Nanoparticle synthesis assisted by machine learning

Nature Reviews Materials, Год журнала: 2021, Номер 6(8), С. 701 - 716

Опубликована: Июль 13, 2021

Язык: Английский

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The Role of Machine Learning in the Understanding and Design of Materials

Journal of the American Chemical Society, Год журнала: 2020, Номер 142(48), С. 20273 - 20287

Опубликована: Ноя. 10, 2020

Developing algorithmic approaches for the rational design and discovery of materials can enable us to systematically find novel materials, which have huge technological social impact. However, such requires a holistic perspective over full multistage process, involves exploring immense spaces, their properties, process engineering as well techno-economic assessment. The complexity all these options using conventional scientific seems intractable. Instead, tools from field machine learning potentially solve some our challenges on way design. Here we review chief advancements methods applications in design, followed by discussion main opportunities currently face together with future discovery.

Язык: Английский

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The rise of self-driving labs in chemical and materials sciences

Nature Synthesis, Год журнала: 2023, Номер 2(6), С. 483 - 492

Опубликована: Янв. 30, 2023

Язык: Английский

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Autonomous experimentation systems for materials development: A community perspective

Matter, Год журнала: 2021, Номер 4(9), С. 2702 - 2726

Опубликована: Июль 26, 2021

Язык: Английский

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Machine learning for a sustainable energy future

Nature Reviews Materials, Год журнала: 2022, Номер 8(3), С. 202 - 215

Опубликована: Окт. 18, 2022

Язык: Английский

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Selective, sensitive, and stable NO2 gas sensor based on porous ZnO nanosheets

Applied Surface Science, Год журнала: 2021, Номер 568, С. 150910 - 150910

Опубликована: Авг. 17, 2021

Язык: Английский

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Data-driven materials research enabled by natural language processing and information extraction

Applied Physics Reviews, Год журнала: 2020, Номер 7(4)

Опубликована: Дек. 1, 2020

Given the emergence of data science and machine learning throughout all aspects society, but particularly in scientific domain, there is increased importance placed on obtaining data. Data materials are heterogeneous, based significant range classes that explored variety properties interest. This leads to many orders magnitude, these may manifest as numerical text or image-based information, which requires quantitative interpretation. The ability automatically consume codify literature across domains—enabled by techniques adapted from field natural language processing—therefore has immense potential unlock generate rich datasets necessary for learning. review focuses progress practices processing mining highlights opportunities extracting additional information beyond contained figures tables articles. We discuss provide examples several reasons pursuit materials, including compilation, hypothesis development, understanding trends within fields. Current emerging methods along with their applications detailed. We, then, challenges domain where future directions prove valuable.

Язык: Английский

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Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning

Chemical Reviews, Год журнала: 2021, Номер 121(16), С. 9927 - 10000

Опубликована: Июль 14, 2021

Transition-metal complexes are attractive targets for the design of catalysts and functional materials. The behavior metal-organic bond, while very tunable achieving target properties, is challenging to predict necessitates searching a wide complex space identify needles in haystacks applications. This review will focus on techniques that make high-throughput search transition-metal chemical feasible discovery with desirable properties. cover development, promise, limitations "traditional" computational chemistry (i.e., force field, semiempirical, density theory methods) as it pertains data generation inorganic molecular discovery. also discuss opportunities leveraging experimental sources. We how advances statistical modeling, artificial intelligence, multiobjective optimization, automation accelerate lead compounds rules. overall objective this showcase bringing together from diverse areas computer science have enabled rapid uncovering structure-property relationships chemistry. aim highlight unique considerations motifs bonding (e.g., variable spin oxidation state, strength/nature) set them their apart more commonly considered organic molecules. uncertainty relative scarcity motivate specific developments machine learning representations, model training, Finally, we conclude an outlook opportunity accelerated complexes.

Язык: Английский

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Machine learning in materials science: From explainable predictions to autonomous design

Computational Materials Science, Год журнала: 2021, Номер 193, С. 110360 - 110360

Опубликована: Март 10, 2021

Язык: Английский

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MatSciBERT: A materials domain language model for text mining and information extraction

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

Опубликована: Май 3, 2022

Abstract A large amount of materials science knowledge is generated and stored as text published in peer-reviewed scientific literature. While recent developments natural language processing, such Bidirectional Encoder Representations from Transformers (BERT) models, provide promising information extraction tools, these models may yield suboptimal results when applied on domain since they are not trained specific notations jargons. Here, we present a materials-aware model, namely, MatSciBERT, corpus publications. We show that MatSciBERT outperforms SciBERT, model corpus, establish state-of-the-art three downstream tasks, named entity recognition, relation classification, abstract classification. make the pre-trained weights publicly accessible for accelerated discovery texts.

Язык: Английский

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