Literature DB >> 29096510

Improving the accuracy of Møller-Plesset perturbation theory with neural networks.

Robert T McGibbon1, Andrew G Taube1, Alexander G Donchev1, Karthik Siva1, Felipe Hernández1, Cory Hargus1, Ka-Hei Law1, John L Klepeis1, David E Shaw1.   

Abstract

Noncovalent interactions are of fundamental importance across the disciplines of chemistry, materials science, and biology. Quantum chemical calculations on noncovalently bound complexes, which allow for the quantification of properties such as binding energies and geometries, play an essential role in advancing our understanding of, and building models for, a vast array of complex processes involving molecular association or self-assembly. Because of its relatively modest computational cost, second-order Møller-Plesset perturbation (MP2) theory is one of the most widely used methods in quantum chemistry for studying noncovalent interactions. MP2 is, however, plagued by serious errors due to its incomplete treatment of electron correlation, especially when modeling van der Waals interactions and π-stacked complexes. Here we present spin-network-scaled MP2 (SNS-MP2), a new semi-empirical MP2-based method for dimer interaction-energy calculations. To correct for errors in MP2, SNS-MP2 uses quantum chemical features of the complex under study in conjunction with a neural network to reweight terms appearing in the total MP2 interaction energy. The method has been trained on a new data set consisting of over 200 000 complete basis set (CBS)-extrapolated coupled-cluster interaction energies, which are considered the gold standard for chemical accuracy. SNS-MP2 predicts gold-standard binding energies of unseen test compounds with a mean absolute error of 0.04 kcal mol-1 (root-mean-square error 0.09 kcal mol-1), a 6- to 7-fold improvement over MP2. To the best of our knowledge, its accuracy exceeds that of all extant density functional theory- and wavefunction-based methods of similar computational cost, and is very close to the intrinsic accuracy of our benchmark coupled-cluster methodology itself. Furthermore, SNS-MP2 provides reliable per-conformation confidence intervals on the predicted interaction energies, a feature not available from any alternative method.

Entities:  

Year:  2017        PMID: 29096510     DOI: 10.1063/1.4986081

Source DB:  PubMed          Journal:  J Chem Phys        ISSN: 0021-9606            Impact factor:   3.488


  12 in total

1.  Psi4 1.4: Open-source software for high-throughput quantum chemistry.

Authors:  Daniel G A Smith; Lori A Burns; Andrew C Simmonett; Robert M Parrish; Matthew C Schieber; Raimondas Galvelis; Peter Kraus; Holger Kruse; Roberto Di Remigio; Asem Alenaizan; Andrew M James; Susi Lehtola; Jonathon P Misiewicz; Maximilian Scheurer; Robert A Shaw; Jeffrey B Schriber; Yi Xie; Zachary L Glick; Dominic A Sirianni; Joseph Senan O'Brien; Jonathan M Waldrop; Ashutosh Kumar; Edward G Hohenstein; Benjamin P Pritchard; Bernard R Brooks; Henry F Schaefer; Alexander Yu Sokolov; Konrad Patkowski; A Eugene DePrince; Uğur Bozkaya; Rollin A King; Francesco A Evangelista; Justin M Turney; T Daniel Crawford; C David Sherrill
Journal:  J Chem Phys       Date:  2020-05-14       Impact factor: 3.488

2.  A Minimum Quantum Chemistry CCSD(T)/CBS Data Set of Dimeric Interaction Energies for Small Organic Functional Groups: Heterodimers.

Authors:  Hsing-Hsiang Huang; Yi-Siang Wang; Sheng D Chao
Journal:  ACS Omega       Date:  2022-05-31

3.  MoleculeNet: a benchmark for molecular machine learning.

Authors:  Zhenqin Wu; Bharath Ramsundar; Evan N Feinberg; Joseph Gomes; Caleb Geniesse; Aneesh S Pappu; Karl Leswing; Vijay Pande
Journal:  Chem Sci       Date:  2017-10-31       Impact factor: 9.825

4.  Accurate Reduced-Cost CCSD(T) Energies: Parallel Implementation, Benchmarks, and Large-Scale Applications.

Authors:  László Gyevi-Nagy; Mihály Kállay; Péter R Nagy
Journal:  J Chem Theory Comput       Date:  2021-01-05       Impact factor: 6.006

5.  Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems.

Authors:  John A Keith; Valentin Vassilev-Galindo; Bingqing Cheng; Stefan Chmiela; Michael Gastegger; Klaus-Robert Müller; Alexandre Tkatchenko
Journal:  Chem Rev       Date:  2021-07-07       Impact factor: 60.622

6.  Computation of host-guest binding free energies with a new quantum mechanics based mining minima algorithm.

Authors:  Peng Xu; Tosaporn Sattasathuchana; Emilie Guidez; Simon P Webb; Kilinoelani Montgomery; Hussna Yasini; Iara F M Pedreira; Mark S Gordon
Journal:  J Chem Phys       Date:  2021-03-14       Impact factor: 3.488

7.  The TensorMol-0.1 model chemistry: a neural network augmented with long-range physics.

Authors:  Kun Yao; John E Herr; David W Toth; Ryker Mckintyre; John Parkhill
Journal:  Chem Sci       Date:  2018-01-18       Impact factor: 9.825

8.  Diagnostics of Data-Driven Models: Uncertainty Quantification of PM7 Semi-Empirical Quantum Chemical Method.

Authors:  James Oreluk; Zhenyuan Liu; Arun Hegde; Wenyu Li; Andrew Packard; Michael Frenklach; Dmitry Zubarev
Journal:  Sci Rep       Date:  2018-09-05       Impact factor: 4.379

9.  Electron density learning of non-covalent systems.

Authors:  Alberto Fabrizio; Andrea Grisafi; Benjamin Meyer; Michele Ceriotti; Clemence Corminboeuf
Journal:  Chem Sci       Date:  2019-09-09       Impact factor: 9.825

10.  Quantum chemical benchmark databases of gold-standard dimer interaction energies.

Authors:  Alexander G Donchev; Andrew G Taube; Elizabeth Decolvenaere; Cory Hargus; Robert T McGibbon; Ka-Hei Law; Brent A Gregersen; Je-Luen Li; Kim Palmo; Karthik Siva; Michael Bergdorf; John L Klepeis; David E Shaw
Journal:  Sci Data       Date:  2021-02-10       Impact factor: 6.444

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