Félix Musil

York College, City University of New York — United States

Materials Science · Physical Sciences

13h-index1.3kcitations39works3.22yr avg
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Research Topics

Machine Learning in Materials Science(17), Computational Drug Discovery Methods(11), Protein Structure and Dynamics(6), X-ray Diffraction in Crystallography(6), Magnetic confinement fusion research(5)

Publications39 total

ClementiGroup/mlcg: noise-alpha: training with noise forces
Zenodo (CERN European Organization for Nuclear Research)·2026Open Access
ACEsuit/mace: v0.3.15
Zenodo (CERN European Organization for Nuclear Research)·2026Open Access
ACEsuit/mace: v0.3.15
Zenodo (CERN European Organization for Nuclear Research)·2026Open Access
ClementiGroup/mlcg: 0.1.3
Zenodo (CERN European Organization for Nuclear Research)·2026Open Access
lab-cosmo/sphericart: v1.0.6
Zenodo (CERN European Organization for Nuclear Research)·2026Open Access
Navigating protein landscapes with a machine-learned transferable coarse-grained model
Nature Chemistry·2025· 25 citedOpen Access
Lattice-Matched Heterogeneous Nucleation Eliminates Defective Buried Interfaces in Halide Perovskites
Chemistry of Materials·2025· 2 cited
Accurate nuclear quantum statistics on machine-learned classical effective potentials
The Journal of Chemical Physics·2024· 4 citedOpen Access
Accurate nuclear quantum statistics on machine-learned classical effective potentials
arXiv (Cornell University)·2024Open Access
Lattice matched heterogeneous nucleation eliminate defective buried interface in halide perovskites
arXiv (Cornell University)·2024· 1 citedOpen Access
Navigating protein landscapes with a machine-learned transferable coarse-grained model
2023· 3 citedOpen Access
Navigating protein landscapes with a machine-learned transferable coarse-grained model
arXiv (Cornell University)·2023· 5 citedOpen Access
Machine learned coarse-grained protein force-fields: Are we there yet?
Current Opinion in Structural Biology·2023· 73 citedOpen Access
Quantum dynamics using path integral coarse-graining
The Journal of Chemical Physics·2022· 39 citedOpen Access
Quantum dynamics using path integral coarse-graining
arXiv (Cornell University)·2022Open Access
Optimal radial basis for density-based atomic representations
Refubium (Universitätsbibliothek der Freien Universität Berlin)·2022· 21 citedOpen Access
Efficient implementation of atom-density representations
Refubium (Universitätsbibliothek der Freien Universität Berlin)·2022· 48 citedOpen Access
A general and efficient framework for atomistic machine learning
Infoscience (Ecole Polytechnique Fédérale de Lausanne)·2021Open Access
Machine Learning at the Atomic Scale
CHIMIA International Journal for Chemistry·2019· 9 citedOpen Access
Chemical Shifts in Molecular Solids by Machine Learning Datasets
NCCR MARVEL·2019· 4 citedOpen Access
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Frequent Co-authors

Michele Ceriotti(14), Cecilia Clementi(10), Iryna Zaporozhets(7), Sandip De(6), Michael J. Willatt(6), Klara Bonneau(6), Frank Noé(6), Federico David Halpern(5), Paolo Ricci(5), Fabio Riva(5), Christoph Wersal(4), Aleksander E. P. Durumeric(4), Nicholas E. Charron(4), Clark Templeton(4), Yaoyi Chen(4), Atharva Kelkar(4), Venkat Kapil(4), Jacopo Venturin(4), Aldo S. Pasos-Trejo(3), Teresa Ingram(3)