The increasing demands of sustainable energy, electronics, and biomedical applications call for next-generation functional materials with unprecedented properties. Of particular interest are emerging materials that display exceptional physical properties, making them promising candidates in energy-efficient microelectronic devices. As the conventional Edisonian approach becomes significantly outpaced by growing societal needs, emerging computational modeling and machine learning (ML) methods are employed for the rational design of materials. However, the complex physical mechanisms, cost of first-principles calculations, and the dispersity and scarcity of data pose challenges to both physics-based and data-driven materials modeling. Moreover, the combinatorial composition-structure design space is high-dimensional and often disjoint, making design optimization nontrivial. In this Account, we review a team effort toward establishing a framework that integrates data-driven and physics-based methods to address these challenges and accelerate materials design. We begin by presenting our integrated materials design framework and its three components in a general context. We then provide an example of applying this materials design framework to metal-insulator transition (MIT) materials, a specific type of emerging materials with practical importance in next-generation memory technologies. We identify multiple new materials which may display this property and propose pathways for their synthesis. Finally, we identify some outstanding challenges in data-driven materials design, such as materials data quality issues and property-performance mismatch. We seek to raise awareness of these overlooked issues hindering materials design, thus stimulating efforts toward developing methods to mitigate the gaps.

翻译：可持续能源、电子学和生物医学应用日益增长的需求，呼唤着具有前所未有特性的下一代功能材料。其中特别令人关注的是展现出卓越物理特性的新兴材料，它们成为节能微电子器件中极具前景的候选者。随着传统爱迪生式试错方法日益无法满足不断增长的社会需求，新兴的计算建模和机器学习（ML）方法被应用于材料的理性设计。然而，复杂的物理机制、第一性原理计算的高昂成本，以及数据的分散性和稀缺性，对基于物理和数据驱动的材料建模都构成了挑战。此外，成分-结构组合设计空间是高维且往往不连续的，这使得设计优化变得非比寻常。在本报告中，我们回顾了一项团队工作，旨在建立一个整合数据驱动与基于物理方法的框架，以应对这些挑战并加速材料设计。我们首先在一般背景下介绍我们集成的材料设计框架及其三个组成部分。随后，我们提供一个应用该材料设计框架于金属-绝缘体转变（MIT）材料的实例，这类特定新兴材料在下一代存储技术中具有实际重要性。我们识别出多种可能展现此特性的新材料，并提出了它们的合成路径。最后，我们指出了数据驱动材料设计中一些突出的挑战，例如材料数据质量问题以及特性与性能不匹配的问题。我们旨在提高对这些阻碍材料设计的被忽视问题的认识，从而激励各方努力开发方法来弥合这些差距。