CAREER: Building an Advanced Cyberinfrastructure for the Data-Driven Design of Chemical Systems and the Exploration of Chemical Space

化学和材料创新是经济发展、繁荣和生活水平提高的关键驱动力。 它还为塑造我们社会的能源、环境可持续性和资源等紧迫问题提供了解决方案。 该研究计划旨在通过改变创造潜在创新的过程来提高化学界应对这些挑战的能力。这项研究促进了从试错搜索向理性设计的转变。 这些联合收割机通过将机器学习等工具引入化学环境，将传统化学研究与现代数据科学相结合。 该项目通过建立网络基础设施，使数据驱动的研究成为化学界可行且广泛使用的主张，从而成为化学企业不可或缺的一部分，从而推动和推进这一新兴领域。 本研究开发的工具和方法为化学空间的大规模探索和更好地理解决定复杂化学系统行为的隐藏机制提供了手段。 这些见解可能会加速、简化并最终改变化学品开发过程。 该项目还解决了随之而来的需要，使教育适应这一新的研究领域，以充分装备下一代科学家和工程师，为未来的尖端研发建立一支有能力和熟练的劳动力队伍，并确保美国学生在国际就业市场上的竞争力。 通过促进少数群体参与这一充满希望的领域，它有助于持续推动我们社会的平等机会。 因此，该项目促进了科学的进步，促进了NSF使命所述的繁荣和福利。虽然人们越来越认同数据驱动的发现和合理设计的价值，但这种方法还远未成为化学界日常研究的主流。 这项工作解决了三个主要障碍：（i）由于缺乏可用和可访问的工具，数据驱动的研究超出了大多数化学家的范围和范围，（ii）关于如何使数据科学为化学研究工作的许多基本和实际问题尚未解决，（iii）数据科学不是化学家正规培训的一部分，因此，社区中的许多人缺乏必要的经验和专业知识来利用它。这项研究围绕着创建一个开放的通用软件生态系统，该生态系统融合了计算机建模，虚拟高通量筛选和大数据分析（即，使用机器学习、信息学和数据库技术对结果数据集进行验证、挖掘和建模）整合到一个综合研究基础设施中。 一个关键的考虑因素是使这个生态系统尽可能全面，强大和用户友好，以便感兴趣的研究人员可以随时使用，而不需要广泛的专业知识。 它还作为一个开发平台和测试平台，用于基础方法、算法和协议的创新，即，它使社区能够有系统和有效地评估不同技术的效用和性能，包括作为该项目一部分而引入的新技术。 目前正在开发一种Meta机器学习方法，以制定为网络基础设施提供附加值的准则和最佳做法。 这项工作是由具体的分子设计问题，这有助于证明整体方法的有效性。 从数据驱动研究的质量新奇及其固有的跨学科性所产生的教育挑战，通过利用计算和数据科学与工程的新研究生课程进行跨学科课程和课程开发，创建交互式教材以及技能建设黑客计划来解决。 该奖项是与化学、化学理论、模型和计算方法项目部联合颁发的。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Innovation in chemistry and materials is a key driver of economic development, prosperity, and a rising standard of living. It also offers solutions to pressing problems on energy, environmental sustainability, and resources that shape our society. This research program is designed to boost the chemistry community's capacity to address these challenges by transforming the process that creates underlying innovation. The research promotes a shift away from trial-and-error searches and towards rational design. These combine traditional chemical research with modern data science by introducing tools such as machine learning into the chemical context. This project enables and advances this emerging field by building a cyberinfrastructure that makes data-driven research a viable and widely accessible proposition for the chemistry community, and thereby an integral part of the chemical enterprise. Tools and methods developed in this research provide the means for the large-scale exploration of chemical space and for a better understanding of the hidden mechanisms that determine the behavior of complex chemical systems. These insights can potentially accelerate, streamline, and ultimately transform the chemical development process. The project also tackles the concomitant need to adapt education to this new research landscape in order to adequately equip the next generation of scientists and engineers, to build a competent and skilled workforce for the cutting-edge R&D of the future, and to ensure the competitiveness of US students in the international job market. By promoting minority participation in this promising field, it contributes to a sustained push towards equal opportunity in our society. This project thus promotes the progress of science and advances prosperity and welfare as stated by NSF's mission. While there is growing agreement on the value of data-driven discovery and rational design, this approach is still far from being a mainstay of everyday research in the chemistry community. This work addresses three key obstacles: (i) data-driven research is beyond the scope and reach of most chemists due to a lack of available and accessible tools, (ii) many fundamental and practical questions on how to make data science work for chemical research remain unresolved, and (iii) data science is not part of the formal training of chemists, and much of the community thus lacks the necessary experience and expertise to utilize it. This research centers around the creation of an open, general-purpose software ecosystem that fuses in silico modeling, virtual high-throughput screening, and big data analytics (i.e., the use of machine learning, informatics, and database technology for the validation, mining, and modeling of resulting data sets) into an integrated research infrastructure. A key consideration is to make this ecosystem as comprehensive, robust, and user-friendly as possible, so that it can readily be employed by interested researchers without the need for extensive expert knowledge. It also serves as a development platform and testbed for innovation in the underlying methods, algorithms, and protocols, i.e., it allows the community to systematically and efficiently evaluate the utility and performance of different techniques, including new ones that are being introduced as part of this project. A meta machine learning approach is being developed to establish guidelines and best practices that provide added value to the cyberinfrastructure. The work is driven by concrete molecular design problems, which serve to demonstrate the efficacy of the overall approach. The educational challenges that arise from the qualitative novelty of data-driven research and its inherent interdisciplinarity are addressesed by leveraging a new graduate program in Computational and Data-Enabled Science and Engineering for cross-cutting course and curricular developments, the creation of interactive teaching materials, and a skill-building hackathon initiative. This award is jointly made with the Division of Chemistry's, Chemical Theory, Models and Computational Methods Program.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.