CDS&E: Better by Design: Establishing Modeling and Optimization Techniques for Producing New Classes of Biomimetic Nanomaterials

具有精确定制特性的新型纳米材料可以实现从制造到能量存储和药物输送等领域的创新，但设计此类材料可能是一个挑战。该项目开发了建模和优化技术，使研究人员能够使用所需的特性来驱动材料和工艺选择。研究人员利用基于贝叶斯机器学习和随机优化的中尺度建模技术和计算方法的力量，在浩瀚的选项宇宙中搜索，以确定有前途的候选者。这种方法为社区分析和设计基于聚合物的纳米材料提供了一种有效的预测工具。从这项研究中获得的知识将通过出版物、会议报告和组织专题讨论会广泛传播。将制定教育和推广方案，以培训多样化的STEM劳动力，并扩大未被充分代表的学生在工程和计算科学领域的参与。将中尺度粗粒化方法与随机优化方法相结合，为软材料领域提供了一种使能工具，促进了对高维搜索空间中的设计探索和不确定条件下的设计优化的认识。该项目的目标是显著降低模拟分子自组装过程的成本和组装材料的特性。研究人员将开发一种描述自组装动力学的介观模型，并模拟和预测组装材料的结构和机械性能。粗粒度方法平衡了对作为优化基础的材料属性的准确性的需求，以及使优化可行所需的计算效率。该计算框架包括中尺度建模、分类和优化步骤，将通过与类肽实验的比较来验证。这项研究将使基于多肽的仿生纳米材料的反向设计成为可能，这些纳米材料具有精确定制的结构和性能，适用于化学/生物传感器、仿生纳米器件和水/离子传输膜等应用。计算方法将通过GitHub共享，LAMMPS子例程和计算机代码将作为开源软件发布给科学界。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。

Novel nanomaterials with precisely tailored characteristics can enable innovation in areas ranging from manufacturing to energy storage and drug delivery but designing such materials can be a challenge. This project develops modeling and optimization techniques that will enable researchers to use desired properties to drive materials and process selection. The researcher harnesses the power of mesoscale modeling techniques and computational methods based on Bayesian machine learning and stochastic optimization to search the vast universe of options to identify promising candidates. This approach gives the community an enabling predictive tool for analysis and design of polymer-based nanomaterials. The knowledge gained from this research will be broadly disseminated through publications, conference presentations and by organizing symposia. Educational and outreach programs will be developed to train a diverse STEM workforce and to broaden participation of underrepresented students in the fields of engineering and computational science. Integrating a mesoscale coarse-graining method with stochastic optimization provides an enabling tool in soft materials and advances knowledge about design exploration in high-dimensional search spaces and design optimization under uncertainty. The goal of this project is to significantly reduce the cost of simulating the molecular self-assembly process and the characteristics of assembled materials. The researcher will develop a mesoscale model that describes the dynamics of self-assembly and simulates and predicts the structures and mechanical properties of assembled materials. A coarse-grained approach balances the need for accuracy in material properties, which is the basis for optimization, and the computational efficiency needed to make the optimization feasible. The computational framework, which includes the mesoscale modeling, classification, and optimization steps, will be validated by comparison with experiments on peptoids. This research will enable inverse design of peptoid-based biomimetic nanomaterials with precisely tailored structures and properties for applications such as chemical/biological sensors, biomimetic nanodevices and water/ion transport membranes. The computational methodology will be shared through GitHub and as LAMMPS subroutines and the computer codes will be released to the scientific community as open-source software.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.