PFI-TT: An Artificial Intelligence Capability to Accelerate Low-Cost Commercial Polymer Design

该创新技术转化伙伴关系（PFI-TT）项目的广泛影响和商业潜力包括为许多行业加速聚合物选择、设计和发现的低成本能力。拟议的项目将推动机器学习的发展，用于传统、费力和昂贵的材料开发的试错方法。聚合物制造行业每年的研发支出总额约为100亿美元，预计在美国每年可节省1亿美元。此外，产品设计工作流程的加速可以大大缩短新产品的上市时间。最后，建议的项目包括创业指导。拟议的项目将有助于创建基于数据驱动的机器学习能力和服务，以加速特定应用的聚合物设计和开发。在底层数据库上“训练”的机器学习（ML）算法产生预测模型，可以(1)对尚未合成的新聚合物的性质进行即时预测，(2)对满足设计目标的新聚合物和现有聚合物提出建议。拟议的项目将推进原型聚合物基因组在线工具的开发。该奖项反映了美国国家科学基金会的法定使命，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。

The broader impact/commercial potential of this Partnerships for Innovation - Technology Translation (PFI-TT) project includes the creation of an accelerated low-cost capability for polymer selection, design and discovery for many industries. The proposed project will advance the development of machine learning for the traditional, laborious and expensive trial-and-error approaches to materials development. The total R&D expenditure of the polymer manufacturing industry is about $10 billion per year, with anticipated savings of one estimated $100 million per year in the United States. In addition, the acceleration of product design workflows could dramatically shorten time-to-market for new products. Finally, the proposed project includes entrepreneurial mentoring.The proposed project will help create a data-driven machine learning based capability and service to achieve accelerated application-specific polymer design and development. Machine learning (ML) algorithms “trained” on an underlying database produce predictive models, which can (1) make instantaneous predictions of properties of a new yet-to-be-synthesized polymer, and (2) make recommendations of new and existing polymers that will meet design objectives. The proposed project will advance the development of a prototype Polymer Genome online tool.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.