SBIR Phase I: Implementing AL-enhanced Machine-Learning for Advanced Electrochemical Manufacturing

SBIR 第一阶段：为先进电化学制造实施 AL 增强机器学习

• 批准号: 2041577
• 负责人: Daniela Blanco
• 金额: $ 25.6万
• 依托单位: SUNTHETICS, INC.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-01 至 2022-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2041577&HistoricalAwards=false
• 关键词: SBIR; Phase; Implementing; AL; enhanced

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to demonstrate feasibility of machine-learning (ML)-guided experimental campaigns that predict, assess, and optimize electroorganic transformations with small experimental datasets. Companies across the chemical industry have pinpointed electrochemistry as a promising avenue for the implementation of more sustainable and energy-efficient manufacturing processes. However, the large cost and effort required in new process development hinders the implementation of electrochemical technologies. ML predictive algorithms can be a powerful tool to accelerate the development and optimization of more sustainable chemical processes, but repeatedly require large amounts of experimental data to train the models. These large datasets are often unavailable and expensive to obtain, which significantly limits the use of ML in the chemical industry. The project will advance future manufacturing by enabling the development of new and more sustainable chemical production routes using 50% less experiments, ultimately unlocking the manufacture of new molecules, medicines, and materials in societal applications. Moreover, by reducing the number of experiments required, the technology will significantly lower emissions and resource consumption in the industry.The proposed project introduces a ML platform capable of guiding experimental campaigns and data collection to enable accurate predictions of reaction behavior with the smallest possible datasets. The approach relies on the combination of chemical engineering and ML knowledge to overcome the optimization limitations found within each field. It will be validated using the electrooxidation of p-methoxytoluene as a model reaction and will elucidate the fundamental limitations and strengths of ML predictive models capturing the complexity of physical systems with small datasets.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.

这项小型企业创新研究（SBIR）I阶段项目的更广泛的影响/商业潜力是证明机器学习（ML）指导的实验活动的可行性，这些活动可以通过小型实验数据集预测，评估和优化电机转换。整个化学工业的公司已将电化学确定为实施更可持续和节能的制造过程的有希望的途径。但是，新工艺开发中所需的巨大成本和精力阻碍了电化学技术的实施。 ML预测算法可以是加速更可持续化学过程的开发和优化的强大工具，但反复需要大量的实验数据来训练模型。这些大型数据集通常无法获得且昂贵，这显着限制了ML在化学工业中的使用。该项目将通过减少50％的实验来开发新的和更可持续的化学生产路线，最终释放社会应用中新分子，药物和材料的制造，从而推动未来的制造。此外，通过减少所需的实验数量，该技术将大大降低行业的排放和资源消耗。拟议的项目引入了一个能够指导实验活动和数据收集的ML平台，以实现对最小可能数据集的反应行为的准确预测。该方法依赖于化学工程和ML知识的组合来克服每个领域内发现的优化局限性。将使用P-甲氧基甲苯的电氧化作为模型反应进行验证，并将阐明ML预测模型的基本局限性和优势，从而捕获物理系统与小数据集的复杂性。该奖项反映了NSF的法规任务，并认为通过基金会的知识优点和广泛的criter scritia crietia criter criter criter criter crietia criter criter criter criter criteria criter crietia criteria criter criteria crietia crietia criteria cripitia crietia crietia croperia croperia cromitia均值得一提。