131 related articles for article (PubMed ID: 32828098)
1. Machine learning Frenkel Hamiltonian parameters to accelerate simulations of exciton dynamics.
Farahvash A; Lee CK; Sun Q; Shi L; Willard AP
J Chem Phys; 2020 Aug; 153(7):074111. PubMed ID: 32828098
[TBL] [Abstract][Full Text] [Related]
2. Increasing Efficiency of Nonadiabatic Molecular Dynamics by Hamiltonian Interpolation with Kernel Ridge Regression.
Wu Y; Prezhdo N; Chu W
J Phys Chem A; 2021 Oct; 125(41):9191-9200. PubMed ID: 34636570
[TBL] [Abstract][Full Text] [Related]
3. Spectral densities for Frenkel exciton dynamics in molecular crystals: A TD-DFTB approach.
Plötz PA; Megow J; Niehaus T; Kühn O
J Chem Phys; 2017 Feb; 146(8):084112. PubMed ID: 28249454
[TBL] [Abstract][Full Text] [Related]
4. A new efficient method for calculation of Frenkel exciton parameters in molecular aggregates.
Plötz PA; Niehaus T; Kühn O
J Chem Phys; 2014 May; 140(17):174101. PubMed ID: 24811619
[TBL] [Abstract][Full Text] [Related]
5. Representing the Molecular Signatures of Disordered Molecular Semiconductors in Size-Extendable Models of Exciton Dynamics.
Lee CK; Willard AP
J Phys Chem B; 2020 Jun; 124(25):5238-5245. PubMed ID: 32422051
[TBL] [Abstract][Full Text] [Related]
6. Modeling the effects of molecular disorder on the properties of Frenkel excitons in organic molecular semiconductors.
Shi L; Willard AP
J Chem Phys; 2018 Sep; 149(9):094110. PubMed ID: 30195311
[TBL] [Abstract][Full Text] [Related]
7. How Geometric Distortions Scatter Electronic Excitations in Conjugated Macromolecules.
Shi T; Li H; Tretiak S; Chernyak VY
J Phys Chem Lett; 2014 Nov; 5(22):3946-52. PubMed ID: 26276475
[TBL] [Abstract][Full Text] [Related]
8. Analytic derivative couplings and first-principles exciton/phonon coupling constants for an ab initio Frenkel-Davydov exciton model: Theory, implementation, and application to compute triplet exciton mobility parameters for crystalline tetracene.
Morrison AF; Herbert JM
J Chem Phys; 2017 Jun; 146(22):224110. PubMed ID: 29166040
[TBL] [Abstract][Full Text] [Related]
9. Machine Learning Diffusion Monte Carlo Energies.
Ryczko K; Krogel JT; Tamblyn I
J Chem Theory Comput; 2022 Dec; 18(12):7695-7701. PubMed ID: 36317712
[TBL] [Abstract][Full Text] [Related]
10. Predicting the DNA Conductance Using a Deep Feedforward Neural Network Model.
Aggarwal A; Vinayak V; Bag S; Bhattacharyya C; Waghmare UV; Maiti PK
J Chem Inf Model; 2021 Jan; 61(1):106-114. PubMed ID: 33320660
[TBL] [Abstract][Full Text] [Related]
11. All-DFTB Approach to the Parametrization of the System-Bath Hamiltonian Describing Exciton-Vibrational Dynamics of Molecular Assemblies.
Plötz PA; Megow J; Niehaus T; Kühn O
J Chem Theory Comput; 2018 Oct; 14(10):5001-5010. PubMed ID: 30141929
[TBL] [Abstract][Full Text] [Related]
12. A first principles approach to the electronic properties of liquid and supercritical CO2.
Cabral BJ; Rivelino R; Coutinho K; Canuto S
J Chem Phys; 2015 Jan; 142(2):024504. PubMed ID: 25591369
[TBL] [Abstract][Full Text] [Related]
13. A model hamiltonian tuned toward high level ab initio calculations to describe the character of excitonic states in perylenebisimide aggregates.
Liu W; Canola S; Köhn A; Engels B; Negri F; Fink RF
J Comput Chem; 2018 Sep; 39(24):1979-1989. PubMed ID: 30315587
[TBL] [Abstract][Full Text] [Related]
14. The roles of electronic exchange and correlation in charge-transfer- to-solvent dynamics: Many-electron nonadiabatic mixed quantum/classical simulations of photoexcited sodium anions in the condensed phase.
Glover WJ; Larsen RE; Schwartz BJ
J Chem Phys; 2008 Oct; 129(16):164505. PubMed ID: 19045282
[TBL] [Abstract][Full Text] [Related]
15. Machine learning exciton dynamics.
Häse F; Valleau S; Pyzer-Knapp E; Aspuru-Guzik A
Chem Sci; 2016 Aug; 7(8):5139-5147. PubMed ID: 30155164
[TBL] [Abstract][Full Text] [Related]
16. Intermolecular coulomb couplings from ab initio electrostatic potentials: application to optical transitions of strongly coupled pigments in photosynthetic antennae and reaction centers.
Madjet ME; Abdurahman A; Renger T
J Phys Chem B; 2006 Aug; 110(34):17268-81. PubMed ID: 16928026
[TBL] [Abstract][Full Text] [Related]
17. Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error.
Faber FA; Hutchison L; Huang B; Gilmer J; Schoenholz SS; Dahl GE; Vinyals O; Kearnes S; Riley PF; von Lilienfeld OA
J Chem Theory Comput; 2017 Nov; 13(11):5255-5264. PubMed ID: 28926232
[TBL] [Abstract][Full Text] [Related]
18. Charge and Exciton Transfer Simulations Using Machine-Learned Hamiltonians.
Krämer M; Dohmen PM; Xie W; Holub D; Christensen AS; Elstner M
J Chem Theory Comput; 2020 Jul; 16(7):4061-4070. PubMed ID: 32491856
[TBL] [Abstract][Full Text] [Related]
19. Chemical diversity in molecular orbital energy predictions with kernel ridge regression.
Stuke A; Todorović M; Rupp M; Kunkel C; Ghosh K; Himanen L; Rinke P
J Chem Phys; 2019 May; 150(20):204121. PubMed ID: 31153160
[TBL] [Abstract][Full Text] [Related]
20. Learning Electron Densities in the Condensed Phase.
Lewis AM; Grisafi A; Ceriotti M; Rossi M
J Chem Theory Comput; 2021 Nov; 17(11):7203-7214. PubMed ID: 34669406
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
