Dr. Štěpán Sršeň

Publications

(1) Müller, C.; Sršeň, Š.; Bachmair, B.; Crespo-Otero, R.; Li, J.; Mausenberger, S.; Pinheiro, M.; Worth, G.; Lopez, S. A.; Westermayr, J. Machine Learning for Nonadiabatic Molecular Dynamics: Best Practices and Recent Progress. Chemical Science 2025, 16 (38), 17542–17567. https://doi.org/10.1039/D5SC05579B.
```
@article{Muller2025,
  author = {M{\"{u}}ller, Carolin and Sr{\v{s}}eň, {\v{S}}t{\v{e}}p{\'{a}}n and Bachmair, Brigitta and Crespo-Otero, Rachel and Li, Jingbai and Mausenberger, Sascha and Pinheiro, Max and Worth, Graham and Lopez, Steven A and Westermayr, Julia},
  doi = {10.1039/D5SC05579B},
  isbn = {1754217567},
  issn = {2041-6539},
  journal = {Chemical Science},
  month = oct,
  number = {38},
  pages = {17542--17567},
  publisher = {The Royal Society of Chemistry},
  title = {{Machine learning for nonadiabatic molecular dynamics: best practices and recent progress}},
  url = {https://pubs.rsc.org/en/content/articlelanding/2025/sc/d5sc05579b},
  volume = {16},
  year = {2025}
}
```
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Exploring molecular excited states holds immense significance across organic chemistry, chemical biology, and materials science. Understanding the photophysical properties of molecular chromophores is crucial for designing nature-inspired functional molecules, with applications ranging from photosynthesis to pharmaceuticals. Non-adiabatic molecular dynamics simulations are powerful tools to investigate the photochemistry of molecules and materials, but demand extensive computing resources, especially for complex molecules and environments. To address these challenges, the integration of machine learning has emerged. Machine learning algorithms can be used to analyse vast datasets and accelerate discoveries by identifying relationships between geometrical features and ground as well as excited-state properties. However, challenges persist, including the acquisition of accurate excited-state data and managing the complexity of the data. This article provides an overview of recent and best practices in machine learning for non-adiabatic molecular dynamics, focusing on pre-processing, surface fitting, and post-processing of data.
(2) Sršeň, Š.; Lilienfeld, O. A. von; Slavíček, P. Fast and Accurate Excited States Predictions: Machine Learning and Diabatization. Phys. Chem. Chem. Phys. 2024, 26 (5), 4306–4319. https://doi.org/10.1039/D3CP05685F.
```
@article{Srsen2024,
  author = {Sr{\v{s}}eň, {\v{S}}t{\v{e}}p{\'{a}}n and von Lilienfeld, O. Anatole and Slav{\'{i}}{\v{c}}ek, Petr},
  doi = {10.1039/D3CP05685F},
  issn = {1463-9084},
  journal = {Phys. Chem. Chem. Phys.},
  month = jan,
  number = {5},
  pages = {4306--4319},
  publisher = {The Royal Society of Chemistry},
  title = {{Fast and accurate excited states predictions: machine learning and diabatization}},
  url = {https://pubs.rsc.org/en/content/articlelanding/2024/cp/d3cp05685f},
  volume = {26},
  year = {2024}
}
```
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The efficiency of machine learning algorithms for electronically excited states is far behind ground-state applications. One of the underlying problems is the insufficient smoothness of the fitted potential energy surfaces and other properties in the vicinity of state crossings and conical intersections, which is a prerequisite for an efficient regression. Smooth surfaces can be obtained by switching to the diabatic basis. However, diabatization itself is still an outstanding problem. We overcome these limitations by solving both problems at once. We use a machine learning approach combining clustering and regression techniques to correct for the deficiencies of property-based diabatization which, in return, provides us with smooth surfaces that can be easily fitted. Our approach extends the applicability of property-based diabatization to multidimensional systems. We utilize the proposed diabatization scheme to achieve higher prediction accuracy for adiabatic states and we show its performance by reconstructing global potential energy surfaces of excited states of nitrosyl fluoride and formaldehyde. While the proposed methodology is independent of the specific property-based diabatization and regression algorithm, we show its performance for kernel ridge regression and a very simple diabatization based on transition multipoles. Compared to most other algorithms based on machine learning, our approach needs only a small amount of training data.

(3) Sršeň, Š. Zpověď Vědce o Vědecké Zodpovědnosti. In Krakatit 100: Úvahy nejen o atomovém věku; Čejková, J., Ed.; Academix: Praha, 2024.

@incollection{Srsen2024Zpoved,
  address = {Praha},
  author = {Sr{\v{s}}eň, {\v{S}}t{\v{e}}p{\'{a}}n},
  booktitle = {Krakatit 100: {\'{U}}vahy nejen o atomov{\'{e}}m v{\v{e}}ku},
  chapter = {9},
  editor = {{\v{C}}ejkov{\'{a}}, Jitka},
  publisher = {Academix},
  title = {{Zpov{\v{e}}ď v{\v{e}}dce o v{\v{e}}deck{\'{e}} zodpov{\v{e}}dnosti}},
  year = {2024}
}

(4) Ovad, T.; Sapunar, M.; Sršeň, Š.; Slavíček, P.; Mašín, Z.; Jones, N. C.; Hoffmann, S. V.; Ranković, M.; Fedor, J. Excitation and Fragmentation of the Dielectric Gas C4F7N: Electrons vs Photons. J. Chem. Phys. 2023, 158 (1), 14303. https://doi.org/10.1063/5.0130216.
```
@article{Ovad2023,
  author = {Ovad, Tom{\'{a}}{\v{s}} and Sapunar, Marin and Sr{\v{s}}eň, {\v{S}}t{\v{e}}p{\'{a}}n and Slav{\'{i}}{\v{c}}ek, Petr and Ma{\v{s}}{\'{i}}n, Zden{\v{e}}k and Jones, Nykola C. and Hoffmann, S{\o}ren Vr{\o}nning and Rankovi{\'{c}}, Milo{\v{s}} and Fedor, Juraj},
  doi = {10.1063/5.0130216},
  issn = {10897690},
  journal = {J. Chem. Phys.},
  month = jan,
  number = {1},
  pages = {14303},
  pmid = {36610949},
  publisher = {American Institute of Physics Inc.},
  title = {{Excitation and fragmentation of the dielectric gas C4F7N: Electrons vs photons}},
  url = {https://pubs.aip.org/aip/jcp/article/158/1/014303/2867526/Excitation-and-fragmentation-of-the-dielectric-gas},
  volume = {158},
  year = {2023}
}
```
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C4F7N is a promising candidate for the replacement of sulfur hexafluoride as an insulating medium, and it is important to understand the chemical changes initiated in the molecule by collision with free electrons, specifically the formation of neutral fragments. The first step of neutral fragmentation is electronic excitation, yet neither the absorption spectrum in the vacuum ultraviolet (VUV) region nor the electron energy loss spectrum have previously been reported. Here, we experimentally probed the excited states by VUV photoabsorption spectroscopy and electron energy loss spectroscopy (EELS). We found that the distribution of states populated upon electron impact with low-energy electrons is significantly different from that following photoabsorption. This difference was confirmed and interpreted with ab initio modeling of both VUV and EELS spectra. We propose here a new computational protocol for the simulation of EELS spectra combining the Born approximation with approximate forms of correlated wave functions, which allows us to calculate the (usually very expensive) scattering cross sections at a cost similar to the calculation of oscillator strengths. Finally, we perform semi-classical non-adiabatic dynamics simulations to investigate the possible neutral fragments of the molecule formed through electron-induced neutral dissociation. We show that the product distribution is highly non-statistical.
(5) Sršeň, Š.; Slavíček, P. Optimal Representation of the Nuclear Ensemble: Application to Electronic Spectroscopy. J. Chem. Theory Comput. 2021, 17 (10), 6395–6404. https://doi.org/10.1021/acs.jctc.1c00749.
```
@article{Srsen2021,
  author = {Sr{\v{s}}eň, {\v{S}}t{\v{e}}p{\'{a}}n and Slav{\'{i}}{\v{c}}ek, Petr},
  doi = {10.1021/acs.jctc.1c00749},
  issn = {1549-9618},
  journal = {J. Chem. Theory Comput.},
  month = sep,
  number = {10},
  pages = {6395--6404},
  publisher = {American Chemical Society},
  title = {{Optimal Representation of the Nuclear Ensemble: Application to Electronic Spectroscopy}},
  url = {https://pubs.acs.org/doi/10.1021/acs.jctc.1c00749},
  volume = {17},
  year = {2021}
}
```
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Nuclear densities are frequently represented by an ensemble of nuclear configurations or points in the phase space in various contexts of molecular simulations. The size of the ensemble directly affects the accuracy and computational cost of subsequent calculations of observable quantities. In the present work, we address the question of how many configurations do we need and how to select them most efficiently. We focus on the nuclear ensemble method in the context of electronic spectroscopy, where thousands of sampled configurations are usually needed for sufficiently converged spectra. The proposed representative sampling technique allows for a dramatic reduction of the sample size. By using an exploratory method, we model the density from a large sample in the space of transition properties. The representative subset of nuclear configurations is optimized by minimizing its Kullback-Leibler divergence to the full density with simulated annealing. High-level calculations are then performed only for the selected subset of configurations. We tested the algorithm on electronic absorption spectra of three molecules: (E)-azobenzene, the simplest Criegee intermediate, and hydrated nitrate anion. Typically, dozens of nuclear configurations provided sufficiently accurate spectra. A strongly forbidden transition of the nitrate anion presented the most challenging case due to rare geometries with disproportionately high transition intensities. This problematic case was easily diagnosed within the present approach. We also discuss various exploratory methods and a possible extension to dynamical simulations.
(6) Sršeň, Š.; Sita, J.; Slavíček, P.; Ladányi, V.; Heger, D. Limits of the Nuclear Ensemble Method for Electronic Spectra Simulations: Temperature Dependence of the (E)-Azobenzene Spectrum. J. Chem. Theory Comput. 2020, 16 (10), 6428–6438. https://doi.org/10.1021/acs.jctc.0c00579.
```
@article{Srsen2020,
  author = {Sr{\v{s}}eň, {\v{S}}t{\v{e}}p{\'{a}}n and Sita, Jaroslav and Slav{\'{i}}{\v{c}}ek, Petr and Lad{\'{a}}nyi, V{\'{i}}t and Heger, Dominik},
  doi = {10.1021/acs.jctc.0c00579},
  issn = {1549-9618},
  journal = {J. Chem. Theory Comput.},
  month = oct,
  number = {10},
  pages = {6428--6438},
  pmid = {32794758},
  publisher = {American Chemical Society (ACS)},
  title = {{Limits of the Nuclear Ensemble Method for Electronic Spectra Simulations: Temperature Dependence of the (E)-Azobenzene Spectrum}},
  url = {https://pubs.acs.org/doi/10.1021/acs.jctc.0c00579},
  volume = {16},
  year = {2020}
}
```
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We explore the range of applicability of the nuclear ensemble method (NEM) for quantitative simulations of absorption spectra and their temperature variations. We formulate a "good practice"for the NEM based on statistical theory. Special attention is paid to proper treatment of uncertainty estimation including the convergence with the number of samples, which is often neglected in the field. As a testbed, we have selected a well-known chromophore, (E)-azobenzene. We measured its temperature difference UV-vis absorption spectra in methanol, which displayed two dominant features: a moderate increase in the intensity of the n\pi∗ band and a pronounced decrease in intensity of the low-energy part of the \pi\pi∗ band. We attributed both features to increasing non-Condon effects with temperature. We show that the NEM based on the path integral molecular dynamics combined with range-separated hybrid functionals provides quantitatively accurate spectra and their differences. Experimentally, the depletion of the absorption in the \pi\pi∗ band showed a characteristic vibrational progression that cannot be reproduced with the NEM. We show that hundreds of thousands of samples are necessary to achieve an accuracy sufficient for the unambiguous explanation of the observed temperature effects. We provide a detailed analysis of the temperature effects on the spectrum based on the harmonic model of the system combined with the NEM. We also rationalize the vibrational structure of the spectrum using the Franck-Condon principle.
(7) Med, J.; Sršeň, Š.; Slavíček, P.; Domaracka, A.; Indrajith, S.; Rousseau, P.; Fárník, M.; Fedor, J.; Kočišek, J. Vibrationally Mediated Stabilization of Electrons in Nonpolar Matter. J. Phys. Chem. Lett. 2020, 11 (7), 2482–2489. https://doi.org/10.1021/acs.jpclett.0c00278.
```
@article{Med2020,
  author = {Med, Jakub and Sr{\v{s}}eň, {\v{S}}t{\v{e}}p{\'{a}}n and Slav{\'{i}}{\v{c}}ek, Petr and Domaracka, A. and Indrajith, S. and Rousseau, P. and F{\'{a}}rn{\'{i}}k, M. and Fedor, J. and Ko{\v{c}}i{\v{s}}ek, J.},
  doi = {10.1021/acs.jpclett.0c00278},
  issn = {19487185},
  journal = {J. Phys. Chem. Lett.},
  month = apr,
  number = {7},
  pages = {2482--2489},
  pmid = {32154726},
  publisher = {American Chemical Society},
  title = {{Vibrationally Mediated Stabilization of Electrons in Nonpolar Matter}},
  url = {https://pubs.acs.org/doi/10.1021/acs.jpclett.0c00278},
  volume = {11},
  year = {2020}
}
```
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We explore solvation of electrons in nonpolar matter, here represented by butadiene clusters. Isolated butadiene supports only the existence of transient anions (resonances). Two-dimensional electron energy loss spectroscopy shows that the resonances lead to an efficient vibrational excitation of butadiene, which can result into the almost complete loss of energy of the interacting electron. Cluster-beam experiments show that molecular clusters of butadiene form stable anions, however only at sizes of more than 9 molecular units. We have calculated the distribution of electron affinities of clusters using classical and path integral molecular dynamics simulations. There is almost a continuous transition from the resonant to the bound anions with an increase in cluster size. The comparison of the classical and quantum dynamics reveals that the electron binding is strongly supported by molecular vibrations, brought about by nuclear zero-point motion and thermal agitation. We also inspected the structure of the solvated electron, finding it well localized.

(8) Slavíček, P.; Sršeň, Š. O Inteligenci Umělého Života a o Životě Umělé Inteligence. In Robot 100: Sto rozumů; Čejková, J., Ed.; VŠCHT Praha: Praha, 2020.

@incollection{Slavicek2020,
  address = {Praha},
  author = {Slav{\'{i}}{\v{c}}ek, Petr and Sr{\v{s}}eň, {\v{S}}t{\v{e}}p{\'{a}}n},
  booktitle = {Robot 100: Sto rozumů},
  chapter = {15},
  editor = {{\v{C}}ejkov{\'{a}}, Jitka},
  publisher = {V{\v{S}}CHT Praha},
  title = {{O inteligenci um{\v{e}}l{\'{e}}ho {\v{z}}ivota a o {\v{z}}ivot{\v{e}} um{\v{e}}l{\'{e}} inteligence}},
  year = {2020}
}

(9) Pohl, M. N.; Muchová, E.; Seidel, R.; Ali, H.; Sršeň, Š.; Wilkinson, I.; Winter, B.; Slavíček, P. Do Water’s Electrons Care about Electrolytes? Chem. Sci. 2019, 10 (3), 848–865. https://doi.org/10.1039/C8SC03381A.

@article{Pohl2019,
  author = {Pohl, Marvin N. and Muchov{\'{a}}, Eva and Seidel, Robert and Ali, Hebatallah and Sr{\v{s}}eň, {\v{S}}t{\v{e}}p{\'{a}}n and Wilkinson, Iain and Winter, Bernd and Slav{\'{i}}{\v{c}}ek, Petr},
  doi = {10.1039/C8SC03381A},
  issn = {2041-6520},
  journal = {Chem. Sci.},
  month = jan,
  number = {3},
  pages = {848--865},
  publisher = {The Royal Society of Chemistry},
  title = {{Do water's electrons care about electrolytes?}},
  url = {https://pubs.rsc.org/en/content/articlelanding/2019/sc/c8sc03381a},
  volume = {10},
  year = {2019}
}

(10) Sršeň, Š.; Hollas, D.; Slavíček, P. UV Absorption of Criegee Intermediates: Quantitative Cross Sections from High-Level Ab Initio Theory. Phys. Chem. Chem. Phys. 2018, 20 (9), 6421–6430. https://doi.org/10.1039/C8CP00199E.

@article{Srsen2018,
  author = {Sr{\v{s}}eň, {\v{S}}t{\v{e}}p{\'{a}}n and Hollas, Daniel and Slav{\'{i}}{\v{c}}ek, Petr},
  doi = {10.1039/C8CP00199E},
  journal = {Phys. Chem. Chem. Phys.},
  month = feb,
  number = {9},
  pages = {6421--6430},
  publisher = {The Royal Society of Chemistry},
  title = {{UV absorption of Criegee intermediates: quantitative cross sections from high-level ab initio theory}},
  url = {https://pubs.rsc.org/en/content/articlelanding/2018/cp/c8cp00199e},
  volume = {20},
  year = {2018}
}

(11) Berka, K.; Sršeň, Š.; Slavíček, P. Je Strojové Učení Budoucností Teoretické Chemie? (Is Machine Learning the Future of Theoretical Chemistry?). Chemicke Listy 2018, 112 (10), 640–647.
```
@article{Berka2018a,
  author = {Berka, Karel and Sr{\v{s}}eň, {\v{S}}t{\v{e}}p{\'{a}}n and Slav{\'{i}}{\v{c}}ek, Petr},
  issn = {12137103},
  journal = {Chemicke Listy},
  keywords = {Artificial intelligence,Machine learning,Neural networks,QSAR,Quantum chemistry,Theoretical chemistry},
  number = {10},
  pages = {640--647},
  title = {{Je strojov{\'{e}} u{\v{c}}en{\'{i}} budoucnost{\'{i}} teoretick{\'{e}} chemie? (Is machine learning the future of theoretical chemistry?)}},
  url = {http://www.chemicke-listy.cz/ojs3/index.php/chemicke-listy/article/view/3187},
  volume = {112},
  year = {2018}
}
```
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The application of the methods of machine learning in chemistry is briefly summarized in the present work. We first explain the basic concepts of artificial intelligence and machine learning. Next, the applications in two particular areas are discussed: searching relations between the structure and biological activity of molecules and using the techniques of machine learning in quantum chemistry as well as in other fields of theoretical chemistry. The evolutionary character of the machine learning approaches is emphasized. A fast development is witnessed in the field which, however, gradually follows the previous development in using statistical techniques in chemistry.

Dr. Štěpán Sršeň

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