Predicting electronic structures at any length scale with machine learning

Fiedler, Lenz; Modine, Normand A.; Schmerler, Steve; Vogel, Dayton J.; Popoola, Gabriel; Thompson, Aidan P.; Rajamanickam, Sivasankaran; Cangi, Attila

doi:10.1038/s41524-023-01070-z

Cited by 29 publications

(14 citation statements)

References 79 publications

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“…Such an approach allows one to extend the prediction of electronic structures to much larger length scales, overcoming a key limitation of DFT. [140] Combining ML and DFT calculations enables a systematic investigation of the intrinsic connection between the structure and properties of potential materials. The DFT-ML framework screens structures with desirable target values by the established ML models and subsequently validates these materials via DFT calculations.…”

Section: Machine Learning For Simulations: Make It Faster Make It Bettermentioning

confidence: 99%

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Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures

Kuznetsova,

Coogan,

Botov

et al. 2024

Advanced Materials

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Machine learning holds significant research potential in the field of nanotechnology, enabling nanomaterial structure and property predictions, facilitating the design of novel materials, and reducing the need for time‐consuming and labour‐intensive experiments and simulations. In contrast to their achiral counterparts, the application of machine learning for chiral nanomaterials and nanostructures is still in its infancy, with a limited number of publications to date. This is despite the great potential of machine learning to advance the development of new sustainable chiral materials with high values of optical activity, circularly polarised luminescence, and enantioselectivity, as well as for the analysis of structural chirality by electron microscopy. In this review, we provide an analysis of machine learning methods used in studying achiral nanomaterials, subsequently offering guidance on adapting and extending this work to chiral nanomaterials. We present an overview of chiral nanomaterials within the framework of synthesis‐structure‐property‐application relationships and provide insights on how to leverage machine learning for the study of these highly complex relationships. We also review and discuss some key recent publications on the application of machine learning for chiral nanomaterials. Finally, the review captures the key achievements, ongoing challenges, and the prospective outlook for this very important research field.This article is protected by copyright. All rights reserved

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Section: Machine Learning For Simulations: Make It Faster Make It Bettermentioning

confidence: 99%

“…[145] In summary, the advancement of ML-assisted computer simulations is propelling rapidly toward the capability of the prediction of properties of any possible material with near ab initio precision, in increasingly shorter times [146] and at increasingly massive length scales. [140]…”

Section: Machine Learning For Simulations: Make It Faster Make It Bettermentioning

confidence: 99%

Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures

Kuznetsova,

Coogan,

Botov

et al. 2024

Advanced Materials

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“…[33,189,190] It can be seen that with powerful data analysis capabilities and low research costs, AI has been widely used in property prediction, material structure search, and new material design. At the application level, AI not only has great advantages over traditional calculation methods in different fields, but also has more and more achievements in different material modeling tasks, such as electronic structure, [51,[191][192][193] ionic conductivity, [83,94,194] stability, [195][196][197][198] mechanical property, [199][200][201] optical property, [202][203][204] magnetism, [205,206] [53] Copyright 2021, The Authors, published by Springer Nature.…”

Section: Other Explorationsmentioning

confidence: 99%

MatGPT: A Vane of Materials Informatics from Past, Present, to Future

Wang,

Chen,

Tao

et al. 2023

Advanced Materials

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Combining materials science, artificial intelligence (AI), physical chemistry and other disciplines, materials informatics is continuously accelerating the vigorous development of new materials. The emergence of “GPT AI” shows that the scientific research field has entered the era of intelligent civilization with “data” as the basic factor and “algorithm + computing power” as the core productivity. The continuous innovation of AI will impact the cognitive laws and scientific methods, and reconstruct the knowledge and wisdom system. This leads us to think more about materials informatics, both in terms of opportunities and challenges. In this review, we provide a comprehensive discussion of AI models, materials infrastructures, and review the current advances in the discovery and design of new materials. With the rise of new research paradigms triggered by “AI for Science”, we propose the vane of materials informatics: “MatGPT”, and carry out the technical path planning from the aspects of data, descriptors, generative models, pre‐training models, directed design models, collaborative training, experimental robots, as well as the efforts and preparations needed to develop a new generation of materials informatics. Finally, we discuss the challenges and constraints faced by materials informatics, in order to achieve a more digital, intelligent and automated construction of materials informatics with the joint efforts of more interdisciplinary scientists. This article is protected by copyright. All rights reserved

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“…The materials learning algorithms (MALA) framework. [139] (b) The Mat2Spec model architecture. [140] (c) Corpus training process.…”

Section: Process Optimizationmentioning

confidence: 99%

“…Representative machine learning methods. (a)The materials learning algorithms (MALA) framework [139]. (b) The Mat2Spec model architecture [140].…”

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confidence: 99%

Applications and potentials of machine learning in optoelectronic materials research: An overview and perspectives

Zhang 张,

Fu 付

2023

Chinese Phys. B

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Optoelectronic materials are essential for today's scientific and technological development, and machine learning provides new ideas and tools for their research. In this paper, we first summarize the development history of optoelectronic materials and how materials informatics drives the innovation and progress of optoelectronic materials and devices. Then, we introduce the development of machine learning and its general process in optoelectronic materials and describe the specific implementation methods. We focus on the cases of machine learning in several application scenarios of optoelectronic materials and devices, including the methods related to crystal structure, properties (defects, electronic structure) research, materials and devices optimization, material characterization, and process optimization. In summarizing the algorithms and feature representations used in different studies, it is noted that prior knowledge can improve optoelectronic materials design, research, and decision-making processes. Finally, the prospect of machine learning applications in optoelectronic materials is discussed, along with current challenges and future directions. This paper comprehensively describes the application value of machine learning in optoelectronic materials research and aims to provide reference and guidance for the continuous development of this field.

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Predicting electronic structures at any length scale with machine learning

Cited by 29 publications

References 79 publications

Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures

Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures

MatGPT: A Vane of Materials Informatics from Past, Present, to Future

Applications and potentials of machine learning in optoelectronic materials research: An overview and perspectives

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