Combining machine learning and high-throughput experimentation to discover photocatalytically active organic molecules

Li, Xiaobo; Maffettone, Phillip M.; Che, Yu; Liu, Tao; Chen, Linjiang; Cooper, Andrew I.

doi:10.1039/d1sc02150h

Cited by 70 publications

(65 citation statements)

References 57 publications

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“…We found no simple correlation between the H 2 O 2 production rate and any single physical property such as band gap, fluorescence lifetime, particle size, or water contact angle ( Figure S8 ), much as for sacrificial hydrogen evolution. 15 , 16 …”

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confidence: 99%

Linear Conjugated Polymers for Solar-Driven Hydrogen Peroxide Production: The Importance of Catalyst Stability

et al. 2021

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Hydrogen peroxide (H2O2) is one of the most important industrial oxidants. In principle, photocatalytic H2O2 synthesis from oxygen and H2O using sunlight could provide a cleaner alternative route to the current anthraquinone process. Recently, conjugated organic materials have been studied as photocatalysts for solar fuels synthesis because they offer synthetic tunability over a large chemical space. Here, we used high-throughput experiments to discover a linear conjugated polymer, poly(3-4-ethynylphenyl)ethynyl)pyridine (DE7), which exhibits efficient photocatalytic H2O2 production from H2O and O2 under visible light illumination for periods of up to 10 h or so. The apparent quantum yield was 8.7% at 420 nm. Mechanistic investigations showed that the H2O2 was produced via the photoinduced stepwise reduction of O2. At longer photolysis times, however, this catalyst decomposed, suggesting a need to focus the photostability of organic photocatalysts, as well as the initial catalytic production rates.

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confidence: 99%

Linear Conjugated Polymers for Solar-Driven Hydrogen Peroxide Production: The Importance of Catalyst Stability

et al. 2021

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“…[ 227–229 ] Here we restrict ourselves to supervised learning algorithms whereas for unsupervised approaches we refer the reader to study the work by Hinton and Sejnowski. [ 230–234 ] Various HTE data analysis tools have been used to assist this process. A detailed explanation of standard statistical practices used for HTE data analysis has been described by Malo et al.…”

Section: Data Driven Methods For the Analysis Of High‐throughput Expe...mentioning

confidence: 99%

“…[227][228][229] Here we restrict ourselves to supervised learning algorithms whereas for unsupervised approaches we refer the reader to study the work by Hinton and Sejnowski. [230][231][232][233][234] Various HTE data analysis tools have been used to assist this process. A detailed explanation of standard statistical practices used for HTE data analysis has been described by Malo et al [235] Due to high-dimension of the feature space, e.g., reflecting the number of components in an electrolyte in battery research, direct interpretation of the experimental or simulation data is somewhat hampered.…”

Section: Data Driven Methods For the Analysis Of High-throughput Expe...mentioning

confidence: 99%

High‐Throughput Experimentation and Computational Freeway Lanes for Accelerated Battery Electrolyte and Interface Development Research

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Heuer

et al. 2021

Advanced Energy Materials

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The timely arrival of novel materials plays a key role in bringing advances to society, as the pace at which major technological breakthroughs take place is usually dictated by the discovery rate at which novel materials are identified within chemical space. High‐throughput experimentation and computation strategy, now widely considered as a watershed in accelerating the discovery and optimization of novel materials in virtually every field, enables simultaneous screening, synthesis and characterization of large arrays of different material classes toward identification of the lead candidates for given system and targeted application. However, the ability to acquire data, through the continued advancement of automation platforms and workflows especially in the field of battery research and development, often outpaces the ability to optimally leverage obtained data for improved decision‐making. Closing this gap inevitably calls for adapted algorithms, development of reliable predictive models and enhanced integration with machine learning, deep learning, and artificial intelligence. This Review aims to highlight state‐of‐the‐art achievements along with an assessment of current and future challenges as well as resulting perspectives toward accelerated development of advanced battery electrolytes and their interfaces.

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“…Furthermore, purely structure-driven machine learning models exhibit surprisingly comparable performance to expensive quantum chemical descriptor-driven models in predicting experimental hydrogen evolution rates. 107 …”

Section: Catalyst Screening Design and Discoverymentioning

confidence: 99%

Photoredox Chemistry with Organic Catalysts: Role of Computational Methods

et al. 2021

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Organic catalysts have the potential to carry out a wide range of otherwise thermally inaccessible reactions via photoredox routes. Early demonstrated successes of organic photoredox catalysts include one-electron CO 2 reduction and H 2 generation via water splitting. Photoredox systems are challenging to study and design owing to the sheer number and diversity of phenomena involved, including light absorption, emission, intersystem crossing, partial or complete charge transfer, and bond breaking or formation. Designing a viable photoredox route therefore requires consideration of a host of factors such as absorption wavelength, solvent, choice of electron donor or acceptor, and so on. Quantum chemistry methods can play a critical role in demystifying photoredox phenomena. Using one-electron CO 2 reduction with phenylene-based chromophores as an illustrative example, this perspective highlights recent developments in quantum chemistry that can advance our understanding of photoredox processes and proposes a way forward for driving the design and discovery of organic catalysts.

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Combining machine learning and high-throughput experimentation to discover photocatalytically active organic molecules

Abstract: 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...

Cited by 70 publications

References 57 publications

Linear Conjugated Polymers for Solar-Driven Hydrogen Peroxide Production: The Importance of Catalyst Stability

Linear Conjugated Polymers for Solar-Driven Hydrogen Peroxide Production: The Importance of Catalyst Stability

High‐Throughput Experimentation and Computational Freeway Lanes for Accelerated Battery Electrolyte and Interface Development Research

Photoredox Chemistry with Organic Catalysts: Role of Computational Methods

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