arXiv subjects

Zhenggang Lan

Publications and source records attributed to Zhenggang Lan.

At least 19 recordsLinked to original sources

arXiv2026

A quantum chemistry dataset containing S0-S1 conical-intersection structures of 259k molecules

Conical intersections are key to photoinduced reactions, but comprehensive datasets of their structures remain rare. To address this gap, we built the QCDGE-CI dataset, which contains ground-state and minimum-energy conical-intersection structures for more than 259k small molecules with up to ten heavy atoms (C, N, O, or F). The minimum-energy conical-intersection geometries were computed using the semi-empirical OM2/MRCI method and evaluated through systematic benchmarks with an enlarged active space and CASSCF calculations. These benchmarks support the reliability of the geometries. Cheminformatics analyses further reveal the broad chemical diversity covered by the dataset. The dataset is designed to support machine-learning and data-driven studies of excited-state chemistry, including nonadiabatic processes.

physics.chem-ph↗

arXiv2026

Nonadiabatic Molecular Dynamics on Real-time Excited-State Surfaces via Machine Learning Hamiltonians

Simulating the coupled, nonequilibrium dynamics of electrons and nuclei is a central challenge in chemistry, physics, and materials science, governing phenomena from photocatalysis to quantum information. The primary bottleneck has been the lack of a general, accurate, and efficient method for modeling the complete excited-state landscape: the potential energy surfaces, forces, and non-adiabatic couplings for multiple electronic states. While machine learning has revolutionized ground-state simulations and shown promise for excited states in molecules, a unified framework that solves the complete multi-state problem for general condensed matter systems has remained elusive. Here we introduce on-the-fly N${^2}$AMD (Neural network NAMD), a machine learning framework that makes on-the-fly NAMD in solids a reality. By employing an equivariant neural network to predict the system Hamiltonian, the framework delivers excited-state energies, forces, and non-adiabatic coupling vectors at a fraction of the cost of ab initio calculations. Crucially, it allows simulations with hybrid functional accuracy, a level of approach previously inaccessible for NAMD. We showcase its capabilities with three topical examples: correcting order-of-magnitude errors in carrier dynamics predicted by conventional procedure in a MoS$_2$/WS$_2$ heterostructure, simulating previously inaccessible photoinduced ferroelectric switching, and capturing real-time polaron formation in TiO$_2$ at the hybrid-functional level. On-the-fly N${^2}$AMD moves beyond the limitations of equilibrium theory, establishing a new paradigm for the predictive, first-principles design of materials operating far from equilibrium.

physics.comp-ph↗

arXiv2026

Perspective on a challenge: predicting the photochemistry of cyclobutanone

This Perspective is part of a Special Topic that explored the maturity of nonadiabatic molecular dynamics for predicting photochemical processes. In 2023, a prediction challenge was issued to the community of computational photochemists to simulate the photochemistry of cyclobutanone, photoexcited at 200 nm, and the resulting time-resolved MeV-UED signal. The challenge attracted 15 theoretical predictions from more than 70 researchers, employing a wide range of strategies for electronic structure and nonadiabatic molecular dynamics to predict the time-resolved MeV-UED signal before the experiment had been conducted at SLAC (Stanford, USA). The MeV-UED instrument at Shanghai Jiao Tong University was also used to provide a second independent time-resolved MeV-UED signal for the photochemistry of cyclobutanone. This Perspective discusses the various approaches and strategies used by the participants to predict the photochemistry of cyclobutanone. This work also summarizes the strengths and weaknesses of various methods used for photoexcitation, electronic structure, nonadiabatic dynamics, and calculation of observables, as agreed by the participants during a CECAM workshop dedicated to the results of the challenge and organized in Lausanne in April 2025. This Perspective also collects all the predicted time-resolved MeV-UED signals into a single figure, together with the experimental signal. This challenge (i) demonstrated the qualitative predictive power of nonadiabatic molecular dynamics and (ii) underscore the impact of electronic-structure theory on the outcome of the excited-state dynamics and the need for its careful benchmarking. This effort allowed the community to share practical strategies to perform nonadiabatic dynamics (discussed in the present Perspective) and constitutes a 'calibration' exercise for computational photochemistry.

physics.chem-ph↗

arXiv2026

Bridging Visual Intuition and Chemical Expertise: An Autonomous Analysis Framework for Nonadiabatic Dynamics Simulations via Mentor-Engineer-Student Collaboration

Analyzing nonadiabatic molecular dynamics trajectories traditionally heavily relies on expert intuition and visual pattern recognition, a process that is difficult to formalize. We present VisU, a vision-driven framework that leverages the complementary strengths of two state-of-the-art large language models to establish a "virtual research collective." This collective operates through a "Mentor-Engineer-Student" paradigm that mimics the collaborative intelligence of a professional chemistry laboratory. Within this ecosystem, the Mentor provides physical intuition through visual reasoning, while the Engineer adaptively constructs analysis scripts, and the Student executes the pipeline and manages the data and results. VisU autonomously orchestrates a four-stage workflow comprising Preprocessing, Recursive Channel Discovery, Important-Motion Identification, and Validation/Summary. This systematic approach identifies reaction channels and key nuclear motions while generating professional academic reports. By bridging visual insight with chemical expertise, VisU establishes a new paradigm for human-AI collaboration in the analysis of excited-state dynamics simulation results, significantly reducing dependence on manual interpretation and enabling more intuitive, scalable mechanistic discovery.

physics.chem-ph↗

arXiv2025

An Automated Framework for Analyzing Structural Evolution in On-the-fly Non-adiabatic Molecular Dynamics Using Autoencoder and Multiple Molecular Descriptors

A major challenge in nonadiabatic molecular dynamics is to automatically and objectively identify the key reaction coordinates that drive molecules toward distinct excited-state decay channels. Traditional manual analyses are inefficient and rely heavily on expert intuition, creating a bottleneck for interpreting complex photochemical processes. To overcome this, we introduce a fully automated machine-learning framework that directly extracts these coordinates from on-the-fly trajectory surface hopping data. By combining an Autoencoder for nonlinear dimensionality reduction with clustering and information entropy analysis, our method autonomously maps reaction channels and pinpoints their governing structural motions. When applied to keto isocytosine and the methaniminium cation, the framework objectively revealed invovled reaction channels and corresponding active coordinates with high efficiency and accuracy. This work establishes an effective paradigm for mechanistic insight in excited-state dynamics, transforming raw trajectory data into clear, interpretable reaction mechanisms.

physics.chem-ph↗

arXiv2025

Chemistry-Enhanced Diffusion-Based Framework for Small-to-Large Molecular Conformation Generation

Obtaining 3D conformations of realistic polyatomic molecules at the quantum chemistry level remains challenging, and although recent machine learning advances offer promise, predicting large-molecule structures still requires substantial computational effort. Here, we introduce StoL, a diffusion model-based framework that enables rapid and knowledge-free generation of large molecular structures from small-molecule data. Remarkably, StoL assembles molecules in a LEGO-style fashion from scratch, without seeing the target molecules or any structures of comparable size during training. Given a SMILES input, it decomposes the molecule into chemically valid fragments, generates their 3D structures with a diffusion model trained on small molecules, and assembles them into diverse conformations. This fragment-based strategy eliminates the need for large-molecule training data while maintaining high scalability and transferability. By embedding chemical principles into key steps, StoL ensures faster convergence, chemically rational structures, and broad configurational coverage, as confirmed against DFT calculations.

physics.chem-ph↗

arXiv2025

WaveMixings.jl: a Julia package for performing on-the-fly time-resolved nonlinear electronic spectra from quasi-classical trajectories

We present an efficient numerical implementation of the quasi-classical doorway-window approximation, specifically designed for on-the-fly simulations of time-resolved nonlinear spectroscopic signals in the Julia package WaveMixings.jl. The package contains modules that facilitate standard tasks such as input/output data handling, data filtering, and post-processing, among others. WaveMixings.jl includes implementations of various methods developed by the authors, including integral and dispersed transient absorption pump-probe signals and two-dimensional spectra. By developing WaveMixings.jl we aim to create a versatile platform to perform simulations and develop methodologies within the quasi-classical doorway-window approximation framework.

physics.chem-ph↗

arXiv2025

Super-resolution femtosecond electron diffraction reveals electronic and nuclear dynamics at conical intersections

Conical intersections play a pivotal role in excited-state quantum dynamics. Capturing transient molecular structures near conical intersections remains challenging due to the rapid timescales and subtle structural changes involved. We overcome this by combining the enhanced temporal resolution of mega-electron-volt ultrafast electron diffraction with a super-resolution real-space inversion algorithm, enabling visualization of nuclear and electronic motions at conical intersections with sub-angstrom resolution, surpassing the diffraction limit. We apply this technique to the textbook example of the ring-opening reaction of 1,3-cyclohexadiene, which proceeds through two conical intersections within 100 femtoseconds. The super-resolved transient structures near conical intersections reveal a C-C bond length difference of less than 0.4 angstrom and an approximately 30-femtosecond traversal time of the nuclear wave packet between them. These findings establish super-resolution ultrafast scattering as a transformative tool for uncovering quantum dynamics in molecules and open new avenues for studying light-matter interactions at the most fundamental level.

physics.chem-ph↗

arXiv2025

Roadmap for Molecular Benchmarks in Nonadiabatic Dynamics

Simulating the coupled electronic and nuclear response of a molecule to light excitation requires the application of nonadiabatic molecular dynamics. However, when faced with a specific photophysical or photochemical problem, selecting the most suitable theoretical approach from the wide array of available techniques is not a trivial task. The challenge is further complicated by the lack of systematic method comparisons and rigorous testing on realistic molecular systems. This absence of comprehensive molecular benchmarks remains a major obstacle to advances within the field of nonadiabatic molecular dynamics. A CECAM workshop, Standardizing Nonadiabatic Dynamics: Towards Common Benchmarks, was held in May 2024 to address this issue. This Perspective highlights the key challenges identified during the workshop in defining molecular benchmarks for nonadiabatic dynamics. Specifically, this work outlines some preliminary observations on essential components needed for simulations and proposes a roadmap aiming to establish, as an ultimate goal, a community-driven, standardized molecular benchmark set.

physics.chem-ph↗

arXiv2024

Advancing Nonadiabatic Molecular Dynamics Simulations for Solids: Achieving Supreme Accuracy and Efficiency with Machine Learning

Non-adiabatic molecular dynamics (NAMD) simulations have become an indispensable tool for investigating excited-state dynamics in solids. In this work, we propose a general framework, N$^2$AMD which employs an E(3)-equivariant deep neural Hamiltonian to boost the accuracy and efficiency of NAMD simulations. The preservation of Euclidean symmetry of Hamiltonian enables N$^2$AMD to achieve state-of-the-art performance. Distinct from conventional machine learning methods that predict key quantities in NAMD, N$^2$AMD computes these quantities directly with a deep neural Hamiltonian, ensuring supreme accuracy, efficiency, and consistency. Furthermore, N$^2$AMD demonstrates excellent generalizability and enables seamless integration with advanced NAMD techniques and infrastructures. Taking several extensively investigated semiconductors as the prototypical system, we successfully simulate carrier recombination in both pristine and defective systems at large scales where conventional NAMD often significantly underestimates or even qualitatively incorrectly predicts lifetimes. This framework not only boosts the efficiency and precision of NAMD simulations but also opens new avenues to advance materials research.

physics.comp-ph↗

arXiv2024

QCDGE database, Quantum Chemistry Database with Ground- and Excited-state Properties of 450 Kilo Molecules

Due to rapid advancements in deep learning techniques, the demand for large-volume high-quality databases grows significantly in chemical research. We developed a quantum-chemistry database that includes 443,106 small organic molecules with sizes up to 10 heavy atoms including carbon (C), nitrogen (N), oxygen (O), and fluorine (F). Ground-state geometry optimizations and frequency calculations of all compounds were performed at the B3LYP/6-31G* level with the BJD3 dispersion correction, while the excited-state single-point calculations were conducted at the $ω$B97X-D/6-31G* level. Totally twenty seven molecular properties, such as geometric, thermodynamic, electronic and energetic properties, were gathered from these calculations. Meanwhile, we also established a comprehensive protocol for the construction of a high-volume quantum-chemistry database. Our QCDGE (Quantum Chemistry Database with Ground- and Excited-State Properties) database contains a substantial volume of data, exhibits high chemical diversity, and most importantly includes excited-state information. This database, along with its construction protocol, is expected to have a significant impact on the broad applications of machine learning studies across different fields of chemistry, especially in the area of excited-state research.

physics.chem-ph↗

arXiv2024

The photodissociation dynamics and ultrafast electron diffraction image of cyclobutanone from the surface hopping dynamics simulation

The comprehension of nonadiabatic dynamics in polyatomic systems relies heavily on the simultaneous advancements in theoretical and experimental domains. The gas-phase electron diffraction (GUED) technique has attracted widespread attention as a promising tool for observing the photochemical and photophysical features at all-atomic level with high temporal and spatial resolutions. In this work, the GUED spectra were predicted to perform a double-blind test of accuracy in excited-state simulation for cyclobutanone based on the trajectory surface hopping method, with respect to the benchmark data obtained by upcoming MeV-GUED experiments at the Stanfold Linear Accelerator Laboratory. The results show that the ultrafast nonadiabatic dynamics occurs in the photoinduced dynamics, and two C2 and C3 channels play dominant roles in the nonadiabatic reactions of cyclobutanone. The simulated UED signal can be directly interpreted by atomic movements, providing a unique view to monitor the time-dependent evolution of the molecular structure in the femtosecond dynamics.

physics.chem-ph↗

arXiv2024

Ultrafast Excited-State Energy Transfer in Phenylene Ethynylene Dendrimer: Quantum Dynamics with Tensor Network Method

Photo-induced excited-state energy transfer (EET) processes play an important role in the solar energy conversions. The phenylene ethynylene (PE) dendrimers display great potential in improving the efficiency of solar cells, because of their excellent photo-harvesting and exciton-transport properties. In this work, we investigated the intramolecular EET dynamics in a dendrimer composed of two linear PE units (2-ring and 3-ring) using the full quantum dynamics based on the tensor network method. We first constructed a diabatic model Hamiltonian based on the electronic structure calculations. Using this diabatic vibronic coupling model, we tried to obtain the main features of the EET dynamics in terms of the several diabatic models with different numbers of vibrational modes (from 4 modes to 129 modes) and to explore the corresponding vibronic coupling interactions. The results show that the EET in the current PE dendrimer is an ultrafast process. Four modes with A' symmetry play dominant roles in the dynamics, other 86 modes with A' symmetry can damp the electronic coherence, and the modes of A" symmetry do not show the significant influence on the EET process. Overall, the first-order intrastate vibronic coupling terms show the dominant roles in the EET dynamics, while the second-order intrastate vibronic coupling terms give the visible impact here by damping the electronic coherence and slowing down the overall EET process. This work provides a valuable understanding of the physical insight in the EET dynamics of PE dendrimers.

physics.chem-ph↗

arXiv2023

Studies of Nonadiabatic Dynamics in the Singlet Fission Processes of Pentacene Dimer via Tensor Train Decomposition Method

Singlet fission (SF) is a very significant photophysical phenomenon and possesses potential applications. In this work, we try to give the rather detailed theoretical investigation of the SF process in the stacked polyacene dimer by combining the high-level quantum chemistry calculations, and the quantum dynamics simulations based on the tensor train decomposition method. Starting from the construction of the linear vibronic coupling model, we explore the pure electronic dynamics and the vibronic dynamics in the SF processes. The role of vibrational modes in nonadiabatic dynamics is addressed. The results show that the super-exchange mechanism mediated by the charge-transfer state is found in both pure electronic dynamics and the nonadiabatic dynamics. Particularly, the vibrational modes with the frequency resonance with the adiabatic energy gap play very import roles in the SF dynamics. This work not only provides a deep and detailed understanding of the SF process, but also verifies the efficiency of the tensor train decomposition method that can serve as the reference dynamics method to explore the dynamics behaviors of complex systems.

physics.chem-ph↗

arXiv2023

Automated Exploration of Reaction Network and Mechanism via Meta-dynamics Nanoreactor

We developed an automated approach to construct the complex reaction network and explore the reaction mechanism for several reactant molecules. The nanoreactor type molecular dynamics was employed to generate possible chemical reactions, in which the meta-dynamics was taken to overcome reaction barriers and the semi-empirical GFN2-xTB method was used to reduce computational cost. The identification of reaction events from trajectories was conducted by using the hidden Markov model based on the evolution of the molecular connectivity. This provided the starting points for the further transition state searches at the more accurate electronic structure levels to obtain the reaction mechanism. Then the whole reaction network with multiply pathways was obtained. The feasibility and efficiency of this automated construction of the reaction network was examined by two examples. The first reaction under study was the HCHO + NH3 biomolecular reaction. The second example focused on the reaction network for a multi-species system composed of dozens of HCN and H2O compounds. The result indicated that the proposed approach was a valuable and effective tool for the automated exploration of reaction networks.

physics.chem-ph↗

arXiv2022

Realization of the Trajectory Propagation in the MM-SQC Dynamics by Using Machine Learning

The supervised machine learning (ML) approach is applied to realize the trajectory-based nonadiabatic dynamics within the framework of the symmetrical quasi-classical dynamics method based on the Meyer-Miller mapping Hamiltonian (MM-SQC). After the construction of the long short-term memory recurrent neural network (LSTM-RNN) model, it is used to perform the entire trajectory evolutions from initial sampling conditions. The proposed idea is proven to be reliable and accurate in the simulations of the dynamics of several site-exciton electron-phonon coupling models, which cover two-site and three-site systems with biased and unbiased energy levels, as well as include a few or many phonon modes. The LSTM-RNN approach also shows the powerful ability to obtain the accurate and stable results for the long-time evolutions. It indicates that the LSTM-RNN model perfectly captures of dynamical correction information in the trajectory evolution in the MM-SQC dynamics. Our work provides the possibility to employ the ML methods in the simulation of the trajectory-based nonadiabatic dynamic of complex systems with a large number of degrees of freedoms.

quant-ph↗

arXiv2022

Automatic Evolution of Machine-Learning based Quantum Dynamics with Uncertainty Analysis

The machine learning approaches are applied in the dynamical simulation of open quantum systems. The long short-term memory recurrent neural network (LSTM-RNN) models are used to simulate the long-time quantum dynamics, which are built based on the key information of the short-time evolution. We employ various hyperparameter optimization methods, including the simulated annealing, Bayesian optimization with tree-structured parzen estimator and random search, to achieve the automatic construction and adjustment of the LSTM-RNN models. The implementation details of three hyperparameter optimization methods are examined, and among them the simulated annealing approach is strongly recommended due to its excellent performance. The uncertainties of the machine learning models are comprehensively analyzed by the combination of bootstrap sampling and Monte-Carlo dropout approaches, which give the prediction confidence of the LSTM-RNN models in the simulation of the open quantum dynamics. This work builds an effective machine learning approach to simulate the dynamics evolution of open quantum systems. In addition, the current study provides an efficient protocol to build the optimal neural networks and to estimate the trustiness of the machine learning models.

quant-ph↗

arXiv2021

An automatic approach to explore multi-reaction mechanism for medium-sized bimolecular reactions via collision dynamics simulations and transition state searches

We develop a broadly-applicable computational method for the automatic exploration of the bimolecular multi-reaction mechanism. The current methodology mainly involves the high-energy Born-Oppenheimer molecular dynamics (BOMD) simulation and the successive reaction pathway construction. Several computational tricks are introduced, which include the selection of the reactive regions based on the electronic-structure calculations and the employment of the virtual collision-dynamics simulations with monitoring atomic distance before BOMD. These prescreening steps largely reduce the number of trajectories in the BOMD simulations and significantly save computational cost. The hidden Markov model combined with modified atomic connectivity matrix is taken for the detection of reaction events in each BOMD trajectory. Starting from several geometries close to reaction events, the further intermediate optimization and transition-state searches are conducted. The proposed method allows us to build the complicated multi-reaction mechanism of medium-sized bimolecular systems automatically. Here we examine the feasibility and efficiency of the current method by its performance in searching the mechanisms of two prototype reactions in environmental science, which are the penicillin G anion + H2O and the penicillin G anion + OH radical reactions. The result indicates that the proposed theoretical method is a powerful protocol for the automatic searching of the bimolecular reaction mechanisms for medium-sized compounds.

physics.chem-ph↗