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项目的更广泛的影响/商业潜力是开发软件平台，以预测可再生材料（例如生物聚合物）的性质。 在不断上升的环境问题之中，亚马逊，沃尔玛和H＆M等电子商务领导者致力于在2030年之前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，这些解决方案优先考虑生物降解性，而不是在回收过程中投资而不是投资。此外，成本注意事项需要重点关注生物聚合物的成本效益用于包装。拟议的技术提供了一种工具，可以自定义生物聚合物的化学行为，这些工具可用于创建其应用所需的技术性能规范。提出的软件平台可能有可能通过启用可再生材料的特性的发现和预测，包括消费电子产品，储能，生物燃料，食品提取和制药研究。该I-Corps基于对Simplation量身定制的Simultator的开发，以最大程度地提高采样，效率控制，并缩放了Sconalectal Dynamelictials Mimalsicals。 所提出的模型将增强学习控制政策作为归纳偏见，优化了目标功能，例如生成特定构象，从而最大程度地减少了计算成本。该技术应用于聚合物链的领域，在研究化学性化学聚合物链构象构象构象上的采样效率提高了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。最初的目标是将该模型应用于由生物聚合物制成的包装膜。 与类似的物业预测技术不同，该模型基于物理学，这意味着不需要经验数据，并且在传统的计算机中运行，使得更广泛的受众访问。将利用基于RL的控制策略来彻底探索配置空间，以增强对机械属性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。 所提出的技术可用于预测和调整生物聚合物的特定特性，例如机械强度，柔韧性或化学无反应性，对于创建量身定制的包装解决方案至关重要。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识和更广泛影响的评估来通过评估来获得支持的。