Collaborative Research: Designing Functional Materials with Optimal Learning

新的产品和材料加工方法通常需要确定更坚固、更轻、更便宜或在某些方面更好的新材料。用试错法寻找新材料的成本可能很高，而且往往效果不佳。有了这个奖项，新的数学方法和计算机软件将被开发出来，以加速材料的发现。计划中的方法将把可用的选择缩小到最有可能成功的选择，使新材料和新工艺的发现更可靠，成本更低。这种方法将被用于柔性有机太阳能电池的材料进行演示，但这种方法也可以适用于用于制药或食品添加剂的材料。计划采用贝叶斯实验设计和机器学习的新的最佳学习方法来根据先前的数据和领域的专业知识预测材料的性质，并智能地建议物理和计算实验，这些实验将提供最支持发现的信息。这些新的数学技术有望极大地加速材料设计，以更可靠的方式和更少的实验工作提供更好的材料。这种方法将在一系列现有的候选材料、溶剂选择和工艺条件下进行有机半导体材料的搜索，并在此搜索中整合物理和计算实验。测试案例是沉积在碳纳米管(CNT)上的扭曲六苯并冠烯(c-HBC)全有机太阳能电池系统。这一复杂的体系涉及到c-HBC和CNT在不同加工条件下的络合等问题，这为优化学习和计算机模拟方法预测加工-结构-功能三位一体提供了严格的测试。这种方法广泛适用于各种材料设计问题。

New products and material processing methods often require the identification of novel materials that are stronger, lighter, cheaper, or better in some way. Searching for new materials with a trial-and-error approach can be expensive and often ineffective. With this award, new mathematical methods and computer software will be developed to accelerate materials discovery. The planned approach will narrow the available options to those that are most likely to succeed, making discovery of new materials and processes more reliable and less costly. Demonstration of the approach will be made for materials to be used in flexible organic solar cells, but the methods could also be amenable to materials for use in pharmaceuticals or to food additives.A new optimal learning approach to materials design is planned that uses advances in Bayesian experimental design and machine learning to predict material properties from previous data and domain expertise, and to intelligently suggest physical and computational experiments that will provide information that is most supportive of discovery. These new mathematical techniques promise to greatly accelerate materials design, providing better materials more reliably and with less experimental effort. The approach will be demonstrated in the search for organic semiconductor materials over a set of existing candidates, solvent choices, and processing conditions, and integrate both physical and computational experiments in this search. The test case is an all-organic solar cell system of contorted hexabenzocoronenes (c-HBC), deposited on carbon nanotubes (CNT). This complex system involves issues including complexation between c-HBC and CNT at different processing conditions, etc., which provide a stringent test of optimal learning and computer simulation methods to predict the processing-structure-function triad. This approach is broadly applicable to a diverse set of materials design problems.