Bridging the complexity gap in computational heterogeneous catalysis with machine learning

Nature Catalysis, Journal Year: 2023, Volume and Issue: 6(2), P. 122 - 136

Published: Feb. 23, 2023

Language: Английский

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Machine Learning-Assisted Low-Dimensional Electrocatalysts Design for Hydrogen Evolution Reaction

Nano-Micro Letters, Journal Year: 2023, Volume and Issue: 15(1)

Published: Oct. 13, 2023

Abstract Efficient electrocatalysts are crucial for hydrogen generation from electrolyzing water. Nevertheless, the conventional "trial and error" method producing advanced is not only cost-ineffective but also time-consuming labor-intensive. Fortunately, advancement of machine learning brings new opportunities discovery design. By analyzing experimental theoretical data, can effectively predict their evolution reaction (HER) performance. This review summarizes recent developments in low-dimensional electrocatalysts, including zero-dimension nanoparticles nanoclusters, one-dimensional nanotubes nanowires, two-dimensional nanosheets, as well other electrocatalysts. In particular, effects descriptors algorithms on screening investigating HER performance highlighted. Finally, future directions perspectives electrocatalysis discussed, emphasizing potential to accelerate electrocatalyst discovery, optimize performance, provide insights into electrocatalytic mechanisms. Overall, this work offers an in-depth understanding current state its research.

Language: Английский

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Autonomous, multiproperty-driven molecular discovery: From predictions to measurements and back

Science, Journal Year: 2023, Volume and Issue: 382(6677)

Published: Dec. 21, 2023

A closed-loop, autonomous molecular discovery platform driven by integrated machine learning tools was developed to accelerate the design of molecules with desired properties. We demonstrated two case studies on dye-like molecules, targeting absorption wavelength, lipophilicity, and photooxidative stability. In first study, experimentally realized 294 unreported across three automatic iterations design-make-test-analyze cycles while exploring structure-function space four rarely reported scaffolds. each iteration, property prediction models that guided exploration learned structure-property diverse scaffold derivatives, which were multistep syntheses a variety reactions. The second study exploited trained explored chemical previously discover nine top-performing within lightly space.

Language: Английский

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Representations of Materials for Machine Learning

Annual Review of Materials Research, Journal Year: 2023, Volume and Issue: 53(1), P. 399 - 426

Published: April 18, 2023

High-throughput data generation methods and machine learning (ML) algorithms have given rise to a new era of computational materials science by the relations between composition, structure, properties exploiting such for design. However, build these connections, must be translated into numerical form, called representation, that can processed an ML model. Data sets in vary format (ranging from images spectra), size, fidelity. Predictive models scope interest. Here, we review context-dependent strategies constructing representations enable use as inputs or outputs models. Furthermore, discuss how modern techniques learn transfer chemical physical information tasks. Finally, outline high-impact questions not been fully resolved thus require further investigation.

Language: Английский

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Machine Learning Descriptors for Data‐Driven Catalysis Study

Advanced Science, Journal Year: 2023, Volume and Issue: 10(22)

Published: May 16, 2023

Traditional trial-and-error experiments and theoretical simulations have difficulty optimizing catalytic processes developing new, better-performing catalysts. Machine learning (ML) provides a promising approach for accelerating catalysis research due to its powerful predictive abilities. The selection of appropriate input features (descriptors) plays decisive role in improving the accuracy ML models uncovering key factors that influence activity selectivity. This review introduces tactics utilization extraction descriptors ML-assisted experimental research. In addition effectiveness advantages various descriptors, their limitations are also discussed. Highlighted both 1) newly developed spectral performance prediction 2) novel paradigm combining computational through suitable intermediate descriptors. Current challenges future perspectives on application techniques presented.

Language: Английский

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Morphing matter: from mechanical principles to robotic applications

Soft Science, Journal Year: 2023, Volume and Issue: 3(4)

Published: Oct. 31, 2023

The adaptability of natural organisms in altering body shapes response to the environment has inspired development artificial morphing matter. These materials encode ability transform their geometrical configurations specific stimuli and have diverse applications soft robotics, wearable electronics, biomedical devices. However, achieving intricate three-dimensional from a two-dimensional flat state is challenging, as it requires manipulations surface curvature controlled manner. In this review, we first summarize mechanical principles extensively explored for realizing matter, both at material structural levels. We then highlight its robotics field. Moreover, offer insights into open challenges opportunities that rapidly growing field faces. This review aims inspire researchers uncover innovative working create multifunctional matter various engineering fields.

Language: Английский

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Zero-dimensional nano-carbons: Synthesis, properties, and applications

Applied Physics Reviews, Journal Year: 2024, Volume and Issue: 11(2)

Published: April 19, 2024

Zero-dimensional (0D) nano-carbons, including graphene quantum dots, nanodiamonds, and carbon represent the new generation of carbon-based nanomaterials with exceptional properties arising from diverse phenomena, such as surface, size, edge effects, which strongly depend on carbon–carbon bond configuration (sp2, sp3, a mixture sp2 sp3) particle size. Their unique physicochemical properties, optical, electronic, magnetic, reactivity, catalytic are valuable for energy conversion storage, sensing, catalysis, optoelectronic devices, modern nanotechnologies, biomedical, many other applications. This review aims to provide insights into distinctive effects 0D nano-carbon microstructures their that crucial cutting-edge fundamental studies broad range multifunctional The key synthesis methods different types nano-carbons current advances characterization computational techniques study structures structure–property relationships also discussed. concludes status, challenges, future opportunities in this rapidly developing research field.

Language: Английский

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Designing membranes with specific binding sites for selective ion separations

Nature Water, Journal Year: 2024, Volume and Issue: 2(8), P. 706 - 718

Published: Aug. 8, 2024

Language: Английский

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Role of the human-in-the-loop in emerging self-driving laboratories for heterogeneous catalysis

Nature Catalysis, Journal Year: 2025, Volume and Issue: 8(1), P. 13 - 19

Published: Jan. 29, 2025

Language: Английский

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Rise of machine learning potentials in heterogeneous catalysis: Developments, applications, and prospects

Chemical Engineering Journal, Journal Year: 2024, Volume and Issue: 494, P. 152757 - 152757

Published: June 2, 2024

Language: Английский

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