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Ab Initio Reactive Computer Aided Molecular Design

Journal Article · · Accounts of Chemical Research
 [1]
  1. Stanford Univ., CA (United States). Dept. of Chemistry and PULSE Inst.; SLAC National Accelerator Lab., Menlo Park, CA (United States)
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.
Research Organization:
SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States)
Sponsoring Organization:
National Science Foundation (NSF); USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22)
Grant/Contract Number:
AC02-76SF00515
OSTI ID:
1390717
Journal Information:
Accounts of Chemical Research, Journal Name: Accounts of Chemical Research Journal Issue: 3 Vol. 50; ISSN 0001-4842
Publisher:
American Chemical SocietyCopyright Statement
Country of Publication:
United States
Language:
English

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Semiempirical molecular orbital models based on the neglect of diatomic differential overlap approximation journal October 2018
Water models for biomolecular simulations: Water models for biomolecular simulations journal November 2017
Making machine learning a useful tool in the accelerated discovery of transition metal complexes journal July 2019
Enumeration of de novo inorganic complexes for chemical discovery and machine learning journal January 2020
A quantitative uncertainty metric controls error in neural network-driven chemical discovery journal January 2019
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