Martin Šípka

Charles University — Czech Republic

Materials Science · Physical Sciences

4h-index86citations18works18.02yr avg
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Research Topics

Machine Learning in Materials Science(7), Neural Networks and Applications(6), Model Reduction and Neural Networks(5), Advanced Thermodynamics and Statistical Mechanics(3), Advanced Data Processing Techniques(3)

Publications18 total

A reactive neural network framework for water-loaded acidic zeolites
Nature Communications·2024· 35 citedOpen Access
Lack-of-fit reduction in non-equilibrium thermodynamics applied to the Kac–Zwanzig model
Journal of Non-Equilibrium Thermodynamics·2024· 1 cited
Supplementary data (CC BY-NC-SA 4.0): A reactive neural network framework for water-loaded acidic zeolites
Zenodo (CERN European Organization for Nuclear Research)·2023Open Access
Direct Poisson neural networks: learning non-symplectic mechanical systems
Journal of Physics A Mathematical and Theoretical·2023· 4 citedOpen Access
Supplementary data (CC BY 4.0): A reactive neural network framework for water-loaded acidic zeolites
Zenodo (CERN European Organization for Nuclear Research)·2023Open Access
Supplementary data (CC BY-NC-SA 4.0): A reactive neural network framework for water-loaded acidic zeolites
Zenodo (CERN European Organization for Nuclear Research)·2023Open Access
A reactive neural network framework for water-loaded acidic zeolites
arXiv (Cornell University)·2023· 4 citedOpen Access
Direct Poisson neural networks: Learning non-symplectic mechanical systems
arXiv (Cornell University)·2023Open Access
Constructing Collective Variables Using Invariant Learned Representations
Journal of Chemical Theory and Computation·2023· 20 citedOpen Access
Differentiable Simulations for Enhanced Sampling of Rare Events
arXiv (Cornell University)·2023· 4 citedOpen Access
On compatibility of the natural configuration framework with general equation for non-equilibrium reversible–irreversible coupling (GENERIC): Derivation of anisotropic rate-type models
Journal of Non-Newtonian Fluid Mechanics·2022· 7 cited
Understanding chemical reactions via variational autoencoder and atomic representations
arXiv (Cornell University)·2022Open Access
Understanding chemical reactions via variational autoencoder and atomic\n representations
arXiv (Cornell University)·2022· 2 citedOpen Access
Learning the GENERIC evolution
arXiv (Cornell University)·2021· 2 citedOpen Access
Learning Physics from Data: A Thermodynamic Interpretation
Springer proceedings in mathematics & statistics·2021· 6 citedOpen Access
On compatibility of the Natural configuration framework with GENERIC: Derivation of anisotropic rate-type models
arXiv (Cornell University)·2020· 1 citedOpen Access
Modeling of anisotropic viscoelastic fluids
2020
Gravitational collapse of scalar field
2017

Frequent Co-authors

Andreas Erlebach(8), Lukáš Grajciar(8), Michal Pavelka(7), Indranil Saha(5), Petr Nachtigall(5), Christopher J. Heard(5), Miroslav Grmela(3), Petr Pelech(2), Karel Tůma(2), Martin Sýkora(2), Oğul Esen(2), Francisco Chinesta(1), Elías Cueto(1), Beatriz Moya(1), Johannes C. B. Dietschreit(1), Rafael Gómez‐Bombarelli(1), Kateřina Mladá(1)