SBIR Phase I: AI Robotics-driven Material Discovery Platform

SBIR 第一阶段：人工智能机器人驱动的材料发现平台

• 批准号: 1938253
• 负责人: Xuejun Wang
• 金额: $ 22.44万
• 依托单位: AUTOMAT SOLUTIONS, INC.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-03-15 至 2021-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1938253&HistoricalAwards=false
• 关键词: SBIR; Phase; AI; Robotics; driven

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to accelerate the development of new high-performance battery materials with an Artificial Intelligence (AI) robotics-driven material development platform. The platform uses machine learning and robotic high-throughput automation to accelerate effective experiment planning and minimize errors. It will potentially have a substantial positive impact on the commercialization of superior battery materials (projected to be a $14 B market by 2025), to support growth of electric vehicles and other sustainable transportation. This Small Business Innovation Research (SBIR) Phase I project aims to build a material development platform featuring a closed-loop machine learning and robotic high-throughput automation, and to develop a high-performance polymer electrolyte product for lithium batteries. The platform can potentially change how material innovation is performed and enable accelerated discovery of electrolytes and other battery materials. The platform’s workflow iterates the following: (1) initial electrolyte knowledge base collection; (2) machine-learning model training using the knowledge base; (3) new electrolyte prescription by the model; (4) parallelized experimental validation via high-throughput equipment; and (5) knowledge base updates. Phase I will help to (1) build key electrochemical and mechanical modules on the robotic system for electrolyte development, (2) improve machine learning models in terms of feasibility, flexibility, and the capability of optimizing multiple objective functions, and (3) develop the polymer electrolyte formulation in order to improve its three primary properties, including ionic conductivity, voltage stability, and mechanical modulus. It is anticipated that the platform will achieve high productivity and effectiveness, significantly improve electrolyte properties, and identify an electrolyte that meets commercialization system requirements.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.

该小型企业创新研究（SBIR）第一阶段项目的更广泛影响/商业潜力是通过人工智能（AI）机器人驱动的材料开发平台加速新型高性能电池材料的开发。该平台使用机器学习和机器人高通量自动化来加速有效的实验规划并最大限度地减少错误。这将对上级电池材料的商业化产生巨大的积极影响（预计到2025年将成为140 B美元的市场），以支持电动汽车和其他可持续交通的增长。 该小型企业创新研究（SBIR）第一阶段项目旨在建立一个具有闭环机器学习和机器人高通量自动化的材料开发平台，并开发用于锂电池的高性能聚合物电解质产品。该平台可能会改变材料创新的方式，并加速电解质和其他电池材料的发现。该平台的工作流程迭代如下：（1）初始电解质知识库收集;（2）使用知识库进行机器学习模型训练;（3）通过模型进行新的电解质处方;（4）通过高通量设备进行并行实验验证;以及（5）知识库更新。第一阶段将有助于（1）在机器人系统上构建关键的电化学和机械模块，用于电解质开发，（2）在可行性，灵活性和优化多个目标函数的能力方面改进机器学习模型，以及（3）开发聚合物电解质配方，以改善其三个主要特性，包括离子电导率，电压稳定性和机械模量。预计该平台将实现高生产率和有效性，显著改善电解质性能，并确定满足商业化系统要求的电解质。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。