The current research status and prospects of AI in chemical science

IF 4.8 2区材料科学 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY

Progress in Natural Science: Materials International Pub Date : 2024-10-01 DOI:10.1016/j.pnsc.2024.08.003

Minghao Yuan , Qinglang Guo , Yingxue Wang

{"title":"The current research status and prospects of AI in chemical science","authors":"Minghao Yuan , Qinglang Guo , Yingxue Wang","doi":"10.1016/j.pnsc.2024.08.003","DOIUrl":null,"url":null,"abstract":"<div><div>This paper primarily examines the utilization and obstacles of AI in the domain of chemistry. Machine learning facilitates the advancement of chemical research at every level through the use of AI. AI has significantly contributed to enhancing the efficiency of chemical experiments and manufacturing, as well as reducing costs, throughout the many phases of chemical study, application, and production. Its impact is particularly notable in the development of new materials and the discovery of drugs. Nevertheless, the implementation of AI in the domain of chemistry encounters numerous obstacles, including inadequate data quality, limited model interpretability, and data privacy concerns. To address these issues, it is imperative for the scientific and technological community to foster multidisciplinary collaboration, develop a more comprehensive and practical AI framework, and investigate more secure data security technologies. In the future, as AI continues to advance, the relationship between AI and chemical research will become more dependable and intimate. This will lead to increased efficiency, safety, and cost-effectiveness in chemical research, ushering in a new era in the field of chemistry.</div></div>","PeriodicalId":20742,"journal":{"name":"Progress in Natural Science: Materials International","volume":"34 5","pages":"Pages 859-872"},"PeriodicalIF":4.8000,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Progress in Natural Science: Materials International","FirstCategoryId":"88","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1002007124001898","RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}

引用次数: 0

Abstract

This paper primarily examines the utilization and obstacles of AI in the domain of chemistry. Machine learning facilitates the advancement of chemical research at every level through the use of AI. AI has significantly contributed to enhancing the efficiency of chemical experiments and manufacturing, as well as reducing costs, throughout the many phases of chemical study, application, and production. Its impact is particularly notable in the development of new materials and the discovery of drugs. Nevertheless, the implementation of AI in the domain of chemistry encounters numerous obstacles, including inadequate data quality, limited model interpretability, and data privacy concerns. To address these issues, it is imperative for the scientific and technological community to foster multidisciplinary collaboration, develop a more comprehensive and practical AI framework, and investigate more secure data security technologies. In the future, as AI continues to advance, the relationship between AI and chemical research will become more dependable and intimate. This will lead to increased efficiency, safety, and cost-effectiveness in chemical research, ushering in a new era in the field of chemistry.

查看原文本刊更多论文

人工智能在化学科学中的研究现状与前景

本文主要探讨人工智能在化学领域的应用和障碍。通过使用人工智能，机器学习在各个层面促进了化学研究的发展。在化学研究、应用和生产的多个阶段，人工智能为提高化学实验和生产效率以及降低成本做出了巨大贡献。其影响在新材料开发和药物发现方面尤为明显。然而，在化学领域实施人工智能会遇到许多障碍，包括数据质量不高、模型可解释性有限以及数据隐私问题。要解决这些问题，科技界必须促进多学科合作，开发更全面、更实用的人工智能框架，并研究更安全的数据安全技术。未来，随着人工智能的不断进步，人工智能与化学研究之间的关系将变得更加可靠和密切。这将提高化学研究的效率、安全性和成本效益，开创化学领域的新时代。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Progress in Natural Science: Materials International 综合性期刊-综合性期刊

CiteScore

8.60

自引率

2.10%

发文量

2812

审稿时长

49 days

期刊介绍： Progress in Natural Science: Materials International provides scientists and engineers throughout the world with a central vehicle for the exchange and dissemination of basic theoretical studies and applied research of advanced materials. The emphasis is placed on original research, both analytical and experimental, which is of permanent interest to engineers and scientists, covering all aspects of new materials and technologies, such as, energy and environmental materials; advanced structural materials; advanced transportation materials, functional and electronic materials; nano-scale and amorphous materials; health and biological materials; materials modeling and simulation; materials characterization; and so on. The latest research achievements and innovative papers in basic theoretical studies and applied research of material science will be carefully selected and promptly reported. Thus, the aim of this Journal is to serve the global materials science and technology community with the latest research findings. As a service to readers, an international bibliography of recent publications in advanced materials is published bimonthly.