Smart Data Approaches for the Inverse Design of Soft Materials

传统的设计新的智能材料的方法需要昂贵的实验来探索大量的参数组合。这个过程可以通过计算机模拟在“虚拟实验室”中加速，但它需要一种非传统的方法。直到最近，计算机模拟的焦点一直是正问题：给定分子结构、组成等参数的值，就可以得到材料的性质。但是，在给定一组期望的材料属性的情况下，我们寻求引起这些属性的参数的值的反问题，对于加速新材料的设计和进步更为关键。逆设计问题是一个极具挑战性的高维全局优化问题，是该项目的主要推力之一。设想的数学和计算框架将广泛适用于各种材料，如嵌段共聚物和纳米结构软材料。这个项目将对教育产生实质性的影响，增加来自代表不足群体的学生和本科生的参与传统上，计算机模拟进行一些有经验的模型参数的选择。每个模拟通常都很昂贵，因此，即使使用最先进的计算资源，对于真实模型来说，也只有有限数量的模拟是可行的。当参数空间是中高维的并且不可能完全探索该空间时，这是一个特别严重的问题。在这个项目中，主要研究人员将开发一种概率方法来解决逆设计问题，为正问题设计更好的方法，并为建立计算更高效的粗粒度模型进行统计推断。这是一种智能数据方法，在这种方法中，将首次在多嵌段共聚体的场论模型的背景下探索使用关于要优化或要推断的目标函数的先验信念来指导参数的采样。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The traditional approach to the design of new, smart materials requires costly experimentation to explore a vast combination of parameters. This process can be accelerated in a "virtual lab" with computer simulations but it requires a non-traditional approach. Until recently, the focus of computer simulations has been the forward problem: given values of the parameters such as molecular architecture, composition, etc., find the material properties. But the inverse problem in which given a desired set of material properties we seek values of the parameters that give rise to those properties, is more critical for accelerating the design and advancement of new materials. This inverse design problem, which is an extraordinarily challenging, high-dimensional global optimization problem, is one of the main thrusts of this project. The envisioned mathematical and computational framework will be broadly applicable to a wide variety of materials such as block copolymers and nano-structured soft materials. This project will have a substantial impact in education with an increased participation of students from underrepresented groups and undergraduates Traditionally, computer simulations are performed for some educated choices of the model parameters. Each simulation is in general costly so that, even with state of the art computing resources, only a limited number of simulations is feasible for realistic models. This is a particularly serious problem when the parameter space is moderate to high dimensional and is impossible to fully explore that space. In this project, the principal investigator will develop a probabilistic approach to solve the inverse design problem, to design superior methods for the forward problem, and to perform statistical inference for the construction of computationally more efficient, coarse-grained models. This is a smart-data approach, in which the use of prior belief about the objective function to be optimized or to be inferred (based prior evaluations) to guide the sampling of the parameter will be explored for the first time in the context of field-theoretic models of multi-block copolymers.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.