Phenol Photostatic Spectra and Quantum‐Classical Photodynamic Deprotonation DOI
Vladimir A. Pomogaev, E. N. Bocharnikova, O. N. Tchaikovskaya

et al.

International Journal of Quantum Chemistry, Journal Year: 2024, Volume and Issue: 124(21)

Published: Oct. 22, 2024

ABSTRACT The spectral‐luminescence properties and photochemical conversions of phenol were analyzed for an isolated molecule as well in water solvents a continuum implicit model explicit atomistic surroundings. This involved employing cut‐edge hybrid quantum‐classical methodologies to generate static optical spectra the excited dissipative crossing potential energy curves. A combination electronic excitations, gradient calculations, embedding electrostatic fitting charges on molecular dynamic propagation trajectories provided statistically averaged absorption spectra. mixed‐reference spin‐flip multiconfigurational linear response method based reference triplet preprocessed time‐dependent density‐functional theory was utilized determine conical intersections between lowest ground states, two‐stage transitions from second excitation state. Non‐adiabatic dynamics defined photodissipative their lifetimes, points through trajectory surface hopping together with approaches. Dyson orbitals extended Koopmans' theorem applied reveal nature states at key photodynamic trajectories. Potential hydroxyl group cleavage predicted searching turns “swift” OH deprotonation |π→⟩ transition along propagations contrast “long” processes leading benzene ring deformation stable bond.

Language: Английский

Machine Learning of Reactive Potentials DOI
Yinuo Yang, Shuhao Zhang,

Kavindri Ranasinghe

et al.

Annual Review of Physical Chemistry, Journal Year: 2024, Volume and Issue: 75(1), P. 371 - 395

Published: June 28, 2024

In the past two decades, machine learning potentials (MLPs) have driven significant developments in chemical, biological, and material sciences. The construction training of MLPs enable fast accurate simulations analysis thermodynamic kinetic properties. This review focuses on application to reaction systems with consideration bond breaking formation. We development MLP models, primarily neural network kernel-based algorithms, recent applications reactive (RMLPs) at different scales. show how RMLPs are constructed, they speed up calculation dynamics, facilitate study trajectories, rates, free energy calculations, many other calculations. Different data sampling strategies applied building also discussed a focus collect structures for rare events further improve their performance active learning.

Language: Английский

Citations

19
MLatom Software Ecosystem for Surface Hopping Dynamics in Python with Quantum Mechanical and Machine Learning Methods DOI
Lina Zhang, Sebastian V. Pios, Mikołaj Martyka

et al.

Journal of Chemical Theory and Computation, Journal Year: 2024, Volume and Issue: 20(12), P. 5043 - 5057

Published: June 5, 2024

We present an open-source MLatom@XACS software ecosystem for on-the-fly surface hopping nonadiabatic dynamics based on the Landau–Zener–Belyaev–Lebedev algorithm. The can be performed via Python API with a wide range of quantum mechanical (QM) and machine learning (ML) methods, including ab initio QM (CASSCF ADC(2)), semiempirical methods (e.g., AM1, PM3, OMx, ODMx), many types ML potentials KREG, ANI, MACE). Combinations also used. While user build their own combinations, we provide AIQM1, which is Δ-learning used out-of-the-box. showcase how AIQM1 reproduces isomerization yield trans-azobenzene at low cost. example scripts that, in dozens lines, enable to obtain final population plots by simply providing initial geometry molecule. Thus, those perform optimization, normal mode calculations, condition sampling, parallel trajectories propagation, analysis, result plotting. Given capabilities MLatom training different models, this seamlessly integrated into protocols building models dynamics. In future, deeper more efficient integration Newton-X will vast functionalities dynamics, such as fewest-switches hopping, facilitate similar workflows API.

Language: Английский

Citations

11
Machine learning accelerated nonadiabatic dynamics simulations of materials with excitonic effects DOI Open Access

Sheng-Ze Wang,

Fang Qiu, Xiang‐Yang Liu

et al.

The Journal of Chemical Physics, Journal Year: 2025, Volume and Issue: 162(2)

Published: Jan. 8, 2025

This study presents an efficient methodology for simulating nonadiabatic dynamics of complex materials with excitonic effects by integrating machine learning (ML) models simplified Tamm–Dancoff approximation (sTDA) calculations. By leveraging ML models, we accurately predict ground-state wavefunctions using unconverged Kohn–Sham (KS) Hamiltonians. These ML-predicted KS Hamiltonians are then employed sTDA-based excited-state calculations (sTDA/ML). The results demonstrate that energies, time-derivative couplings, and absorption spectra from sTDA/ML accurate enough compared those conventional density functional theory based sTDA (sTDA/DFT) Furthermore, sTDA/ML-based molecular simulations on two different systems, namely chloro-substituted silicon quantum dot monolayer black phosphorus, achieve more than 100 times speedup the linear response time-dependent DFT simulations. work highlights potential ML-accelerated studying complicated photoinduced large offering significant computational savings without compromising accuracy.

Language: Английский

Citations

1
Machine learning photodynamics uncover blocked non-radiative mechanisms in aggregation-induced emission DOI
Li Wang,

Christian Salguero,

Steven A. Lopez

et al.

Chem, Journal Year: 2024, Volume and Issue: 10(7), P. 2295 - 2310

Published: May 14, 2024

Language: Английский

Citations

7
Dynamic vertical triplet energies: Understanding and predicting triplet energy transfer DOI
Mihai V. Popescu, Robert S. Paton

Chem, Journal Year: 2024, Volume and Issue: 10(11), P. 3428 - 3443

Published: July 26, 2024

Language: Английский

Citations

6
Nonadiabatic Field: A Conceptually Novel Approach for Nonadiabatic Quantum Molecular Dynamics DOI Creative Commons
Baihua Wu, Bingqi Li, Xin He

et al.

Journal of Chemical Theory and Computation, Journal Year: 2025, Volume and Issue: unknown

Published: April 7, 2025

Reliable trajectory-based nonadiabatic quantum dynamics methods at the atomic/molecular level are critical for practical understanding and rational design of many important processes in real large/complex systems, where dynamical behavior electrons that nuclei coupled. The paper reports latest progress field (NaF), a conceptually novel approach with independent trajectories. Substantially different from mainstreams Ehrenfest-like surface hopping methods, nuclear force NaF involves arising coupling between electronic states, addition to adiabatic contributed by single state. is capable faithfully describing interplay motion broad regime, which covers relevant states keep coupled wide range or all time bifurcation characteristic essential. derived exact generalized phase space formulation coordinate-momentum variables, constraint (CPS) employed discrete electronic-state degrees freedom (DOFs) infinite Wigner used continuous DOFs. We propose efficient integrators equations both diabatic representations. Since formalism CPS not unique, can principle be implemented various representations correlation function (TCF) time-dependent property. They applied suite representative gas-phase condensed-phase benchmark models numerically results available comparison. It shown relatively insensitive representation TCF will potential tool reliable simulations mechanical transition systems.

Language: Английский

Citations

0
The evolution of machine learning potentials for molecules, reactions and materials DOI
Junfan Xia, Yaolong Zhang, Bin Jiang

et al.

Chemical Society Reviews, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

This review offers a comprehensive overview of the development machine learning potentials for molecules, reactions, and materials over past two decades, evolving from traditional models to state-of-the-art.

Language: Английский

Citations

0
Neural network methods for radiation detectors and imaging DOI Creative Commons
Sijie Lin, Shu Ning, Hui Zhu

et al.

Frontiers in Physics, Journal Year: 2024, Volume and Issue: 12

Published: Feb. 22, 2024

Recent advances in image data proccesing through deep learning allow for new optimization and performance-enhancement schemes radiation detectors imaging hardware. This enables experiments, which includes photon sciences synchrotron X-ray free electron lasers as a subclass, data-endowed artificial intelligence. We give an overview of generation at sources, learning-based methods processing tasks, hardware solutions acceleration. Most existing approaches are trained offline, typically using large amounts computational resources. However, once trained, DNNs can achieve fast inference speeds be deployed to edge devices. A trend is computing with less energy consumption (hundreds watts or less) real-time analysis potential. While popularly used computing, electronic-based accelerators ranging from general purpose processors such central units (CPUs) application-specific integrated circuits (ASICs) constantly reaching performance limits latency, consumption, other physical constraints. These rise next-generation analog neuromorhpic platforms, optical neural networks (ONNs), high parallel, low boost acceleration (LA-UR-23-32395).

Language: Английский

Citations

3
Tutorials: Physics-informed machine learning methods of computing 1D phase-field models DOI Creative Commons
Wei Li, Ruqing Fang,

Junning Jiao

et al.

APL Machine Learning, Journal Year: 2024, Volume and Issue: 2(3)

Published: Aug. 19, 2024

Phase-field models are widely used to describe phase transitions and interface evolution in various scientific disciplines. In this Tutorial, we present two neural network methods for solving them. The first method is based on physics-informed networks (PINNs), which enforce the governing equations boundary/initial conditions loss function. second deep operator (DeepONets), treat as an that maps current state of field variable next state. Both demonstrated with Allen–Cahn equation one dimension, results compared ground truth. This Tutorial also discusses advantages limitations each method, well potential extensions improvements.

Language: Английский

Citations

3
Unsupervised Machine Learning in the Analysis of Nonadiabatic Molecular Dynamics Simulation DOI
Yifei Zhu, Jiawei Peng, Chao Xu

et al.

The Journal of Physical Chemistry Letters, Journal Year: 2024, Volume and Issue: unknown, P. 9601 - 9619

Published: Sept. 13, 2024

The all-atomic full-dimensional-level simulations of nonadiabatic molecular dynamics (NAMD) in large realistic systems has received high research interest recent years. However, such NAMD normally generate an enormous amount time-dependent high-dimensional data, leading to a significant challenge result analyses. Based on unsupervised machine learning (ML) methods, considerable efforts were devoted developing novel and easy-to-use analysis tools for the identification photoinduced reaction channels comprehensive understanding complicated motions simulations. Here, we tried survey advances this field, particularly focus how use ML methods analyze trajectory-based simulation results. Our purpose is offer discussion several essential components protocol, including selection construction descriptors, establishment analytical frameworks, their advantages limitations, persistent challenges.

Language: Английский

Citations

3