I-Corps: AI for predicting polymer properties for biopolymer films

I-Corps：用于预测生物聚合物薄膜聚合物特性的人工智能

• 批准号: 2335930
• 负责人: Svafa Gronfeldt
• 金额: $ 5万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2024-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2335930&HistoricalAwards=false
• 关键词: Corps; AI; predicting; polymer; properties

The broader impact/commercial potential of this I-Corps project is the development of a software platform to predict the properties of renewable materials such as biopolymers. Amid rising environmental concerns, e-commerce leaders such as Amazon, Walmart, and H&M have committed to reducing plastic waste and carbon emissions by 2030. Achieving this goal may depend on sustainable packaging solutions that prioritize biodegradability over investment in recycling processes. In addition, cost considerations necessitate a focus on cost-effective production of biopolymers for packaging. The proposed technology provides a tool enabling the customization of the chemical behavior of biopolymers that may be used to create the technical performance specifications required for their applications. The proposed software platform may have the potential to impact multiple industries by enabling the discovery and prediction of renewable materials' properties including consumer electronics, energy storage, biofuels, food extraction, and pharmaceutical research.This I-Corps project is based on the development of simulator tailored to maximize sampling control, efficiency, and scalability in molecular dynamics simulations. The proposed model employs reinforcement learning control policies as an inductive bias, optimizing objective functions like generating specific conformations, which minimizes computational cost. Applied to the realm of polymer chains, the proposed technology has shown a 40% improvement in sampling efficiency over studied chemistry polymer chain conformations with a target radius of gyration, illustrating the power of machine learning control policies in optimizing complex simulations. The initial goal is to apply this model to packaging films made from biopolymers. Unlike similar technologies for property prediction, this model is physics-based, which means empirical data are not required, and it runs in conventional computers, making it accessible to a wider audience. RL-based control policies will be leveraged to explore the configuration space thoroughly to enhance control over the simulation's conformations for mechanical property prediction, which is a key aspect in designing effective packaging materials. The proposed technology may be used to predict and adjust specific properties of biopolymers, such as mechanical strength, flexibility, or chemical non-reactivity that are crucial for creating tailored packaging solutions.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 I-Corps 项目更广泛的影响/商业潜力是开发一个软件平台来预测生物聚合物等可再生材料的特性。 随着环境问题日益严重，亚马逊、沃尔玛和 H&M 等电子商务领导者已承诺到 2030 年减少塑料废物和碳排放。实现这一目标可能取决于可持续包装解决方案，该解决方案优先考虑生物可降解性而不是回收过程的投资。此外，出于成本考虑，需要重点关注用于包装的生物聚合物的经济高效生产。所提出的技术提供了一种能够定制生物聚合物化学行为的工具，可用于创建其应用所需的技术性能规范。拟议的软件平台可能会通过发现和预测可再生材料的特性（包括消费电子产品、能源存储、生物燃料、食品提取和制药研究）来影响多个行业。该 I-Corps 项目基于专为最大限度地提高分子动力学模拟中的采样控制、效率和可扩展性而定制的模拟器。 所提出的模型采用强化学习控制策略作为归纳偏差，优化目标函数，例如生成特定构象，从而最大限度地减少计算成本。应用于聚合物链领域时，所提出的技术比具有目标回转半径的化学聚合物链构象的采样效率提高了 40%，这说明了机器学习控制策略在优化复杂模拟方面的力量。最初的目标是将这种模型应用于由生物聚合物制成的包装薄膜。 与类似的财产预测技术不同，该模型是基于物理的，这意味着不需要经验数据，并且它在传统计算机中运行，使其可供更广泛的受众使用。将利用基于强化学习的控制策略来彻底探索配置空间，以增强对机械性能预测的模拟构象的控制，这是设计有效包装材料的关键方面。 拟议的技术可用于预测和调整生物聚合物的特定性能，例如机械强度、柔韧性或化学非反应性，这些对于创建定制包装解决方案至关重要。该奖项反映了 NSF 的法定使命，并被认为值得通过以下方式获得支持：使用基金会的智力价值和更广泛的影响审查标准进行评估。