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Li X, Lubbers N, Tretiak S, Barros K, Zhang Y. Machine Learning Framework for Modeling Exciton Polaritons in Molecular Materials. J Chem Theory Comput 2024; 20:891-901. [PMID: 38168674 DOI: 10.1021/acs.jctc.3c01068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
A light-matter hybrid quasiparticle, called a polariton, is formed when molecules are strongly coupled to an optical cavity. Recent experiments have shown that polariton chemistry can manipulate chemical reactions. Polariton chemistry is a collective phenomenon, and its effects increase with the number of molecules in a cavity. However, simulating an ensemble of molecules in the excited state coupled to a cavity mode is theoretically and computationally challenging. Recent advances in machine learning (ML) techniques have shown promising capabilities in modeling ground-state chemical systems. This work presents a general protocol to predict excited-state properties, such as energies, transition dipoles, and nonadiabatic coupling vectors with the hierarchically interacting particle neural network. ML predictions are then applied to compute the potential energy surfaces and electronic spectra of a prototype azomethane molecule in the collective coupling scenario. These computational tools provide a much-needed framework to model and understand many molecules' emerging excited-state polariton chemistry.
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Affiliation(s)
- Xinyang Li
- Physics and Chemistry of Materials, Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Nicholas Lubbers
- Information Sciences, Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Sergei Tretiak
- Physics and Chemistry of Materials, Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
- Center for Integrated Nanotechnologies, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Kipton Barros
- Physics and Chemistry of Materials, Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Yu Zhang
- Physics and Chemistry of Materials, Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
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2
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Kumar A, DeGregorio N, Ricard T, Iyengar SS. Graph-Theoretic Molecular Fragmentation for Potential Surfaces Leads Naturally to a Tensor Network Form and Allows Accurate and Efficient Quantum Nuclear Dynamics. J Chem Theory Comput 2022; 18:7243-7259. [PMID: 36332133 DOI: 10.1021/acs.jctc.2c00484] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Molecular fragmentation methods have revolutionized quantum chemistry. Here, we use a graph-theoretically generated molecular fragmentation method, to obtain accurate and efficient representations for multidimensional potential energy surfaces and the quantum time-evolution operator, which plays a critical role in quantum chemical dynamics. In doing so, we find that the graph-theoretic fragmentation approach naturally reduces the potential portion of the time-evolution operator into a tensor network that contains a stream of coupled lower-dimensional propagation steps to potentially achieve quantum dynamics with reduced complexity. Furthermore, the fragmentation approach used here has previously been shown to allow accurate and efficient computation of post-Hartree-Fock electronic potential energy surfaces, which in many cases has been shown to be at density functional theory cost. Thus, by combining the advantages of molecular fragmentation with the tensor network formalism, the approach yields an on-the-fly quantum dynamics scheme where both the electronic potential calculation and nuclear propagation portion are enormously simplified through a single stroke. The method is demonstrated by computing approximations to the propagator and to potential surfaces for a set of coupled nuclear dimensions within a protonated water wire problem exhibiting the Grotthuss mechanism of proton transport. In all cases, our approach has been shown to reduce the complexity of representing the quantum propagator, and by extension action of the propagator on an initial wavepacket, by several orders, with minimal loss in accuracy.
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Affiliation(s)
- Anup Kumar
- Department of Chemistry, and the Indiana University Quantum Science and Engineering Center (IU-QSEC), Indiana University, Bloomington, Indiana 47405, United States
| | - Nicole DeGregorio
- Department of Chemistry, and the Indiana University Quantum Science and Engineering Center (IU-QSEC), Indiana University, Bloomington, Indiana 47405, United States
| | - Timothy Ricard
- Department of Chemistry, and the Indiana University Quantum Science and Engineering Center (IU-QSEC), Indiana University, Bloomington, Indiana 47405, United States
| | - Srinivasan S Iyengar
- Department of Chemistry, and the Indiana University Quantum Science and Engineering Center (IU-QSEC), Indiana University, Bloomington, Indiana 47405, United States
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3
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Habershon S. Program Synthesis of Sparse Algorithms for Wave Function and Energy Prediction in Grid-Based Quantum Simulations. J Chem Theory Comput 2022; 18:2462-2478. [PMID: 35293216 PMCID: PMC9009083 DOI: 10.1021/acs.jctc.2c00035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
We have recently shown how program synthesis (PS), or the concept of "self-writing code", can generate novel algorithms that solve the vibrational Schrödinger equation, providing approximations to the allowed wave functions for bound, one-dimensional (1-D) potential energy surfaces (PESs). The resulting algorithms use a grid-based representation of the underlying wave function ψ(x) and PES V(x), providing codes which represent approximations to standard discrete variable representation (DVR) methods. In this Article, we show how this inductive PS strategy can be improved and modified to enable prediction of both vibrational wave functions and energy eigenvalues of representative model PESs (both 1-D and multidimensional). We show that PS can generate algorithms that offer some improvements in energy eigenvalue accuracy over standard DVR schemes; however, we also demonstrate that PS can identify accurate numerical methods that exhibit desirable computational features, such as employing very sparse (tridiagonal) matrices. The resulting PS-generated algorithms are initially developed and tested for 1-D vibrational eigenproblems, before solution of multidimensional problems is demonstrated; we find that our new PS-generated algorithms can reduce calculation times for grid-based eigenvector computation by an order of magnitude or more. More generally, with further development and optimization, we anticipate that PS-generated algorithms based on effective Hamiltonian approximations, such as those proposed here, could be useful in direct simulations of quantum dynamics via wave function propagation and evaluation of molecular electronic structure.
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Affiliation(s)
- Scott Habershon
- Department of Chemistry, University of Warwick, Coventry, CV4 7AL, United Kingdom
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4
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Richings GW, Habershon S. Predicting Molecular Photochemistry Using Machine-Learning-Enhanced Quantum Dynamics Simulations. Acc Chem Res 2022; 55:209-220. [PMID: 34982533 DOI: 10.1021/acs.accounts.1c00665] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The processes which occur after molecules absorb light underpin an enormous range of fundamental technologies and applications, including photocatalysis to enable new chemical transformations, sunscreens to protect against the harmful effects of UV overexposure, efficient photovoltaics for energy generation from sunlight, and fluorescent probes to image the intricate details of complex biomolecular structures. Reflecting this broad range of applications, an enormously versatile set of experiments are now regularly used to interrogate light-driven chemical dynamics, ranging from the typical ultrafast transient absorption spectroscopy used in many university laboratories to the inspiring central facilities around the world, such as the next-generation of X-ray free-electron lasers.Computer simulations of light-driven molecular and material dynamics are an essential route to analyzing the enormous amount of transient electronic and structural data produced by these experimental sources. However, to date, the direct simulation of molecular photochemistry remains a frontier challenge in computational chemical science, simultaneously demanding the accurate treatment of molecular electronic structure, nuclear dynamics, and the impact of nonadiabatic couplings.To address these important challenges and to enable new computational methods which can be integrated with state-of-the-art experimental capabilities, the past few years have seen a burst of activity in the development of "direct" quantum dynamics methods, merging the machine learning of potential energy surfaces (PESs) and nonadiabatic couplings with accurate quantum propagation schemes such as the multiconfiguration time-dependent Hartree (MCTDH) method. The result of this approach is a new generation of direct quantum dynamics tools in which PESs are generated in tandem with wave function propagation, enabling accurate "on-the-fly" simulations of molecular photochemistry. These simulations offer an alternative route toward gaining quantum dynamics insights, circumventing the challenge of generating ab initio electronic structure data for PES fitting by instead only demanding expensive energy evaluations as and when they are needed.In this Account, we describe the chronological evolution of our own contributions to this field, focusing on describing the algorithmic developments that enable direct MCTDH simulations for complex molecular systems moving on multiple coupled electronic states. Specifically, we highlight active learning strategies for generating PESs during grid-based quantum chemical dynamics simulations, and we discuss the development and impact of novel diabatization schemes to enable direct grid-based simulations of photochemical dynamics; these developments are highlighted in a series of benchmark molecular simulations of systems containing multiple nuclear degrees of freedom moving on multiple coupled electronic states. We hope that the ongoing developments reported here represent a major step forward in tools for modeling excited-state chemistry such as photodissociation, proton and electron transfer, and ultrafast energy dissipation in complex molecular systems.
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Affiliation(s)
- Gareth W. Richings
- Department of Chemistry, University of Warwick, Coventry, United Kingdom CV4 7AL
| | - Scott Habershon
- Department of Chemistry, University of Warwick, Coventry, United Kingdom CV4 7AL
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5
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Richings GW, Habershon S. Analyzing Grid-Based Direct Quantum Molecular Dynamics Using Non-Linear Dimensionality Reduction. Molecules 2021; 26:molecules26247418. [PMID: 34946499 PMCID: PMC8708769 DOI: 10.3390/molecules26247418] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Revised: 11/30/2021] [Accepted: 12/02/2021] [Indexed: 11/18/2022] Open
Abstract
Grid-based schemes for simulating quantum dynamics, such as the multi-configuration time-dependent Hartree (MCTDH) method, provide highly accurate predictions of the coupled nuclear and electronic dynamics in molecular systems. Such approaches provide a multi-dimensional, time-dependent view of the system wavefunction represented on a coordinate grid; in the case of non-adiabatic simulations, additional information about the state populations adds a further layer of complexity. As such, wavepacket motion on potential energy surfaces which couple many nuclear and electronic degrees-of-freedom can be extremely challenging to analyse in order to extract physical insight beyond the usual expectation-value picture. Here, we show that non-linear dimensionality reduction (NLDR) methods, notably diffusion maps, can be adapted to extract information from grid-based wavefunction dynamics simulations, providing insight into key nuclear motions which explain the observed dynamics. This approach is demonstrated for 2-D and 9-D models of proton transfer in salicylaldimine, as well as 8-D and full 12-D simulations of cis-trans isomerization in ethene; these simulations demonstrate how NLDR can provide alternative views of wavefunction dynamics, and also highlight future developments.
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6
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Westermayr J, Marquetand P. Machine Learning for Electronically Excited States of Molecules. Chem Rev 2021; 121:9873-9926. [PMID: 33211478 PMCID: PMC8391943 DOI: 10.1021/acs.chemrev.0c00749] [Citation(s) in RCA: 162] [Impact Index Per Article: 54.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Indexed: 12/11/2022]
Abstract
Electronically excited states of molecules are at the heart of photochemistry, photophysics, as well as photobiology and also play a role in material science. Their theoretical description requires highly accurate quantum chemical calculations, which are computationally expensive. In this review, we focus on not only how machine learning is employed to speed up such excited-state simulations but also how this branch of artificial intelligence can be used to advance this exciting research field in all its aspects. Discussed applications of machine learning for excited states include excited-state dynamics simulations, static calculations of absorption spectra, as well as many others. In order to put these studies into context, we discuss the promises and pitfalls of the involved machine learning techniques. Since the latter are mostly based on quantum chemistry calculations, we also provide a short introduction into excited-state electronic structure methods and approaches for nonadiabatic dynamics simulations and describe tricks and problems when using them in machine learning for excited states of molecules.
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Affiliation(s)
- Julia Westermayr
- Institute
of Theoretical Chemistry, Faculty of Chemistry, University of Vienna, Währinger Strasse 17, 1090 Vienna, Austria
| | - Philipp Marquetand
- Institute
of Theoretical Chemistry, Faculty of Chemistry, University of Vienna, Währinger Strasse 17, 1090 Vienna, Austria
- Vienna
Research Platform on Accelerating Photoreaction Discovery, University of Vienna, Währinger Strasse 17, 1090 Vienna, Austria
- Data
Science @ Uni Vienna, University of Vienna, Währinger Strasse 29, 1090 Vienna, Austria
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7
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Abstract
Electronically excited states of molecules are at the heart of photochemistry, photophysics, as well as photobiology and also play a role in material science. Their theoretical description requires highly accurate quantum chemical calculations, which are computationally expensive. In this review, we focus on not only how machine learning is employed to speed up such excited-state simulations but also how this branch of artificial intelligence can be used to advance this exciting research field in all its aspects. Discussed applications of machine learning for excited states include excited-state dynamics simulations, static calculations of absorption spectra, as well as many others. In order to put these studies into context, we discuss the promises and pitfalls of the involved machine learning techniques. Since the latter are mostly based on quantum chemistry calculations, we also provide a short introduction into excited-state electronic structure methods and approaches for nonadiabatic dynamics simulations and describe tricks and problems when using them in machine learning for excited states of molecules.
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Affiliation(s)
- Julia Westermayr
- Institute of Theoretical Chemistry, Faculty of Chemistry, University of Vienna, Währinger Strasse 17, 1090 Vienna, Austria
| | - Philipp Marquetand
- Institute of Theoretical Chemistry, Faculty of Chemistry, University of Vienna, Währinger Strasse 17, 1090 Vienna, Austria
- Vienna Research Platform on Accelerating Photoreaction Discovery, University of Vienna, Währinger Strasse 17, 1090 Vienna, Austria
- Data Science @ Uni Vienna, University of Vienna, Währinger Strasse 29, 1090 Vienna, Austria
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8
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Westermayr J, Gastegger M, Schütt KT, Maurer RJ. Perspective on integrating machine learning into computational chemistry and materials science. J Chem Phys 2021; 154:230903. [PMID: 34241249 DOI: 10.1063/5.0047760] [Citation(s) in RCA: 67] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023] Open
Abstract
Machine learning (ML) methods are being used in almost every conceivable area of electronic structure theory and molecular simulation. In particular, ML has become firmly established in the construction of high-dimensional interatomic potentials. Not a day goes by without another proof of principle being published on how ML methods can represent and predict quantum mechanical properties-be they observable, such as molecular polarizabilities, or not, such as atomic charges. As ML is becoming pervasive in electronic structure theory and molecular simulation, we provide an overview of how atomistic computational modeling is being transformed by the incorporation of ML approaches. From the perspective of the practitioner in the field, we assess how common workflows to predict structure, dynamics, and spectroscopy are affected by ML. Finally, we discuss how a tighter and lasting integration of ML methods with computational chemistry and materials science can be achieved and what it will mean for research practice, software development, and postgraduate training.
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Affiliation(s)
- Julia Westermayr
- Department of Chemistry, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, United Kingdom
| | - Michael Gastegger
- Machine Learning Group, Technische Universität Berlin, 10587 Berlin, Germany
| | - Kristof T Schütt
- Machine Learning Group, Technische Universität Berlin, 10587 Berlin, Germany
| | - Reinhard J Maurer
- Department of Chemistry, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, United Kingdom
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9
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Abstract
Theoretical simulations of electronic excitations and associated processes in molecules are indispensable for fundamental research and technological innovations. However, such simulations are notoriously challenging to perform with quantum mechanical methods. Advances in machine learning open many new avenues for assisting molecular excited-state simulations. In this Review, we track such progress, assess the current state of the art and highlight the critical issues to solve in the future. We overview a broad range of machine learning applications in excited-state research, which include the prediction of molecular properties, improvements of quantum mechanical methods for the calculations of excited-state properties and the search for new materials. Machine learning approaches can help us understand hidden factors that influence photo-processes, leading to a better control of such processes and new rules for the design of materials for optoelectronic applications.
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10
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Ha JK, Kim K, Min SK. Machine Learning-Assisted Excited State Molecular Dynamics with the State-Interaction State-Averaged Spin-Restricted Ensemble-Referenced Kohn-Sham Approach. J Chem Theory Comput 2021; 17:694-702. [PMID: 33470100 DOI: 10.1021/acs.jctc.0c01261] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
We present a machine learning-assisted excited state molecular dynamics (ML-ESMD) based on the ensemble density functional theory framework. Since we represent a diabatic Hamiltonian in terms of generalized valence bond ansatz within the state-interaction state-averaged spin-restricted ensemble-referenced Kohn-Sham (SI-SA-REKS) method, we can avoid singularities near conical intersections, which are crucial in excited state molecular dynamics simulations. We train the diabatic Hamiltonian elements and their analytical gradients with the SchNet architecture to construct machine learning models, while the phase freedom of off-diagonal elements of the Hamiltonian is cured by introducing the phase-less loss function. Our machine learning models show reasonable accuracy with mean absolute errors of ∼0.1 kcal/mol and ∼0.5 kcal/mol/Å for the diabatic Hamiltonian elements and their gradients, respectively, for penta-2,4-dieniminium cation. Moreover, by exploiting the diabatic representation, our models can predict correct conical intersection structures and their topologies. In addition, our ML-ESMD simulations give almost identical result with a direct dynamics at the same level of theory.
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Affiliation(s)
- Jong-Kwon Ha
- Department of Chemistry, School of Natural Science, Ulsan National Institute of Science and Technology (UNIST), 50 UNIST-gil, Ulju-gun, Ulsan 44919, South Korea
| | - Kicheol Kim
- Department of Chemistry, School of Natural Science, Ulsan National Institute of Science and Technology (UNIST), 50 UNIST-gil, Ulju-gun, Ulsan 44919, South Korea
| | - Seung Kyu Min
- Department of Chemistry, School of Natural Science, Ulsan National Institute of Science and Technology (UNIST), 50 UNIST-gil, Ulju-gun, Ulsan 44919, South Korea
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11
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Richings GW, Habershon S. Direct Grid-Based Nonadiabatic Dynamics on Machine-Learned Potential Energy Surfaces: Application to Spin-Forbidden Processes. J Phys Chem A 2020; 124:9299-9313. [DOI: 10.1021/acs.jpca.0c06125] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Gareth W. Richings
- Department of Chemistry, University of Warwick, Coventry CV4 7AL, United Kingdom
| | - Scott Habershon
- Department of Chemistry, University of Warwick, Coventry CV4 7AL, United Kingdom
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12
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Westermayr J, Marquetand P. Deep learning for UV absorption spectra with SchNarc: First steps toward transferability in chemical compound space. J Chem Phys 2020; 153:154112. [DOI: 10.1063/5.0021915] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Affiliation(s)
- J. Westermayr
- Faculty of Chemistry, Institute of Theoretical Chemistry, University of Vienna, Währinger Str. 17, 1090 Vienna, Austria
| | - P. Marquetand
- Faculty of Chemistry, Institute of Theoretical Chemistry, University of Vienna, Währinger Str. 17, 1090 Vienna, Austria
- Vienna Research Platform on Accelerating Photoreaction Discovery, University of Vienna, Währinger Str. 17, 1090 Vienna, Austria
- Faculty of Chemistry, Data Science @ Uni Vienna, University of Vienna, Währinger Str. 29, 1090 Vienna, Austria
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13
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Sibaev M, Polyak I, Manby FR, Knowles PJ. Molecular second-quantized Hamiltonian: Electron correlation and non-adiabatic coupling treated on an equal footing. J Chem Phys 2020; 153:124102. [DOI: 10.1063/5.0018930] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Affiliation(s)
- Marat Sibaev
- School of Chemistry, Cardiff University, Main Building, Park Place, Cardiff CF10 3AT, United Kingdom
| | - Iakov Polyak
- School of Chemistry, Cardiff University, Main Building, Park Place, Cardiff CF10 3AT, United Kingdom
| | - Frederick R. Manby
- School of Chemistry, University of Bristol, Cantock’s Close, Bristol BS8 1TS, United Kingdom
| | - Peter J. Knowles
- School of Chemistry, Cardiff University, Main Building, Park Place, Cardiff CF10 3AT, United Kingdom
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14
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Westermayr J, Marquetand P. Machine learning and excited-state molecular dynamics. MACHINE LEARNING-SCIENCE AND TECHNOLOGY 2020. [DOI: 10.1088/2632-2153/ab9c3e] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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15
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Ravi S, Mukherjee S, Mukherjee B, Adhikari S, Sathyamurthy N, Baer M. Non-adiabatic coupling as a frictional force in (He, H, H)+ dynamics and the formation of HeH2+. Mol Phys 2020. [DOI: 10.1080/00268976.2020.1811907] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Affiliation(s)
- Satyam Ravi
- School of Chemical Sciences, Indian Association for the Cultivation of Science, Kolkata, India
| | - Soumya Mukherjee
- School of Chemical Sciences, Indian Association for the Cultivation of Science, Kolkata, India
| | - Bijit Mukherjee
- School of Chemical Sciences, Indian Association for the Cultivation of Science, Kolkata, India
| | - Satrajit Adhikari
- School of Chemical Sciences, Indian Association for the Cultivation of Science, Kolkata, India
| | | | - Michael Baer
- The Fritz Haber Center for Molecular Dynamics, The Hebrew University of Jerusalem, Jerusalem, Israel
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16
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Abstract
We present a machine learning (ML) method to accelerate the nuclear ensemble approach (NEA) for computing absorption cross sections. ML-NEA is used to calculate cross sections on vast ensembles of nuclear geometries to reduce the error due to insufficient statistical sampling. The electronic properties-excitation energies and oscillator strengths-are calculated with a reference electronic structure method only for a relatively few points in the ensemble. The KREG model (kernel-ridge-regression-based ML combined with the RE descriptor) as implemented in MLatom is used to predict these properties for the remaining tens of thousands of points in the ensemble without incurring much of additional computational cost. We demonstrate for two examples, benzene and a 9-dicyanomethylene derivative of acridine, that ML-NEA can produce statistically converged cross sections even for very challenging cases and even with as few as several hundreds of training points.
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Affiliation(s)
- Bao-Xin Xue
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, Department of Chemistry, and College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, P. R. China
| | | | - Pavlo O Dral
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, Department of Chemistry, and College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, P. R. China
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17
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Westermayr J, Gastegger M, Marquetand P. Combining SchNet and SHARC: The SchNarc Machine Learning Approach for Excited-State Dynamics. J Phys Chem Lett 2020; 11:3828-3834. [PMID: 32311258 PMCID: PMC7246974 DOI: 10.1021/acs.jpclett.0c00527] [Citation(s) in RCA: 86] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2020] [Accepted: 04/20/2020] [Indexed: 05/26/2023]
Abstract
In recent years, deep learning has become a part of our everyday life and is revolutionizing quantum chemistry as well. In this work, we show how deep learning can be used to advance the research field of photochemistry by learning all important properties-multiple energies, forces, and different couplings-for photodynamics simulations. We simplify such simulations substantially by (i) a phase-free training skipping costly preprocessing of raw quantum chemistry data; (ii) rotationally covariant nonadiabatic couplings, which can either be trained or (iii) alternatively be approximated from only ML potentials, their gradients, and Hessians; and (iv) incorporating spin-orbit couplings. As the deep-learning method, we employ SchNet with its automatically determined representation of molecular structures and extend it for multiple electronic states. In combination with the molecular dynamics program SHARC, our approach termed SchNarc is tested on two polyatomic molecules and paves the way toward efficient photodynamics simulations of complex systems.
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Affiliation(s)
- Julia Westermayr
- Institute
of Theoretical Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 17, 1090 Vienna, Austria
| | - Michael Gastegger
- Machine
Learning Group, Technical University of
Berlin, 10587 Berlin, Germany
| | - Philipp Marquetand
- Institute
of Theoretical Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 17, 1090 Vienna, Austria
- Vienna
Research Platform on Accelerating Photoreaction Discovery, University of Vienna, Währinger Str. 17, 1090 Vienna, Austria
- Data
Science @ Uni Vienna, University of Vienna, Währinger Str. 29, 1090 Vienna, Austria
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18
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Westermayr J, Faber FA, Christensen AS, von Lilienfeld OA, Marquetand P. Neural networks and kernel ridge regression for excited states dynamics of CH2NH$_2^+$: From single-state to multi-state representations and multi-property machine learning models. MACHINE LEARNING-SCIENCE AND TECHNOLOGY 2020. [DOI: 10.1088/2632-2153/ab88d0] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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19
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Richings GW, Habershon S. A new diabatization scheme for direct quantum dynamics: Procrustes diabatization. J Chem Phys 2020; 152:154108. [DOI: 10.1063/5.0003254] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Gareth W. Richings
- Department of Chemistry, University of Warwick, Coventry CV4 7AL, United Kingdom
| | - Scott Habershon
- Department of Chemistry, University of Warwick, Coventry CV4 7AL, United Kingdom
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20
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Neville SP, Seidu I, Schuurman MS. Propagative block diagonalization diabatization of DFT/MRCI electronic states. J Chem Phys 2020; 152:114110. [DOI: 10.1063/1.5143126] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Affiliation(s)
- Simon P. Neville
- Department of Chemistry and Biomolecular Sciences, University of Ottawa, 10 Marie Curie, Ottawa, Ontario K1N 6N5, Canada
| | - Issaka Seidu
- Department of Chemistry and Biomolecular Sciences, University of Ottawa, 10 Marie Curie, Ottawa, Ontario K1N 6N5, Canada
| | - Michael S. Schuurman
- Department of Chemistry and Biomolecular Sciences, University of Ottawa, 10 Marie Curie, Ottawa, Ontario K1N 6N5, Canada
- National Research Council of Canada, 100 Sussex Drive, Ottawa, Ontario K1A 0R6, Canada
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21
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Guan Y, Yarkony DR. Accurate Neural Network Representation of the Ab Initio Determined Spin-Orbit Interaction in the Diabatic Representation Including the Effects of Conical Intersections. J Phys Chem Lett 2020; 11:1848-1858. [PMID: 32062966 DOI: 10.1021/acs.jpclett.0c00074] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
A method for fitting ab initio determined spin-orbit coupling interactions, in the Breit-Pauli approximation, based on quasidiabatic representations using neural network fits is reported. The algorithm generalizes our recently reported neural network approach for representing the dipole interaction. The S0, S1, and T1 states of formaldehyde are used as an example. First, the two singlet states S0 and S1 are diabatized with a modified Boys Localization diabatization method. Second, the spin-orbit coupling between singlet and triplet states is transformed to the diabatic representation. This removes the discontinuities in the adiabatic representation. The diabatized spin-orbit couplings are then fit with smooth neural network functions. The analytic representation of spin-orbit coupling interactions in a diabatic basis by neural networks will make accurate full-dimensional quantum dynamical treatment of both internal conversion and intersystem crossing possible, which will help us to gain better understanding of both processes.
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Affiliation(s)
- Yafu Guan
- Department of Chemistry, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - David R Yarkony
- Department of Chemistry, Johns Hopkins University, Baltimore, Maryland 21218, United States
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22
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Karandashev K, Vaníček J. A combined on-the-fly/interpolation procedure for evaluating energy values needed in molecular simulations. J Chem Phys 2019; 151:174116. [PMID: 31703487 DOI: 10.1063/1.5124469] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
We propose an algorithm for molecular dynamics or Monte Carlo simulations that uses an interpolation procedure to estimate potential energy values from energies and gradients evaluated previously at points of a simplicial mesh. We chose an interpolation procedure that is exact for harmonic systems and considered two possible mesh types: Delaunay triangulation and an alternative anisotropic triangulation designed to improve performance in anharmonic systems. The mesh is generated and updated on the fly during the simulation. The procedure is tested on two-dimensional quartic oscillators and on the path integral Monte Carlo evaluation of the HCN/DCN equilibrium isotope effect.
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Affiliation(s)
- Konstantin Karandashev
- Laboratory of Theoretical Physical Chemistry, Institut des Sciences et Ingénierie Chimiques, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
| | - Jiří Vaníček
- Laboratory of Theoretical Physical Chemistry, Institut des Sciences et Ingénierie Chimiques, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
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23
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Kananenka AA, Yao K, Corcelli SA, Skinner JL. Machine Learning for Vibrational Spectroscopic Maps. J Chem Theory Comput 2019; 15:6850-6858. [DOI: 10.1021/acs.jctc.9b00698] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Affiliation(s)
- Alexei A. Kananenka
- Pritzker School of Molecular Engineering, The University of Chicago, Chicago, Illinois 60637, United States
- Department of Physics and Astronomy, University of Delaware, Newark, Delaware 19716, United States
| | - Kun Yao
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, Indiana 46556, United States
| | - Steven A. Corcelli
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, Indiana 46556, United States
| | - J. L. Skinner
- Pritzker School of Molecular Engineering, The University of Chicago, Chicago, Illinois 60637, United States
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24
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Baiardi A, Reiher M. Large-Scale Quantum Dynamics with Matrix Product States. J Chem Theory Comput 2019; 15:3481-3498. [DOI: 10.1021/acs.jctc.9b00301] [Citation(s) in RCA: 48] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Affiliation(s)
- Alberto Baiardi
- Laboratorium für Physikalische Chemie, ETH Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland
| | - Markus Reiher
- Laboratorium für Physikalische Chemie, ETH Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland
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25
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Polyak I, Richings GW, Habershon S, Knowles PJ. Direct quantum dynamics using variational Gaussian wavepackets and Gaussian process regression. J Chem Phys 2019; 150:041101. [PMID: 30709252 DOI: 10.1063/1.5086358] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
The method of direct variational quantum nuclear dynamics in a basis of Gaussian wavepackets, combined with the potential energy surfaces fitted on-the-fly using Gaussian process regression, is described together with its implementation. Enabling exact and efficient analytic evaluation of Hamiltonian matrix elements, this approach allows for black-box quantum dynamics of multidimensional anharmonic molecular systems. Example calculations of intra-molecular proton transfer on the electronic ground state of salicylaldimine are provided, and future algorithmic improvements as well as the potential for multiple-state non-adiabatic dynamics are discussed.
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Affiliation(s)
- Iakov Polyak
- School of Chemistry, Cardiff University, Main Building, Park Place, Cardiff CF10 3AT, United Kingdom
| | - Gareth W Richings
- Department of Chemistry and Centre for Scientific Computing, University of Warwick, Coventry CV4 7AL, United Kingdom
| | - Scott Habershon
- Department of Chemistry and Centre for Scientific Computing, University of Warwick, Coventry CV4 7AL, United Kingdom
| | - Peter J Knowles
- School of Chemistry, Cardiff University, Main Building, Park Place, Cardiff CF10 3AT, United Kingdom
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26
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Richings G, Robertson C, Habershon S. Can we use on-the-fly quantum simulations to connect molecular structure and sunscreen action? Faraday Discuss 2019; 216:476-493. [DOI: 10.1039/c8fd00228b] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
Direct MCTDH quantum dynamics simulations, with automatic active coordinate generation, applied to potential molecular sunscreens.
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Affiliation(s)
- Gareth W. Richings
- Department of Chemistry and Centre for Scientific Computing
- University of Warwick
- Coventry
- UK
| | - Christopher Robertson
- Department of Chemistry and Centre for Scientific Computing
- University of Warwick
- Coventry
- UK
| | - Scott Habershon
- Department of Chemistry and Centre for Scientific Computing
- University of Warwick
- Coventry
- UK
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27
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Robertson C, González-Vázquez J, Corral I, Díaz-Tendero S, Díaz C. Nonadiabatic scattering of NO off Au3
clusters: A simple and robust diabatic state manifold generation method for multiconfigurational wavefunctions. J Comput Chem 2018; 40:794-810. [DOI: 10.1002/jcc.25764] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2018] [Revised: 10/28/2018] [Accepted: 11/01/2018] [Indexed: 01/19/2023]
Affiliation(s)
- Christopher Robertson
- Department of Chemistry and Centre for Scientific Computing; University Of Warwick, CV4 7AL; Coventry United Kingdom
| | - Jesús González-Vázquez
- Departamento de Química Módulo 13; Universidad Autónoma de Madrid, 28049; Madrid Spain
- Institute for Advanced Research in Chemistry (IAdChem); Universidad Autónoma de Madrid, 28049; Madrid Spain
| | - Ines Corral
- Institute for Advanced Research in Chemistry (IAdChem); Universidad Autónoma de Madrid, 28049; Madrid Spain
- Departamento de Química Módulo 13; Universidad Autónoma de Madrid, 28049; Madrid Spain
| | - Sergio Díaz-Tendero
- Condensed Matter Physics Center (IFIMAC); Universidad Autónoma de Madrid, 28049; Madrid Spain
- Departamento de Química Módulo 13; Universidad Autónoma de Madrid, 28049; Madrid Spain
- Institute for Advanced Research in Chemistry (IAdChem); Universidad Autónoma de Madrid, 28049; Madrid Spain
| | - Cristina Díaz
- Departamento de Química Módulo 13; Universidad Autónoma de Madrid, 28049; Madrid Spain
- Institute for Advanced Research in Chemistry (IAdChem); Universidad Autónoma de Madrid, 28049; Madrid Spain
- Condensed Matter Physics Center (IFIMAC); Universidad Autónoma de Madrid, 28049; Madrid Spain
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28
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Richings GW, Robertson C, Habershon S. Improved on-the-Fly MCTDH Simulations with Many-Body-Potential Tensor Decomposition and Projection Diabatization. J Chem Theory Comput 2018; 15:857-870. [DOI: 10.1021/acs.jctc.8b00819] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Gareth W. Richings
- Department of Chemistry and Centre for Scientific Computing, University of Warwick, Coventry, CV4 7AL, U.K
| | - Christopher Robertson
- Department of Chemistry and Centre for Scientific Computing, University of Warwick, Coventry, CV4 7AL, U.K
| | - Scott Habershon
- Department of Chemistry and Centre for Scientific Computing, University of Warwick, Coventry, CV4 7AL, U.K
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29
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Torres A, Prado LR, Bortolini G, Rego LGC. Charge Transfer Driven Structural Relaxation in a Push-Pull Azobenzene Dye-Semiconductor Complex. J Phys Chem Lett 2018; 9:5926-5933. [PMID: 30257563 DOI: 10.1021/acs.jpclett.8b02490] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Photoexcited structural dynamics in azo-compounds may differ fundamentally whether the push-pull photochromic azo-compound is isolated or forms a heterogeneous charge transfer complex, due to a sudden oxidation of the chromophore. Herein, we use a quantum-classical self-consistent approach that incorporates nonadiabatic excited-state electronic quantum dynamics into molecular mechanics to study the photoexcited dynamics of the push-pull azo-compound para-Methyl Red in the gas phase and sensitizing the (101) anatase surface of TiO2. We find that the photoinduced S2/S0 trans-to- cis isomerization of para-Methyl Red in the gas phase occurs through a pedal-like torsion around the ϕCNNC dihedral angle, without evidence to support the inversion mechanism, likewise in the parent azobenzene molecule. However, the photoexcited structural relaxation of the charge transfer complex para-Methyl Red/TiO2 contrasts essentially with the isolated azo-compounds. Immediately after photoexcitation, the excited electron flows into the TiO2 conduction band, with an injection time constant of ≃5 fs, and no indication of isomerization is observed during the 1.5 ps simulations. Instead, a strong vibronic relaxation occurs that excites the NN stretching mode of the azo-group, which is ultimately ascribed to the NA relaxation, and delocalization, of the hole wavepacket.
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Affiliation(s)
- Alberto Torres
- Department of Physics , Universidade Federal de Santa Catarina , Florianópolis , SC 88040-900 , Brazil
| | - Luciano R Prado
- Department of Physics , Universidade Federal de Santa Catarina , Florianópolis , SC 88040-900 , Brazil
| | - Graziele Bortolini
- Department of Physics , Universidade Federal de Santa Catarina , Florianópolis , SC 88040-900 , Brazil
| | - Luis G C Rego
- Department of Physics , Universidade Federal de Santa Catarina , Florianópolis , SC 88040-900 , Brazil
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30
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Komarova KG, Remacle F, Levine R. On the fly quantum dynamics of electronic and nuclear wave packets. Chem Phys Lett 2018. [DOI: 10.1016/j.cplett.2018.03.050] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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31
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Richings GW, Habershon S. MCTDH on-the-fly: Efficient grid-based quantum dynamics without pre-computed potential energy surfaces. J Chem Phys 2018; 148:134116. [DOI: 10.1063/1.5024869] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Gareth W. Richings
- Department of Chemistry and Centre for Scientific Computing, University of Warwick, Coventry CV4 7AL, United Kingdom
| | - Scott Habershon
- Department of Chemistry and Centre for Scientific Computing, University of Warwick, Coventry CV4 7AL, United Kingdom
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32
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Spinlove KE, Richings GW, Robb MA, Worth GA. Curve crossing in a manifold of coupled electronic states: direct quantum dynamics simulations of formamide. Faraday Discuss 2018; 212:191-215. [PMID: 30238103 DOI: 10.1039/c8fd00090e] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
Quantum dynamics simulations are an important tool to evaluate molecular behaviour including the, often key, quantum nature of the system. In this paper we present an algorithm that is able to simulate the time evolution of a molecule after photo-excitation into a manifold of states. The direct dynamics variational multi-configurational Gaussian (DD-vMCG) method circumvents the computational bottleneck problems of traditional grid-based methods by computing the potential energy functions on-the-fly, i.e. only where required. Unlike other commonly used direct dynamics methods, DD-vMCG is fully quantum mechanical. Here, the method is combined with a novel on-the-fly diabatisation scheme to simulate the short-time dynamics of the key molecule formamide and its acid analogue formimidic acid. This is a challenging test system due to the nature and large number of excited states, and eight coupled states are included in the calculations. It is shown that the method is able to provide unbiased information on the product channels open after excitation at different energies and demonstrates the potential to be a practical scheme, limited mainly by the quality of the quantum chemistry used to describe the excited states.
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Affiliation(s)
- K Eryn Spinlove
- Dept. of Chemistry, University College London, 20 Gordon St., London, UK. and School of Chemistry, University of Birmingham, Birmingham, B15 2TT, UK and Dept. of Chemistry, Imperial College London, London, SW7 2AZ, UK
| | - Gareth W Richings
- Dept. of Chemistry, University of Warwick, Gibbet Hill, Coventry, CV4 7AL, UK
| | - Michael A Robb
- Dept. of Chemistry, Imperial College London, London, SW7 2AZ, UK
| | - Graham A Worth
- Dept. of Chemistry, University College London, 20 Gordon St., London, UK.
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33
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Richings GW, Habershon S. Direct Quantum Dynamics Using Grid-Based Wave Function Propagation and Machine-Learned Potential Energy Surfaces. J Chem Theory Comput 2017; 13:4012-4024. [DOI: 10.1021/acs.jctc.7b00507] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Gareth W. Richings
- Department of Chemistry and
Centre for Scientific Computing, University of Warwick, Coventry CV4 7AL, United Kingdom
| | - Scott Habershon
- Department of Chemistry and
Centre for Scientific Computing, University of Warwick, Coventry CV4 7AL, United Kingdom
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34
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Penfold TJ. Accelerating direct quantum dynamics using graphical processing units. Phys Chem Chem Phys 2017; 19:19601-19608. [DOI: 10.1039/c7cp01473b] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
The direct dynamics variational multi-configurational Gaussian (DD-vMCG) method is combined with electronic structure calculations accelerated by Graphical Processing Units (GPUs). This is used to identify GPU acceleration will have a significant effect for both ground and excited state simulations.
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Affiliation(s)
- T. J. Penfold
- School of Chemistry
- Newcastle University
- Newcastle upon Tyne
- UK
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