Literature DB >> 30521337

Improved on-the-Fly MCTDH Simulations with Many-Body-Potential Tensor Decomposition and Projection Diabatization.

Gareth W Richings1, Christopher Robertson1, Scott Habershon1.   

Abstract

We have recently demonstrated how potential energy surface (PES) interpolation methods, such as kernel ridge regression (KRR), can be combined with accurate wave function time-propagation methods, specifically the multiconfiguration time-dependent Hartree (MCTDH) method, to generate a new "on-the-fly" MCTDH scheme (DD-MCTDH) that does not require the pre-fitting of the PES, which is normally required by MCTDH. Specifically, we have shown how our DD-MCTDH strategy can be used to model non-adiabatic dynamics in a 4-mode/2-state model of pyrazine, with ab initio electronic structure calculations performed directly during propagation, requiring around 100 h of computer wall-time. In this Article, we show how the efficiency and accuracy of DD-MCTDH can be dramatically improved further still by (i) using systematic tensor decompositions of the KRR PES, and (ii) using a novel scheme for diabatization within the framework of configuration interaction (CI) methods which only requires local adiabatic electronic states, rather than non-adiabatic coupling matrix elements. The result of these improvements is that our latest version of DD-MCTDH can perform a 12-mode/2-state simulation of pyrazine, with PES evaluations at CAS level, in just 29-90 h on a standard desktop computer; this work therefore represents an enormous step towards direct quantum dynamics with MCTDH.

Entities:  

Year:  2019        PMID: 30521337     DOI: 10.1021/acs.jctc.8b00819

Source DB:  PubMed          Journal:  J Chem Theory Comput        ISSN: 1549-9618            Impact factor:   6.006


  4 in total

1.  Machine Learning for Electronically Excited States of Molecules.

Authors:  Julia Westermayr; Philipp Marquetand
Journal:  Chem Rev       Date:  2020-11-19       Impact factor: 60.622

2.  Combining SchNet and SHARC: The SchNarc Machine Learning Approach for Excited-State Dynamics.

Authors:  Julia Westermayr; Michael Gastegger; Philipp Marquetand
Journal:  J Phys Chem Lett       Date:  2020-05-01       Impact factor: 6.475

3.  Program Synthesis of Sparse Algorithms for Wave Function and Energy Prediction in Grid-Based Quantum Simulations.

Authors:  Scott Habershon
Journal:  J Chem Theory Comput       Date:  2022-03-16       Impact factor: 6.006

4.  Analyzing Grid-Based Direct Quantum Molecular Dynamics Using Non-Linear Dimensionality Reduction.

Authors:  Gareth W Richings; Scott Habershon
Journal:  Molecules       Date:  2021-12-07       Impact factor: 4.411

  4 in total

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