Empowering research in chemistry and materials science through intelligent algorithms

In this review, we explore the integration of intelligent algorithms in chemistry and materials science.We begin by delineating the core principles of Machine Learning, Deep Learning, and optimization algorithms, highlighting their bespoke adaptation to these scientific domains. The focus then shift...

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Vydáno v:Artificial intelligence chemistry Ročník 2; číslo 1; s. 100035
Hlavní autoři: Lin, Jinglong, Mo, Fanyang
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 01.06.2024
Elsevier
Témata:
Deep Learning
Intelligent Algorithms
Machine Learning
Intelligent Algorithms
Machine Learning
Deep Learning
ISSN:2949-7477, 2949-7477
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Shrnutí:In this review, we explore the integration of intelligent algorithms in chemistry and materials science.We begin by delineating the core principles of Machine Learning, Deep Learning, and optimization algorithms, highlighting their bespoke adaptation to these scientific domains. The focus then shifts to the critical processes of data management, including collection, refinement, and feature engineering, alongside strategies for efficient data mining from targeted databases and literatures. Subsequently, we present a concise overview of the diverse applications of these algorithms, emphasizing their transformative impact in both fields. Finally, this review explores the future prospects and challenges of these emerging algorithms.
ISSN:2949-7477
2949-7477
DOI:10.1016/j.aichem.2023.100035