生成式人工智能与未来材料科学

朱宏伟

doi:10.3969/j.issn.0253-9608.2024.01.005

自然杂志 >

2024 , Vol. 46 >Issue 1: 46 - 49

DOI: https://doi.org/10.3969/j.issn.0253-9608.2024.01.005

科学创造未来

生成式人工智能与未来材料科学

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清华大学材料学院，北京 100084

收稿日期: 2023-12-18

网络出版日期: 2024-02-20

基金资助

国家自然科学基金面上项目(52172046)

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Generative artificial intelligence and future materials science

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School of Materials Science and Engineering, Tsinghua University, Beijing 100084, China

Received date: 2023-12-18

Online published: 2024-02-20

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摘要

人工智能是推动数据驱动科学飞速发展的强大动力。文章以深度学习在材料科学中的应用为例，介绍了生成式模型的概念，及其在新材料发现和自动化实验室等领域的应用。生成式人工智能不仅极大地加速了材料的研发进程，同时提高了整个研发过程的透明度和可信度，开启了材料科学研究的新纪元。

关键词： 人工智能; 深度学习; 生成式模型; 材料科学; 自动化实验室

本文引用格式

朱宏伟 . 生成式人工智能与未来材料科学[J]. 自然杂志, 2024 , 46(1) : 46 -49 . DOI: 10.3969/j.issn.0253-9608.2024.01.005

Abstract

Artificial intelligence is a powerful driving force behind the rapid development of data-driven science. This article, using the application of deep learning in materials science as an example, introduces the concept of generative models, and their applications in the discovery of new materials and autonomous laboratories. Generative artificial intelligence not only greatly accelerates the research and development process of materials, but also enhances the transparency and credibility of the entire process, heralding a new era in materials science research.

参考文献

[1] MERCHANT A, BATZNER S, SCHOENHOLZ S S, et al. Scaling deep learning for materials discovery [J]. Nature, 2023, 624(7990):
80-85.
[2] SZYMANSKI N J, RENDY B, FEI Y, et al. An autonomous laboratory for the accelerated synthesis of novel materials [J]. Nature, 2023, 624(7990): 86-91.
[3] BURGER B, MAFFETTONE P M, GUSEV V V, et al. A mobile robotic chemist [J]. Nature, 2020, 583(7815): 237-241.
[4] MEHR S H M, CRAVEN M, LEONOV A I, et al. A universal system for digitization and automatic execution of the chemical synthesis literature [J]. Science, 2020, 370(6512): 101-108.
[5] LECUN Y, BENGIO Y, HINTON G. Deep learning [J]. Nature, 2015, 521(7553): 436-444.
[6] BOND-TAYLOR S, LEACH A, LONG Y, et al. Deep generative modelling: A comparative review of VAEs, GANs, normalizing
flows, energy-based and autoregressive models [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022,

44(11): 7327-7347.

[7] SAEED W, OMLIN C. Explainable AI (XAI): A systematic meta-survey of current challenges and future opportunities [J]. Knowledge-Based Systems, 2023, 263: 110273.
[8] 朱宏伟. 二维材料在人工智能中的应用[J]. 自然杂志, 2022, 44(6): 466-468.
[9] 朱宏伟. 6G时代的新材料[J]. 自然杂志, 2023, 45(2): 109-112.
[10] KASPAR C, RAVOO B J, VAN DER WIEL W G, et al. The rise of intelligent matter [J]. Nature, 2021, 594(7863): 345-355.

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