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Title: Ab Initio Reactive Computer Aided Molecular Design

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 then opens the possibility of new discovery-based approaches to chemical reactivity, such as the recently proposed ab initio nanoreactor. Here, 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.

Authors:
ORCiD logo [1]
  1. Stanford Univ., CA (United States). Dept. of Chemistry and PULSE Inst.; SLAC National Accelerator Lab., Menlo Park, CA (United States)
Publication Date:
Research Org.:
SLAC National Accelerator Lab., Menlo Park, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES); National Science Foundation (NSF)
OSTI Identifier:
1390717
Grant/Contract Number:  
AC02-76SF00515; N00014-14-1-0590; ACI-1450179
Resource Type:
Accepted Manuscript
Journal Name:
Accounts of Chemical Research
Additional Journal Information:
Journal Volume: 50; Journal Issue: 3; Journal ID: ISSN 0001-4842
Publisher:
American Chemical Society
Country of Publication:
United States
Language:
English
Subject:
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY

Citation Formats

Martínez, Todd J. Ab Initio Reactive Computer Aided Molecular Design. United States: N. p., 2017. Web. doi:10.1021/acs.accounts.7b00010.
Martínez, Todd J. Ab Initio Reactive Computer Aided Molecular Design. United States. doi:10.1021/acs.accounts.7b00010.
Martínez, Todd J. Tue . "Ab Initio Reactive Computer Aided Molecular Design". United States. doi:10.1021/acs.accounts.7b00010. https://www.osti.gov/servlets/purl/1390717.
@article{osti_1390717,
title = {Ab Initio Reactive Computer Aided Molecular Design},
author = {Martínez, Todd J.},
abstractNote = {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 then opens the possibility of new discovery-based approaches to chemical reactivity, such as the recently proposed ab initio nanoreactor. Here, 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.},
doi = {10.1021/acs.accounts.7b00010},
journal = {Accounts of Chemical Research},
number = 3,
volume = 50,
place = {United States},
year = {2017},
month = {3}
}

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Cited by: 14 works
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Works referencing / citing this record:

Enumeration of de novo inorganic complexes for chemical discovery and machine learning
journal, January 2020

  • Gugler, Stefan; Janet, Jon Paul; Kulik, Heather J.
  • Molecular Systems Design & Engineering, Vol. 5, Issue 1
  • DOI: 10.1039/c9me00069k

A quantitative uncertainty metric controls error in neural network-driven chemical discovery
journal, January 2019

  • Janet, Jon Paul; Duan, Chenru; Yang, Tzuhsiung
  • Chemical Science, Vol. 10, Issue 34
  • DOI: 10.1039/c9sc02298h

Enumeration of de novo inorganic complexes for chemical discovery and machine learning
journal, January 2020

  • Gugler, Stefan; Janet, Jon Paul; Kulik, Heather J.
  • Molecular Systems Design & Engineering, Vol. 5, Issue 1
  • DOI: 10.1039/c9me00069k

A quantitative uncertainty metric controls error in neural network-driven chemical discovery
journal, January 2019

  • Janet, Jon Paul; Duan, Chenru; Yang, Tzuhsiung
  • Chemical Science, Vol. 10, Issue 34
  • DOI: 10.1039/c9sc02298h