Fetching the paper…
Reading the bibliography…
Atomistic modeling of energetic disorder in organic semiconductors (OSCs) and its effects on the optoelectronic properties of OSCs requires a large number of excited-state electronic-structure calculations, a computationally daunting task for many OSC applications.
1901
Earlier work this paper cites.
1906
Earlier work this paper cites.
1906
Earlier work this paper cites.
Sease, J. W.; Zechmeister, L. Chromatographic and Spectral Characteristics of Some Polythienyls. Journal of the American Chemical Society 1947
1947
Earlier work this paper cites.
Chromatographic and Spectral Characteristics of Some Polythienyls
Sease, J. W. & Zechmeister, L · 1947
Earlier work this paper cites.
McQuarrie, D. A. Statistical Mechanics ; Harper and Row: New York, 1976
1976
Earlier work this paper cites.
Nosé, S. A molecular dynamics method for simulations in the canonical ensemble. Molecular Physics 1984
1984
Earlier work this paper cites.
Hoover, W. G. Canonical dynamics: Equilibrium phase-space distributions. Physical Review A 1985
1985
Earlier work this paper cites.
Reed, A. E.; Weinstock, R. B.; Weinhold, F. Natural population analysis. The Journal of Chemical Physics 1985
1985
Earlier work this paper cites.
Zhao, M.; Singh, B. P.; Prasad, P. N. A systematic study of polarizability and microscopic third-order optical nonlinearity in thiophene oligomers. The Journal of Chemical Physics 1988
1988
Earlier work this paper cites.
A systematic study of polarizability and microscopic third-order optical nonlinearity in thiophene oligomers
Zhao, M., Singh, B. P. & Prasad, P. N · 1988
Earlier work this paper cites.
Fichou, D.; Horowitz, G.; Xu, B.; Garnier, F. Low temperature optical absorption of polycrystalline thin films of α \alpha -quaterthiophene, α \alpha -sexithiophene and α \alpha -octithiophene, three model oligomers of polythiophene. Synthetic Metals 1992
1992
Earlier work this paper cites.
Fave, J.-L. Excitons in Chains of Thiophene Rings. Electronic Properties of Polymers. Berlin, Heidelberg, 1992; pp 60–62
1992
Earlier work this paper cites.
Bässler, H. Charge Transport in Disordered Organic Photoconductors a Monte Carlo Simulation Study. Physica Status Solidi (b) 1993
1993
Earlier work this paper cites.
Darden, T.; York, D.; Pedersen, L. Particle mesh Ewald: An N.log(N) method for Ewald sums in large systems. The Journal of Chemical Physics 1993
1993
Earlier work this paper cites.
Constant pressure molecular dynamics algorithms
Martyna, G. J., Tobias, D. J. & Klein, M. L · 1994
Earlier work this paper cites.
Fulde, P. Electron Correlations in Molecules and Solids ; Springer Berlin Heidelberg: Berlin, Heidelberg, 1995; pp 107–128
1995
Earlier work this paper cites.
Essmann, U.; Perera, L.; Berkowitz, M. L.; Darden, T.; Lee, H.; Pedersen, L. G. A smooth particle mesh Ewald method. The Journal of Chemical Physics 1995
1995
Earlier work this paper cites.
Mukamel, S. Principles of Nonlinear Optical Spectroscopy ; Oxford: New York, 1995
1995
Earlier work this paper cites.
Grebner, D.; Helbig, M.; Rentsch, S. Size-dependent properties of oligothiophenes by picosecond time-resolved spectroscopy. Journal of Physical Chemistry 1995
1995
Earlier work this paper cites.
Size-dependent properties of oligothiophenes by picosecond time-resolved spectroscopy
Grebner, D., Helbig, M. & Rentsch, S · 1995
Earlier work this paper cites.
Becker, R. S.; Seixas de Melo, J.; Maçanita, A. L.; Elisei, F. Comprehensive Evaluation of the Absorption, Photophysical, Energy Transfer, Structural, and Theoretical Properties of α \alpha -Oligothiophenes with One to Seven Rings. The Journal of Physical Chemistry 1996
1996
Earlier work this paper cites.
Lap, D. V.; Grebner, D.; Rentsch, S. Femtosecond Time-Resolved Spectroscopic Studies on Thiophene Oligomers. The Journal of Physical Chemistry A 1997
1997
Earlier work this paper cites.
Femtosecond Time-Resolved Spectroscopic Studies on Thiophene Oligomers
Lap, D. V., Grebner, D. & Rentsch, S · 1997
Earlier work this paper cites.
Fichou, D. In Handbook of Oligo- and Polythiophenes ; Fichou, D., Ed.; Wiley: Weinheim, Germany, 1998
1998
Earlier work this paper cites.
Egelhaaf, H.-J.; Oelkrug, D.; Gebauer, W.; Sokolowski, M.; Umbach, E.; Fischer, T.; Bäuerle, P. Photophysical properties of β \beta -alkylated quater-, octa-, dodeca- and hexadecatiophenes. Optical Materials 1998
1998
Earlier work this paper cites.
de Melo, J. S.; Silva, L. M.; Arnaut, L. G.; Becker, R. S. Singlet and triplet energies of α \alpha -oligothiophenes: A spectroscopic, theoretical, and photoacoustic study: Extrapolation to polythiophene. The Journal of Chemical Physics 1999
1999
Earlier work this paper cites.
Pan, J.-F.; Chua, S.-J.; Huang, W. Conformational analysis (ab initio HF/3-21G*) and optical properties of poly(thiophene-phenylene-thiophene) (PTPT). Chemical Physics Letters 2002
2002
Earlier work this paper cites.
Jorissen, R. N.; Gilson, M. K. Virtual Screening of Molecular Databases Using a Support Vector Machine. Journal of Chemical Information and Modeling 2005
2005
Earlier work this paper cites.
Fabiano, E.; Sala, F. D.; Cingolani, R.; Weimer, M.; Görling, A. Theoretical Study of Singlet and Triplet Excitation Energies in Oligothiophenes. The Journal of Physical Chemistry A 2005
2005
Earlier work this paper cites.
Banks, J. L. et al. Integrated Modeling Program, Applied Chemical Theory (IMPACT). Journal of Computational Chemistry 2005
2005
Earlier work this paper cites.
Dreuw, A.; Head-Gordon, M. Single-Reference ab Initio Methods for the Calculation of Excited States of Large Molecules. Chemical Reviews 2005
2005
Earlier work this paper cites.
Theoretical Study of Singlet and Triplet Excitation Energies in Oligothiophenes
Fabiano, E., Sala, F. D., Cingolani, R., Weimer, M. & Görling, A · 2005
Earlier work this paper cites.
Single-Reference ab Initio Methods for the Calculation of Excited States of Large Molecules
Dreuw, A. & Head-Gordon, M · 2005
Earlier work this paper cites.
Westenhoff, S.; Beenken, W. J. D.; Yartsev, A.; Greenham, N. C. Conformational disorder of conjugated polymers. The Journal of Chemical Physics 2006
2006
Earlier work this paper cites.
Bowers, K. J.; Sacerdoti, F. D.; Salmon, J. K.; Shan, Y.; Shaw, D. E.; Chow, E.; Xu, H.; Dror, R. O.; Eastwood, M. P.; Gregersen, B. A.; Klepeis, J. L.; Kolossvary, I.; Moraes, M. A. Scalable algorithms for molecular dynamics simulations on commodity clusters. Proceedings of the 2006 ACM/IEEE conference on Supercomputing - SC ’06. New York, New York, USA, 2006; p 84
2006
Earlier work this paper cites.
Scalable algorithms for molecular dynamics simulations on commodity clusters
Bowers, K. J. et al · 2006
Earlier work this paper cites.
Taliani, C.; Gebauer, W. Handbook of Oligo- and Polythiophenes ; Wiley-VCH Verlag GmbH: Weinheim, Germany, 2007; pp 361–404
2007
Earlier work this paper cites.
Salzner, U. Theoretical Investigation of Excited States of Oligothiophenes and of Their Monocations. Journal of Chemical Theory and Computation 2007
2007
Earlier work this paper cites.
Electronic Excited States of Conjugated Oligothiophenes
Taliani, C. & Gebauer, W · 2007
Earlier work this paper cites.
Theoretical Investigation of Excited States of Oligothiophenes and of Their Monocations
Salzner, U · 2007
Earlier work this paper cites.
Moulé, A. J.; Meerholz, K. Controlling morphology in polymer-fullerene mixtures. Advanced Materials 2008
2008
Earlier work this paper cites.
Darling, S. B. Isolating the Effect of Torsional Defects on Mobility and Band Gap in Conjugated Polymers. The Journal of Physical Chemistry B 2008
2008
Earlier work this paper cites.
Brédas, J.-L.; Norton, J. E.; Cornil, J.; Coropceanu, V. Molecular Understanding of Organic Solar Cells: The Challenges. Accounts of Chemical Research 2009
2009
Earlier work this paper cites.
Perepichka, I. F.; Perepichka, D. F. In Handbook of Thiophene-Based Materials: Applications in Organic Electronics and Photonics ; Perepichka, I. F., Perepichka, D. F., Eds.; John Wiley & Sons, Ltd: Chichester, UK, 2009
2009
Cited alongside, same era.
Hains, A. W.; Liang, Z.; Woodhouse, M. A.; Gregg, B. A. Molecular Semiconductors in Organic Photovoltaic Cells. Chemical Reviews 2010
2010
Cited alongside, same era.
Troisi, A. Charge transport in high mobility molecular semiconductors: classical models and new theories. Chemical Society Reviews 2011
2011
Cited alongside, same era.
Salzner, U.; Aydin, A. Improved Prediction of Properties of π \pi -Conjugated Oligomers with Range-Separated Hybrid Density Functionals. Journal of Chemical Theory and Computation 2011
2011
Cited alongside, same era.
Behler, J. Atom-centered symmetry functions for constructing high-dimensional neural network potentials. The Journal of Chemical Physics 2011
Smith, J. S.; Isayev, O.; Roitberg, A. E. ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost. Chemical Science 2017
2017
Later among the works it cites.
Botu, V.; Batra, R.; Chapman, J.; Ramprasad, R. Machine Learning Force Fields: Construction, Validation, and Outlook. The Journal of Physical Chemistry C 2017
2017
Later among the works it cites.
Chmiela, S.; Tkatchenko, A.; Sauceda, H. E.; Poltavsky, I.; Schütt, K. T.; Müller, K.-R. Machine learning of accurate energy-conserving molecular force fields. Science Advances 2017
2017
Later among the works it cites.
Gastegger, M.; Behler, J.; Marquetand, P. Machine learning molecular dynamics for the simulation of infrared spectra. Chemical Science 2017
2017
Later among the works it cites.
Schütt, K. T.; Arbabzadah, F.; Chmiela, S.; Müller, K. R.; Tkatchenko, A. Quantum-chemical insights from deep tensor neural networks. Nature Communications 2017
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2011
Cited alongside, same era.
Alemán, C.; Torras, J.; Casanovas, J. Influence of polarity of the medium in the saturation of the electronic properties for π \pi -conjugated oligothiophenes. Chemical Physics Letters 2011
2011
Cited alongside, same era.
Improved Prediction of Properties of π \pi -Conjugated Oligomers with Range-Separated Hybrid Density Functionals
Salzner, U. & Aydin, A · 2011
Cited alongside, same era.
Influence of polarity of the medium in the saturation of the electronic properties for π \pi -conjugated oligothiophenes
Alemán, C., Torras, J. & Casanovas, J · 2011
Cited alongside, same era.
Myers, J. D.; Xue, J. Organic Semiconductors and their Applications in Photovoltaic Devices. Polymer Reviews 2012
2012
Cited alongside, same era.
Feron, K.; Zhou, X.; Belcher, W. J.; Dastoor, P. C. Exciton transport in organic semiconductors: Förster resonance energy transfer compared with a simple random walk. Journal of Applied Physics 2012
2012
Cited alongside, same era.
Feron, K.; Belcher, W.; Fell, C.; Dastoor, P. Organic Solar Cells: Understanding the Role of Förster Resonance Energy Transfer. International Journal of Molecular Sciences 2012
2012
Cited alongside, same era.
Rupp, M.; Tkatchenko, A.; Müller, K.-R.; von Lilienfeld, O. A. Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning. Physical Review Letters 2012
2012
Cited alongside, same era.
2017
Later among the works it cites.
Gilmer, J.; Schoenholz, S. S.; Riley, P. F.; Vinyals, O.; Dahl, G. E. Neural message passing for quantum chemistry. Proceedings of the 34th International Conference on Machine Learning-Volume 70. 2017; pp 1263–1272
2017
Later among the works it cites.
Schütt, K.; Kindermans, P.-J.; Felix, H. E. S.; Chmiela, S.; Tkatchenko, A.; Müller, K.-R. Schnet: A continuous-filter convolutional neural network for modeling quantum interactions. Advances in Neural Information Processing Systems. 2017; pp 991–1001
2017
Later among the works it cites.
Automatic differentiation in pytorch
Adam, P. et al · 2017
Later among the works it cites.
Welborn, M.; Cheng, L.; Miller, T. F. Transferability in Machine Learning for Electronic Structure via the Molecular Orbital Basis. Journal of Chemical Theory and Computation 2018
2018
Later among the works it cites.
2018
Later among the works it cites.
Chmiela, S.; Sauceda, H. E.; Müller, K.-r.; Tkatchenko, A. Towards exact molecular dynamics simulations with machine-learned force fields. Nature Communications 2018
2018
Later among the works it cites.
Gómez-Bombarelli, R.; Wei, J. N.; Duvenaud, D.; Hernández-Lobato, J. M.; Sánchez-Lengeling, B.; Sheberla, D.; Aguilera-Iparraguirre, J.; Hirzel, T. D.; Adams, R. P.; Aspuru-Guzik, A. Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules. ACS Central Science 2018
2018
Later among the works it cites.
Schütt, K. T.; Sauceda, H. E.; Kindermans, P.-J.; Tkatchenko, A.; Müller, K.-R. SchNet - A deep learning architecture for molecules and materials. The Journal of Chemical Physics 2018
2018
Later among the works it cites.
Wu, Z.; Ramsundar, B.; Feinberg, E. N.; Gomes, J.; Geniesse, C.; Pappu, A. S.; Leswing, K.; Pande, V. MoleculeNet: a benchmark for molecular machine learning. Chemical Science. 2018; pp 513–530
2018
Later among the works it cites.
Sun, Q.; Berkelbach, T. C.; Blunt, N. S.; Booth, G. H.; Guo, S.; Li, Z.; Liu, J.; McClain, J. D.; Sayfutyarova, E. R.; Sharma, S.; Wouters, S.; Chan, G. K.-L. PySCF: the Python-based simulations of chemistry framework. Wiley Interdisciplinary Reviews: Computational Molecular Science 2018
2018
Later among the works it cites.
Zuehlsdorff, T. J.; Isborn, C. M. Combining the ensemble and Franck-Condon approaches for calculating spectral shapes of molecules in solution. The Journal of Chemical Physics 2018
2018
Later among the works it cites.
Shi, L.; Willard, A. P. Modeling the effects of molecular disorder on the properties of Frenkel excitons in organic molecular semiconductors. The Journal of Chemical Physics 2018
2018
Later among the works it cites.
PySCF: the Python-based simulations of chemistry framework
Sun, Q. et al · 2018
Later among the works it cites.
Zaspel, P.; Huang, B.; Harbrecht, H.; von Lilienfeld, O. A. Boosting Quantum Machine Learning Models with a Multilevel Combination Technique: Pople Diagrams Revisited. Journal of Chemical Theory and Computation 2019
2019
Closest in time.
Cheng, L.; Welborn, M.; Christensen, A. S.; Miller, T. F. A universal density matrix functional from molecular orbital-based machine learning: Transferability across organic molecules. The Journal of Chemical Physics 2019
2019
Closest in time.
Grisafi, A.; Fabrizio, A.; Meyer, B.; Wilkins, D. M.; Corminboeuf, C.; Ceriotti, M. Transferable Machine-Learning Model of the Electron Density. ACS Central Science 2019
2019
Closest in time.
Fabrizio, A.; Grisafi, A.; Meyer, B.; Ceriotti, M.; Corminboeuf, C. Electron density learning of non-covalent systems. Chemical Science 2019
2019
Closest in time.
Ryczko, K.; Strubbe, D. A.; Tamblyn, I. Deep learning and density-functional theory. Physical Review A 2019
2019
Closest in time.
Wang, J.; Olsson, S.; Wehmeyer, C.; Pérez, A.; Charron, N. E.; de Fabritiis, G.; Noé, F.; Clementi, C. Machine Learning of Coarse-Grained Molecular Dynamics Force Fields. ACS Central Science 2019
2019
Closest in time.
Jinnouchi, R.; Karsai, F.; Kresse, G. On-the-fly machine learning force field generation: Application to melting points. Physical Review B 2019
2019
Closest in time.
Ye, S.; Hu, W.; Li, X.; Zhang, J.; Zhong, K.; Zhang, G.; Luo, Y.; Mukamel, S.; Jiang, J. A neural network protocol for electronic excitations of N -methylacetamide. Proceedings of the National Academy of Sciences 2019
2019
Closest in time.
Ghosh, K.; Stuke, A.; Todorović, M.; Jørgensen, P. B.; Schmidt, M. N.; Vehtari, A.; Rinke, P. Deep Learning Spectroscopy: Neural Networks for Molecular Excitation Spectra. Advanced Science 2019
2019
Closest in time.
Unke, O. T.; Meuwly, M. PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments, and Partial Charges. Journal of Chemical Theory and Computation 2019
2019
Closest in time.
Lu, C.; Liu, Q.; Wang, C.; Huang, Z.; Lin, P.; He, L. Molecular Property Prediction: A Multilevel Quantum Interactions Modeling Perspective. Proceedings of the AAAI Conference on Artificial Intelligence 2019
2019
Closest in time.
St. John, P. C.; Phillips, C.; Kemper, T. W.; Wilson, A. N.; Guan, Y.; Crowley, M. F.; Nimlos, M. R.; Larsen, R. E. Message-passing neural networks for high-throughput polymer screening. The Journal of Chemical Physics 2019
2019
Closest in time.
Ramsundar, B.; Eastman, P.; Walters, P.; Pande, V.; Leswing, K.; Wu, Z. Deep Learning for the Life Sciences ; O’Reilly Media, 2019
2019
Closest in time.
Segatta, F.; Cupellini, L.; Garavelli, M.; Mennucci, B. Quantum Chemical Modeling of the Photoinduced Activity of Multichromophoric Biosystems. Chemical Reviews 2019
2019
Closest in time.
Loco, D.; Cupellini, L. Modeling the absorption lineshape of embedded systems from molecular dynamics: A tutorial review. International Journal of Quantum Chemistry 2019
2019
Closest in time.
Zuehlsdorff, T. J.; Montoya-Castillo, A.; Napoli, J. A.; Markland, T. E.; Isborn, C. M. Optical spectra in the condensed phase: Capturing anharmonic and vibronic features using dynamic and static approaches. The Journal of Chemical Physics 2019
2019
Closest in time.
Zuehlsdorff, T. J.; Isborn, C. M. Modeling absorption spectra of molecules in solution. International Journal of Quantum Chemistry 2019
2019
Closest in time.
Lee, C. K.; Shi, L.; Willard, A. P. Modeling the Influence of Correlated Molecular Disorder on the Dynamics of Excitons in Organic Molecular Semiconductors. The Journal of Physical Chemistry C 2019
2019
Closest in time.
Wu, S.; Kondo, Y.; Kakimoto, M.-a.; Yang, B.; Yamada, H.; Kuwajima, I.; Lambard, G.; Hongo, K.; Xu, Y.; Shiomi, J.; Schick, C.; Morikawa, J.; Yoshida, R. Machine-learning-assisted discovery of polymers with high thermal conductivity using a molecular design algorithm. npj Computational Materials 2019
2019
Closest in time.
Smith, J. S.; Nebgen, B. T.; Zubatyuk, R.; Lubbers, N.; Devereux, C.; Barros, K.; Tretiak, S.; Isayev, O.; Roitberg, A. E. Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning. Nature Communications 2019
2019
Closest in time.
Yamada, H.; Liu, C.; Wu, S.; Koyama, Y.; Ju, S.; Shiomi, J.; Morikawa, J.; Yoshida, R. Predicting Materials Properties with Little Data Using Shotgun Transfer Learning. ACS Central Science 2019
2019
Closest in time.
Deep Graph Library: Towards Efficient and Scalable Deep Learning on Graphs
Wang, M. et al · 2019
Closest in time.
Deep Learning for the Life Sciences (O’Reilly Media, 2019)
Ramsundar, B. et al · 2019
Closest in time.
McMahon, D. P.; Troisi, A. Organic Semiconductors: Impact of Disorder at Different Timescales. ChemPhysChem 2010
2074
Closest in time.
Ramakrishnan, R.; Dral, P. O.; Rupp, M.; von Lilienfeld, O. A. Big Data Meets Quantum Chemistry Approximations: The Δ \Delta -Machine Learning Approach. Journal of Chemical Theory and Computation 2015
2096
Closest in time.