molSimplify: A toolkit for automating discovery in inorganic chemistry

Ioannidis, Efthymios Ioannis; Gani, Terry Z. H.; Kulik, Heather J.

doi:10.1002/jcc.24437

Cited by 163 publications

(255 citation statements)

References 51 publications

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“…The sole exception to this assignment is that the singlet Mn(III) ion energy was not available from the same NIST database 73 , but we define a singlet state as the lowspin state for the Mn(III) complexes. Structures and input files for these calculations were generated using molSimplify 76 , a recently introduced toolkit for automating simulation of transition metal complexes. The molSimplify 76 code uses trained metal-ligand bond lengths and force-field pre-optimization to provide good initial guesses for DFT geometry optimizations.…”

Section: Computational Detailsmentioning

confidence: 99%

“…Structures and input files for these calculations were generated using molSimplify 76 , a recently introduced toolkit for automating simulation of transition metal complexes. The molSimplify 76 code uses trained metal-ligand bond lengths and force-field pre-optimization to provide good initial guesses for DFT geometry optimizations. We carried out exchange tuning from 0% to 100% meta-GGA exchange in 10% increments to ensure a consistent electronic state was smoothly converged.…”

Section: Computational Detailsmentioning

confidence: 99%

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Ligand-Field-Dependent Behavior of Meta-GGA Exchange in Transition-Metal Complex Spin-State Ordering

2017

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Prediction of spin-state ordering in transition metal complexes is essential for understanding catalytic activity and designing functional materials. Semi-local approximations in density functional theory, such as the generalized-gradient approximation (GGA), suffer from several errors notably including delocalization error that give rise to systematic bias for more covalently bound low-spin electronic states. Incorporation of exact exchange is known to counteract this bias, instead favoring high-spin states, in a manner that has recently been identified to be ligand-field dependent. In this work, we introduce a tuning strategy to identify the effect of incorporating the Laplacian of the density (i.e., a meta-GGA) in exchange on spinstate ordering. We employ a diverse test set of M(II) and M(III) first-row transition metal ions from Ti to Cu as well as octahedral complexes of these ions with ligands of increasing field strength (i.e., H 2 O, NH 3 , and CO). We show that the sensitivity of spin-state ordering to meta-GGA exchange is highly ligand-field dependent, stabilizing high-spin states in strong-field (i.e., CO) cases and stabilizing low-spin states in weak-field (i.e., H 2 O, NH 3 , and isolated ions) cases. This diverging behavior leads to generally improved treatment of isolated ions and strong field complexes over a standard GGA but worsened treatment for the hexa-aqua or hexa-ammine complexes. These observations highlight the sensitivity of functional performance to subtle changes in chemical bonding.2

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Section: Computational Detailsmentioning

confidence: 99%

Section: Computational Detailsmentioning

confidence: 99%

Ligand-Field-Dependent Behavior of Meta-GGA Exchange in Transition-Metal Complex Spin-State Ordering

2017

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“…Geometry optimizations in the gas phase on molSimplify-generated 86 structures were carried out using the L-BFGS algorithm in Cartesian coordinates as implemented in DL-FIND 87 .…”

Section: Computational Detailsmentioning

confidence: 99%

Computational Discovery of Hydrogen Bond Design Rules for Electrochemical Ion Separation

2016

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Selective ion separation is a major challenge with far-ranging impact from water desalination to product separation in catalysis. Recently introduced ferrocenium(Fc + )/ferrocene(Fc) polymer electrode materials have been demonstrated experimentally and theoretically to selectively bind carboxylates over perchlorate through weak C-H … O hydrogen bond (HB) interactions that favor carboxylates, despite the comparable size and charge of the two species. However, practical application of this technology in aqueous environments requires further selectivity enhancement. Using a first-principles discovery approach, we investigate the effect of Fc/Fc + functional groups (FGs) on the selectivity and reversibility of formate-Fc + adsorption with respect to perchlorate in aqueous solution. Our wide design space of 44 FGs enables identification of FGs with higher selectivity and rationalization of trends through electronic energy decomposition analysis or geometric hydrogen bonding analysis. Overall, we observe weaker, longer HBs for perchlorate as compared to formate with Fc + . We further identify Fc + functionalizations that simultaneously increase selectivity for formate in aqueous environments but permit rapid release from neutral Fc. We introduce the materiaphore, a 3D abstraction of these design rules, to help guide nextgeneration material optimization for selective ion sorption. This approach is expected to have broad relevance in computational discovery for molecular recognition, sensing, separations, and catalysis.2

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“…For complicated aggregate structures, evolutionary algorithms and, possibly, neural networks might be applied for further refining the aggregate candidate set. Periodic boundary conditions might be used at any growth step to check whether experimental properties of extended supramolecular systems are sufficiently well reproduced at that growth step .…”

Section: Introductionmentioning

confidence: 99%

A program for automatically predicting supramolecular aggregates and its application to urea and porphin

et al. 2018

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Not only the molecular structure but also the presence or absence of aggregates determines many properties of organic materials. Theoretical investigation of such aggregates requires the prediction of a suitable set of diverse structures. Here, we present the open-source program EnergyScan for the unbiased prediction of geometrically diverse sets of small aggregates. Its bottom-up approach is complementary to existing ones by performing a detailed scan of an aggregate's potential energy surface, from which diverse local energy minima are selected. We crossvalidate this approach by predicting both literature-known and heretofore unreported geometries of the urea dimer. We also predict a diverse set of dimers of the less intensely studied case of porphin, which we investigate further using quantum chemistry. For several dimers, we find strong deviations from a reference absorption spectrum, which we explain using computed transition densities. This proof of principle clearly shows that EnergyScan successfully predicts aggregates exhibiting large structural and spectral diversity. © 2018 Wiley Periodicals, Inc.

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molSimplify: A toolkit for automating discovery in inorganic chemistry

Cited by 163 publications

References 51 publications

Ligand-Field-Dependent Behavior of Meta-GGA Exchange in Transition-Metal Complex Spin-State Ordering

Ligand-Field-Dependent Behavior of Meta-GGA Exchange in Transition-Metal Complex Spin-State Ordering

Computational Discovery of Hydrogen Bond Design Rules for Electrochemical Ion Separation

A program for automatically predicting supramolecular aggregates and its application to urea and porphin

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