Convergence Accelerator Phase I (RAISE): MPrint-OKN

NSF融合加速器支持基于团队的多学科努力，以应对国家重要性的挑战，并在不久的将来显示出交付成果的潜力。这一融合加速器第一阶段项目的更广泛的影响和潜在的社会效益是创建一个知识网络和设计平台(MPrint-OKN)，以加速、高度有针对性地创造从药品到智能材料的各种先进产品。MPrint-OKN计划创建预测工具，以吸引渴望将他们的数据贡献给集体整体的用户基础，以便获得帮助他们实现数据价值最大化的强大工具。这种互惠互利的交换将导致收集越来越多的至关重要的科学数据，彻底改变更高效、更低成本和更环保的终端产品的设计和制造。通过将计算机、数据和配方科学的学术、政府和工业研究人员与化学、材料和产品工程师汇聚在一起，MPrint-OKN平台将发挥枢纽的作用，实现两个主要目标：1)创建工具，使合作伙伴能够发现性能更好、环境影响更低、和/或与最先进技术相比更经济的分子系统；2)通过缩短发现周期时间，加快研究从实验室向市场的过渡速度。该团队已经与国家实验室和政府机构(如阿贡、爱达荷州、洛斯阿拉莫斯和NIST)以及公司(包括陶氏、默克、斯伦贝谢和苏伊士)建立了合作伙伴关系。该项目将培养一个充满活力的、协作的、多学科的社区，并在重要的经济部门创造机器学习和数据科学的创新方法，这将有助于美国保持其在数据科学和材料科学方面的全球领导地位。MPrint-OKN的研究目标是创建一个有价值的协作系统，开发和分发最先进的分子模型、机器学习、数据科学和数据可视化工具，供需要用于产品开发的分子系统的许多学科使用。我们寻求通过将先进的工具交到更多的研究人员手中来减少发现和开发下一代基于分子的应用程序的成本和时间。MPrint-OKN的智力优势之一是通过机器学习将量子力学分子表示与实验数据相交而得出的。这些新的机器学习工具的创造将使合作伙伴能够预测分子在复杂系统中如何相互作用，并使人们能够对分子结构和产品性能之间的关系有新的见解。作为对科学工具开发的补充，MPrint-OKN项目将有助于培育来自我们经济所有部门的思想领袖的生态系统，他们依赖分子来驱动他们的创新。这一奖项反映了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。

The NSF Convergence Accelerator supports team-based, multidisciplinary efforts that address challenges of national importance and show potential for deliverables in the near future. The broader impact and potential societal benefit of this Convergence Accelerator Phase I project is to create a knowledge network and design platform (the MPrint-OKN) for the accelerated, highly targeted creation of advanced products ranging from medicines to smart materials. The MPrint-OKN plans to create predictive tools that will attract a user-base eager to contribute their data to the collective whole in order to gain access to powerful tools that help them maximum the value of their data. This mutually beneficial exchange will result in an ever-growing collection of critically important scientific data, revolutionizing the design and manufacture of more efficient, less costly, and environmentally friendly end-products. By converging academic, government, and industrial researchers in computer, data, and formulation science with chemical, materials, and product engineers, the MPrint-OKN platform will act as a hub with two main goals: 1) create tools enabling partners to discover molecular systems that provide improved performance, lower environmental impact, and/or better economics relative to state of the art and 2) accelerate the speed of research transition from laboratory to marketplace by decreasing the discovery cycle time. The team already has partnerships in place with national laboratories and government agencies (such as Argonne, Idaho, Los Alamos, and NIST) as well as corporations (including Dow, Merck, Schlumberger, and Suez). This project will foster a vibrant, collaborative, multi-disciplinary community, and create innovative approaches to machine learning and data science in important economic sectors which will help the U.S. maintain its global leadership in data sciences and material sciences. The research objective of MPrint-OKN is to create a valuable collaborative system that develops and distributes the most advanced molecular models, machine learning, data science, and data visualization tools available to the many disciplines requiring molecular systems for product development. We seek to reduce both the cost and time of discovering and developing next-generation molecular-based applications by placing advanced tools in the hands of more researchers. One aspect of the intellectual merit of the MPrint-OKN is derived by intersecting quantum-mechanical molecular representations with experimental data through machine learning. This creation of these new machine learning tools will enable partners to predict how molecules interact in complex systems and enable new insight into the relationships between molecular structure and product performance. Complementing the development of scientific tools, the MPrint-OKN project will help foster the ecosystem of thought leaders from all sectors of our economy that rely on molecules to drive their innovation.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.