Search details
1.
Deep learning of dynamically responsive chemical Hamiltonians with semiempirical quantum mechanics.
Proc Natl Acad Sci U S A
; 119(27): e2120333119, 2022 Jul 05.
Article
in English
| MEDLINE | ID: mdl-35776544
2.
Neural network atomistic potentials for global energy minima search in carbon clusters.
Phys Chem Chem Phys
; 25(32): 21173-21182, 2023 Aug 16.
Article
in English
| MEDLINE | ID: mdl-37490276
3.
Machine Learning Models Capture Plasmon Dynamics in Ag Nanoparticles.
J Phys Chem A
; 127(17): 3768-3778, 2023 May 04.
Article
in English
| MEDLINE | ID: mdl-37078657
4.
Lightweight and effective tensor sensitivity for atomistic neural networks.
J Chem Phys
; 158(18)2023 May 14.
Article
in English
| MEDLINE | ID: mdl-37158328
5.
Synergy of semiempirical models and machine learning in computational chemistry.
J Chem Phys
; 159(11)2023 Sep 21.
Article
in English
| MEDLINE | ID: mdl-37712780
6.
Non-adiabatic Excited-State Molecular Dynamics: Theory and Applications for Modeling Photophysics in Extended Molecular Materials.
Chem Rev
; 120(4): 2215-2287, 2020 02 26.
Article
in English
| MEDLINE | ID: mdl-32040312
7.
Machine learned Hückel theory: Interfacing physics and deep neural networks.
J Chem Phys
; 154(24): 244108, 2021 Jun 28.
Article
in English
| MEDLINE | ID: mdl-34241371
8.
The effects of site asymmetry on near-degenerate state-to-state vibronic mixing in flexible bichromophores.
J Chem Phys
; 151(8): 084313, 2019 Aug 28.
Article
in English
| MEDLINE | ID: mdl-31470719
9.
Design principles from multiscale simulations to predict nanostructure in self-assembling ionic liquids.
Faraday Discuss
; 206: 159-181, 2017 12 14.
Article
in English
| MEDLINE | ID: mdl-28956588
10.
Vibronic coupling in asymmetric bichromophores: theory and application to diphenylmethane-d(5).
J Chem Phys
; 141(13): 134119, 2014 Oct 07.
Article
in English
| MEDLINE | ID: mdl-25296796
11.
Vibronic coupling in asymmetric bichromophores: experimental investigation of diphenylmethane-d5.
J Chem Phys
; 141(6): 064316, 2014 Aug 14.
Article
in English
| MEDLINE | ID: mdl-25134580
12.
Machine Learning Potentials with the Iterative Boltzmann Inversion: Training to Experiment.
J Chem Theory Comput
; 20(3): 1274-1281, 2024 Feb 13.
Article
in English
| MEDLINE | ID: mdl-38307009
13.
Exploring the frontiers of condensed-phase chemistry with a general reactive machine learning potential.
Nat Chem
; 16(5): 727-734, 2024 May.
Article
in English
| MEDLINE | ID: mdl-38454071
14.
Semi-Empirical Shadow Molecular Dynamics: A PyTorch Implementation.
J Chem Theory Comput
; 19(11): 3209-3222, 2023 Jun 13.
Article
in English
| MEDLINE | ID: mdl-37163680
15.
Uncertainty-driven dynamics for active learning of interatomic potentials.
Nat Comput Sci
; 3(3): 230-239, 2023 Mar.
Article
in English
| MEDLINE | ID: mdl-38177878
16.
Extending machine learning beyond interatomic potentials for predicting molecular properties.
Nat Rev Chem
; 6(9): 653-672, 2022 Sep.
Article
in English
| MEDLINE | ID: mdl-37117713
17.
Teaching a neural network to attach and detach electrons from molecules.
Nat Commun
; 12(1): 4870, 2021 08 11.
Article
in English
| MEDLINE | ID: mdl-34381051
18.
The Rise of Neural Networks for Materials and Chemical Dynamics.
J Phys Chem Lett
; 12(26): 6227-6243, 2021 Jul 08.
Article
in English
| MEDLINE | ID: mdl-34196559
19.
Predicting phosphorescence energies and inferring wavefunction localization with machine learning.
Chem Sci
; 12(30): 10207-10217, 2021 Aug 04.
Article
in English
| MEDLINE | ID: mdl-34447529
20.
Automated discovery of a robust interatomic potential for aluminum.
Nat Commun
; 12(1): 1257, 2021 Feb 23.
Article
in English
| MEDLINE | ID: mdl-33623036