The paradigm of molecular discovery in the chemical and pharmaceutical industry has followed a repetitive succession of screening and synthesis, involving the analysis of individual molecules that were both natural and produced. This ability to generate and screen libraries of compounds has found an echo in solid-state physics with the demand to explore and produce new materials for testing. In response to this demand, a golden age of materials discovery is being developed, with progress on important areas of both basic science and device applications. The confluence of theoretical and simulation methods, together with the availability of computation resources, has established the “materials genome” approach that is used by a growing number of research groups around the world with the goal of innovating on materials through systematic discovery. In this Prospective, an overview of this group of methodologies in tackling the ever-increasing complexity of computational materials science simulations is provided. Computational simulation is highlighted as a major component of rational design and synthesis of new materials with targeted properties, describing progress on databases and large data treatment. Tools for new materials discovery, including progress on the deployment of new data repositories, the implementation of high-throughput simulation approaches, and the development of artificial intelligence algorithms, are discussed.

中文翻译：

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不断发展的材料信息学领域：数据库和人工智能

化学和制药行业的分子发现范式遵循筛选和合成的重复序列，包括对天然和生产的单个分子的分析。这种生成和筛选化合物库的能力在固态物理学中找到了回应，需要探索和生产用于测试的新材料。为了满足这一需求，材料发现的黄金时代正在发展，基础科学和设备应用的重要领域都取得了进展。理论和模拟方法的融合，加上计算资源的可用性，建立了“材料基因组”方法，世界各地越来越多的研究小组正在使用该方法，其目标是通过系统发现对材料进行创新。在本展望中，提供了这组方法的概述，以解决计算材料科学模拟不断增加的复杂性。计算模拟被强调为合理设计和合成具有目标特性的新材料的主要组成部分，描述了数据库和大数据处理的进展。讨论了新材料发现的工具，包括部署新数据存储库的进展、​​高通量模拟方法的实施以及人工智能算法的开发。计算模拟被强调为合理设计和合成具有目标特性的新材料的主要组成部分，描述了数据库和大数据处理的进展。讨论了新材料发现的工具，包括部署新数据存储库的进展、​​高通量模拟方法的实施以及人工智能算法的开发。计算模拟被强调为合理设计和合成具有目标特性的新材料的主要组成部分，描述了数据库和大数据处理的进展。讨论了新材料发现的工具，包括部署新数据存储库的进展、​​高通量模拟方法的实施以及人工智能算法的开发。