Dávid Péter Kovács

University of Cambridge — United Kingdom

Materials Science · Physical Sciences

15h-index1.8kcitations28works73.02yr avg
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Research Topics

Machine Learning in Materials Science(25), Computational Drug Discovery Methods(10), Mass Spectrometry Techniques and Applications(10), Protein Structure and Dynamics(7), Force Microscopy Techniques and Applications(7)

Publications28 total

A foundation model for atomistic materials chemistry
The Journal of Chemical Physics·2025· 65 citedOpen Access
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
The design space of E(3)-equivariant atom-centred interatomic potentials
Nature Machine Intelligence·2025· 118 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
Hyperactive learning for data-driven interatomic potentials
npj Computational Materials·2023· 94 citedOpen Access
Evaluation of the MACE force field architecture: From medicinal chemistry to materials science
The Journal of Chemical Physics·2023· 152 citedOpen Access
First-principles spectroscopy of aqueous interfaces using machine-learned electronic and quantum nuclear effects
Faraday Discussions·2023· 53 citedOpen Access
Tensor-Reduced Atomic Density Representations
Physical Review Letters·2023· 37 citedOpen Access
Evaluation of the MACE Force Field Architecture: from Medicinal Chemistry to Materials Science
arXiv (Cornell University)·2023· 3 citedOpen Access
Hyperactive Learning (HAL) for Data-Driven Interatomic Potentials
arXiv (Cornell University)·2022· 9 citedOpen Access
Tensor-reduced atomic density representations
arXiv (Cornell University)·2022Open Access
MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields
arXiv (Cornell University)·2022· 266 citedOpen Access
The Design Space of E(3)-Equivariant Atom-Centered Interatomic Potentials
arXiv (Cornell University)·2022· 98 citedOpen Access
Linear Atomic Cluster Expansion Force Fields for Organic Molecules: Beyond RMSE
Journal of Chemical Theory and Computation·2021· 111 citedOpen Access
Linear Atomic Cluster Expansion Force Fields for Organic Molecules: beyond RMSE
2021· 13 citedOpen Access
Linear Atomic Cluster Expansion Force Fields forOrganic Molecules: beyond RMSE
2021Open Access
Linear Atomic Cluster Expansion Force Fields for Organic Molecules: beyond RMSE
2021· 6 citedOpen Access
Linear Atomic Cluster Expansion Force Fields for Organic Molecules: beyond RMSE
2021· 1 citedOpen Access
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Frequent Co-authors

Christoph Ortner(18), Gábor Csányi(14), Ilyes Batatia(11), Cas van der Oord(11), Daniel J. Cole(11), Gábor Cśanyi(10), Alice E. A. Allen(9), Jiří Kučera(7), Venkat Kapil(5), James P. Darby(4), Ioan-Bogdan Magdău(4), J. Harry Moore(4), Gregor N. C. Simm(3), Rokas Elijošius(3), Christian Holm(3), Angelos Michaelides(3), Sam Walton Norwood(3), William C. Witt(3), Fabian Zills(3), William McCorkindale(3)