机器学习在材料研究中的背景与应用综述

Robert Ahmed, Christna Ahler
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引用次数: 0

摘要

近几十年来,人工智能(AI)因其促进更高水平自动化和加快整体产出的潜力而获得了相当大的兴趣。训练数据集的数量、处理能力和深度学习技术都有了显著的增加,这些都有利于人工智能在材料科学等领域的广泛应用。试图通过试错来学习新东西是一种缓慢而无效的方法。因此,人工智能,尤其是机器学习,可能会通过从信息中收集规则和构建预测模型来加速这一过程。在传统的计算化学中,人类科学家给出公式,计算机只是处理数字。在本文中,我们将回顾人工智能在新材料创造中的应用方式,例如设计、性能预测和合成。在这些项目中,重点放在人工智能方法实施的细节和比传统方法获得的好处上。最后一部分从算法和基础设施的角度阐述了人工智能未来的发展方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Review on Background and Applications of Machine Learning in Materials Research
In recent decades, Artificial Intelligence (AI) has garnered considerable interest owing to its potential to facilitate greater levels of automation and speed up overall output. There has been a significant increase in the quantity of training data sets, processing capacity, and deep learning techniques that are all favorable to the widespread use of AI in fields like material science. Attempting to learn anything new by trial and error is a slow and ineffective approach. Therefore, AI, and particularly machine learning, may hasten the process by gleaning rules from information and constructing predictive models. In traditional computational chemistry, human scientists give the formulae, and the computer just crunches the numbers. In this article, we take a look back at the ways in which artificial intelligence has been put to use in the creation of new materials, such as in their design, performance prediction, and synthesis. In these programs, an emphasis is placed on the specifics of AI methodology implementation and the benefits gained over more traditional approaches. The last section elaborates, from both an algorithmic and an infrastructural perspective, where AI is headed in the future.
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