Neural networks and kernel ridge regression for excited states dynamics of CH $_2$ NH $_2^+$ : From single-state to multi-state representations and multi-property machine learning models

18 December 2019

Papers citing "Neural networks and kernel ridge regression for excited states dynamics of CH$_2$NH$_2^+$: From single-state to multi-state representations and multi-property machine learning models"

7 / 7 papers shown

Title
Electronic excited states from physically-constrained machine learning Edoardo Cignoni Divya Suman Jigyasa Nigam Lorenzo Cupellini B. Mennucci Michele Ceriotti 63 17 0 01 Nov 2023
Multi-Fidelity Machine Learning for Excited State Energies of Molecules Vivin Vinod Sayan Maity Peter Zaspel Ulrich Kleinekathöfer AI4CE 79 9 0 18 May 2023
Deep Learning for UV Absorption Spectra with SchNarc: First Steps Towards Transferability in Chemical Compound Space Julia Westermayr P. Marquetand 91 53 0 15 Jul 2020
Machine learning for electronically excited states of molecules Julia Westermayr P. Marquetand 71 266 0 10 Jul 2020
Machine learning and excited-state molecular dynamics Julia Westermayr P. Marquetand AI4CE 61 56 0 28 May 2020
Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning Marcel F. Langer Alex Goessmann M. Rupp AI4CE 73 99 0 26 Mar 2020
Combining SchNet and SHARC: The SchNarc machine learning approach for excited-state dynamics Julia Westermayr M. Gastegger P. Marquetand AI4CE 70 131 0 17 Feb 2020