Literature DB >> 28945420

Ab Initio Reactive Computer Aided Molecular Design.

Todd J Martínez1,2.   

Abstract

Few would dispute that theoretical chemistry tools can now provide keen insights into chemical phenomena. Yet the holy grail of efficient and reliable prediction of complex reactivity has remained elusive. Fortunately, recent advances in electronic structure theory based on the concepts of both element- and rank-sparsity, coupled with the emergence of new highly parallel computer architectures, have led to a significant increase in the time and length scales which can be simulated using first principles molecular dynamics. This opens the possibility of new discovery-based approaches to chemical reactivity, such as the recently proposed ab initio nanoreactor. We argue that due to these and other recent advances, the holy grail of computational discovery for complex chemical reactivity is rapidly coming within our reach.

Year:  2017        PMID: 28945420     DOI: 10.1021/acs.accounts.7b00010

Source DB:  PubMed          Journal:  Acc Chem Res        ISSN: 0001-4842            Impact factor:   22.384


  5 in total

Review 1.  The Matter Simulation (R)evolution.

Authors:  Alán Aspuru-Guzik; Roland Lindh; Markus Reiher
Journal:  ACS Cent Sci       Date:  2018-02-06       Impact factor: 14.553

2.  Computational Discovery of the Origins of Life.

Authors:  Jan Meisner; Xiaolei Zhu; Todd J Martínez
Journal:  ACS Cent Sci       Date:  2019-09-17       Impact factor: 14.553

3.  Retrosynthetic Reaction Prediction Using Neural Sequence-to-Sequence Models.

Authors:  Bowen Liu; Bharath Ramsundar; Prasad Kawthekar; Jade Shi; Joseph Gomes; Quang Luu Nguyen; Stephen Ho; Jack Sloane; Paul Wender; Vijay Pande
Journal:  ACS Cent Sci       Date:  2017-09-05       Impact factor: 18.728

4.  A quantitative uncertainty metric controls error in neural network-driven chemical discovery.

Authors:  Jon Paul Janet; Chenru Duan; Tzuhsiung Yang; Aditya Nandy; Heather J Kulik
Journal:  Chem Sci       Date:  2019-07-11       Impact factor: 9.825

5.  Complex reaction processes in combustion unraveled by neural network-based molecular dynamics simulation.

Authors:  Jinzhe Zeng; Liqun Cao; Mingyuan Xu; Tong Zhu; John Z H Zhang
Journal:  Nat Commun       Date:  2020-11-11       Impact factor: 14.919

  5 in total

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