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基于机器学习和知识发现的材料探索

Materials Discovery With Machine Learning and Knowledge Discovery.

作者信息

Oliveira Osvaldo N, Oliveira Maria Cristina F

机构信息

Sao Carlos Institute of Physics (IFSC), University of Sao Paulo, Sao Paulo, Brazil.

Institute of Mathematics and Computer Science (ICMC), University of Sao Paulo, Sao Paulo, Brazil.

出版信息

Front Chem. 2022 Jul 7;10:930369. doi: 10.3389/fchem.2022.930369. eCollection 2022.

Abstract

Machine learning and other artificial intelligence methods are gaining increasing prominence in chemistry and materials sciences, especially for materials design and discovery, and in data analysis of results generated by sensors and biosensors. In this paper, we present a perspective on this current use of machine learning, and discuss the prospects of the future impact of extending the use of machine learning to encompass knowledge discovery as an essential step towards a new paradigm of machine-generated knowledge. The reasons why results so far have been limited are given with a discussion of the limitations of machine learning in tasks requiring interpretation. Also discussed is the need to adapt the training of students and scientists in chemistry and materials sciences, to better explore the potential of artificial intelligence capabilities.

摘要

机器学习和其他人工智能方法在化学和材料科学领域正日益受到关注,特别是在材料设计与发现方面,以及在传感器和生物传感器所产生结果的数据分析中。在本文中,我们阐述了对机器学习当前应用情况的看法,并探讨了将机器学习的应用扩展到知识发现领域的未来影响前景,这是迈向机器生成知识新范式的关键一步。文中给出了迄今为止成果有限的原因,并讨论了机器学习在需要解释的任务中的局限性。此外,还讨论了调整化学和材料科学领域学生和科学家培训方式的必要性,以便更好地挖掘人工智能能力的潜力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/89cf/9300917/8d703f514730/fchem-10-930369-g001.jpg

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