Akinpelu Akinwumi, Bhullar Mangladeep, Yao Yansun
Department of Physics and Engineering Physics, University of Saskatchewan, Saskatoon, Saskatchewan S7N 5E2, Canada.
J Phys Condens Matter. 2024 Aug 14;36(45). doi: 10.1088/1361-648X/ad6bdb.
Experimental exploration of new materials relies heavily on a laborious trial-and-error approach. In addition to substantial time and resource requirements, traditional experiments and computational modelling are typically limited in finding target materials within the enormous chemical space. Therefore, creating innovative techniques to expedite material discovery becomes essential. Recently, machine learning (ML) has emerged as a valuable tool for material discovery, garnering significant attention due to its remarkable advancements in prediction accuracy and time efficiency. This rapidly developing computational technique accelerates the search and optimization process and enables the prediction of material properties at a minimal computational cost, thereby facilitating the discovery of novel materials. We provide a comprehensive overview of recent studies on discovering new materials by predicting materials and their properties using ML techniques. Beginning with an introduction of the fundamental principles of ML methods, we subsequently examine the current research landscape on the applications of ML in predicting material properties that lead to the discovery of novel materials. Finally, we discuss challenges in employing ML within materials science, propose potential solutions, and outline future research directions.
新材料的实验探索在很大程度上依赖于繁琐的反复试验方法。除了需要大量的时间和资源外，传统实验和计算建模在从庞大的化学空间中寻找目标材料方面通常也存在局限性。因此，创建创新技术以加速材料发现变得至关重要。最近，机器学习（ML）已成为材料发现的一种有价值的工具，由于其在预测准确性和时间效率方面取得的显著进展而备受关注。这种快速发展的计算技术加速了搜索和优化过程，并能够以最小的计算成本预测材料特性，从而促进新型材料的发现。我们全面概述了最近关于使用ML技术预测材料及其特性来发现新材料的研究。首先介绍ML方法的基本原理，随后考察ML在预测导致新型材料发现的材料特性方面的当前研究状况。最后，我们讨论在材料科学中应用ML时面临的挑战，提出潜在的解决方案，并概述未来的研究方向。
