• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法，用中文像聊天一样搜索，搜遍4000万医学文献。AI智能推荐，让科研检索更轻松。

立即免费搜索

文件翻译

保留排版，准确专业，支持PDF/Word/PPT等文件格式，支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述，25分钟生成高质量综述，智能提取关键信息，辅助科研写作。

立即免费体验

人工智能驱动的材料科学

Artificial Intelligence-Powered Materials Science.

作者信息

Bai Xiaopeng, Zhang Xingcai

机构信息

Department of Mechanical Engineering, The University of Hong Kong, Hong Kong, 999077, People's Republic of China.

Department of Physics, The Chinese University of Hong Kong, Shatin, Hong Kong, 999077, People's Republic of China.

出版信息

Nanomicro Lett. 2025 Feb 6;17(1):135. doi: 10.1007/s40820-024-01634-8.

DOI:10.1007/s40820-024-01634-8
PMID:39912967
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11803041/
Abstract

The advancement of materials has played a pivotal role in the advancement of human civilization, and the emergence of artificial intelligence (AI)-empowered materials science heralds a new era with substantial potential to tackle the escalating challenges related to energy, environment, and biomedical concerns in a sustainable manner. The exploration and development of sustainable materials are poised to assume a critical role in attaining technologically advanced solutions that are environmentally friendly, energy-efficient, and conducive to human well-being. This review provides a comprehensive overview of the current scholarly progress in artificial intelligence-powered materials science and its cutting-edge applications. We anticipate that AI technology will be extensively utilized in material research and development, thereby expediting the growth and implementation of novel materials. AI will serve as a catalyst for materials innovation, and in turn, advancements in materials innovation will further enhance the capabilities of AI and AI-powered materials science. Through the synergistic collaboration between AI and materials science, we stand to realize a future propelled by advanced AI-powered materials.

摘要

材料的进步在人类文明的发展中发挥了关键作用，而人工智能赋能的材料科学的出现预示着一个新时代的到来，该时代具有以可持续方式应对与能源、环境和生物医学问题相关的不断升级的挑战的巨大潜力。可持续材料的探索与开发对于实现技术先进、环境友好、节能且有利于人类福祉的解决方案将起到关键作用。本综述全面概述了人工智能赋能的材料科学的当前学术进展及其前沿应用。我们预计人工智能技术将在材料研发中得到广泛应用，从而加速新型材料的发展与应用。人工智能将成为材料创新的催化剂，而材料创新的进步反过来又将进一步提升人工智能及人工智能赋能的材料科学的能力。通过人工智能与材料科学的协同合作，我们有望实现一个由先进的人工智能赋能材料推动的未来。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/88fe/11803041/462fdbe80301/40820_2024_1634_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/88fe/11803041/32fa06a1ddfd/40820_2024_1634_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/88fe/11803041/1db7d04003eb/40820_2024_1634_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/88fe/11803041/2ac1bb65a25b/40820_2024_1634_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/88fe/11803041/d3efa25148eb/40820_2024_1634_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/88fe/11803041/635dc67664bf/40820_2024_1634_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/88fe/11803041/f4bbef5f11ef/40820_2024_1634_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/88fe/11803041/462fdbe80301/40820_2024_1634_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/88fe/11803041/32fa06a1ddfd/40820_2024_1634_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/88fe/11803041/1db7d04003eb/40820_2024_1634_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/88fe/11803041/2ac1bb65a25b/40820_2024_1634_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/88fe/11803041/d3efa25148eb/40820_2024_1634_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/88fe/11803041/635dc67664bf/40820_2024_1634_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/88fe/11803041/f4bbef5f11ef/40820_2024_1634_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/88fe/11803041/462fdbe80301/40820_2024_1634_Fig7_HTML.jpg

相似文献

1
Artificial Intelligence-Powered Materials Science.人工智能驱动的材料科学
Nanomicro Lett. 2025 Feb 6;17(1):135. doi: 10.1007/s40820-024-01634-8.
2
Pharmacovigilance in the Era of Artificial Intelligence: Advancements, Challenges, and Considerations.人工智能时代的药物警戒：进展、挑战与思考
Cureus. 2025 Jun 29;17(6):e86972. doi: 10.7759/cureus.86972. eCollection 2025 Jun.
3
Research status, hotspots and perspectives of artificial intelligence applied to pain management: a bibliometric and visual analysis.人工智能应用于疼痛管理的研究现状、热点与展望：一项文献计量学与可视化分析
Updates Surg. 2025 Jun 28. doi: 10.1007/s13304-025-02296-w.
4
AML diagnostics in the 21st century: Use of AI.21世纪的急性髓系白血病诊断：人工智能的应用。
Semin Hematol. 2025 Jun 16. doi: 10.1053/j.seminhematol.2025.06.002.
5
Blockchain Integration With Digital Technology and the Future of Health Care Ecosystems: Systematic Review.区块链与数字技术融合与医疗保健生态系统的未来：系统评价。
J Med Internet Res. 2021 Nov 2;23(11):e19846. doi: 10.2196/19846.
6
Application of artificial intelligence to electronic health record data in long-term care facilities: a scoping review protocol.人工智能在长期护理机构电子健康记录数据中的应用：一项范围综述方案
BMJ Open. 2025 Jul 16;15(7):e098091. doi: 10.1136/bmjopen-2024-098091.
7
Leadership in radiology in the era of technological advancements and artificial intelligence.技术进步与人工智能时代的放射学领导力。
Eur Radiol. 2025 Jun 27. doi: 10.1007/s00330-025-11745-4.
8
Bioanalysis of antihypertensive drugs by LC-MS: a fleeting look at the regulatory guidelines and artificial intelligence.基于液相色谱-质谱联用技术的抗高血压药物生物分析：对监管指南和人工智能的简要审视
Bioanalysis. 2025 Apr;17(7):471-487. doi: 10.1080/17576180.2025.2489917. Epub 2025 Apr 21.
9
The Use of Artificial Intelligence and Wearable Inertial Measurement Units in Medicine: Systematic Review.人工智能与可穿戴惯性测量单元在医学中的应用：系统评价
JMIR Mhealth Uhealth. 2025 Jan 29;13:e60521. doi: 10.2196/60521.
10
AI for IMPACTS Framework for Evaluating the Long-Term Real-World Impacts of AI-Powered Clinician Tools: Systematic Review and Narrative Synthesis.用于评估人工智能驱动的临床医生工具长期现实世界影响的AI for IMPACTS框架：系统评价与叙述性综合分析
J Med Internet Res. 2025 Feb 5;27:e67485. doi: 10.2196/67485.

引用本文的文献

1
Layer-by-layer assembly: an emerging, tailored and robust platform for solar water splitting.逐层组装：一种新兴的、定制化且稳健的太阳能水分解平台。
Chem Sci. 2025 Aug 29. doi: 10.1039/d5sc04478b.
2
Emerging Nonvolatile Memory Technologies in the Future of Microelectronics.微电子未来的新兴非易失性存储技术。
ACS Omega. 2025 Jun 30;10(27):28492-28498. doi: 10.1021/acsomega.5c01414. eCollection 2025 Jul 15.
3
Iron-Based High-Temperature Alloys: Alloying Strategies and New Opportunities.铁基高温合金：合金化策略与新机遇

本文引用的文献

1
Machine-Learning Mental-Fatigue-Measuring μm-Thick Elastic Epidermal Electronics (MMMEEE).机器学习精神疲劳测量微米级厚弹性表皮电子器件（MMMEEE）
Nano Lett. 2024 Dec 25;24(51):16221-16230. doi: 10.1021/acs.nanolett.4c02474. Epub 2024 Nov 27.
2
Microorganism microneedle micro-engine depth drug delivery.微生物微针微引擎深度药物递送。
Nat Commun. 2024 Oct 17;15(1):8947. doi: 10.1038/s41467-024-53280-8.
3
Transvascular transport of nanocarriers for tumor delivery.纳米载体经血管向肿瘤内的转运。
Materials (Basel). 2025 Jun 24;18(13):2989. doi: 10.3390/ma18132989.
4
Correction: Artificial Intelligence-Powered Materials Science.更正：人工智能驱动的材料科学。
Nanomicro Lett. 2025 Apr 10;17(1):211. doi: 10.1007/s40820-025-01731-2.
5
Machine Learning-Driven Prediction of Composite Materials Properties Based on Experimental Testing Data.基于实验测试数据的机器学习驱动的复合材料性能预测
Polymers (Basel). 2025 Mar 5;17(5):694. doi: 10.3390/polym17050694.
Nat Commun. 2024 Sep 17;15(1):8172. doi: 10.1038/s41467-024-52416-0.
4
AI-recognized mitochondrial phenotype enables identification of drug targets.人工智能识别的线粒体表型有助于确定药物靶点。
Nat Comput Sci. 2024 Aug;4(8):563-564. doi: 10.1038/s43588-024-00682-9.
5
Deep learning large-scale drug discovery and repurposing.深度学习在药物发现和再利用中的应用。
Nat Comput Sci. 2024 Aug;4(8):600-614. doi: 10.1038/s43588-024-00679-4. Epub 2024 Aug 21.
6
Virus detection light diffraction fingerprints for biological applications.病毒检测光衍射指纹用于生物应用。
Sci Adv. 2024 Mar 15;10(11):eadl3466. doi: 10.1126/sciadv.adl3466. Epub 2024 Mar 13.
7
Minimum Minutes Machine-Learning Microfluidic Microbe Monitoring Method (M7).最小分钟数机器学习微流控微生物监测方法（M7）
ACS Nano. 2024 Feb 13;18(6):4862-4870. doi: 10.1021/acsnano.3c09733. Epub 2024 Jan 17.
8
An autonomous laboratory for the accelerated synthesis of novel materials.自主式实验室，用于加速新型材料的合成。
Nature. 2023 Dec;624(7990):86-91. doi: 10.1038/s41586-023-06734-w. Epub 2023 Nov 29.
9
Scaling deep learning for materials discovery.深度学习在材料发现中的应用。
Nature. 2023 Dec;624(7990):80-85. doi: 10.1038/s41586-023-06735-9. Epub 2023 Nov 29.
10
Deep-Learning Terahertz Single-Cell Metabolic Viability Study.深度学习太赫兹单细胞代谢活力研究。
ACS Nano. 2023 Nov 14;17(21):21383-21393. doi: 10.1021/acsnano.3c06084. Epub 2023 Sep 28.