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Predictions of High-Order Electric Properties of Molecules: Can We Benefit from Machine Learning?

ABSTRACT: There is an exigency of adopting machine learning techniques to screen and discover new materials which could address many societal and technological challenges. In this work, we follow this trend and employ machine learning to study (high-order) electric properties of organic compounds. The results of quantum-chemistry calculations of polarizability and first hyperpolarizability, obtained for more than 50,000 compounds, served as targets for machine learning-based predictions. The studied set of molecular structures encompasses organic push-pull molecules with variable linker lengths. Moreover, the diversified set of linkers, composed of alternating single/double and single/triple carbon-carbon bonds, was considered. This study demonstrates that the applied machine learning strategy allows us to obtain the correlation coefficients, between predicted and reference values of (hyper)polarizabilities, exceeding 0.9 on training, validation, and test set. However, in order to achieve such satisfactory predictive power, one needs to choose the training set appropriately, as the machine learning methods are very sensitive to the linker-type diversity in the training set, yielding catastrophic predictions in certain cases. Furthermore, the dependence of (hyper)polarizability on the length of spacers was studied in detail, allowing for explanation of the appreciably high accuracy of employed approaches.

SUBMITTER: Tuan-Anh T

PROVIDER: S-EPMC7081434 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Predictions of High-Order Electric Properties of Molecules: Can We Benefit from Machine Learning?

Tuan-Anh Tran T Zaleśny Robert R

ACS omega 20200309 10

There is an exigency of adopting machine learning techniques to screen and discover new materials which could address many societal and technological challenges. In this work, we follow this trend and employ machine learning to study (high-order) electric properties of organic compounds. The results of quantum-chemistry calculations of polarizability and first hyperpolarizability, obtained for more than 50,000 compounds, served as targets for machine learning-based predictions. The studied set o ...[more]

PMID: 32201820

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Project description:Light-absorbing organic molecules are useful components in photocatalysts, but it is difficult to formulate reliable structure-property design rules. More than 100 million unique chemical compounds are documented in the PubChem database, and a significant sub-set of these are π-conjugated, light-absorbing molecules that might in principle act as photocatalysts. Nature has used natural selection to evolve photosynthetic assemblies; by contrast, our ability to navigate the enormous potential search space of organic photocatalysts in the laboratory is limited. Here, we integrate experiment, computation, and machine learning to address this challenge. A library of 572 aromatic organic molecules was assembled with diverse compositions and structures, selected on the basis of availability in our laboratory, rather than more sophisticated criteria. This training library was then assessed experimentally for sacrificial photocatalytic hydrogen evolution using a high-throughput, automated method. Quantum chemical calculations and machine learning were used to visualise, interpret, and ultimately to predict the photocatalytic activities of these molecules, covering a much broader chemical space than for previous polymer photocatalyst libraries. By applying unsupervised learning to the molecular structures, we identified structural features that were common in molecules with high catalytic activity. Further analysis using calculated molecular descriptors within a suite of supervised classification algorithms revealed that light absorption, exciton electron affinity, electron affinity, exciton binding energy, and singlet-triplet energy gap had correlations with the photocatalytic performance. These trained predictive models can be used in future studies as filters to deprioritise or discard would-be low-activity candidate molecules from experiments, and to prioritize more favourable candidates. As a demonstration, we used virtual in silico experiments to show that it was possible to halve the experimental cost of finding 50% of the most active photocatalysts by using the machine learning model as an experimental advisor. We further showed that the ML advisor trained on the 572-molecule library could be used to make predictions for an unseen set of 96 molecules, achieving equivalent predictive accuracies to those in the initial training set. This marks a step toward the machine-learning assisted discovery of molecular organic photocatalysts and the approach might also be applied to problems beyond photocatalytic hydrogen evolution, such as CO2 reduction and photoredox chemistry.

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Predictions of High-Order Electric Properties of Molecules: Can We Benefit from Machine Learning?

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Predictions of High-Order Electric Properties of Molecules: Can We Benefit from Machine Learning?

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