Electronic spectra from TDDFT and machine learning in chemical space

Ramakrishnan, Raghunathan; Hartmann, Mia; Tapavicza, Enrico; von Lilienfeld, O. Anatole

doi:10.1063/1.4928757

Title: Electronic spectra from TDDFT and machine learning in chemical space

Journal Article · Tue Aug 25 00:00:00 EDT 2015 · Journal of Chemical Physics

DOI:https://doi.org/10.1063/1.4928757· OSTI ID:1392463

Ramakrishnan, Raghunathan ^[1]; Hartmann, Mia ^[2]; Tapavicza, Enrico ^[2]; von Lilienfeld, O. Anatole ^[3]

Univ. of Basel (Switzerland)
California State Univ. (CalState), Long Beach, CA (United States)
Univ. of Basel (Switzerland); Argonne National Lab. (ANL), Argonne, IL (United States). Argonne Leadership Computing Facility (ALCF)

Due to its favorable computational efficiency, time-dependent (TD) density functional theory (DFT) enables the prediction of electronic spectra in a high-throughput manner across chemical space. Its predictions, however, can be quite inaccurate. In this work, we resolve this issue with machine learning models trained on deviations of reference second-order approximate coupled-cluster (CC2) singles and doubles spectra from TDDFT counterparts, or even from DFT gap. We applied this approach to low-lying singlet-singlet vertical electronic spectra of over 20 000 synthetically feasible small organic molecules with up to eight CONF atoms. The prediction errors decay monotonously as a function of training set size. For a training set of 10 000 molecules, CC2 excitation energies can be reproduced to within ±0.1 eV for the remaining molecules. Analysis of our spectral database via chromophore counting suggests that even higher accuracies can be achieved. Based on the evidence collected, we discuss open challenges associated with data-driven modeling of high-lying spectra and transition intensities.

View Accepted Manuscript (DOE)

View Accepted Manuscript (Publisher)

Cite

Export

Save

Research Organization:: Argonne National Laboratory (ANL), Argonne, IL (United States)

Sponsoring Organization:: USDOE Office of Science (SC); Swiss National Science Foundation (SNF)

Grant/Contract Number:: AC02-06CH11357; PP00P2_138932

OSTI ID:: 1392463

Alternate ID(s):: OSTI ID: 1229636

Journal Information:: Journal of Chemical Physics, Vol. 143, Issue 8; ISSN 0021-9606

Publisher:: American Institute of Physics (AIP)Copyright Statement

Country of Publication:: United States

Language:: English

Citation Metrics:

Cited by: 138 works

Citation information provided by
Web of Science

References (72)

Recognizing molecular patterns by machine learning: An agnostic structural definition of the hydrogen bond Gasparotto, Piero; Ceriotti, Michele The Journal of Chemical Physics, Vol. 141, Issue 17 https://doi.org/10.1063/1.4900655	journal	November 2014
Generalized Gradient Approximation Made Simple Perdew, John P.; Burke, Kieron; Ernzerhof, Matthias Physical Review Letters, Vol. 77, Issue 18, p. 3865-3868 https://doi.org/10.1103/PhysRevLett.77.3865	journal	October 1996
Photoelectrochemical cells Grätzel, Michael Nature, Vol. 414, Issue 6861 https://doi.org/10.1038/35104607	journal	November 2001
Fourier series of atomic radial distribution functions: A molecular fingerprint for machine learning models of quantum chemical properties von Lilienfeld, O. Anatole; Ramakrishnan, Raghunathan; Rupp, Matthias arXiv https://doi.org/10.48550/arxiv.1307.2918	preprint	January 2013
Machine learning for many-body physics: The case of the Anderson impurity model Arsenault, Louis-François; Lopez-Bezanilla, Alejandro; von Lilienfeld, O. Anatole Physical Review B, Vol. 90, Issue 15 https://doi.org/10.1103/PhysRevB.90.155136	journal	October 2014
Entangled trajectory molecular dynamics in multidimensional systems: Two-dimensional quantum tunneling through the Eckart barrier Martens, Craig C. The Journal of Chemical Physics, Vol. 137, Issue 3 https://doi.org/10.1063/1.4736559	journal	July 2012
Machine learning of molecular electronic properties in chemical compound space Montavon, Gregoire; Rupp, Matthias; Gobre, Vivekanand IOP Publishing https://doi.org/10.5451/unibas-ep53261	text	January 2013
Machine learning for many-body physics: The case of the Anderson impurity model Arsenault, Louis-Francois; Lopez-Bezanilla, Alejandro; Von Lilienfeld, O. Anatole American Physical Society https://doi.org/10.5451/unibas-ep53262	text	January 2014
Modeling electronic quantum transport with machine learning Lopez-Bezanilla, Alejandro; Von Lilienfeld, O. Anatole American Physical Society https://doi.org/10.5451/unibas-ep53263	text	January 2014
Many Molecular Properties from One Kernel in Chemical Space Raghunathan, Ramakrishnan,; Anatole, von Lilienfeld, O. Schweizerische Chemische Gesellschaft https://doi.org/10.5451/unibas-ep53265	text	January 2015
Fourier series of atomic radial distribution functions: A molecular fingerprint for machine learning models of quantum chemical properties Von Lilienfeld, O. Anatole; Ramakrishnan, Raghunathan; Rupp, Matthias Wiley https://doi.org/10.5451/unibas-ep53267	text	January 2015
Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space Hansen, Katja; Biegler, Franziska; Ramakrishnan, Raghunathan The Journal of Physical Chemistry Letters, Vol. 6, Issue 12 https://doi.org/10.1021/acs.jpclett.5b00831	journal	June 2015
Adiabatic time-dependent density functional methods for excited state properties Furche, Filipp; Ahlrichs, Reinhart The Journal of Chemical Physics, Vol. 117, Issue 16 https://doi.org/10.1063/1.1508368	journal	October 2002
Highly Efficient and Photostable Photosensitizer Based on BODIPY Chromophore Yogo, Takatoshi; Urano, Yasuteru; Ishitsuka, Yukiko Journal of the American Chemical Society, Vol. 127, Issue 35 https://doi.org/10.1021/ja0528533	journal	September 2005
Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach Ramakrishnan, Raghunathan; Dral, Pavlo O.; Rupp, Matthias American Chemical Society https://doi.org/10.5451/unibas-ep43348	text	January 2015
Many Molecular Properties from One Kernel in Chemical Space Ramakrishnan, Raghunathan; von Lilienfeld, O. Anatole CHIMIA International Journal for Chemistry, Vol. 69, Issue 4 https://doi.org/10.2533/chimia.2015.182	journal	April 2015
Machine learning of molecular electronic properties in chemical compound space Montavon, Grégoire; Rupp, Matthias; Gobre, Vivekanand New Journal of Physics, Vol. 15, Issue 9 https://doi.org/10.1088/1367-2630/15/9/095003	journal	September 2013
Benchmarking Coupled Cluster Methods on Valence Singlet Excited States Kánnár, Dániel; Szalay, Péter G. Journal of Chemical Theory and Computation, Vol. 10, Issue 9 https://doi.org/10.1021/ct500495n	journal	July 2014
Optimizing transition states via kernel-based machine learning Pozun, Zachary D.; Hansen, Katja; Sheppard, Daniel The Journal of Chemical Physics, Vol. 136, Issue 17 https://doi.org/10.1063/1.4707167	journal	May 2012
Excited-State Dynamics of Isolated and Microsolvated Cinnamate-Based UV-B Sunscreens Tan, Eric M. M.; Hilbers, Michiel; Buma, Wybren J. The Journal of Physical Chemistry Letters, Vol. 5, Issue 14 https://doi.org/10.1021/jz501140b	journal	July 2014
Improvements on the direct SCF method: Improved Direct SCF Method Häser, Marco; Ahlrichs, Reinhart Journal of Computational Chemistry, Vol. 10, Issue 1 https://doi.org/10.1002/jcc.540100111	journal	January 1989
Self-Consistent Equations Including Exchange and Correlation Effects Kohn, W.; Sham, L. J. Physical Review, Vol. 140, Issue 4A, p. A1133-A1138 https://doi.org/10.1103/PhysRev.140.A1133	journal	November 1965
Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning Rupp, Matthias; Tkatchenko, Alexandre; Müller, Klaus-Robert Physical Review Letters, Vol. 108, Issue 5 https://doi.org/10.1103/PhysRevLett.108.058301	journal	January 2012
Improving the performance of doped π-conjugated polymers for use in organic light-emitting diodes Gross, Markus; Müller, David C.; Nothofer, Heinz-Georg Nature, Vol. 405, Issue 6787 https://doi.org/10.1038/35015037	journal	June 2000
Excitation energies in density functional theory: An evaluation and a diagnostic test Peach, Michael J. G.; Benfield, Peter; Helgaker, Trygve The Journal of Chemical Physics, Vol. 128, Issue 4 https://doi.org/10.1063/1.2831900	journal	January 2008
Photoelectrochemical Cells Nozik, A. J. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol. 295, Issue 1414 https://doi.org/10.1098/rsta.1980.0141	journal	February 1980
Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach Ramakrishnan, Raghunathan; Dral, Pavlo O.; Rupp, Matthias Journal of Chemical Theory and Computation, Vol. 11, Issue 5 https://doi.org/10.1021/acs.jctc.5b00099	journal	April 2015
Energy Transfer Tunes Phosphorescent Color of Single-Dopant White OLEDs Han, Juan; Chen, XueBo; Shen, Lin Chemistry - A European Journal, Vol. 17, Issue 50 https://doi.org/10.1002/chem.201102702	journal	November 2011
A vision for data science Mattmann, Chris A. Nature, Vol. 493, Issue 7433 https://doi.org/10.1038/493473a	journal	January 2013
Correlated electron dynamics with time-dependent quantum Monte Carlo: Three-dimensional helium Christov, Ivan P. The Journal of Chemical Physics, Vol. 135, Issue 4 https://doi.org/10.1063/1.3615061	journal	July 2011
Enumeration of 166 Billion Organic Small Molecules in the Chemical Universe Database GDB-17 Ruddigkeit, Lars; van Deursen, Ruud; Blum, Lorenz C. Journal of Chemical Information and Modeling, Vol. 52, Issue 11 https://doi.org/10.1021/ci300415d	journal	November 2012
Unravelling the details of vitamin D photosynthesis by non-adiabatic molecular dynamics simulations Tapavicza, Enrico; Meyer, Alexander M.; Furche, Filipp Physical Chemistry Chemical Physics, Vol. 13, Issue 47 https://doi.org/10.1039/c1cp21292c	journal	January 2011
Density Estimation for Statistics and Data Analysis Silverman, Bernard W. https://doi.org/10.1201/9781315140919	book	January 1998
Extensive TD-DFT Benchmark: Singlet-Excited States of Organic Molecules Jacquemin, Denis; Wathelet, Valérie; Perpète, Eric A. Journal of Chemical Theory and Computation, Vol. 5, Issue 9 https://doi.org/10.1021/ct900298e	journal	August 2009
Inverse Strategies for Molecular Design Kuhn, Christoph; Beratan, David N. The Journal of Physical Chemistry, Vol. 100, Issue 25 https://doi.org/10.1021/jp960518i	journal	January 1996
A Simplex Method for Function Minimization Nelder, J. A.; Mead, R. The Computer Journal, Vol. 7, Issue 4 https://doi.org/10.1093/comjnl/7.4.308	journal	January 1965
Kernel density estimation via diffusion Botev, Z. I.; Grotowski, J. F.; Kroese, D. P. The Annals of Statistics, Vol. 38, Issue 5 https://doi.org/10.1214/10-AOS799	journal	October 2010
Relaxed active space: Fixing tailored-CC with high order coupled cluster. I Melnichuk, Anna; Bartlett, Rodney J. The Journal of Chemical Physics, Vol. 137, Issue 21 https://doi.org/10.1063/1.4767900	journal	December 2012
Kernel density estimation via diffusion Botev, Z. I.; Grotowski, J. F.; Kroese, D. P. arXiv https://doi.org/10.48550/arxiv.1011.2602	text	January 2010
Finding Density Functionals with Machine Learning Snyder, John C.; Rupp, Matthias; Hansen, Katja Physical Review Letters, Vol. 108, Issue 25 https://doi.org/10.1103/PhysRevLett.108.253002	journal	June 2012
A Computational Investigation of Organic Dyes for Dye-Sensitized Solar Cells: Benchmark, Strategies, and Open Issues Pastore, Mariachiara; Mosconi, Edoardo; De Angelis, Filippo The Journal of Physical Chemistry C, Vol. 114, Issue 15 https://doi.org/10.1021/jp100713r	journal	March 2010
Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning Rupp, Matthias; Tkatchenko, Alexandre; Müller, Klaus-Robert arXiv https://doi.org/10.48550/arxiv.1109.2618	text	January 2011
Using Molecular Similarity to Develop Reliable Models of Chemical Reactions in Complex Environments Ediz, Volkan; Monda, Anthony C.; Brown, Robert P. Journal of Chemical Theory and Computation, Vol. 5, Issue 12 https://doi.org/10.1021/ct9004195	journal	November 2009
The big challenges of big data Marx, Vivien Nature, Vol. 498, Issue 7453 https://doi.org/10.1038/498255a	journal	June 2013
Double excitations within time-dependent density functional theory linear response Maitra, Neepa T.; Zhang, Fan; Cave, Robert J. The Journal of Chemical Physics, Vol. 120, Issue 13 https://doi.org/10.1063/1.1651060	journal	April 2004
Fourier series of atomic radial distribution functions: A molecular fingerprint for machine learning models of quantum chemical properties von Lilienfeld, O. Anatole; Ramakrishnan, Raghunathan; Rupp, Matthias International Journal of Quantum Chemistry, Vol. 115, Issue 16 https://doi.org/10.1002/qua.24912	journal	April 2015
Time-Dependent Density Functional Response Theory for Molecules Casida, Mark E. Recent Advances in Density Functional Methods https://doi.org/10.1142/9789812830586_0005	book	November 1995
CC2 excitation energy calculations on large molecules using the resolution of the identity approximation Hättig, Christof; Weigend, Florian The Journal of Chemical Physics, Vol. 113, Issue 13 https://doi.org/10.1063/1.1290013	journal	January 2000
A new hybrid exchange–correlation functional using the Coulomb-attenuating method (CAM-B3LYP) Yanai, Takeshi; Tew, David P.; Handy, Nicholas C. Chemical Physics Letters, Vol. 393, Issue 1-3, p. 51-57 https://doi.org/10.1016/j.cplett.2004.06.011	journal	July 2004
Using molecular similarity to construct accurate semiempirical electronic structure theories Janesko, Benjamin G.; Yaron, David The Journal of Chemical Physics, Vol. 121, Issue 12 https://doi.org/10.1063/1.1785771	journal	September 2004
Oscillator Strength: How Does TDDFT Compare to EOM-CCSD? Caricato, Marco; Trucks, Gary W.; Frisch, Michael J. Journal of Chemical Theory and Computation, Vol. 7, Issue 2 https://doi.org/10.1021/ct100662n	journal	December 2010
Recognizing molecular patterns by machine learning: an agnostic structural definition of the hydrogen bond Gasparotto, Piero; Ceriotti, Michele arXiv https://doi.org/10.48550/arxiv.1410.5447	text	January 2014
Balanced basis sets of split valence, triple zeta valence and quadruple zeta valence quality for H to Rn: Design and assessment of accuracy Weigend, Florian; Ahlrichs, Reinhart Physical Chemistry Chemical Physics, Vol. 7, Issue 18, p. 3297-3305 https://doi.org/10.1039/b508541a	journal	January 2005
Density Estimation for Statistics and Data Analysis Dehnad, Khosrow Technometrics, Vol. 29, Issue 4 https://doi.org/10.1080/00401706.1987.10488295	journal	November 1987
Design strategies of metal free-organic sensitizers for dye sensitized solar cells: Role of donor and acceptor monomers Tseng, Chieh-Yu; Taufany, Fadlilatul; Nachimuthu, Santhanamoorthi Organic Electronics, Vol. 15, Issue 6 https://doi.org/10.1016/j.orgel.2014.03.022	journal	June 2014
Modeling electronic quantum transport with machine learning Lopez-Bezanilla, Alejandro; von Lilienfeld, O. Anatole Physical Review B, Vol. 89, Issue 23 https://doi.org/10.1103/PhysRevB.89.235411	journal	June 2014
Fast and accurate modeling of molecular atomization energies with machine learning Rupp, Matthias; Tkatchenko, Alexandre; Müller, Klaus-Robert American Physical Society https://doi.org/10.5451/unibas-ep43360	text	January 2012
Modeling Electronic Quantum Transport with Machine Learning Lopez-Bezanilla, Alejandro; von Lilienfeld, O. Anatole arXiv https://doi.org/10.48550/arxiv.1401.8277	text	January 2014
Assessing Excited State Methods by Adiabatic Excitation Energies Send, Robert; Kühn, Michael; Furche, Filipp Journal of Chemical Theory and Computation, Vol. 7, Issue 8 https://doi.org/10.1021/ct200272b	journal	July 2011
Assessment of the Perdew–Burke–Ernzerhof exchange-correlation functional Ernzerhof, Matthias; Scuseria, Gustavo E. The Journal of Chemical Physics, Vol. 110, Issue 11 https://doi.org/10.1063/1.478401	journal	March 1999
Toward reliable density functional methods without adjustable parameters: The PBE0 model Adamo, Carlo; Barone, Vincenzo The Journal of Chemical Physics, Vol. 110, Issue 13 https://doi.org/10.1063/1.478522	journal	April 1999
Inhomogeneous Electron Gas Hohenberg, P.; Kohn, W. Physical Review, Vol. 136, Issue 3B, p. B864-B871 https://doi.org/10.1103/PhysRev.136.B864	journal	November 1964
Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies Hansen, Katja; Montavon, Grégoire; Biegler, Franziska Journal of Chemical Theory and Computation, Vol. 9, Issue 8 https://doi.org/10.1021/ct400195d	journal	July 2013
First principles view on chemical compound space: Gaining rigorous atomistic control of molecular properties von Lilienfeld, O. A. arXiv https://doi.org/10.48550/arxiv.1209.5033	preprint	January 2012
First principles view on chemical compound space: Gaining rigorous atomistic control of molecular properties von Lilienfeld, O. Anatole International Journal of Quantum Chemistry, Vol. 113, Issue 12 https://doi.org/10.1002/qua.24375	journal	February 2013
Ultrafast Infrared Spectroscopy of Riboflavin: Dynamics, Electronic Structure, and Vibrational Mode Analysis Wolf, Matthias M. N.; Schumann, Christian; Gross, Ruth The Journal of Physical Chemistry B, Vol. 112, Issue 42 https://doi.org/10.1021/jp804231c	journal	October 2008
Machine learning of molecular electronic properties in chemical compound space Montavon, Grégoire; Rupp, Matthias; Gobre, Vivekanand ETH Zurich https://doi.org/10.3929/ethz-b-000071765	text	January 2013
Long-range charge-transfer excited states in time-dependent density functional theory require non-local exchange Dreuw, Andreas; Weisman, Jennifer L.; Head-Gordon, Martin The Journal of Chemical Physics, Vol. 119, Issue 6 https://doi.org/10.1063/1.1590951	journal	August 2003
Rationale for mixing exact exchange with density functional approximations Perdew, John P.; Ernzerhof, Matthias; Burke, Kieron The Journal of Chemical Physics, Vol. 105, Issue 22, p. 9982-9985 https://doi.org/10.1063/1.472933	journal	December 1996
Quantum chemistry structures and properties of 134 kilo molecules Ramakrishnan, Raghunathan; Dral, Pavlo O.; Rupp, Matthias Scientific Data, Vol. 1, Issue 1 https://doi.org/10.1038/sdata.2014.22	journal	August 2014
Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space Hansen, Katja; Biegler, Franziska; Ramakrishnan, Raghunathan American Chemical Society https://doi.org/10.5451/unibas-ep43351	text	January 2015
Quantum chemistry structures and properties of 134 kilo molecules Raghunathan, Ramakrishnan,; O., Dral, Pavlo; Matthias, Rupp, Springer Nature https://doi.org/10.5451/unibas-ep43357	text	January 2014

Cited By (26)

Visualization of very large high-dimensional data sets as minimum spanning trees Probst, Daniel; Reymond, Jean-Louis Journal of Cheminformatics, Vol. 12, Issue 1 https://doi.org/10.1186/s13321-020-0416-x	journal	February 2020
Size‐Extensive Molecular Machine Learning with Global Representations Jung, Hyunwook; Stocker, Sina; Kunkel, Christian ChemSystemsChem, Vol. 2, Issue 4 https://doi.org/10.1002/syst.201900052	journal	February 2020
MoleculeNet: A Benchmark for Molecular Machine Learning Wu, Zhenqin; Ramsundar, Bharath; Feinberg, Evan N. arXiv https://doi.org/10.48550/arxiv.1703.00564	preprint	January 2017
Evolutionary Approach to Constructing a Deep Feedforward Neural Network for Prediction of Electronic Coupling Elements in Molecular Materials Çaylak, Onur; Yaman, Anil; Baumeier, Björn Journal of Chemical Theory and Computation, Vol. 15, Issue 3 https://doi.org/10.1021/acs.jctc.8b01285	journal	February 2019
Quantum chemical elucidation of the turn-on luminescence mechanism in two new Schiff bases as selective chemosensors of Zn ²⁺ : synthesis, theory and bioimaging applications Berrones-Reyes, Jessica C.; Muñoz-Flores, Blanca M.; Cantón-Diáz, Arelly M. RSC Advances, Vol. 9, Issue 53 https://doi.org/10.1039/c9ra05010h	journal	January 2019
Chemical diversity in molecular orbital energy predictions with kernel ridge regression Stuke, Annika; Todorović, Milica; Rupp, Matthias The Journal of Chemical Physics, Vol. 150, Issue 20 https://doi.org/10.1063/1.5086105	journal	May 2019
Machine Learning, Quantum Chemistry, and Chemical Space Ramakrishnan, Raghunathan; von Lilienfeld, O. Anatole Reviews in Computational Chemistry, Vol. 30 https://doi.org/10.1002/9781119356059.ch5	book	January 2017
Quantum Machine Learning in Chemical Compound Space von Lilienfeld, O. Anatole Angewandte Chemie International Edition, Vol. 57, Issue 16 https://doi.org/10.1002/anie.201709686	journal	March 2018
Recent advances and applications of machine learning in solid-state materials science Schmidt, Jonathan; Marques, Mário R. G.; Botti, Silvana npj Computational Materials, Vol. 5, Issue 1 https://doi.org/10.1038/s41524-019-0221-0	journal	August 2019
Radiative decay channel assessment to understand the sensing mechanism of a fluorescent turn‐on Al ³⁺ chemosensor Treto‐Suárez, Manuel A.; Hidalgo‐Rosa, Yoan; Schott, Eduardo International Journal of Quantum Chemistry, Vol. 120, Issue 3 https://doi.org/10.1002/qua.26083	journal	October 2019
MoleculeNet: a benchmark for molecular machine learning Wu, Zhenqin; Ramsundar, Bharath; Feinberg, Evan N. Chemical Science, Vol. 9, Issue 2 https://doi.org/10.1039/c7sc02664a	journal	January 2018
Building attention and edge message passing neural networks for bioactivity and physical–chemical property prediction Withnall, M.; Lindelöf, E.; Engkvist, O. Journal of Cheminformatics, Vol. 12, Issue 1 https://doi.org/10.1186/s13321-019-0407-y	journal	January 2020
Prediction model of band-gap for AX binary compounds by combination of density functional theory calculations and machine learning techniques Lee, Joohwi; Seko, Atsuto; Shitara, Kazuki arXiv https://doi.org/10.48550/arxiv.1509.00973	text	January 2015
Enriched optimization of molecular properties under constraints: an electrochromic example Rinderspacher, B. Christopher; Elward, Jennifer M. Molecular Systems Design & Engineering, Vol. 3, Issue 3 https://doi.org/10.1039/c7me00126f	journal	January 2018
PotentialNet for Molecular Property Prediction Feinberg, Evan N.; Sur, Debnil; Wu, Zhenqin arXiv https://doi.org/10.48550/arxiv.1803.04465	preprint	January 2018
Visualization of Very Large High-Dimensional Data Sets as Minimum Spanning Trees Probst, Daniel; Reymond, Jean-Louis ChemRxiv. Theoretical and Computational Chemistry https://doi.org/10.26434/chemrxiv.9698861	posted_content	November 2019
Nonadiabatic Excited-State Dynamics with Machine Learning Dral, Pavlo O.; Barbatti, Mario; Thiel, Walter The Journal of Physical Chemistry Letters, Vol. 9, Issue 19 https://doi.org/10.1021/acs.jpclett.8b02469	journal	September 2018
Structure-based sampling and self-correcting machine learning for accurate calculations of potential energy surfaces and vibrational levels Dral, Pavlo O.; Owens, Alec; Yurchenko, Sergei N. The Journal of Chemical Physics, Vol. 146, Issue 24 https://doi.org/10.1063/1.4989536	journal	June 2017
Atomic structures and orbital energies of 61,489 crystal-forming organic molecules Stuke, Annika; Kunkel, Christian; Golze, Dorothea Scientific Data, Vol. 7, Issue 1 https://doi.org/10.1038/s41597-020-0385-y	journal	February 2020
Machine learning model for non-equilibrium structures and energies of simple molecules Iype, E.; Urolagin, S. The Journal of Chemical Physics, Vol. 150, Issue 2 https://doi.org/10.1063/1.5054968	journal	January 2019
Quantum Machine Learning in Chemistry and Materials Huang, Bing; Symonds, Nadine O.; von Lilienfeld, O. Anatole Handbook of Materials Modeling https://doi.org/10.1007/978-3-319-44677-6_67	book	January 2020
Machine learning modeling of Wigner intracule functionals for two electrons in one-dimension Bhavsar, Rutvij; Ramakrishnan, Raghunathan The Journal of Chemical Physics, Vol. 150, Issue 14 https://doi.org/10.1063/1.5089597	journal	April 2019
Visualization of Very Large High-Dimensional Data Sets as Minimum Spanning Trees Probst, Daniel; Reymond, Jean-Louis ChemRxiv https://doi.org/10.26434/chemrxiv.9698861.v2	journal	November 2019
Atomic structures and orbital energies of 61,489 crystal-forming organic molecules Stuke, Annika; Kunkel, Christian; Golze, Dorothea arXiv https://doi.org/10.48550/arxiv.2001.08954	preprint	January 2020
Solid harmonic wavelet scattering for predictions of molecule properties Eickenberg, Michael; Exarchakis, Georgios; Hirn, Matthew The Journal of Chemical Physics, Vol. 148, Issue 24 https://doi.org/10.1063/1.5023798	journal	June 2018
Constant size descriptors for accurate machine learning models of molecular properties Collins, Christopher R.; Gordon, Geoffrey J.; von Lilienfeld, O. Anatole The Journal of Chemical Physics, Vol. 148, Issue 24 https://doi.org/10.1063/1.5020441	journal	June 2018

Similar Records

Electronic spectra from TDDFT and machine learning in chemical space

Journal Article · Fri Aug 28 00:00:00 EDT 2015 · Journal of Chemical Physics · OSTI ID:1392463

Ramakrishnan, Raghunathan; Hartmann, Mia; Tapavicza, Enrico; +1 more

Electronic spectra from TDDFT and machine learning in chemical space

Journal Article · Fri Aug 28 00:00:00 EDT 2015 · Journal of Chemical Physics · OSTI ID:1392463

Ramakrishnan, Raghunathan; Hartmann, Mia; Tapavicza, Enrico; +1 more

Development of a TDDFT-Based Protocol with Local Hybrid Functionals for the Screening of Potential Singlet Fission Chromophores

Journal Article · Fri Sep 01 00:00:00 EDT 2017 · Journal of Chemical Theory and Computation · OSTI ID:1392463

Grotjahn, Robin; Maier, Toni M.; Michl, Josef; +1 more

Related Subjects

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY

Title: Electronic spectra from TDDFT and machine learning in chemical space

Citation Formats

References (72)

Cited By (26)

Similar Records

Related Subjects