Predicting the outcomes of organic reactions via machine learning: are current descriptors sufficient?

Skoraczyński, Grzegorz; Dittwald, Piotr; Miasojedow, Błażej; Szymkuć, Sara; Gajewska, EP; Grzybowski, Bartosz A.; Gambin, Anna

doi:10.1038/s41598-017-02303-0

Cited by 133 publications

(122 citation statements)

References 41 publications

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“…However, it is important to point out that side chains may significantly affect solubility and morphology of materials. [56,65,[82][83][84] Technically, we split the dataset into two subsets of 250 (training and validating sets) and 30 (testing set) data points. IP(v), λ h and E bind were calculated for each system.…”

Section: Descriptors For Machine Learning Modelsmentioning

confidence: 99%

Toward Predicting Efficiency of Organic Solar Cells via Machine Learning and Improved Descriptors

Sahu

Rao

Troisi

et al. 2018

Advanced Energy Materials

206

220

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include the strong electron-electron interactions and strong electron-phonon couplings as well as the complicated donor/ acceptor (D/A) interface morphology, which are fundamentally different from inorganic semiconductors. [23,24] Therefore, the accurate simulating of OPVs requires high-level theoretical methods in quantum chemistry, quantum dynamics and statistical mechanics, and in recent years there have been substantial progress for the theoretical understanding of many microscopic processes such as charge transport, [25] exciton dissociation, [26,27] singlet fission, [28][29][30] etc. These methods are useful for few benchmark systems but cannot be used to explore the chemical space, i.e., screen a large number of candidate materials.The predictive power of a theoretical model by high-throughput virtual screening has been explored in the recent years. [31][32][33][34][35][36][37][38][39][40] For example, in the Harvard Clean Energy Project (CEP), [41] Aspuru-Guzik and co-workers have screened ≈2.3 million compounds by employing the Scharber model [42,43] to find out efficient new donor molecules for OPVs. Here, the computed energies of the highest occupied molecular orbital (HOMO)/the lowest unoccupied molecular orbital (LUMO) of organic molecules are calibrated to experimentally determined values, and then employing an averaging scheme, energies of HOMO/LUMO are estimated to use them as input in the Scharber model. However, the prediction of PCE of an OPV is much more challenging compared to the energies of orbitals. Very recently, the same research team [44] noticed that the Scharber model, widely used in these virtual screening works, is not accurate enough for the prediction of PCE, and calibration of PCE by considering molecular similarity, molecular weight and band gap energy leads to improvement in correlation between experimental (49 OPVs) and calculated PCE (Pearson's coefficient (r) is increased from 0.30 to 0.43). Further, the Scharber model is only optimized for the PC 61 BM acceptor [42] and a more general model is desired to take account of nonfullerene molecules.In an OPV, energy conversion is accomplished by four consecutive steps: i) absorption of photons and exciton formation, ii) exciton diffusion to D/A interface, iii) exciton dissociation, and iv) transport of holes/electrons to the respective electrode. Taking account of all these processes, the efficiency of a device depends on many microscopic properties of the organic material such as optical gap, charge-carrier mobility, ionization To design efficient materials for organic photovoltaics (OPVs), it is essential to identify the largest number of parameters that control their properties and build a model using these parameters (known as descriptors) for the prediction of the power conversion efficiency (PCE). By constructing a dataset for 280 small molecule OPV systems, it is found that for all high-performing devices, frontier molecular orbitals of donor molecules are nearly degenerated and in such cases, orbitals other than just high...

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Section: Descriptors For Machine Learning Modelsmentioning

confidence: 99%

Toward Predicting Efficiency of Organic Solar Cells via Machine Learning and Improved Descriptors

Sahu

Rao

Troisi

et al. 2018

Advanced Energy Materials

206

220

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“…[9][10][11][12][13][14][15] This should not come as a surprise, as it builds on a long tradition of using stereoelectronic parameters, Tolman's perhaps most prominently among them, 16 in this field. 17 Homogeneous catalysis is not (yet) data-rich enough to be considered amenable to "Big Data" approaches, [18][19][20][21] and machine-learning approaches, while used in this area, 9,[22][23][24][25] are still very much in their infancy, 26 but this provides a convenient opportunity to survey and collate available descriptors.…”

Section: Introductionmentioning

confidence: 99%

Computational Ligand Descriptors for Catalyst Design

2019

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Ligands, especially phosphines and carbenes, can play a key role in modifying and controlling homogeneous organometallic catalysts, and they often provide a convenient approach to fine-tuning the performance of known catalysts. The measurable outcomes of such catalyst modifications (yields, rates, selectivity) can be set into context by establishing their relationship to steric and electronic descriptors of ligand properties, and such models can guide the discovery, optimisation and design of catalysts.In this review we present a survey of calculated ligand descriptors, with a particular focus on homogeneous organometallic catalysis. A range of different approaches to calculating steric and electronic parameters are set out and compared, and we have collected descriptors for a range of representative ligand sets, including 30 monodentate phosphorus(III) donor ligands, 23 bidentate P,Pdonor ligands and 30 carbenes, with a view to providing a useful resource for analysis to practitioners. In addition, several case studies of applications of such descriptors, covering both maps and models, have been reviewed, illustrating how descriptor-led studies of catalysis can inform experiments and highlighting good practice for model comparison and evaluation.

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“…Im Jahr 2017 testeten Gambin et al. KI‐Algorithmen, um eine große Anzahl (450 000 Fälle) von vielfältigen organischen Reaktionen vorherzusagen, und betonten, dass die Identifizierung neuer chemoinformatischer Deskriptoren für zukünftige Entwicklungen essentiell sein könnte . Neben anderen wichtigen Versuchen, organische Reaktionen anhand von KI vorherzusagen und zu optimieren, sind die jüngsten Studien von Zare et al .…”

Section: Methodsunclassified