Elements: Software: Autonomous, Robust, and Optimal In-Silico Experimental Design Platform for Accelerating Innovations in Materials Discovery

加快开发具有理想性能的新材料是一个关键的挑战，因为它可以促进科学、工程和医学等多个领域的进步，对经济增长做出重大贡献。例如，美国材料基因组计划呼吁通过整合实验、计算机模拟和数据分析，以极小的成本将新材料从发现到部署的时间减少一半。然而，目前在材料发现方面的主流实践依赖于反复试验和/或高通量筛选方法，这不能有效地开发出具有目标性能的材料的巨大设计空间。此外，由于材料合成和表征的技术限制，材料成分、结构和性能的测量往往包含相当大的误差，这使得这一探索更加具有挑战性。该项目旨在开发一个强大的自主材料发现软件平台，该平台可以将目前的反复试验的做法转变为信息学驱动的做法，从而有可能以极大的成本和时间加快发现新材料。在整个项目中，PI和Co-PI将指导学生，并让他们掌握必要的技能，以解决涉及材料科学、计算、优化和人工智能的跨学科问题。该项目的研究成果将被融入到PI和Co-Pis教授的课程中，从而丰富学生的学习经验。该项目的目标是开发一个有效的硅内实验设计平台，以加速发现新材料。该平台将建立在最优贝叶斯学习和实验设计方法的基础上，这些方法可以将材料、物理和化学的科学原理转化为预测模型，同时考虑到模型和数据的不确定性。最优的贝叶斯实验设计框架将使智能数据的收集能够帮助有效地探索材料设计空间，而不依赖于缓慢且昂贵的试错和/或高通量筛选方法。开发的方法将被集成到MSGalaxy，这是一个模块化的科学工作流程管理系统，产生一个可访问、可重复和透明的计算平台，用于加速材料发现，允许轻松灵活的定制以及不同学科的研究人员的协同贡献。该项目得到了计算机与信息科学与工程局的高级网络基础设施办公室和数学和物理科学局的材料研究部的支持。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。

Accelerating the development of novel materials that have desirable properties is a critical challenge as it can facilitate advances in diverse fields across science, engineering, and medicine with significant contributions to economic growth. For example, the US Materials Genome Initiative calls for cutting the time for bringing new materials from discovery to deployment by half at a fraction of the cost, by integrating experiments, computer simulations, and data analytics. However, the current prevailing practice in materials discovery relies on trial-and-error experimental campaigns and/or high-throughput screening approaches, which cannot efficiently explore the huge design space to develop materials with the targeted properties. Furthermore, measurements of material composition, structure, and properties often contain considerable errors due to technical limitations in materials synthesis and characterization, making this exploration even more challenging. This project aims to develop a software platform for robust autonomous materials discovery that can shift the current trial-and-error practice to an informatics-driven one that can potentially expedite the discovery of novel materials at substantially reduced cost and time. Throughout the project, the PI and Co-PIs will mentor students and equip them with the skills necessary to tackle interdisciplinary problems that involve materials science, computing, optimization, and artificial intelligence. Research findings in the project will be incorporated into the courses taught by the PI and Co-PIs, thereby enriching the learning experience of students.The objective of this project is to develop an effective in-silico experimental design platform to accelerate the discovery of novel materials. The platform will be built on optimal Bayesian learning and experimental design methodologies that can translate scientific principles in materials, physics, and chemistry into predictive models, in a way that takes model and data uncertainty into account. The optimal Bayesian experimental design framework will enable the collection of smart data that can help exploring the material design space efficiently, without relying on slow and costly trial-and-error and/or high-throughput screening approaches. The developed methodologies will be integrated into MSGalaxy, a modular scientific workflow management system, resulting in an accessible, reproducible, and transparent computational platform for accelerated materials discovery that allows easy and flexible customization as well as synergistic contributions from researchers across different disciplines.This project is supported by the Office of Advanced Cyberinfrastructure in the Directorate for Computer & Information Science & Engineering and the Division of Materials Research in the Directorate of Mathematical and Physical Sciences.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.