CAREER: Teaching Machines to Design Self-Assembling Materials

职业：教授机器设计自组装材料

• 批准号: 1841800
• 负责人: Andrew Ferguson
• 金额: $ 9万
• 依托单位: University of Chicago
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-06-01 至 2020-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1841800&HistoricalAwards=false
• 关键词: CAREER; Teaching; Machines; Design; Self

TECHNICAL SUMMARYThis CAREER award supports theoretical and computational research and education in the understanding and design of self-assembling biomaterials. Self-assembly of structured aggregates by the spontaneous organization of their constituent building blocks is prevalent in the natural world, and is an attractive route to fabricate artificial materials with desirable properties that cannot be easily produced by other means. The design of building blocks programmed to self-assemble custom materials is a grand challenge in materials science.In this work, the PI will integrate statistical mechanics theory with nonlinear machine learning algorithms to establish a new theoretical and computational approach to understand and program the self-assembly of nanostructured biomaterials. Using these tools, the PI will extract from molecular simulations the pathways and mechanisms by which building blocks self-assemble into structured aggregates. This methodology overcomes a key scientific challenge by integrating thermodynamics and kinetics in a unified framework that identifies both what stable aggregates form (thermodynamics) and how they assemble (kinetics and mechanisms). The collective order parameters unveiled by this approach are good descriptors of the slow dynamical motions driving assembly, and present a natural parameterization for kinetically meaningful free energy landscapes that link building block properties to collective assembly behavior. By "sculpting" the landscape topography through rational manipulation of building block structure and chemistry the PI's group will program the assembly of desired structures that are thermodynamically stable and kinetically accessible (design).The PI will apply a new approach to three technologically important self-assembling biomaterials: 1) "patchy colloid" polyhedral clusters for small molecule encapsulation, 2) ultra-short peptide mineralization templates for silica nanotubes for controlled drug release, heavy metal ion adsorption, and catalysis, and 3) antimicrobial peptide amphiphile nanostructures for antibiotic resistant bacteria. This work will establish new basic understanding and control of materials assembly, and accelerate development of new structural and functional biomaterials. The integrated education and outreach plan incorporates the scientific outcomes into education and outreach, and supports graduate training, undergraduate research, and mentoring of underrepresented minority groups. The PI will create a new materials science course to equip the next generation workforce with computational tools, support undergraduate students in performing portions of the work, and promote the recruitment, retention, and success of students of color through mentorship of minority students and high school outreach.NONTECHNICAL SUMMARYThis CAREER award supports a theoretical and computational research program to design microscopic building blocks with the ability to spontaneously self-organize into materials with desirable properties. This way of making materials is known as "bottom-up self-assembly", as opposed to more familiar "top-down" manufacturing. Imagine if it will be possible one day to design molecules with just the right shape and properties so that shaking them in a flask spontaneously self-assembled a solar cell! In this work, the PI will combine ideas from the fields of thermodynamics and machine learning (sometimes known as artificial intelligence) to establish a new tool to allow computers to learn both what structures can be formed by a particular building block, and how they assemble. The PI will then flip this problem to use our tool to help reverse-engineer building blocks to assemble custom materials. The PI's group will apply these tools to the design of three useful biological materials: 1) micron-sized particles possessing directional sticky patches that assemble polyhedral clusters to hold and deliver small molecules, 2) short peptides that assemble networks to template the synthesis of silica nanotubes for drug delivery, cleanup of heavy metal pollutants, and catalysis of chemical reactions, and 3) longer peptides that assemble into nanometer sized rods that can kill antibiotic resistant bacteria such as the MRSA "superbug".This award also supports an integrated research and education program in which the scientific results from this work will enrich and enhance undergraduate and graduate classes, and high school outreach activities. Undergraduate students will directly participate in the scientific research by working with the PI during the summer months. The PI will also design and teach a new class providing hands-on experience in the computational materials modeling, analysis, and design, and maintain his commitment to promote the recruitment and success of students of color through mentorship of undergraduate and graduate minority students.

技术总结这个职业奖项支持在理解和设计自组装生物材料方面的理论和计算研究和教育。结构聚集体的自组装是自然界中普遍存在的一种结构聚集体的自发组织，是一种具有理想性能的人工材料的制备途径，而这些材料是用其他方法无法轻易生产的。自组装材料的设计是材料科学中的一大挑战。在这项工作中，PI将把统计力学理论和非线性机器学习算法结合起来，建立一种新的理论和计算方法来理解和编程纳米结构生物材料的自组装。使用这些工具，PI将从分子模拟中提取构建块自组装成结构化聚集体的路径和机制。这种方法克服了一个关键的科学挑战，将热力学和动力学整合在一个统一的框架中，确定稳定的聚集体形成什么(热力学)和它们如何组装(动力学和机制)。该方法揭示的集体有序参数很好地描述了驱动组装的慢动力学运动，并为将积木的性质与集体组装行为联系起来的具有运动学意义的自由能景观提供了一种自然的参数化。通过合理地操纵积木结构和化学，PI的团队将对所需结构的组装进行编程，这些结构是热力学稳定和动态可达的(设计)。PI将把一种新的方法应用于三种具有重要技术意义的自组装生物材料：1)用于小分子封装的“片状胶体”多面体簇，2)用于控制药物释放、重金属离子吸附和催化的二氧化硅纳米管的超短肽矿化模板，以及3)用于抗生素耐药性细菌的抗菌肽两亲性纳米结构。这项工作将建立对材料组装的新的基本认识和控制，并加快新型结构和功能生物材料的开发。综合教育和外展计划将科学成果纳入教育和外展，并支持研究生培训、本科生研究和对代表不足的少数群体的指导。PI将创建一门新的材料科学课程，为下一代劳动力配备计算工具，支持本科生完成部分工作，并通过对少数族裔学生的指导和高中出游促进有色人种学生的招聘、留住和成功。非技术总结这个职业奖项支持一个理论和计算研究计划，该计划旨在设计具有自发自组织能力的具有理想性能的材料的微观构建块。这种制造材料的方法被称为“自下而上的自组装”，而不是更常见的“自上而下”的制造。想象一下，如果有一天可以设计出形状和性质恰到好处的分子，这样在烧瓶中摇晃它们，就可以自发地自组装成太阳能电池！在这项工作中，PI将结合热力学和机器学习(有时称为人工智能)领域的想法，建立一种新工具，使计算机能够学习特定构件可以形成什么结构，以及它们是如何组装的。然后PI将翻转这个问题，使用我们的工具帮助反向工程构建块来组装自定义材料。PI的团队将把这些工具应用于设计三种有用的生物材料：1)微米尺寸的颗粒，具有定向粘性斑块，聚集多面体簇以容纳和传递小分子；2)短肽，组装网络以模板合成二氧化硅纳米管，用于药物输送、重金属污染物的清除和化学反应的催化；3)较长的多肽，组装成纳米尺寸的杆状，可以杀死耐抗生素细菌，如MRSA“超级细菌”。该奖项还支持一个综合研究和教育计划，在该计划中，这项工作的科学成果将丰富和加强本科生和研究生的课程，以及高中的推广活动。本科生将在暑期几个月与PI一起直接参与科学研究。PI还将设计和教授一个新的课程，提供计算材料建模、分析和设计方面的实践经验，并坚持他的承诺，通过指导本科生和研究生来促进有色人种学生的招生和成功。

项目成果

Inverse Design of Self-Assembling Diamond Photonic Lattices from Anisotropic Colloidal Clusters

各向异性胶体团簇自组装金刚石光子晶格的逆向设计

• DOI: 10.1021/acs.jpcb.0c08723
• 发表时间: 2021
• 期刊: The Journal of Physical Chemistry B
• 作者: Ma, Yutao;Aulicino, Joseph C.;Ferguson, Andrew L.
• 通讯作者: Ferguson, Andrew L.