2020
DOI: 10.1021/acscatal.9b04952
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Automated in Silico Design of Homogeneous Catalysts

Abstract: Catalyst discovery is increasingly relying on computational chemistry, and many of the computational tools are currently being automated. The state of this automation and the degree to which it may contribute to speeding up development of catalysts are the subject of this Perspective. We also consider the main challenges associated with automated catalyst design, in particular the generation of promising and chemically realistic candidates, the tradeoff between accuracy and cost in estimating the catalytic per… Show more

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Cited by 158 publications
(186 citation statements)
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“…The hybrid DFT training data used for these models The Journal of Physical Chemistry A pubs.acs.org/JPCA Article were obtained with B3LYP 86−88 using an LANL2DZ effective core potential 89 on the transition metal and 6-31G* on the remaining atoms with a developer version of TeraChem. 90,91 Following conventions in prior work, the HS or LS bond lengths predicted for d 6 Fe(II) are quintets and closed-shell singlets, respectively, and the d 5 Fe(III) HS and LS states are sextets and doublets, respectively. Intermediate spin states are neglected due to the higher probability of transitioning between HS and LS states experimentally.…”
Section: Computational Detailsmentioning
confidence: 99%
“…The hybrid DFT training data used for these models The Journal of Physical Chemistry A pubs.acs.org/JPCA Article were obtained with B3LYP 86−88 using an LANL2DZ effective core potential 89 on the transition metal and 6-31G* on the remaining atoms with a developer version of TeraChem. 90,91 Following conventions in prior work, the HS or LS bond lengths predicted for d 6 Fe(II) are quintets and closed-shell singlets, respectively, and the d 5 Fe(III) HS and LS states are sextets and doublets, respectively. Intermediate spin states are neglected due to the higher probability of transitioning between HS and LS states experimentally.…”
Section: Computational Detailsmentioning
confidence: 99%
“…[9][10][11] Neural networks [12][13][14][15][16] and other ML models have been used successfully in a wide range of applications, with numerous examples in materials science 17-21 and drug discovery. [22][23][24][25][26] ML and data-driven approaches are also making a rapid progress in catalytic, [27][28][29][30][31][32][33][34][35][36][37][38][39][40][41] organic, [42][43][44][45][46][47] inorganic 48,49 and theoretical [50][51][52][53][54][55][56] chemistry.…”
Section: Introductionmentioning
confidence: 99%
“…The next step then was the identification of a convenient set of descriptors in order to implement a well-working QSAR model. Whereas this step is nowadays routine in pharmaceutical chemistry [ 9 ], with several software packages and libraries of descriptors available commercially, examples in organometallic catalysis are still rare [ 97 ], likely because—as we noted above— ‘activity’ in the latter context indicates a whole set of performance properties that descend from different electronic and steric factors [ 10 , 11 , 12 , 13 , 84 , 98 ] and, moreover, can be difficult to quantify. In the following we define as a ‘clear-box’ QSAR model one which makes use of chemically intuitive descriptors with an evident meaning for the investigated systems (at odds with a ‘black-box’ QSAR model in which the descriptors are chosen tentatively out of very large sets, and selected for the best-working combination based on complex statistical procedures [ 9 ]).…”
Section: Resultsmentioning
confidence: 99%