Literature DB >> 15525089

"Learn on the fly": a hybrid classical and quantum-mechanical molecular dynamics simulation.

Gabor Csányi1, T Albaret, M C Payne, A De Vita.   

Abstract

We describe and test a novel molecular dynamics method which combines quantum-mechanical embedding and classical force model optimization into a unified scheme free of the boundary region, and the transferability problems which these techniques, taken separately, involve. The scheme is based on the idea of augmenting a unique, simple parametrized force model by incorporating in it, at run time, the quantum-mechanical information necessary to ensure accurate trajectories. The scheme is tested on a number of silicon systems composed of up to approximately 200 000 atoms.

Entities:  

Year:  2004        PMID: 15525089     DOI: 10.1103/PhysRevLett.93.175503

Source DB:  PubMed          Journal:  Phys Rev Lett        ISSN: 0031-9007            Impact factor:   9.161


  20 in total

Review 1.  Big-Data Science in Porous Materials: Materials Genomics and Machine Learning.

Authors:  Kevin Maik Jablonka; Daniele Ongari; Seyed Mohamad Moosavi; Berend Smit
Journal:  Chem Rev       Date:  2020-06-10       Impact factor: 60.622

2.  Further analysis and comparative study of intermolecular interactions using dimers from the S22 database.

Authors:  Laszlo Fusti Molnar; Xiao He; Bing Wang; Kenneth M Merz
Journal:  J Chem Phys       Date:  2009-08-14       Impact factor: 3.488

3.  Concurrent coupling of realistic and ideal models of liquids and solids in Hamiltonian adaptive resolution simulations.

Authors:  Maziar Heidari; Robinson Cortes-Huerto; Kurt Kremer; Raffaello Potestio
Journal:  Eur Phys J E Soft Matter       Date:  2018-05-23       Impact factor: 1.890

4.  Toward Determining ATPase Mechanism in ABC Transporters: Development of the Reaction Path-Force Matching QM/MM Method.

Authors:  Y Zhou; P Ojeda-May; M Nagaraju; J Pu
Journal:  Methods Enzymol       Date:  2016-07-01       Impact factor: 1.600

5.  Internal force corrections with machine learning for quantum mechanics/molecular mechanics simulations.

Authors:  Jingheng Wu; Lin Shen; Weitao Yang
Journal:  J Chem Phys       Date:  2017-10-28       Impact factor: 3.488

6.  Molecular Dynamics Simulations with Quantum Mechanics/Molecular Mechanics and Adaptive Neural Networks.

Authors:  Lin Shen; Weitao Yang
Journal:  J Chem Theory Comput       Date:  2018-02-26       Impact factor: 6.006

7.  Solvation Free Energy Calculations with Quantum Mechanics/Molecular Mechanics and Machine Learning Models.

Authors:  Pan Zhang; Lin Shen; Weitao Yang
Journal:  J Phys Chem B       Date:  2019-01-15       Impact factor: 2.991

8.  Multiscale Quantum Mechanics/Molecular Mechanics Simulations with Neural Networks.

Authors:  Lin Shen; Jingheng Wu; Weitao Yang
Journal:  J Chem Theory Comput       Date:  2016-09-06       Impact factor: 6.006

Review 9.  Multiscale methods for macromolecular simulations.

Authors:  Paul Sherwood; Bernard R Brooks; Mark S P Sansom
Journal:  Curr Opin Struct Biol       Date:  2008-09-17       Impact factor: 6.809

10.  Force matching as a stepping stone to QM/MM CB[8] host/guest binding free energies: a SAMPL6 cautionary tale.

Authors:  Phillip S Hudson; Kyungreem Han; H Lee Woodcock; Bernard R Brooks
Journal:  J Comput Aided Mol Des       Date:  2018-10-01       Impact factor: 3.686

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