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RESEARCH PAPER · The Journal of Physical Chemistry A · 2021

Increasing Efficiency of Nonadiabatic Molecular Dynamics by Hamiltonian Interpolation with Kernel Ridge Regression

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15 direct citing papers in this saved network. Explore the next layer with second-order citations.

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42 direct citing works

Increasing Efficiency of Nonadiabatic Molecular Dynamics by Hamiltonian Interpolation with Kernel Ridge RegressionOriginalExtending the Time Scales of Nonadiabatic Molecular Dynamics via Machine Learning in the Time Domain42Interpolating Nonadiabatic Molecular Dynamics Hamiltonian with Inverse Fast Fourier Transform20Interpolating Moving Ridge Regression (IMRR): A machine learning algorithm to predict energy gradients for ab initio molecular dynamics simulations3Fluctuations at Metal Halide Perovskite Grain Boundaries Create Transient Trap States: Machine Learning Assisted Ab Initio Analysis43Recent advances in machine learning for electronic excited state molecular dynamics simulations2Non-Adiabatic Dynamics in Condensed Matter and Nanoscale Systems0Interpolating Nonadiabatic Molecular Dynamics Hamiltonian with Bidirectional Long Short-Term Memory Networks19Advances of machine learning in materials science: Ideas and techniques105Photocatalytic activity of dual defect modified graphitic carbon nitride is robust to tautomerism: machine learning assisted ab initio quantum dynamics22Fast robust kernel regression through sign gradient descent with early stopping1Tethered Gaussian wavepackets for quantum dynamics simulations: Sticking together for better convergence0Excited-State Wave Functions and Energies Predicted by Machine Learning Based on Graph Neural Networks6Machine Learning Accelerated Non‐Adiabatic Molecular Dynamics Elucidates Local Polarization Effects on Non‐radiative Recombination in Halide Perovskites2Efficient Nonadiabatic Molecular Dynamics with Machine Learning Hamiltonian Interpolation0Animal versus human oral drug bioavailability: machine learning says yes – they correlate0

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  1. Extending the Time Scales of Nonadiabatic Molecular Dynamics via Machine Learning in the Time Domain

    The Journal of Physical Chemistry Letters · 2021-12-16

    Alexey V. Akimov

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  2. Interpolating Nonadiabatic Molecular Dynamics Hamiltonian with Inverse Fast Fourier Transform

    The Journal of Physical Chemistry Letters · 2022-01-03

    Bipeng Wang, Weibin Chu, Oleg V. Prezhdo

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  3. Interpolating Moving Ridge Regression (IMRR): A machine learning algorithm to predict energy gradients for ab initio molecular dynamics simulations

    Chemical Physics · 2022-02-23

    Kazuumi Fujioka, Rui Sun

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  4. Fluctuations at Metal Halide Perovskite Grain Boundaries Create Transient Trap States: Machine Learning Assisted Ab Initio Analysis

    ACS Applied Materials & Interfaces · 2022-12-07

    Yifan Wu, Dongyu Liu, Weibin Chu, Bipeng Wang, Andrey S. Vasenko, Oleg V. Prezhdo

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  5. Recent advances in machine learning for electronic excited state molecular dynamics simulations

    Royal Society of Chemistry eBooks · 2022-12-19

    Brigitta Bachmair, Madlen Maria Reiner, Maximilian Xaver Tiefenbacher, Philipp Marquetand

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  6. Non-Adiabatic Dynamics in Condensed Matter and Nanoscale Systems

    Elsevier eBooks · 2023-04-14

    Oleg V. Prezhdo, Carlos Mora Perez

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  7. Interpolating Nonadiabatic Molecular Dynamics Hamiltonian with Bidirectional Long Short-Term Memory Networks

    The Journal of Physical Chemistry Letters · 2023-08-02

    Bipeng Wang, Ludwig Winkler, Yifan Wu, Klaus‐Robert Müller, Huziel E. Sauceda, Oleg V. Prezhdo

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  8. Advances of machine learning in materials science: Ideas and techniques

    Frontiers of Physics · 2023-11-24

    Sue Sin Chong, Yi Sheng Ng, Hui‐Qiong Wang, Jin‐Cheng Zheng

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  9. Photocatalytic activity of dual defect modified graphitic carbon nitride is robust to tautomerism: machine learning assisted ab initio quantum dynamics

    Nanoscale · 2024-01-01

    Sraddha Agrawal, Bipeng Wang, Yifan Wu, David Casanova, Oleg V. Prezhdo

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  10. Fast robust kernel regression through sign gradient descent with early stopping

    Electronic Journal of Statistics · 2025-01-01

    Oskar Allerbo

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  11. Tethered Gaussian wavepackets for quantum dynamics simulations: Sticking together for better convergence

    The Journal of Chemical Physics · 2025-07-10

    L. Cruz Rodríguez, Scott Habershon

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  12. Excited-State Wave Functions and Energies Predicted by Machine Learning Based on Graph Neural Networks

    Journal of Chemical Theory and Computation · 2025-09-04

    Xiang‐Yang Liu, Dongyi Xiao, Wei‐Hai Fang, Ganglong Cui

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  13. Machine Learning Accelerated Non‐Adiabatic Molecular Dynamics Elucidates Local Polarization Effects on Non‐radiative Recombination in Halide Perovskites

    Advanced Science · 2026-05-29

    Bing Yang, Xiaoli Wei, Bo Cai, Yang Yan, Xinghai Zhu, Ying Liu, Junmin Xia, Lihui Liu, Kun Cao, Wei Shen, Pengfei Xia, Shufen Chen, Siyu Chen, Jin Zhao

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  14. Efficient Nonadiabatic Molecular Dynamics with Machine Learning Hamiltonian Interpolation

    The Journal of Physical Chemistry Letters · 2026-06-23

    Yifan Wu, Bipeng Wang, Mohit Chaudhary, David Casanova, Oleg V. Prezhdo

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  15. Animal versus human oral drug bioavailability: machine learning says yes – they correlate

    AAPS Open · 2026-09-02

    Yusuf Ali Mohammed, Madalyn Heaton-Ward, Simon Gaisford, Moe Elbadawi

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