Recent advances and applications of deep learning methods in materials science

npj Computational Materials, Год журнала: 2022, Номер 8(1)

Опубликована: Апрель 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.

Язык: Английский

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Photocatalytic CO2 reduction

Nature Reviews Methods Primers, Год журнала: 2023, Номер 3(1)

Опубликована: Авг. 10, 2023

Язык: Английский

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Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery

Chemical Reviews, Год журнала: 2022, Номер 122(16), С. 13478 - 13515

Опубликована: Июль 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.

Язык: Английский

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Materials for extreme environments

Nature Reviews Materials, Год журнала: 2022, Номер 8(2), С. 81 - 88

Опубликована: Ноя. 9, 2022

Язык: Английский

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Human- and machine-centred designs of molecules and materials for sustainability and decarbonization

Nature Reviews Materials, Год журнала: 2022, Номер 7(12), С. 991 - 1009

Опубликована: Авг. 24, 2022

Язык: Английский

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AlphaFlow: autonomous discovery and optimization of multi-step chemistry using a self-driven fluidic lab guided by reinforcement learning

Nature Communications, Год журнала: 2023, Номер 14(1)

Опубликована: Март 14, 2023

Closed-loop, autonomous experimentation enables accelerated and material-efficient exploration of large reaction spaces without the need for user intervention. However, advanced materials with complex, multi-step processes data sparse environments remains a challenge. In this work, we present AlphaFlow, self-driven fluidic lab capable discovery complex chemistries. AlphaFlow uses reinforcement learning integrated modular microdroplet reactor performing steps variable sequence, phase separation, washing, continuous in-situ spectral monitoring. To demonstrate power toward high dimensionality chemistries, use to discover optimize synthetic routes shell-growth core-shell semiconductor nanoparticles, inspired by colloidal atomic layer deposition (cALD). Without prior knowledge conventional cALD parameters, successfully identified optimized novel route, up 40 that outperformed sequences. Through capabilities closed-loop, learning-guided systems in exploring solving challenges nanoparticle syntheses, while relying solely on in-house generated from miniaturized microfluidic platform. Further application chemistries beyond can lead fundamental generation as well route discoveries optimization.

Язык: Английский

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Emerging Trends in Machine Learning: A Polymer Perspective

ACS Polymers Au, Год журнала: 2023, Номер 3(3), С. 239 - 258

Опубликована: Янв. 18, 2023

In the last five years, there has been tremendous growth in machine learning and artificial intelligence as applied to polymer science. Here, we highlight unique challenges presented by polymers how field is addressing them. We focus on emerging trends with an emphasis topics that have received less attention review literature. Finally, provide outlook for field, outline important areas science discuss advances from greater material community.

Язык: Английский

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From Platform to Knowledge Graph: Evolution of Laboratory Automation

JACS Au, Год журнала: 2022, Номер 2(2), С. 292 - 309

Опубликована: Янв. 10, 2022

High-fidelity computer-aided experimentation is becoming more accessible with the development of computing power and artificial intelligence tools. The advancement experimental hardware also empowers researchers to reach a level accuracy that was not possible in past. Marching toward next generation self-driving laboratories, orchestration both resources lies at focal point autonomous discovery chemical science. To achieve such goal, algorithmically data representations standardized communication protocols are indispensable. In this perspective, we recategorize recently introduced approach based on Materials Acceleration Platforms into five functional components discuss recent case studies focus representation exchange scheme between different components. Emerging technologies for interoperable multi-agent systems discussed their applications automation. We hypothesize knowledge graph technology, orchestrating semantic web systems, will be driving force bring knowledge, evolving our way automating laboratory.

Язык: Английский

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Recent Advances and Applications of Machine Learning in Experimental Solid Mechanics: A Review

Applied Mechanics Reviews, Год журнала: 2023, Номер 75(6)

Опубликована: Июль 17, 2023

Abstract For many decades, experimental solid mechanics has played a crucial role in characterizing and understanding the mechanical properties of natural novel artificial materials. Recent advances machine learning (ML) provide new opportunities for field, including design, data analysis, uncertainty quantification, inverse problems. As number papers published recent years this emerging field is growing exponentially, it timely to conduct comprehensive up-to-date review ML applications mechanics. Here, we first an overview common algorithms terminologies that are pertinent review, with emphasis placed on physics-informed physics-based methods. Then, thorough coverage traditional areas mechanics, fracture biomechanics, nano- micromechanics, architected materials, two-dimensional Finally, highlight some current challenges applying multimodality multifidelity datasets, quantifying predictions, proposing several future research directions. This aims valuable insights into use methods variety examples researchers integrate their experiments.

Язык: Английский

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Advances in machine learning-aided design of reinforced polymer composite and hybrid material systems

Hybrid Advances, Год журнала: 2023, Номер 2, С. 100026 - 100026

Опубликована: Фев. 4, 2023

Reinforced composite is a preferred choice of material for the design industrial lightweight structures. As late, materials analysis and development utilizing machine learning algorithms have been getting expanding consideration accomplished extraordinary upgrades in both time productivity expectation exactness. This review encapsulates recent advances learning-based reinforced during last half-decade. It summarizes limitations traditional methods presents detailed protocol technology; implementation was covered, with an emphasis on importance data hygiene. Machine integration process selection, sourcing techniques were also examined. The evaluation looked at emerging digital tools platforms implementing algorithms. In addition, essential effort made to identify research gaps define areas further research. indeed designed provide some direction future into use design.

Язык: Английский

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