Molecular excited states through a machine learning lens.

Ponseca, C. S., Chábera, P., Uhlig, J., Persson, P. & Sundström, V. Ultrafast electron dynamics in solar energy conversion. Chem. Rev. 117, 10940–11024 (2017).

pubmed: 28805062

Brunk, E. & Rothlisberger, U. Mixed quantum mechanical/molecular mechanical molecular dynamics simulations of biological systems in ground and electronically excited states. Chem. Rev. 115, 6217–6263 (2015).

pubmed: 25880693

Zhang, B. & Sun, L. Artificial photosynthesis: opportunities and challenges of molecular catalysts. Chem. Soc. Rev. 48, 2216–2264 (2019).

pubmed: 30895997

Gozem, S., Luk, H. L., Schapiro, I. & Olivucci, M. Theory and simulation of the ultrafast double-bond isomerization of biological chromophores. Chem. Rev. 117, 13502–13565 (2017).

pubmed: 29083892

Chakraborty, P., Karsili, T. N. V., Marchetti, B. & Matsika, S. Mechanistic insights into photoinduced damage of DNA and RNA nucleobases in the gas phase and in bulk solution. Faraday Discuss. 207, 329–350 (2018).

pubmed: 29364301

Yang, Z. et al. Recent advances in organic thermally activated delayed fluorescence materials. Chem. Soc. Rev. 46, 915–1016 (2017).

pubmed: 28117864

Kaloni, T. P., Giesbrecht, P. K., Schreckenbach, G. & Freund, M. S. Polythiophene: from fundamental perspectives to applications. Chem. Mater. 29, 10248–10283 (2017).

Zhang, J. Z. & Reisner, E. Advancing photosystem II photoelectrochemistry for semi-artificial photosynthesis. Nat. Rev. Chem. 4, 6–21 (2019).

Bennett, K., Kowalewski, M. & Mukamel, S. Probing electronic and vibrational dynamics in molecules by time-resolved photoelectron, Auger-electron, and X-ray photon scattering spectroscopy. Faraday Discuss. 177, 405–428 (2015).

pubmed: 25730500 pmcid: 4401660

Gao, Y., Nie, W., Wang, X., Fan, F. & Li, C. Advanced space- and time-resolved techniques for photocatalyst studies. Chem. Commun. 56, 1007–1021 (2020).

Mancuso, J. L., Mroz, A. M., Le, K. N. & Hendon, C. H. Electronic structure modeling of metal–organic frameworks. Chem. Rev. 120, 8641–8715 (2020).

pubmed: 32672939

Taniguchi, M., Du, H. & Lindsey, J. S. PhotochemCAD 3: diverse modules for photophysical calculations with multiple spectral databases. Photochem. Photobiol. 94, 277–289 (2018).

pubmed: 29166541

Norman, P. & Dreuw, A. Simulating X-ray spectroscopies and calculating core-excited states of molecules. Chem. Rev. 118, 7208–7248 (2018).

pubmed: 29894157

Yonehara, T., Hanasaki, K. & Takatsuka, K. Fundamental approaches to nonadiabaticity: Toward a chemical theory beyond the Born–Oppenheimer paradigm. Chem. Rev. 112, 499–542 (2011).

pubmed: 22077497

Baryshnikov, G., Minaev, B. & Ågren, H. Theory and calculation of the phosphorescence phenomenon. Chem. Rev. 117, 6500–6537 (2017).

pubmed: 28388041

Crespo-Otero, R. & Barbatti, M. Recent advances and perspectives on nonadiabatic mixed quantum-classical dynamics. Chem. Rev. 118, 7026–7068 (2018).

pubmed: 29767966

Curchod, B. F. E. & Martínez, T. J. Ab initio nonadiabatic quantum molecular dynamics. Chem. Rev. 118, 3305–3336 (2018).

pubmed: 29465231

Kumpulainen, T., Lang, B., Rosspeintner, A. & Vauthey, E. Ultrafast elementary photochemical processes of organic molecules in liquid solution. Chem. Rev. 117, 10826–10939 (2017).

pubmed: 27957848

Lischka, H. et al. Multireference approaches for excited states of molecules. Chem. Rev. 118, 7293–7361 (2018).

pubmed: 30040389

Kozma, B. et al. A new benchmark set for excitation energy of charge transfer states: systematic investigation of coupled cluster type methods. J. Chem. Theory Comput. 16, 4213–4225 (2020).

pubmed: 32502351 pmcid: 7467641

Laurent, A. D. & Jacquemin, D. TD-DFT benchmarks: A review. Int. J. Quant. Chem. 113, 2019–2039 (2013).

Peach, M. J., Benfield, P., Helgaker, T. & Tozer, D. J. Excitation energies in density functional theory: an evaluation and a diagnostic test. J. Chem. Phys. 128, 044118 (2008).

pubmed: 18247941

Dral, P. O. Quantum chemistry in the age of machine learning. J. Phys. Chem. Lett. 11, 2336–2347 (2020).

pubmed: 32125858

Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O. & Walsh, A. Machine learning for molecular and materials science. Nature 559, 547–555 (2018).

pubmed: 30046072

von Lilienfeld, O. A., Müller, K.-R. & Tkatchenko, A. Exploring chemical compound space with quantum-based machine learning. Nat. Rev. Chem. 4, 347–358 (2020).

Otto, M. & Hörchner, U. in Software Development in Chemistry 4 (ed. Gasteiger, J.) 377–384 (Springer, 1990).

Zupan, J. & Gasteiger, J. Neural networks: A new method for solving chemical problems or just a passing phase? Anal. Chim. Acta 248, 1–30 (1991).

Bos, M. & Weber, H. T. Comparison of the training of neural networks for quantitative X-ray fluorescence spectrometry by a genetic algorithm and backward error propagation. Anal. Chim. Acta 247, 97–105 (1991).

Dral, P. O. in Advances in Quantum Chemistry: Chemical Physics and Quantum Chemistry Vol. 81 (eds Ruud, K. & Brändas, E. J.) 291–324 (Academic Press, 2020).

Liu, F., Du, L., Zhang, D. & Gao, J. Direct learning hidden excited state interaction patterns from ab initio dynamics and its implication as alternative molecular mechanism models. Sci. Rep. 7, 8737 (2017).

pubmed: 28821842 pmcid: 5562909

Ye, S. et al. A neural network protocol for electronic excitations of N-methylacetamide. Proc. Natl Acad. Sci. USA 116, 11612–11617 (2019).

pubmed: 31147467

Xue, B.-X., Barbatti, M. & Dral, P. O. Machine learning for absorption cross sections. J. Phys. Chem. A 124, 7199–7210 (2020).

pubmed: 32786977 pmcid: 7511037

Zhang, Y. et al. Efficient and accurate simulations of vibrational and electronic spectra with symmetry-preserving neural network models for tensorial properties. J. Phys. Chem. B 124, 7284–7290 (2020).

pubmed: 32786714

Chen, M. S., Zuehlsdorff, T. J., Morawietz, T., Isborn, C. M. & Markland, T. E. Exploiting machine learning to efficiently predict multidimensional optical spectra in complex environments. J. Phys. Chem. Lett. 11, 7559–7568 (2020).

pubmed: 32808797

Westermayr, J. & Marquetand, P. Deep learning for UV absorption spectra with SchNarc: First steps toward transferability in chemical compound space. J. Chem. Phys. 153, 154112 (2020).

pubmed: 33092371

Carbogno, C., Behler, J., Reuter, K. & Gross, A. Signatures of nonadiabatic O

Chen, W.-K., Liu, X.-Y., Fang, W., Dral, P. O. & Cui, G. Deep learning for nonadiabatic excited-state dynamics. J. Phys. Chem. Lett. 9, 6702–6708 (2018).

pubmed: 30403870

Dral, P. O., Barbatti, M. & Thiel, W. Nonadiabatic excited-state dynamics with machine learning. J. Phys. Chem. Lett. 9, 5660–5663 (2018).

pubmed: 30200766 pmcid: 6174422

Hu, D., Xie, Y., Li, X., Li, L. & Lan, Z. Inclusion of machine learning kernel ridge regression potential energy surfaces in on-the-fly nonadiabatic molecular dynamics simulation. J. Phys. Chem. Lett. 9, 2725–2732 (2018).

pubmed: 29732893

Chen, W. K., Fang, W. H. & Cui, G. Integrating machine learning with the multilayer energy-based fragment method for excited states of large systems. J. Phys. Chem. Lett. 10, 7836–7841 (2019).

pubmed: 31786927

Westermayr, J. et al. Machine learning enables long time scale molecular photodynamics simulations. Chem. Sci. 10, 8100–8107 (2019).

pubmed: 31857878 pmcid: 6849489

Chu, W., Saidi, W. A. & Prezhdo, O. V. Long-lived hot electron in a metallic particle for plasmonics and catalysis: Ab initio nonadiabatic molecular dynamics with machine learning. ACS Nano 14, 10608–10615 (2020).

pubmed: 32806073

Westermayr, J., Faber, F. A., Christensen, A. S., von Lilienfeld, O. A. & Marquetand, P. Neural networks and kernel ridge regression for excited states dynamics of CH

Westermayr, J., Gastegger, M. & Marquetand, P. Combining SchNet and SHARC: The SchNarc machine learning approach for excited-state dynamics. J. Phys. Chem. Lett. 11, 3828–3834 (2020).

pubmed: 32311258 pmcid: 7246974

Westermayr, J. & Marquetand, P. Machine learning and excited-state molecular dynamics. Mach. Learn. Sci. Technol. 1, 043001 (2020).

Westermayr, J. & Marquetand, P. Machine learning for electronically excited states of molecules. Chem. Rev. https://doi.org/10.1021/acs.chemrev.0c00749 (2020).

doi: 10.1021/acs.chemrev.0c00749 pubmed: 33211478

Nantasenamat, C., Isarankura-Na-Ayudhya, C., Tansila, N., Naenna, T. & Prachayasittikul, V. Prediction of GFP spectral properties using artificial neural network. J. Comput. Chem. 28, 1275–1289 (2007).

pubmed: 17299836

Nantasenamat, C. et al. Quantitative structure–property relationship study of spectral properties of green fluorescent protein with support vector machine. Chemom. Intell. Lab. Syst. 120, 42–52 (2013).

Ye, Z.-R. et al. Predicting the emission wavelength of organic molecules using a combinatorial QSAR and machine learning approach. RSC Adv. 10, 23834–23841 (2020).

Wang, J. N. et al. An accurate and efficient method to predict the electronic excitation energies of BODIPY fluorescent dyes. J. Comput. Chem. 34, 566–575 (2013).

pubmed: 23115129

Olivares-Amaya, R. et al. Accelerated computational discovery of high-performance materials for organic photovoltaics by means of cheminformatics. Energy Environ. Sci. 4, 4849–4861 (2011).

Pyzer-Knapp, E. O., Li, K. & Aspuru-Guzik, A. Learning from the Harvard Clean Energy Project: The use of neural networks to accelerate materials discovery. Adv. Funct. Mater. 25, 6495–6502 (2015).

Pyzer-Knapp, E. O., Simm, G. N. & Aspuru Guzik, A. A Bayesian approach to calibrating high-throughput virtual screening results and application to organic photovoltaic materials. Mater. Horiz. 3, 226–233 (2016).

Lopez, S. A., Sanchez-Lengeling, B., de Goes Soares, J. & Aspuru-Guzik, A. Design principles and top non-fullerene acceptor candidates for organic photovoltaics. Joule 1, 857–870 (2017).

Venkatraman, V., Astrand, P. O. & Alsberg, B. K. Quantitative structure–property relationship modeling of Gratzel solar cell dyes. J. Comput. Chem. 35, 214–226 (2014).

pubmed: 24222335

Venkatraman, V. & Alsberg, B. K. A quantitative structure-property relationship study of the photovoltaic performance of phenothiazine dyes. Dye. Pigm. 114, 69–77 (2015).

Venkatraman, V., Foscato, M., Jensen, V. R. & Alsberg, B. K. Evolutionary de novo design of phenothiazine derivatives for dye-sensitized solar cells. J. Mater. Chem. A 3, 9851–9860 (2015).

Li, H. et al. A cascaded QSAR model for efficient prediction of overall power conversion efficiency of all-organic dye-sensitized solar cells. J. Comput. Chem. 36, 1036–1046 (2015).

pubmed: 25773984

Tortorella, S., Marotta, G., Cruciani, G. & De Angelis, F. Quantitative structure–property relationship modeling of ruthenium sensitizers for solar cells applications: novel tools for designing promising candidates. RSC Adv. 5, 23865–23873 (2015).

Tortorella, S., De Angelis, F. & Cruciani, G. Quantitative structure-property relationship modeling of small organic molecules for solar cells applications. J. Chemom. 32, e2957 (2018).

Sahu, H., Rao, W., Troisi, A. & Ma, H. Toward predicting efficiency of organic solar cells via machine learning and improved descriptors. Adv. Energy Mater. 8, 1801032 (2018).

Padula, D., Simpson, J. D. & Troisi, A. Combining electronic and structural features in machine learning models to predict organic solar cells properties. Mater. Horiz. 6, 343–349 (2019).

Wen, Y., Fu, L., Li, G., Ma, J. & Ma, H. Accelerated discovery of potential organic dyes for dye-sensitized solar cells by interpretable machine learning models and virtual screening. Sol. RRL 31, 2000110 (2020).

Lee, M.-H. Robust random forest based non-fullerene organic solar cells efficiency prediction. Org. Electron. 76, 105465 (2020).

Manzhos, S. Machine learning for the solution of the Schrödinger equation. Mach. Learn. Sci. Technol. 1, 013002 (2020).

Coe, J. P. Machine learning configuration interaction. J. Chem. Theory Comput. 14, 5739–5749 (2018).

pubmed: 30285426

Babaei, M., Azar, Y. T. & Sadeghi, A. Locality meets machine learning: Excited and ground-state energy surfaces of large systems at the cost of small ones. Phys. Rev. B 101, 115132 (2020).

Reshetnyak, Y. K., Koshevnik, Y. & Burstein, E. A. Decomposition of protein tryptophan fluorescence spectra into log-normal components. III. Correlation between fluorescence and microenvironment parameters of individual tryptophan residues. Biophys. J. 81, 1735–1758 (2001).

pubmed: 11509384 pmcid: 1301649

Hixon, J. & Reshetnyak, Y. Algorithm for the analysis of tryptophan fluorescence spectra and their correlation with protein structural parameters. Algorithms 2, 1155–1176 (2009).

Li, X., Xie, Y., Hu, D. & Lan, Z. Analysis of the geometrical evolution in on-the-fly surface-hopping nonadiabatic dynamics with machine learning dimensionality reduction approaches: classical multidimensional scaling and isometric feature mapping. J. Chem. Theory Comput. 13, 4611–4623 (2017).

pubmed: 28862858

Li, X., Hu, D., Xie, Y. & Lan, Z. Analysis of trajectory similarity and configuration similarity in on-the-fly surface-hopping simulation on multi-channel nonadiabatic photoisomerization dynamics. J. Chem. Phys. 149, 244104 (2018).

pubmed: 30599730

Zhou, G., Chu, W. & Prezhdo, O. V. Structural deformation controls charge losses in MAPbI

Sanchez-Lengeling, B. & Aspuru-Guzik, A. Inverse molecular design using machine learning: Generative models for matter engineering. Science 361, 360–365 (2018).

pubmed: 30049875

Gomez-Bombarelli, R. et al. Automatic chemical design using a data-driven continuous representation of molecules. ACS Cent. Sci. 4, 268–276 (2018).

pubmed: 29532027 pmcid: 5833007

Jørgensen, P. B. et al. Machine learning-based screening of complex molecules for polymer solar cells. J. Chem. Phys. 148, 241735 (2018).

pubmed: 29960358

Peng, S. P. & Zhao, Y. Convolutional neural networks for the design and analysis of non-fullerene acceptors. J. Chem. Inf. Model. 59, 4993–5001 (2019).

pubmed: 31710212

Sumita, M., Yang, X., Ishihara, S., Tamura, R. & Tsuda, K. Hunting for organic molecules with artificial intelligence: molecules optimized for desired excitation energies. ACS Cent. Sci. 4, 1126–1133 (2018).

pubmed: 30276245 pmcid: 6161049

Sumita, M. et al. De novo creation of a naked-eye-detectable fluorescent molecule based on quantum-chemical computation and machine learning. Preprint at https://doi.org/10.26434/chemrxiv.14306522.v1 (2021).

Chapelle, O., Schölkopf, B. & Zien, A. Semi-Supervised Learning (MIT Press, 2006).

Duan, C., Liu, F., Nandy, A. & Kulik, H. J. Semi-supervised machine learning enables the robust detection of multireference character at low cost. J. Phys. Chem. Lett. 11, 6640–6648 (2020).

pubmed: 32692570

Ju, C.-W., Bai, H., Li, B. & Liu, R. Machine learning enables highly accurate predictions of photophysical properties of organic fluorescent materials: emission wavelengths and quantum yields. J. Chem. Inf. Model. 61, 1053–1065 (2021).

pubmed: 33620207

Smith, J. S. et al. Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning. Nat. Commun. 10, 2903 (2019).

pubmed: 31263102 pmcid: 6602931

Pronobis, W., Schütt, K. T., Tkatchenko, A. & Müller, K.-R. Capturing intensive and extensive DFT/TDDFT molecular properties with machine learning. Eur. Phys. J. B 91, 178 (2018).

Ramakrishnan, R., Hartmann, M., Tapavicza, E. & von Lilienfeld, O. A. Electronic spectra from TDDFT and machine learning in chemical space. J. Chem. Phys. 143, 084111 (2015).

pubmed: 26328822

Wu, Z. et al. MoleculeNet: a benchmark for molecular machine learning. Chem. Sci. 9, 513–530 (2018).

pubmed: 29629118

Kang, B., Seok, C. & Lee, J. Prediction of molecular electronic transitions using random forests. J. Chem. Inf. Model. 60, 5984–5994 (2020).

pubmed: 33090804

Ma, J. et al. Transferable multilevel attention neural network for accurate prediction of quantum chemistry properties via multitask learning. J. Chem. Inf. Model. 61, 1066–1082 (2021).

pubmed: 33629839

Ramakrishnan, R., Dral, P. O., Rupp, M. & von Lilienfeld, O. A. Big data meets quantum chemistry approximations: The Δ-machine learning approach. J. Chem. Theory Comput. 11, 2087–2096 (2015).

pubmed: 26574412

Wang, X. et al. Improving the accuracy of density-functional theory calculation: the statistical correction approach. J. Phys. Chem. A 108, 8514–8525 (2004).

Li, H. et al. Improving the accuracy of density-functional theory calculation: the genetic algorithm and neural network approach. J. Chem. Phys. 126, 144101 (2007).

pubmed: 17444695

Gao, T. et al. An accurate density functional theory calculation for electronic excitation energies: the least-squares support vector machine. J. Chem. Phys. 130, 184104 (2009).

pubmed: 19449905

Cui, J. et al. AdaBoost ensemble correction models for TDDFT calculated absorption energies. IEEE Access 7, 38397–38406 (2019).

Lee, C.-K. et al. Transfer learning with graph neural networks for optoelectronic properties of conjugated oligomers. J. Chem. Phys. 154, 024906 (2021).

pubmed: 33445906

Paul, A. et al. in International Joint Conference on Neural Networks (IJCNN) (2019).

Pilania, G., Gubernatis, J. E. & Lookman, T. Multi-fidelity machine learning models for accurate bandgap predictions of solids. Comput. Mater. Sci. 129, 156–163 (2017).

Thompson, K. & Martı́nez, T. J. Ab initio/interpolated quantum dynamics on coupled electronic states with full configuration interaction wave functions. J. Chem. Phys. 110, 1376–1382 (1999).

Netzloff, H. M., Collins, M. A. & Gordon, M. S. Growing multiconfigurational potential energy surfaces with applications to X+H

pubmed: 16674215

Montavon, G. et al. Machine learning of molecular electronic properties in chemical compound space. New J. Phys. 15, 095003 (2013).

Stuke, A. et al. Chemical diversity in molecular orbital energy predictions with kernel ridge regression. J. Chem. Phys. 150, 204121 (2019).

pubmed: 31153160

Ramakrishnan, R., Dral, P. O., Rupp, M. & von Lilienfeld, O. A. Quantum chemistry structures and properties of 134 kilo molecules. Sci. Data 1, 140022 (2014).

pubmed: 25977779 pmcid: 4322582

Liang, J. et al. QM-symex, update of the QM-sym database with excited state information for 173 kilo molecules. Sci. Data 7, 400 (2020).

pubmed: 33208742 pmcid: 7675965

Huang, B. & von Lilienfeld, O. A. Communication: Understanding molecular representations in machine learning: The role of uniqueness and target similarity. J. Chem. Phys. 145, 161102 (2016).

pubmed: 27802646

Ramakrishnan, R. & von Lilienfeld, O. A. Many molecular properties from one kernel in chemical space. CHIMIA 69, 182–186 (2015).

pubmed: 26672132

Lee, J., Seko, A., Shitara, K., Nakayama, K. & Tanaka, I. Prediction model of band gap for inorganic compounds by combination of density functional theory calculations and machine learning techniques. Phys. Rev. B 93, 115104 (2016).

Zhuo, Y., Mansouri Tehrani, A. & Brgoch, J. Predicting the band gaps of inorganic solids by machine learning. J. Phys. Chem. Lett. 9, 1668–1673 (2018).

pubmed: 29532658

Isayev, O. et al. Universal fragment descriptors for predicting properties of inorganic crystals. Nat. Commun. 8, 15679 (2017).

pubmed: 28580961 pmcid: 5465371

Pereira, F. et al. Machine learning methods to predict density functional theory B3LYP energies of HOMO and LUMO orbitals. J. Chem. Inf. Model. 57, 11–21 (2017).

pubmed: 28033004

Faber, F. A. et al. Prediction errors of molecular machine learning models lower than hybrid DFT error. J. Chem. Theory Comput. 13, 5255–5264 (2017).

pubmed: 28926232

Faber, F. A., Christensen, A. S., Huang, B. & von Lilienfeld, O. A. Alchemical and structural distribution based representation for universal quantum machine learning. J. Chem. Phys. 148, 241717 (2018).

pubmed: 29960351

Liu, F., Duan, C. & Kulik, H. J. Rapid detection of strong correlation with machine learning for transition-metal complex high-throughput screening. J. Phys. Chem. Lett. 11, 8067–8076 (2020).

pubmed: 32864977

Lu, S. et al. Accelerated discovery of stable lead-free hybrid organic-inorganic perovskites via machine learning. Nat. Commun. 9, 3405 (2018).

pubmed: 30143621 pmcid: 6109147

Pilania, G. et al. Machine learning bandgaps of double perovskites. Sci. Rep. 6, 19375 (2016).

pubmed: 26783247 pmcid: 4726030

Ghosh, K. et al. Deep learning spectroscopy: neural networks for molecular excitation spectra. Adv. Sci. 6, 1801367 (2019).

Pinheiro, G. A. et al. Machine learning prediction of nine molecular properties based on the SMILES representation of the QM9 quantum-chemistry dataset. J. Phys. Chem. A 124, 9854–9866 (2020).

pubmed: 33174750

Westermayr, J. & Maurer, R. J. Physically inspired deep learning of molecular excitations and photoemission spectra. Preprint at https://arxiv.org/abs/2103.09948v1 (2021).

Lopez, S. A. et al. The Harvard organic photovoltaic dataset. Sci. Data. 3, 160086 (2016).

pubmed: 27676312 pmcid: 5037972

Hoja, J. et al. QM7-X, a comprehensive dataset of quantum-mechanical properties spanning the chemical space of small organic molecules. Sci. Data 8, 43 (2021).

pubmed: 33531509 pmcid: 7854709

Stuke, A. et al. Atomic structures and orbital energies of 61,489 crystal-forming organic molecules. Sci. Data. 7, 58 (2020).

pubmed: 32071311 pmcid: 7029047

Nakata, M., Shimazaki, T., Hashimoto, M. & Maeda, T. PubChemQC PM6: data sets of 221 million molecules with optimized molecular geometries and electronic properties. J. Chem. Inf. Model. 60, 5891–5899 (2020).

pubmed: 33104339

Rosen, A. S. et al. Machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery. Matter 4, 1578–1597 (2021).

Gonze, X. & Scheffler, M. Exchange and correlation kernels at the resonance frequency: implications for excitation energies in density-functional theory. Phys. Rev. Lett. 82, 4416–4419 (1999).

Häse, F., Valleau, S., Pyzer-Knapp, E. & Aspuru-Guzik, A. Machine learning exciton dynamics. Chem. Sci. 7, 5139–5147 (2016).

pubmed: 30155164 pmcid: 6020119

Tong, Q. et al. Combining machine learning potential and structure prediction for accelerated materials design and discovery. J. Phys. Chem. Lett. 11, 8710–8720 (2020).

pubmed: 32955889

Lu, C. et al. Deep learning for optoelectronic properties of organic semiconductors. J. Phys. Chem. C 124, 7048–7060 (2020).

Richings, G. W. & Habershon, S. Direct grid-based nonadiabatic dynamics on machine-learned potential energy surfaces: application to spin-forbidden processes. J. Phys. Chem. A 124, 9299–9313 (2020).

pubmed: 33104337

Ueno, S. & Tanimura, Y. Modeling and simulating the excited-state dynamics of a system with condensed phases: A machine learning approach. Preprint at https://arxiv.org/abs/2102.02427v2 (2021).

Zheng, C. et al. Automated generation and ensemble-learned matching of X-ray absorption spectra. NPJ Comput. Mater. 4, 12 (2018).

Torrisi, S. B. et al. Random forest machine learning models for interpretable X-ray absorption near-edge structure spectrum-property relationships. NPJ Comput. Mater. 6, 109 (2020).

Timoshenko, J. & Frenkel, A. I. “Inverting” X-ray absorption spectra of catalysts by machine learning in search for activity descriptors. ACS Catal. 9, 10192–10211 (2019).

Rodríguez, M. & Kramer, T. Machine learning of two-dimensional spectroscopic data. Chem. Phys. 520, 52–60 (2019).

Xie, C., Zhu, X., Yarkony, D. R. & Guo, H. Permutation invariant polynomial neural network approach to fitting potential energy surfaces. IV. Coupled diabatic potential energy matrices. J. Chem. Phys. 149, 144107 (2018).

pubmed: 30316273

Carbone, M. R., Topsakal, M., Lu, D. & Yoo, S. Machine-learning X-ray absorption spectra to quantitative accuracy. Phys. Rev. Lett. 124, 156401 (2020).

pubmed: 32357067

Rankine, C. D., Madkhali, M. M. M. & Penfold, T. J. A deep neural network for the rapid prediction of X-ray absorption spectra. J. Phys. Chem. A 124, 4263–4270 (2020).

pubmed: 32369378

Aarva, A., Deringer, V. L., Sainio, S., Laurila, T. & Caro, M. A. Understanding X-ray spectroscopy of carbonaceous materials by combining experiments, density functional theory, and machine learning. Part I: fingerprint spectra. Chem. Mater. 31, 9243–9255 (2019).

Aarva, A., Deringer, V. L., Sainio, S., Laurila, T. & Caro, M. A. Understanding X-ray spectroscopy of carbonaceous materials by combining experiments, density functional theory, and machine learning. Part II: quantitative fitting of spectra. Chem. Mater. 31, 9256–9267 (2019).

Kolesnichenko, P. V., Zhang, Q., Zheng, C., Fuhrer, M. S. & Davis, J. A. Multidimensional analysis of excitonic spectra of monolayers of tungsten disulphide: toward computer-aided identification of structural and environmental perturbations of 2D materials. Mach. Learn. Sci. Technol. 2, 025021 (2021).

Mukherjee, B. et al. Beyond Born–Oppenheimer theory for spectroscopic and scattering processes. Int. Rev. Phys. Chem. 38, 287–341 (2019).

Nelson, T., Naumov, A., Fernandez-Alberti, S. & Tretiak, S. Nonadiabatic excited-state molecular dynamics: On-the-fly limiting of essential excited states. Chem. Phys. 481, 84–90 (2016).

Ardiansyah, M. & Brorsen, K. R. Mixed quantum-classical dynamics with machine learning-based potentials via Wigner sampling. J. Phys. Chem. A 124, 9326–9331 (2020).

pubmed: 33107739

Li, J. et al. Automatic discovery of photoisomerization mechanisms with nanosecond machine learning photodynamics simulations. Chem. Sci. 12, 5302–5314 (2021).

Posenitskiy, E. Non-adiabatic molecular dynamics of PAH-related complexes. PhD thesis, Univ. Paul Sabatier (2020).

Richings, G. W. et al. Quantum dynamics simulations using Gaussian wavepackets: the vMCG method. Int. Rev. Phys. Chem. 34, 269–308 (2015).

Koch, W. & Zhang, D. H. Communication: Separable potential energy surfaces from multiplicative artificial neural networks. J. Chem. Phys. 141, 021101 (2014).

pubmed: 25027992

Frankcombe, T. J., Collins, M. A. & Worth, G. A. Converged quantum dynamics with modified Shepard interpolation and Gaussian wave packets. Chem. Phys. Lett. 489, 242–247 (2010).

Alborzpour, J. P., Tew, D. P. & Habershon, S. Efficient and accurate evaluation of potential energy matrix elements for quantum dynamics using Gaussian process regression. J. Chem. Phys. 145, 174112 (2016).

pubmed: 27825241

Richings, G. W. & Habershon, S. Direct grid-based quantum dynamics on propagated diabatic potential energy surfaces. Chem. Phys. Lett. 683, 228–233 (2017).

Richings, G. W. & Habershon, S. Direct quantum dynamics using grid-based wave function propagation and machine-learned potential energy surfaces. J. Chem. Theory Comput. 13, 4012–4024 (2017).

pubmed: 28719206

Richings, G. W. & Habershon, S. MCTDH on-the-fly: Efficient grid-based quantum dynamics without pre-computed potential energy surfaces. J. Chem. Phys. 148, 134116 (2018).

pubmed: 29626895

Polyak, I., Richings, G. W., Habershon, S. & Knowles, P. J. Direct quantum dynamics using variational Gaussian wavepackets and Gaussian process regression. J. Chem. Phys. 150, 041101 (2019).

pubmed: 30709252

Zhang, D. H., Collins, M. A. & Lee, S. Y. First-principles theory for the H + H

pubmed: 11062123

Crespos, C., Collins, M. A., Pijper, E. & Kroes, G. J. Application of the modified Shepard interpolation method to the determination of the potential energy surface for a molecule-surface reaction: H

pubmed: 15268379

Evenhuis, C. R. & Collins, M. A. Interpolation of diabatic potential energy surfaces. J. Chem. Phys. 121, 2515–2527 (2004).

pubmed: 15281848

Evenhuis, C. R., Lin, X., Zhang, D. H., Yarkony, D. & Collins, M. A. Interpolation of diabatic potential-energy surfaces: quantum dynamics on ab initio surfaces. J. Chem. Phys. 123, 134110 (2005).

pubmed: 16223278

Godsi, O., Evenhuis, C. R. & Collins, M. A. Interpolation of multidimensional diabatic potential energy matrices. J. Chem. Phys. 125, 104105 (2006).

pubmed: 16999513

Evenhuis, C. & Martinez, T. J. A scheme to interpolate potential energy surfaces and derivative coupling vectors without performing a global diabatization. J. Chem. Phys. 135, 224110 (2011).

pubmed: 22168683

Lenzen, T. & Manthe, U. Neural network based coupled diabatic potential energy surfaces for reactive scattering. J. Chem. Phys. 147, 084105 (2017).

pubmed: 28863526

Guan, Y., Fu, B. & Zhang, D. H. Construction of diabatic energy surfaces for LiFH with artificial neural networks. J. Chem. Phys. 147, 224307 (2017).

pubmed: 29246076

Yuan, J., He, D., Wang, S., Chen, M. & Han, K. Diabatic potential energy surfaces of MgH

pubmed: 29457183

Williams, D. M. G. & Eisfeld, W. Neural network diabatization: A new ansatz for accurate high-dimensional coupled potential energy surfaces. J. Chem. Phys. 149, 204106 (2018).

pubmed: 30501255

Guan, Y., Zhang, D. H., Guo, H. & Yarkony, D. R. Representation of coupled adiabatic potential energy surfaces using neural network based quasi-diabatic Hamiltonians: 1,2

pubmed: 30523350

Guan, Y., Guo, H. & Yarkony, D. R. Neural network based quasi-diabatic Hamiltonians with symmetry adaptation and a correct description of conical intersections. J. Chem. Phys. 150, 214101 (2019).

pubmed: 31176323

Yin, Z., Guan, Y., Fu, B. & Zhang, D. H. Two-state diabatic potential energy surfaces of ClH

pubmed: 31498342

Zheng, F., Gao, X. & Eisfeld, A. Excitonic wave function reconstruction from near-field spectra using machine learning techniques. Phys. Rev. Lett. 123, 163202 (2019).

pubmed: 31702362

Guan, Y., Guo, H. & Yarkony, D. R. Extending the representation of multistate coupled potential energy surfaces to include properties operators using neural networks: application to the 1,2

pubmed: 31743017

Shen, Y. & Yarkony, D. R. Construction of quasi-diabatic Hamiltonians that accurately represent ab initio determined adiabatic electronic states coupled by conical intersections for systems on the order of 15 atoms. Application to cyclopentoxide photoelectron detachment in the full 39 degrees of freedom. J. Phys. Chem. A 124, 4539–4548 (2020).

pubmed: 32374600

Shu, Y. & Truhlar, D. G. Diabatization by machine intelligence. J. Chem. Theory Comput. 16, 6456–6464 (2020).

pubmed: 32886513

Shu, Y., Varga, Z., Sampaio de Oliveira-Filho, A. G. & Truhlar, D. G. Permutationally restrained diabatization by machine intelligence. J. Chem. Theory Comput. 17, 1106–1116 (2021).

pubmed: 33405927

Ha, J. K., Kim, K. & Min, S. K. Machine learning-assisted excited state molecular dynamics with the state-interaction state-averaged spin-restricted ensemble-referenced Kohn-Sham approach. J. Chem. Theory Comput. 17, 694–702 (2021).

pubmed: 33470100

Guan, Y. & Yarkony, D. R. Accurate neural network representation of the ab initio determined spin–orbit interaction in the diabatic representation including the effects of conical intersections. J. Phys. Chem. Lett. 11, 1848–1858 (2020).

pubmed: 32062966

Zheng, J., Xu, X., Meana-Pañeda, R. & Truhlar, D. G. Army ants tunneling for classical simulations. Chem. Sci. 5, 2091–2099 (2014).

Chen, W. K., Zhang, Y., Jiang, B., Fang, W. H. & Cui, G. Efficient construction of excited-state Hessian matrices with machine learning accelerated multilayer energy-based fragment method. J. Phys. Chem. A 124, 5684–5695 (2020).

pubmed: 32530626

Krämer, M. et al. Charge and exciton transfer simulations using machine-learned Hamiltonians. J. Chem. Theory Comput. 16, 4061–4070 (2020).

pubmed: 32491856

Moiseyev, N. Non-Hermitian Quantum Mechanics (Cambridge Univ. Press, 2011).

Yang, B., He, B., Wan, J., Kubal, S. & Zhao, Y. Applications of neural networks to dynamics simulation of Landau-Zener transitions. Chem. Phys. 528, 110509 (2020).

Herrera Rodriguez, L. E. & Kananenka, A. A. Convolutional neural networks for long-time dissipative quantum dynamics. J. Phys. Chem. Lett. 12, 2476–2483 (2021).

pubmed: 33666085

Ueno, S. & Tanimura, Y. Modeling intermolecular and intramolecular modes of liquid water using multiple heat baths: machine learning approach. J. Chem. Theory Comput. 16, 2099–2108 (2020).

pubmed: 32150682

Häse, F., Kreisbeck, C. & Aspuru-Guzik, A. Machine learning for quantum dynamics: deep learning of excitation energy transfer properties. Chem. Sci. 8, 8419–8426 (2017).

pubmed: 29619189 pmcid: 5863613

Hachmann, J. et al. The Harvard clean energy project: large-scale computational screening and design of organic photovoltaics on the world community grid. J. Phys. Chem. Lett. 2, 2241–2251 (2011).

Zhuo, Y. & Brgoch, J. Opportunities for next-generation luminescent materials through artificial intelligence. J. Phys. Chem. Lett. 12, 764–772 (2021).

pubmed: 33423499

Misra, M., Andrienko, D., Baumeier, B., Faulon, J. L. & von Lilienfeld, O. A. Toward quantitative structure–property relationships for charge transfer rates of polycyclic aromatic hydrocarbons. J. Chem. Theory Comput. 7, 2549–2555 (2011).

pubmed: 26606628

Gomez-Bombarelli, R. et al. Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach. Nat. Mater. 15, 1120–1127 (2016).

pubmed: 27500805

Atahan-Evrenk, S. A quantitative structure–property study of reorganization energy for known p-type organic semiconductors. RSC Adv. 8, 40330–40337 (2018).

Atahan-Evrenk, S. & Atalay, F. B. Prediction of intramolecular reorganization energy using machine learning. J. Phys. Chem. A 123, 7855–7863 (2019).

pubmed: 31204476

Lee, M. H. Machine learning for understanding the relationship between the charge transport mobility and electronic energy levels for n-type organic field-effect transistors. Adv. Electron. Mater. 5, 1900573 (2019).

Kar, S., Roy, J., Leszczynska, D. & Leszczynski, J. Power conversion efficiency of arylamine organic dyes for dye-sensitized solar cells (DSSCs) explicit to cobalt electrolyte: understanding the structural attributes using a direct QSPR approach. Computation 5, 2 (2017).

Kar, S., Roy, J. K. & Leszczynski, J. In silico designing of power conversion efficient organic lead dyes for solar cells using todays innovative approaches to assure renewable energy for future. NPJ Comput. Mater. 3, 22 (2017).

Li, Z. et al. Robot-accelerated perovskite investigation and discovery. Chem. Mater. 32, 5650–5663 (2020).

Chudakov, D. M., Matz, M. V., Lukyanov, S. & Lukyanov, K. A. Fluorescent proteins and their applications in imaging living cells and tissues. Physiol. Rev. 90, 1103–1163 (2010).

pubmed: 20664080

da Silva, R. S., Marins, L. F., Almeida, D. V., Dos Santos Machado, K. & Werhli, A. V. A comparison of classifiers for predicting the class color of fluorescent proteins. Comput. Biol. Chem. 83, 107089 (2019).

pubmed: 31323386

Scharber, M. C. et al. Design rules for donors in bulk-heterojunction solar cells — Towards 10% energy-conversion efficiency. Adv. Mater. 18, 789–794 (2006).

Bella, F., Nair, J. R. & Gerbaldi, C. Towards green, efficient and durable quasi-solid dye-sensitized solar cells integrated with a cellulose-based gel-polymer electrolyte optimized by a chemometric DoE approach. RSC Adv. 3, 15993–16001 (2013).

Pugliese, D. et al. A chemometric approach for the sensitization procedure of ZnO flowerlike microstructures for dye-sensitized solar cells. ACS Appl. Mater. Interfaces 5, 11288–11295 (2013).

pubmed: 24102107

Bella, F., Sacco, A., Pugliese, D., Laurenti, M. & Bianco, S. Additives and salts for dye-sensitized solar cells electrolytes: what is the best choice? J. Power Sources 264, 333–343 (2014).

Bella, F., Mobarak, N. N., Jumaah, F. N. & Ahmad, A. From seaweeds to biopolymeric electrolytes for third generation solar cells: An intriguing approach. Electrochim. Acta 151, 306–311 (2015).

Liu, D., Tan, Y., Khoram, E. & Yu, Z. Training deep neural networks for the inverse design of nanophotonic structures. ACS Photonics 5, 1365–1369 (2018).

Liu, R. et al. Causal inference machine learning leads original experimental discovery in CdSe/CdS core/shell nanoparticles. J. Phys. Chem. Lett. 11, 7232–7238 (2020).

pubmed: 32787235

Hong, S. et al. Defect healing in layered materials: a machine learning-assisted characterization of MoS

pubmed: 31046288

Huang, Y. et al. Structure–property correlation study for organic photovoltaic polymer materials using data science approach. J. Phys. Chem. C 124, 12871–12882 (2020).

Himanen, L., Geurts, A., Foster, A. S. & Rinke, P. Data-driven materials science: status, challenges, and perspectives. Adv. Sci. 6, 1900808 (2019).

Huwig, K., Fan, C. & Springborg, M. From properties to materials: An efficient and simple approach. J. Chem. Phys. 147, 234105 (2017).

pubmed: 29272928

Kanal, I. Y., Owens, S. G., Bechtel, J. S. & Hutchison, G. R. Efficient computational screening of organic polymer photovoltaics. J. Phys. Chem. Lett. 4, 1613–1623 (2013).

pubmed: 26282968

Rajan, A. C. et al. Machine-learning-assisted accurate band gap predictions of functionalized MXene. Chem. Mater. 30, 4031–4038 (2018).

Mishra, A. et al. Accelerated data-driven accurate positioning of the band edges of MXenes. J. Phys. Chem. Lett. 10, 780–785 (2019).

pubmed: 30730142

Kunkel, C., Schober, C., Oberhofer, H. & Reuter, K. Knowledge discovery through chemical space networks: the case of organic electronics. J. Mol. Model. 25, 87 (2019).

pubmed: 30847684

Kunkel, C., Schober, C., Margraf, J. T., Reuter, K. & Oberhofer, H. Finding the right bricks for molecular legos: a data mining approach to organic semiconductor design. Chem. Mater. 31, 969–978 (2019).

Đord¯evic´, N. et al. Machine learning for analysis of time-resolved luminescence data. ACS Photonics 5, 4888–4895 (2018).

Musil, F. et al. Machine learning for the structure–energy–property landscapes of molecular crystals. Chem. Sci. 9, 1289–1300 (2018).

pubmed: 29675175

Lederer, J., Kaiser, W., Mattoni, A. & Gagliardi, A. Machine learning–based charge transport computation for pentacene. Adv. Theory Simul. 2, 1800136 (2018).

Çaylak, O., Yaman, A. & Baumeier, B. Evolutionary approach to constructing a deep feedforward neural network for prediction of electronic coupling elements in molecular materials. J. Chem. Theory Comput. 15, 1777–1784 (2019).

pubmed: 30753071 pmcid: 6581422

Wang, C. I., Braza, M. K. E., Claudio, G. C., Nellas, R. B. & Hsu, C. P. Machine learning for predicting electron transfer coupling. J. Phys. Chem. A 123, 7792–7802 (2019).

pubmed: 31429287

Wang, C. I., Joanito, I., Lan, C. F. & Hsu, C. P. Artificial neural networks for predicting charge transfer coupling. J. Chem. Phys. 153, 214113 (2020).

pubmed: 33291923

Bag, S., Aggarwal, A. & Maiti, P. K. Machine learning prediction of electronic coupling between the guanine bases of DNA. J. Phys. Chem. A 124, 7658–7664 (2020).

pubmed: 32876443

Rinderle, M., Kaiser, W., Mattoni, A. & Gagliardi, A. Machine-learned charge transfer integrals for multiscale simulations in organic thin films. J. Phys. Chem. C 124, 17733–17743 (2020).

Miller, E. D., Jones, M. L., Henry, M. M., Stanfill, B. & Jankowski, E. Machine learning predictions of electronic couplings for charge transport calculations of P3HT. AIChE J. 65, e16760 (2019).

Thawani, A. R. et al. The photoswitch dataset: a molecular machine learning benchmark for the advancement of synthetic chemistry. Preprint at https://doi.org/10.26434/chemrxiv.12609899.v1 (2020).

Roch, L. M. et al. From absorption spectra to charge transfer in nanoaggregates of oligomers with machine learning. ACS Nano 14, 6589–6598 (2020).

pubmed: 32338888

Schütt, K. T., Gastegger, M., Tkatchenko, A., Müller, K.-R. & Maurer, R. J. Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions. Nat. Commun. 10, 5024 (2019).

pubmed: 31729373 pmcid: 6858523

Kaelbling, L. P., Littman, M. L. & Moore, A. W. Reinforcement learning: a survey. J. Artif. Intell. Res. 4, 237–285 (1996).

Granda, J. M., Donina, L., Dragone, V., Long, D. L. & Cronin, L. Controlling an organic synthesis robot with machine learning to search for new reactivity. Nature 559, 377–381 (2018).

pubmed: 30022133 pmcid: 6223543

MacLeod, B. P. et al. Self-driving laboratory for accelerated discovery of thin-film materials. Sci. Adv. 6, eaaz8867 (2020).

pubmed: 32426501 pmcid: 7220369

Bolovinos, A., Philis, J., Pantos, E., Tsekeris, P. & Andritsopoulos, G. The methylbenzenes vis-à-vis benzene. J. Mol. Spectrosc. 94, 55–68 (1982).

Casida, M. E. & Huix-Rotllant, M. Progress in time-dependent density-functional theory. Annu. Rev. Phys. Chem. 63, 287–323 (2012).

pubmed: 22242728

Pulay, P. A perspective on the CASPT2 method. Int. J. Quant. Chem. 111, 3273–3279 (2011).

Hättig, C. in Computational Nanoscience: Do It Yourself! NIC Series (eds Grotendort, J., Blügel, S. & Marx, D.) 1–34 (Forschungszentrum Jülich, 2006).

Dreuw, A. & Wormit, M. The algebraic diagrammatic construction scheme for the polarization propagator for the calculation of excited states. Wiley Interdiscip. Rev. Comput. Mol. Sci. 5, 82–95 (2015).

Golze, D., Dvorak, M. & Rinke, P. The GW compendium: A practical guide to theoretical photoemission spectroscopy. Front. Chem. 7, 377 (2019).

pubmed: 31355177 pmcid: 6633269

Kranz, J. J. et al. Time-dependent extension of the long-range corrected density functional based tight-binding method. J. Chem. Theory Comput. 13, 1737–1747 (2017).

pubmed: 28272887

Thiel, W. Semiempirical quantum-chemical methods. Wiley Interdiscip. Rev. Comput. Mol. Sci. 4, 145–157 (2014).

Weingart, O. Combined quantum and molecular mechanics (QM/MM) approaches to simulate ultrafast photodynamics in biological systems. Curr. Org. Chem. 21, 586–601 (2017).

Plasser, F., Gómez, S., Menger, M. F. S. J., Mai, S. & González, L. Highly efficient surface hopping dynamics using a linear vibronic coupling model. Phys. Chem. Chem. Phys. 21, 57–69 (2019).

Niu, Y., Peng, Q., Deng, C., Gao, X. & Shuai, Z. Theory of excited state decays and optical spectra: application to polyatomic molecules. J. Phys. Chem. A 114, 7817–7831 (2010).

pubmed: 20666533

Hastie, T., Tibshirani, R. & Friedman, J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction 2nd edn 763 (Springer, 2009).

Kingma, D. P. & Welling, M. Auto-encoding variational bayes. Preprint at https://arxiv.org/abs/1312.6114 (2013).

Settles, B. Active Learning Literature Survey. Computer Sciences Technical Report 1648 (Univ. Wisconsin, 2009).

Dral, P. O., Owens, A., Yurchenko, S. N. & Thiel, W. Structure-based sampling and self-correcting machine learning for accurate calculations of potential energy surfaces and vibrational levels. J. Chem. Phys. 146, 244108 (2017).

pubmed: 28668062

Schütt, K. T. et al. SchNet: A continuous-filter convolutional neural network for modeling quantum interactions. Adv. Neural Inf. Process. Syst. 30, 992–1002 (2017).

Zhang, L., Han, J., Wang, H., Car, R. & E, W. Deep potential molecular dynamics: a scalable model with the accuracy of quantum mechanics. Phys. Rev. Lett. 120, 143001 (2018).

pubmed: 29694129

Zhang, L. F. et al. End-to-end symmetry preserving inter-atomic potential energy model for finite and extended systems. Adv. Neural Inf. Process. Syst. 31, 4436–4446 (2018).

Rupp, M., Tkatchenko, A., Müller, K.-R. & von Lilienfeld, O. A. Fast and accurate modeling of molecular atomization energies with machine learning. Phys. Rev. Lett. 108, 058301 (2012).

pubmed: 22400967

Hansen, K. et al. Machine learning predictions of molecular properties: accurate many-body potentials and nonlocality in chemical space. J. Phys. Chem. Lett. 6, 2326–2331 (2015).

pubmed: 26113956 pmcid: 4476293

Bartók, A. P. et al. Machine learning unifies the modeling of materials and molecules. Sci. Adv. 3, e1701816 (2017).

pubmed: 29242828 pmcid: 5729016

Dral, P. O. MLatom: a program package for quantum chemical research assisted by machine learning. J. Comput. Chem. 40, 2339–2347 (2019).

pubmed: 31219626

Schütt, K. T. et al. SchNetPack: a deep learning toolbox for atomistic systems. J. Chem. Theory Comput. 15, 448–455 (2019).

pubmed: 30481453

Wang, H., Zhang, L., Han, J. & E, W. DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics. Comput. Phys. Commun. 228, 178–184 (2018).

Himanen, L. et al. DScribe: Library of descriptors for machine learning in materials science. Comput. Phys. Commun. 247, 106949 (2020).

Heller, E. J. The Semiclassical Way to Dynamics and Spectroscopy (Princeton Univ. Press, 2018).

Mukamel, S. Multidimensional femtosecond correlation spectroscopies of electronic and vibrational excitations. Annu. Rev. Phys. Chem. 51, 691–729 (2000).

pubmed: 11031297

Bai, S., Mansour, R., Stojanovic, L., Toldo, J. M. & Barbatti, M. On the origin of the shift between vertical excitation and band maximum in molecular photoabsorption. J. Mol. Model. 26, 107 (2020).

pubmed: 32318882 pmcid: 7174274

Crespo-Otero, R. & Barbatti, M. Spectrum simulation and decomposition with nuclear ensemble: formal derivation and application to benzene, furan and 2-phenylfuran. Theor. Chem. Acc. 131, 1237 (2012).

Segarra-Marti, J. et al. Modeling multidimensional spectral lineshapes from first principles: application to water-solvated adenine. Faraday Discuss. 221, 219–244 (2019).

pubmed: 31544178

Biczysko, M., Bloino, J., Santoro, F. & Barone, V. in Computational Strategies for Spectroscopy: From Small Molecules to Nano Systems (ed. Barone, V.) (Wiley, 2011).

Petrenko, T. & Neese, F. Analysis and prediction of absorption band shapes, fluorescence band shapes, resonance Raman intensities, and excitation profiles using the time-dependent theory of electronic spectroscopy. J. Chem. Phys. 127, 164319 (2007).

pubmed: 17979350

Tanimura, Y. & Kubo, R. Time evolution of a quantum system in contact with a nearly Gaussian-Markoffian noise bath. J. Phys. Soc. Jpn. 58, 101–114 (1989).

Worth, G. A. & Cederbaum, L. S. Beyond Born-Oppenheimer: molecular dynamics through a conical intersection. Annu. Rev. Phys. Chem. 55, 127–158 (2004).

pubmed: 15117250

Worth, G. A., Meyer, H. D., Köppel, H., Cederbaum, L. S. & Burghardt, I. Using the MCTDH wavepacket propagation method to describe multimode non-adiabatic dynamics. Int. Rev. Phys. Chem. 27, 569–606 (2008).

Barbatti, M. & Crespo-Otero, R. in Density-Functional Methods for Excited States (eds Ferré, N., Filatov, M., & Huix-Rotllant, M.) 415–444 (Springer, 2016).

Nelson, T. R. et al. Non-adiabatic excited-state molecular dynamics: theory and applications for modeling photophysics in extended molecular materials. Chem. Rev. 120, 2215–2287 (2020).

pubmed: 32040312

Yue, L. et al. Benchmark performance of global switching versus local switching for trajectory surface hopping molecular dynamics simulation: cis↔trans azobenzene photoisomerization. ChemPhysChem 18, 1274–1287 (2017).

pubmed: 28213950

Suchan, J., Janos, J. & Slavicek, P. Pragmatic approach to photodynamics: mixed Landau–Zener surface hopping with intersystem crossing. J. Chem. Theory Comput. 16, 5809–5820 (2020).

pubmed: 32687703

Köppel, H. in Conical Intersections (eds Domcke, W., Yarkony, D. R., & Köppel, H.) 175–204 (World Scientific, 2004).

Li, S. L., Truhlar, D. G., Schmidt, M. W. & Gordon, M. S. Model space diabatization for quantum photochemistry. J. Chem. Phys. 142, 064106 (2015).

pubmed: 25681886

Zhu, X. & Yarkony, D. R. Toward eliminating the electronic structure bottleneck in nonadiabatic dynamics on the fly: An algorithm to fit nonlocal, quasidiabatic, coupled electronic state Hamiltonians based on ab initio electronic structure data. J. Chem. Phys. 132, 104101 (2010).

pubmed: 20232941

Wittenbrink, N., Venghaus, F., Williams, D. & Eisfeld, W. A new approach for the development of diabatic potential energy surfaces: Hybrid block-diagonalization and diabatization by ansatz. J. Chem. Phys. 145, 184108 (2016).

pubmed: 27846705

Molecular excited states through a machine learning lens.

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Pavlo O Dral (PO)

Mario Barbatti (M)

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