Recent advances and applications of machine learning in solid-state materials science

npj Computational Materials, Год журнала: 2019, Номер 5(1)

Опубликована: Авг. 8, 2019

Abstract One of the most exciting tools that have entered material science toolbox in recent years is machine learning. This collection statistical methods has already proved to be capable considerably speeding up both fundamental and applied research. At present, we are witnessing an explosion works develop apply learning solid-state systems. We provide a comprehensive overview analysis research this topic. As starting point, introduce principles, algorithms, descriptors, databases materials science. continue with description different approaches for discovery stable prediction their crystal structure. Then discuss numerous quantitative structure–property relationships various replacement first-principle by review how active surrogate-based optimization can improve rational design process related examples applications. Two major questions always interpretability physical understanding gained from models. consider therefore facets importance Finally, propose solutions future paths challenges computational

Язык: Английский

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Metal halide perovskites for light-emitting diodes

Nature Materials, Год журнала: 2020, Номер 20(1), С. 10 - 21

Опубликована: Сен. 14, 2020

Язык: Английский

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Artificial intelligence: A powerful paradigm for scientific research

The Innovation, Год журнала: 2021, Номер 2(4), С. 100179 - 100179

Опубликована: Окт. 29, 2021

•"Can machines think?" The goal of artificial intelligence (AI) is to enable mimic human thoughts and behaviors, including learning, reasoning, predicting, so on.•"Can AI do fundamental research?" coupled with machine learning techniques impacting a wide range sciences, mathematics, medical science, physics, etc.•"How does accelerate New research applications are emerging rapidly the support by infrastructure, data storage, computing power, algorithms, frameworks. Artificial promising (ML) well known from computer science broadly affecting many aspects various fields technology, industry, even our day-to-day life. ML have been developed analyze high-throughput view obtaining useful insights, categorizing, making evidence-based decisions in novel ways, which will promote growth fuel sustainable booming AI. This paper undertakes comprehensive survey on development application different information materials geoscience, life chemistry. challenges that each discipline meets, potentials handle these challenges, discussed detail. Moreover, we shed light new trends entailing integration into scientific discipline. aim this provide broad guideline sciences potential infusion AI, help motivate researchers deeply understand state-of-the-art AI-based thereby continuous sciences.

Язык: Английский

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Machine learning in materials science

InfoMat, Год журнала: 2019, Номер 1(3), С. 338 - 358

Опубликована: Сен. 1, 2019

Abstract Traditional methods of discovering new materials, such as the empirical trial and error method density functional theory (DFT)‐based method, are unable to keep pace with development materials science today due their long cycles, low efficiency, high costs. Accordingly, its computational cost short cycle, machine learning is coupled powerful data processing prediction performance being widely used in material detection, analysis, design. In this article, we discuss basic operational procedures analyzing properties via learning, summarize recent applications algorithms several mature fields science, improvements that required for wide‐ranging application.

Язык: Английский

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Chiral-perovskite optoelectronics

Nature Reviews Materials, Год журнала: 2020, Номер 5(6), С. 423 - 439

Опубликована: Март 10, 2020

Язык: Английский

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From DFT to machine learning: recent approaches to materials science–a review

Journal of Physics Materials, Год журнала: 2019, Номер 2(3), С. 032001 - 032001

Опубликована: Фев. 19, 2019

Abstract Recent advances in experimental and computational methods are increasing the quantity complexity of generated data. This massive amount raw data needs to be stored interpreted order advance materials science field. Identifying correlations patterns from large amounts complex is being performed by machine learning algorithms for decades. Recently, community started invest these methodologies extract knowledge insights accumulated review follows a logical sequence starting density functional theory as representative instance electronic structure methods, subsequent high-throughput approach, used generate Ultimately, data-driven strategies which include mining, screening, techniques, employ generated. We show how approaches modern uncover complexities design novel with enhanced properties. Finally, we point present research problems, challenges, potential future perspectives this new exciting

Язык: Английский

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Low‐Dimensional Metal Halide Perovskite Photodetectors

Advanced Materials, Год журнала: 2020, Номер 33(7)

Опубликована: Дек. 21, 2020

Abstract Metal halide perovskites (MHPs) have been a hot research topic due to their facile synthesis, excellent optical and optoelectronic properties, record‐breaking efficiency of corresponding devices. Nowadays, the development miniaturized high‐performance photodetectors (PDs) has fueling demand for novel photoactive materials, among which low‐dimensional MHPs attracted burgeoning interest. In this report, photodetection performance, stability MHPs, including 0D, 1D, 2D layered nonlayered nanostructures, as well heterostructures are reviewed. Recent advances in synthesis approaches summarized key concepts understanding properties related PD applications introduced. More importantly, recent progress PDs based on is presented, strategies improving performance perovskite highlighted. By discussing advances, strategies, existing challenges, report provides perspectives MHP‐based future.

Язык: Английский

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A Critical Review of Machine Learning of Energy Materials

Advanced Energy Materials, Год журнала: 2020, Номер 10(8)

Опубликована: Янв. 29, 2020

Abstract Machine learning (ML) is rapidly revolutionizing many fields and starting to change landscapes for physics chemistry. With its ability solve complex tasks autonomously, ML being exploited as a radically new way help find material correlations, understand materials chemistry, accelerate the discovery of materials. Here, an in‐depth review application energy materials, including rechargeable alkali‐ion batteries, photovoltaics, catalysts, thermoelectrics, piezoelectrics, superconductors, presented. A conceptual framework first provided in science, with broad overview different techniques well best practices. This followed by critical discussion how applied concluded perspectives on major challenges opportunities this exciting field.

Язык: Английский

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High-performance quasi-2D perovskite light-emitting diodes: from materials to devices

Light Science & Applications, Год журнала: 2021, Номер 10(1)

Опубликована: Март 19, 2021

Abstract Quasi-two-dimensional (quasi-2D) perovskites have attracted extraordinary attention due to their superior semiconducting properties and emerged as one of the most promising materials for next-generation light-emitting diodes (LEDs). The outstanding optical originate from structural characteristics. In particular, inherent quantum-well structure endows them with a large exciton binding energy strong dielectric- quantum-confinement effects; corresponding transfer among different n -value species thus results in high photoluminescence quantum yields (PLQYs), particularly at low excitation intensities. review herein presents an overview quasi-2D perovskite materials, spectral tunability methodologies thin films, well application high-performance LEDs. We then summarize challenges potential research directions towards developing stable PeLEDs. provides systematic timely summary community deepen understanding resulting LED devices.

Язык: Английский

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Opportunities and Challenges for Machine Learning in Materials Science

Annual Review of Materials Research, Год журнала: 2020, Номер 50(1), С. 71 - 103

Опубликована: Май 6, 2020

Advances in machine learning have impacted myriad areas of materials science, ranging from the discovery novel to improvement molecular simulations, with likely many more important developments come. Given rapid changes this field, it is challenging understand both breadth opportunities as well best practices for their use. In review, we address aspects problems by providing an overview where has recently had significant impact and then provide a detailed discussion on determining accuracy domain applicability some common types models. Finally, discuss challenges community fully utilize capabilities learning.

Язык: Английский

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