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7 results for “nonadiabatic dynamic”
Assessing Nonadiabatic Dynamics Methods in Long Timescales
<p>In this study, we employ the multiconfiguration time-dependent Hartree (MCTDH) and its multi-layer variants (ML-MCTDH), ab initio multiple spawning (AIMS), and fewest-switches surface hopping (FSSH) methodologies to simulate the excited-state dynamics of a weakly-coupled multi-dimensional (10 dimensional) Spin-Boson model Hamiltonian designed for a long timescale decay behavior. </p> <p>The pre-print of the article can be found at <a href="https://chemrxiv.org/engage/chemrxiv/article-details/66f68454cec5d6c142647b0a">https://chemrxiv.org/engage/chemrxiv/article-details/66f68454cec5d6c142647b0a</a></p> <p>MUKHERJEE S, Lassmann Y, Mattos RS, Demoulin B, Curchod BFE, Barbatti M. Assessing Nonadiabatic Dynamics Methods in Long Timescales. ChemRxiv. 2024; doi:10.26434/chemrxiv-2024-j7xxl </p> <p> </p> <p><strong>The dataset contains</strong></p> <ul> <li>MCTDH.tar.bz2 - 10 sets of text files for MCTDH simulations </li> <li>ML-MCTDH.tar.vz2 - 10 sets of text files for ML-MCTDH simulations</li> <li>DC-FSSH.tar.bz2 - HDF5 output files for 2000 independent decoherence-corrected FSSH trajectories and a Python script for Wigner Sampling initial conditions </li> <li>CSS-AIMS.tar.gz - output text files for 44 independent cannibalistic stochastic selection approach of AIMS trajectories</li> </ul>
Data for: Efficient geometric integrators for nonadiabatic quantum dynamics. II. The diabatic representation
<p>Data for publication: J. Roulet, S. Choi, J. Vanicek, Efficient geometric integrators for nonadiabatic quantum dynamics. II. The diabatic representation, J. Chem. Phys. <strong>150</strong>, 204113 (2019)</p> <p>Contains the data for reproducing the figures in the abovementioned publication.</p>
Legion: A Platform for Gaussian Wavepacket Nonadiabatic Dynamics
<p>These datasets contain the initial conditions and trajectories generated by the newly developed Legion using <em>Ab Initio</em> Multiple Spawning (AIMS) for both fulvene with different initial condistions and DMABN. We performed the dynamics varying multiple parameters and introduced new approximations that avoid the need to compute the nonadiabatic coupling (NACV).</p> <p>The initial condition for fulvene were obtained from (doi.org/10.1039/D0CP01353F and doi.org/10.1021/acs.jctc.3c01159) and for DMABN were obtained from (doi.org/10.1039/D0CP01353F). For fulvene the dynamics used CASSCF in the <strong>Columbus software (version 7.2)</strong> and <strong>OpenMolcas (version 24.06)</strong>. For CASPT2 it used only OpenMolcas. The DMABN dynamics were performed with TDDFT using <strong>ORCA (version 5.0.4)</strong> and <strong>Gaussian (version 16)</strong>.</p>
Pyrene fluorescence data obtained by nonadiabatic dynamics calculations
<p> </p> <p>The dataset includes all trajectories of the nonadiabatic molecular dynamics simulations of pyrene. Each tar.gz directory contains two directories: INPUT and RESULTS.</p> <p>In the INPUT directory, one has the input files for the Newton-X dynamics calculations and the subdirectory JOB_AD with the inputs for the ADC(2) calculations with TURBOMOLE. One also has the files "final_output.1.2" and "final_output.1.3" with the data used to obtain the emission spectrum.</p> <p>In the RESULTS directory, one has the 20 trajectories for the S2 state and 52 trajectories initiated in S7-S8. For the latter, the trajectories numbered 1-32 started in S7, and those numbered 33-52 started in S8. One also has a directory called TRAJ1_uncorrected that corresponds to the data for TRAJ1 without the kinetic energy correction. The RESULTS directory contains all the output files from the Newton-X dynamics calculation, including the file dyn.out with the atomic coordinates, velocities, and energies for each time step. There is also a file called "get_points.py," a python script used to extract the data (from the dyn.out files) for the emission spectrum.</p>
Supporting data and code for the published paper: Machine Learning Nonadiabatic Dynamics: Eliminating Phase Freedom of Nonadiabatic Couplings with the State-Interaction State-Averaged Spin-Restricted Ensemble-Referenced Kohn–Sham Approach
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Revealing the nonadiabatic tunneling dynamics in solid-state high harmonic generation
<p>Dataset of the publication “Revealing the nonadiabatic tunneling dynamics in solid-state high harmonic generation“, by Ruixin Zuo, Xiaohong Song, Shuai Ben, Torsten Meier, and Weifeng Yang, published in PHYSICAL REVIEW RESEARCH 5, L022040 (2023) ( <a href="https://doi.org/10.1103/PhysRevResearch.5.L022040">https://doi.org/10.1103/PhysRevResearch.5.L022040</a> )<br> The zip file includes the data on which the plots 2 – 9 are based.</p>
Data for "Coupled cluster theory for nonadiabatic dynamics: nuclear gradients and nonadiabatic couplings in similarity constrained coupled cluster theory"
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OpenNeuro
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