CDS&E: Collaborative Research: Designing New Zintl Phases with Motif-based Learning and Ab Initio Methods

非技术总结该奖项支持一类有用但未得到充分探索的材料的计算和数据科学设计，这种材料具有适用于热电、电池和光伏的独特性质。随着计算资源的扩展、算法的进步和数据库的增长，具有定制属性的材料的数据驱动设计已经变得越来越可行。然而，在实践中探索广阔的搜索空间仍然具有挑战性，数据驱动的模型仍然面临着精度和可解释性有限的困难。在这个项目中，研究小组将研究中心假设，即某些类别材料的材料性质，如锌相，通过结构内原子组之间的相互作用而不是单个原子之间的相互作用来更准确地建模。这种物理直觉将被用来建立一套新的方法，将先进的计算机模拟与最先进的机器学习(ML)模型相结合，以实现快速逆向材料设计。除了提高精度和速度外，这些方法还将能够解释和科学地理解材料中原子组之间的重要相互作用，这些相互作用有助于所需的性质。研究团队将使用这些方法来发现新的Zintl相，并与实验合作者合作验证他们的发现。由计算材料科学家和计算机科学家组成的跨学科研究团队处于有利地位，能够在这些具有挑战性的基础和技术兴趣问题上推进知识。该项目还支持培养研究生和本科生成为材料和数据科学交叉点跨学科研究的下一代领导者。在这方面计划开展的活动包括举办跨学科研讨会和讲习班，讨论如何利用数据发现材料，并编写课程材料，将材料应用于多学科课程。该项目还将通过ML上关于材料科学的播客来支持公众参与。TECHNICAL SUMMARY该奖项支持一类被称为Zintl相的材料的计算和数据科学支持的逆向设计，这些材料在热电、电池和光伏等领域有应用。随着计算能力和方法的进步，新材料的计算设计变得越来越可行。然而，对于从头算方法来说，探索包含数百万化合物的大型化学空间仍然是计算上的难题。机器学习(ML)已经成为加速这种大规模探索的一种手段，但流行的ML方法在准确性和可解释性方面仍然存在局限性。在这个项目中，研究小组将开发新的材料设计计算方法，重点是通过结构内基元(原子组)之间的相互作用而不是单个原子之间的相互作用来更好地模拟晶体性质这一中心思想。该团队将应用这一想法来设计新的Zintl相，其中结构主题强烈影响功能特性。Zintl相是一类未被开发的化合物，为发现和设计新相提供了机会。该项目的最终目标是发现具有新组成和结构类型的锌相，并了解它们的组成、结构主题和功能特性之间的关系，从而实现具有定制性能的锌相的反向设计。该团队将使用化学装饰和乐高积木状的组装来“构建”Zintl相，并开发分层图形表示算法来自动学习结构主题和功能属性之间的关系。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

NONTECHNICAL SUMMARYThis award supports computational and data-science-enabled design of a useful but under-explored class of materials called Zintl phases, which have unique properties suitable for thermoelectrics, batteries, and photovoltaics. Data-driven design of materials with tailored properties has become increasingly viable with the expansion of computing resources, algorithmic advances, and growing databases. However, it remains challenging in practice to explore vast search spaces, and data-driven models still face difficulties with limited accuracy and interpretability. In this project, the research team will study the central hypothesis that materials properties for certain classes of materials such as Zintl phases are more accurately modeled through interactions between groups of atoms within a structure rather than interactions between individual atoms. This physical intuition will be used to build a new suite of methods integrating advanced computer simulations with state-of-the-art machine learning (ML) models for enabling fast inverse materials design. In addition to improving accuracy and speed, these methods will be designed to enable interpretation and scientific understanding of the important interactions between groups of atoms in the material that contribute to desired properties. The research team will use these methods to discover new Zintl phases and work with experimental collaborators to validate their discoveries. The interdisciplinary research team of computational materials scientists and computer scientists is well positioned to advance knowledge in these challenging problems of fundamental and technological interest. This project also supports training of graduate and undergraduate students to be the next generation of leaders in interdisciplinary research at the intersection of materials and data science. Activities planned within this context include the development of cross-disciplinary seminars and workshops on data-enabled materials discovery and course materials introducing materials applications into ML courses. The project will also support public engagement through a podcast on ML for materials science.TECHNICAL SUMMARYThis award supports computational and data science-enabled inverse design of a class of materials called Zintl phases that have applications in thermoelectrics, batteries, and photovoltaics, among others. Computational design of new materials has become increasingly viable with advances in computing power and methodologies. However, exploration of large chemical spaces with millions of compounds is still computationally intractable for ab-initio methods. Machine learning (ML) has emerged as a means to accelerate such large explorations, but popular ML methods still have limitations in accuracy and interpretability. In this project, the research team will develop novel computational methods for materials design focused on the central idea that crystal properties are better modeled through interactions between motifs (groups of atoms) within a structure rather than between individual atoms. The team will apply this idea to design new Zintl phases, where the structural motifs strongly influence functional properties. Zintl phases are an under-explored class of compounds, presenting opportunities to discover and design new phases. The ultimate goal of this project is to discover Zintl phases with new compositions and structure types and learn relationships between their compositions, structural motifs, and functional properties that will allow inverse design of Zintl phases with tailored properties. The team will use chemical decorations and Lego-like assembly to "construct" Zintl phases and develop hierarchical graph representation algorithms to automatically learn the relationships between structural motifs and functional properties.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.