Tracking the Electron Density Changes in Excited States: A Computational Study of Pyrazine DOI
Sebastian V. Pios, Jiaji Zhang, Maxim F. Gelin

et al.

The Journal of Physical Chemistry Letters, Journal Year: 2024, Volume and Issue: 15(42), P. 10609 - 10613

Published: Oct. 15, 2024

The development of X-ray free-electron lasers has enabled ultrafast diffraction (XRD) experiments, which are capable resolving electronic and vibrational transitions structural changes in molecules or capturing molecular movies. While time-resolved XRD attracted more attention, the extraction information from signals is challenging requires theoretical support. In this work, we combined scattering theory a trajectory surface hopping approach to resolve dynamical structure photoexcited by studying time evolution electron density between excited states ground state. Using pyrazine molecule as an example, show that key features reaction pathways can be identified, enabling capture associated with for molecule.

Language: Английский

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
AI in computational chemistry through the lens of a decade-long journey DOI Creative Commons
Pavlo O. Dral

Chemical Communications, Journal Year: 2024, Volume and Issue: 60(24), P. 3240 - 3258

Published: Jan. 1, 2024

This article gives a perspective on the progress of AI tools in computational chemistry through lens author's decade-long contributions put wider context trends this rapidly expanding field. over last decade is tremendous: while ago we had glimpse what was to come many proof-of-concept studies, now witness emergence AI-based that are mature enough make faster and more accurate simulations increasingly routine. Such turn allow us validate even revise experimental results, deepen our understanding physicochemical processes nature, design better materials, devices, drugs. The rapid introduction powerful rise unique challenges opportunities discussed too.

Language: Английский

Citations

7
Physics-Informed Active Learning for Accelerating Quantum Chemical Simulations DOI

Yi-Fan Hou,

Lina Zhang, Quanhao Zhang

et al.

Journal of Chemical Theory and Computation, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 12, 2024

Quantum chemical simulations can be greatly accelerated by constructing machine learning potentials, which is often done using active (AL). The usefulness of the constructed potentials limited high effort required and their insufficient robustness in simulations. Here, we introduce end-to-end AL for robust data-efficient with affordable investment time resources minimum human interference. Our protocol based on physics-informed sampling training points, automatic selection initial data, uncertainty quantification, convergence monitoring. versatility this shown our implementation quasi-classical molecular dynamics simulating vibrational spectra, conformer search a key biochemical molecule, time-resolved mechanism Diels-Alder reaction. These investigations took us days instead weeks pure quantum calculations high-performance computing cluster.

Language: Английский

Citations

6
On-the-Fly Simulation of Two-Dimensional Fluorescence–Excitation Spectra DOI
Sebastian V. Pios, Maxim F. Gelin, Luis Contreras

et al.

The Journal of Physical Chemistry Letters, Journal Year: 2024, Volume and Issue: 15(34), P. 8728 - 8735

Published: Aug. 20, 2024

Two-dimensional (2D) fluorescence-excitation (2D-FLEX) spectroscopy is a recently proposed nonlinear femtosecond technique for the detection of photoinduced dynamics. The method records time-resolved fluorescence signal in its excitation- and detection-frequency dependence hence combines exclusive excited state dynamics (fluorescence) with signals resolved both excitation emission frequencies (2D electronic spectroscopy). In this work, we develop an on-the-fly protocol simulation 2D-FLEX spectra molecular systems, which based on interfacing classical doorway-window representation spectroscopic responses trajectory surface hopping simulations. Applying methodology to gas-phase pyrazine, show that can deliver detailed information otherwise obtainable via attosecond spectroscopy.

Language: Английский

Citations

4
Machine Learning Nonadiabatic Dynamics: Eliminating Phase Freedom of Nonadiabatic Couplings with the State-Interaction State-Averaged Spin-Restricted Ensemble-Referenced Kohn–Sham Approach DOI
Sung Wook Moon, Soohaeng Yoo Willow,

Tae Park

et al.

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

Published: Feb. 4, 2025

Excited-state molecular dynamics (ESMD) simulations near conical intersections (CIs) pose significant challenges when using machine learning potentials (MLPs). Although MLPs have gained recognition for their integration into mixed quantum-classical (MQC) methods, such as trajectory surface hopping (TSH), and capacity to model correlated electron–nuclear efficiently, difficulties persist in managing nonadiabatic dynamics. Specifically, singularities at CIs double-valued coupling elements result discontinuities that disrupt the smoothness of predictive functions. Partial solutions been provided by diabatic Hamiltonians with phaseless loss functions these challenges. However, a definitive method addressing caused has yet be developed. Here, we introduce term, Δ2, derived from square off-diagonal Hamiltonian state-interaction state-averaged spin-restricted ensemble-referenced Kohn–Sham (SI-SA-REKS, briefly SSR)(2,2) formalism. This approach improves stability accuracy MLP issues arising CI We apply this penta-2,4-dieniminium cation (PSB3), demonstrating its effectiveness improving training ML-based Our results show Δ2-based ML-ESMD can reproduce ab initio ESMD simulations, underscoring potential efficiency broader applications, particularly large-scale long-time scale simulations.

Language: Английский

Citations

0
Development of time-resolved luminescence measurement instruments for biosensing and bioimaging – An overview DOI
Benjamin Sreenan, Vala Kafil,

Donovan Wells

et al.

Measurement, Journal Year: 2025, Volume and Issue: unknown, P. 117201 - 117201

Published: March 1, 2025

Language: Английский

Citations

0
Transient-Absorption Pump-Probe Spectra as Information-Rich Observables: Case Study of Fulvene DOI Creative Commons
Zhaofa Li, Jiawei Peng, Yifei Zhu

et al.

Molecules, Journal Year: 2025, Volume and Issue: 30(7), P. 1439 - 1439

Published: March 24, 2025

Conical intersections (CIs) are the most efficient channels of photodeactivation and energy transfer, while femtosecond spectroscopy is main experimental tool delivering information on molecular CI-driven photoinduced processes. In this work, we undertake a comprehensive ab initio investigation CI-mediated internal conversion in fulvene by simulating evolutions electronic populations, bond lengths angles, time-resolved transient absorption (TA) pump-probe (PP) spectra. TA PP spectra evaluated fly combining symmetrical quasiclassical/Meyer–Miller–Stock–Thoss (SQC/MMST) dynamics doorway-window representation spectroscopic signals. We show that simulated reveal not only population but also key nuclear motions as well mode–mode couplings. demonstrate signals observables: They can be considered information-rich purely theoretical observables, which deliver more than conventional populations. This extracted appropriate analyses

Language: Английский

Citations

0
Study on the Photoinduced Isomerization Mechanism of Hydrazone Derivatives Molecular Switch DOI Creative Commons
Xiaojuan Pang,

Kaiyue Zhao,

Chenghao Yang

et al.

ACS Omega, Journal Year: 2025, Volume and Issue: unknown

Published: April 23, 2025

Language: Английский

Citations

0
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
Charting electronic-state manifolds across molecules with multi-state learning and gap-driven dynamics via efficient and robust active learning DOI Creative Commons
Mikołaj Martyka, Lina Zhang, Fuchun Ge

et al.

Published: Aug. 6, 2024

We present a robust protocol for affordable learning of the electronic-state manifold to accelerate photophysical and photochemical molecular simulations. The solves several pertinent issues precluding widespread use machine (ML) in excited-state introduce novel physics-informed multi-state ML model that can learn an arbitrary number excited states across molecules with accuracy better or similar ground-state energies established potentials. also gap-driven dynamics meticulous accelerated sampling small-gap regions: which proves crucial stable surface-hopping dynamics. Put together, enable efficient active furnishing models Our active-learning includes based on uncertainty quantification, ensuring quality each adiabatic surface, low error energy gaps, precise calculation hopping probability. thresholds quantification are automatically chosen statistical physical considerations. will be made available next release open-source MLatom as described at https://github.com/dralgroup/al-namd

Language: Английский

Citations

2