Recent advances and applications of deep learning methods in materials science

npj Computational Materials, Journal Year: 2022, Volume and Issue: 8(1)

Published: April 5, 2022

Deep learning (DL) is one of the fastest growing topics in materials data science, with rapidly emerging applications spanning atomistic, image-based, spectral, and textual modalities. DL allows analysis unstructured automated identification features. Recent development large databases has fueled application methods atomistic prediction particular. In contrast, advances image spectral have largely leveraged synthetic enabled by high quality forward models as well generative unsupervised methods. this article, we present a high-level overview deep-learning followed detailed discussion recent developments deep simulation, imaging, analysis, natural language processing. For each modality discuss involving both theoretical experimental data, typical modeling approaches their strengths limitations, relevant publicly available software datasets. We conclude review cross-cutting work related to uncertainty quantification field brief perspective on challenges, potential growth areas for science. The science presents an exciting avenue future discovery design.

Language: Английский

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Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery

Chemical Reviews, Journal Year: 2022, Volume and Issue: 122(16), P. 13478 - 13515

Published: July 21, 2022

Electrocatalysts and photocatalysts are key to a sustainable future, generating clean fuels, reducing the impact of global warming, providing solutions environmental pollution. Improved processes for catalyst design better understanding electro/photocatalytic essential improving effectiveness. Recent advances in data science artificial intelligence have great potential accelerate electrocatalysis photocatalysis research, particularly rapid exploration large materials chemistry spaces through machine learning. Here comprehensive introduction to, critical review of, learning techniques used research provided. Sources electro/photocatalyst current approaches representing these by mathematical features described, most commonly methods summarized, quality utility models evaluated. Illustrations how applied novel discovery elucidate electrocatalytic or photocatalytic reaction mechanisms The offers guide scientists on selection research. application catalysis represents paradigm shift way advanced, next-generation catalysts will be designed synthesized.

Language: Английский

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Machine learning for a sustainable energy future

Nature Reviews Materials, Journal Year: 2022, Volume and Issue: 8(3), P. 202 - 215

Published: Oct. 18, 2022

Language: Английский

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Benchmarking graph neural networks for materials chemistry

npj Computational Materials, Journal Year: 2021, Volume and Issue: 7(1)

Published: June 3, 2021

Abstract Graph neural networks (GNNs) have received intense interest as a rapidly expanding class of machine learning models remarkably well-suited for materials applications. To date, number successful GNNs been proposed and demonstrated systems ranging from crystal stability to electronic property prediction surface chemistry heterogeneous catalysis. However, consistent benchmark these remains lacking, hindering the development evaluation new in field. Here, we present workflow testing platform, MatDeepLearn, quickly reproducibly assessing comparing other models. We use this platform optimize evaluate selection top performing on several representative datasets computational chemistry. From our investigations note importance hyperparameter find roughly similar performances once optimized. identify strengths over conventional cases with compositionally diverse its overall flexibility respect inputs, due learned rather than defined representations. Meanwhile weaknesses are also observed including high data requirements, suggestions further improvement applications discussed.

Language: Английский

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Molecular excited states through a machine learning lens

Nature Reviews Chemistry, Journal Year: 2021, Volume and Issue: 5(6), P. 388 - 405

Published: May 20, 2021

Language: Английский

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Emerging porous organic polymers for biomedical applications

Chemical Society Reviews, Journal Year: 2022, Volume and Issue: 51(4), P. 1377 - 1414

Published: Jan. 1, 2022

This review summarizes and discusses the recent progress in porous organic polymers for diverse biomedical applications such as drug delivery, biomacromolecule immobilization, phototherapy, biosensing, bioimaging, antibacterial applications.

Language: Английский

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Ab Initio Machine Learning in Chemical Compound Space

Chemical Reviews, Journal Year: 2021, Volume and Issue: 121(16), P. 10001 - 10036

Published: Aug. 13, 2021

Chemical compound space (CCS), the set of all theoretically conceivable combinations chemical elements and (meta-)stable geometries that make up matter, is colossal. The first-principles based virtual sampling this space, for example, in search novel molecules or materials which exhibit desirable properties, therefore prohibitive but smallest subsets simplest properties. We review studies aimed at tackling challenge using modern machine learning techniques on (i) synthetic data, typically generated quantum mechanics methods, (ii) model architectures inspired by mechanics. Such Quantum Machine Learning (QML) approaches combine numerical efficiency statistical surrogate models with an ab initio view matter. They rigorously reflect underlying physics order to reach universality transferability across CCS. While state-of-the-art approximations problems impose severe computational bottlenecks, recent QML developments indicate possibility substantial acceleration without sacrificing predictive power

Language: Английский

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Material Evolution with Nanotechnology, Nanoarchitectonics, and Materials Informatics: What will be the Next Paradigm Shift in Nanoporous Materials?

Advanced Materials, Journal Year: 2021, Volume and Issue: 34(7)

Published: Oct. 13, 2021

Abstract Materials science and chemistry have played a central significant role in advancing society. With the shift toward sustainable living, it is anticipated that development of functional materials will continue to be vital for sustaining life on our planet. In recent decades, rapid progress has been made owing advances experimental, analytical, computational methods, thereby producing several novel useful materials. However, most problems material are highly complex. Here, best strategy via implementation three key concepts discussed: nanotechnology as game changer, nanoarchitectonics an integrator, informatics super‐accelerator. Discussions from conceptual viewpoints example developments, chiefly focused nanoporous materials, presented. It coupling these strategies together open advanced routes swift design exploratory search truly solving real‐world problems. These result evolution

Language: Английский

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Recent advances in computational modeling of MOFs: From molecular simulations to machine learning

Coordination Chemistry Reviews, Journal Year: 2023, Volume and Issue: 484, P. 215112 - 215112

Published: March 21, 2023

The reticular chemistry of metal–organic frameworks (MOFs) allows for the generation an almost boundless number materials some which can be a substitute traditionally used porous in various fields including gas storage and separation, catalysis, drug delivery. MOFs their potential applications are growing so quickly that, when novel synthesized, testing them all possible is not practical. High-throughput computational screening approaches based on molecular simulations have been widely to investigate identify optimal specific application. Despite resources, given enormous MOF material space, identification promising requires more efficient terms time effort. Leveraging data-driven science techniques offer key benefits such as accelerated design discovery pathways via establishment machine learning (ML) models interpretation complex structure-performance relationships that reach beyond expert intuition. In this review, we present scientific breakthroughs propelled modeling discuss state-of-the-art extending from ML algorithms. Finally, provide our perspective opportunities challenges future big discovery.

Language: Английский

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High-throughput predictions of metal–organic framework electronic properties: theoretical challenges, graph neural networks, and data exploration

npj Computational Materials, Journal Year: 2022, Volume and Issue: 8(1)

Published: May 17, 2022

Abstract With the goal of accelerating design and discovery metal–organic frameworks (MOFs) for electronic, optoelectronic, energy storage applications, we present a dataset predicted electronic structure properties thousands MOFs carried out using multiple density functional approximations. Compared to more accurate hybrid functionals, find that widely used PBE generalized gradient approximation (GGA) severely underpredicts MOF band gaps in largely systematic manner semi-conductors insulators without magnetic character. However, an even larger less predictable disparity gap prediction is with open-shell 3 d transition metal cations. regards partial atomic charges, different approximations predict similar charges overall, although functionals tend shift electron away from centers onto ligand environments compared GGA point reference. Much significant differences are observed when comparing charge partitioning schemes. We conclude by computed train machine-learning models can rapidly all four considered this work, paving way future high-throughput screening studies. To encourage exploration reuse theoretical calculations presented curated data made publicly available via interactive user-friendly web application on Materials Project.

Language: Английский

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