications·2025· 1 citedOpen Access
MACE-OFF: Short-Range Transferable Machine Learning Force Fields for Organic Molecules
Journal of the American Chemical Society·2025· 108 citedOpen Access
Optimizing Data Distribution and Kernel Performance for Efficient Training of Chemistry Foundation Models: A Case Study with MACE
arXiv (Cornell University)·2025Open Access
Crash testing machine learning force fields for molecules, materials, and interfaces: molecular dynamics in the TEA challenge 2023
Chemical Science·2025· 12 citedOpen Access
Crash testing machine learning force fields for molecules, materials, and interfaces: model analysis in the TEA Challenge 2023
Chemical Science·2025· 10 citedOpen Access
BoostMD: Accelerating molecular sampling by leveraging ML force field features from previous time-steps
arXiv (Cornell University)·2024Open Access
Crash Testing Machine Learning Force Fields for Molecules, Materials, and Interfaces: Model Analysis in the TEA Challenge 2023
2024· 7 cited
Crash Testing Machine Learning Force Fields for Molecules, Materials, and Interfaces: Molecular Dynamics in the TEA Challenge 2023
2024· 5 cited
Data-efficient fine-tuning of foundational models for first-principles quality sublimation enthalpies
Faraday Discussions·2024· 33 citedOpen Access
Benchmarking of machine learning interatomic potentials for reactive hydrogen dynamics at metal surfaces
Machine Learning Science and Technology·2024· 20 citedOpen Access
Data-efficient fine-tuning of foundational models for first-principles quality sublimation enthalpies
arXiv (Cornell University)·2024· 2 citedOpen Access
Benchmarking of machine learning interatomic potentials for reactive hydrogen dynamics at metal surfaces
arXiv (Cornell University)·2024· 4 citedOpen Access
Energy-conserving equivariant GNN for elasticity of lattice architected metamaterials
arXiv (Cornell University)·2024· 1 citedOpen Access
A foundation model for atomistic materials chemistry
arXiv (Cornell University)·2023· 229 citedOpen Access
MACE-OFF: Transferable Short Range Machine Learning Force Fields for Organic Molecules
arXiv (Cornell University)·2023· 65 citedOpen Access
A Geometric Insight into Equivariant Message Passing Neural Networks on Riemannian Manifolds
arXiv (Cornell University)·2023Open Access
Evaluation of the MACE force field architecture: From medicinal chemistry to materials science
The Journal of Chemical Physics·2023· 152 citedOpen Access
Tensor-Reduced Atomic Density Representations
Physical Review Letters·2023· 37 citedOpen Access
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Frequent Co-authors

Gábor Cśanyi(14), Dávid Péter Kovács(11), Gábor Csányi(8), Christoph Ortner(7), Venkat Kapil(6), Johannes T. Margraf(6), Nicholas J. Browning(6), Xavier R. Advincula(4), James P. Darby(4), Flaviano Della Pia(4), Ioan-Bogdan Magdău(4), Angelos Michaelides(4), J. Harry Moore(4), Igor Poltavsky(4), Mirela Puleva(4), Anton Charkin-Gorbulin(4), Grégory Fonseca(4), Stefan Chmiela(4), Mengnan Cui(4), J. Thorben Frank(4)