CAREER: Teaching Machines to Design Self-Assembling Materials

该职业奖支持理论和计算研究以及对自组装生物材料的理解和设计的教育。通过自发组织其组成构件的结构聚集体的自组装在自然界中很普遍，并且是制造具有理想性能的人工材料的有吸引力的途径，这些材料不易通过其他方法生产。设计可自组装定制材料的构建模块是材料科学中的一个巨大挑战。在这项工作中，PI将把统计力学理论与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。利用这些工具，PI将从分子模拟中提取构建块自组装成结构聚集体的途径和机制。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，确定了稳定的聚集形式（热力学）和它们如何聚集（动力学和机制）。该方法揭示的集体顺序参数是驱动装配的缓慢动力学运动的良好描述符，并为将建筑块属性与集体装配行为联系起来的动力学上有意义的自由能景观提供了自然的参数化。通过对建筑块结构和化学的合理操纵来“雕刻”景观地形，PI的团队将对所需结构的组装进行编程，这些结构在热力学上是稳定的，在动力学上是可接近的（设计）。PI将采用一种新方法来研究三种技术上重要的自组装生物材料：1)用于小分子包封的“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)用于耐药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，并加速新型结构和功能生物材料的开发。综合教育和推广计划将科学成果纳入教育和推广，并支持研究生培训、本科生研究和对代表性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，并通过对少数民族学生的指导和高中推广，促进有色人种学生的招聘、保留和成功。该职业奖支持一个理论和计算研究项目，旨在设计具有自发自组织成具有理想性能的材料能力的微观构建块。这种制造材料的方式被称为“自下而上的自组装”，与我们更熟悉的“自上而下”的制造方式相反。想象一下，如果有一天能设计出形状和性质都恰到好处的分子，在烧瓶里摇晃它们，就能自动组装成太阳能电池！在这项工作中，PI将结合热力学和机器学习（有时被称为人工智能）领域的想法，建立一个新的工具，使计算机能够学习特定构建块可以形成什么结构，以及它们如何组装。然后PI将翻转这个问题，使用我们的工具来帮助逆向工程构建块来组装定制材料。PI的小组将把这些工具应用于三种有用的生物材料的设计：1)微米大小的颗粒，具有定向粘片，聚集多面体簇，以容纳和传递小分子；2)短肽，聚集网络，以模板合成二氧化硅纳米管，用于药物输送，清除重金属污染物，催化化学反应；3)长肽，组装成纳米大小的棒，可以杀死耐抗生素细菌，如MRSA“超级细菌”。该奖项还支持一项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和研究生课程，以及高中的推广活动。本科生将在夏季与PI一起直接参与科学研究。PI还将设计和教授一门新的课程，提供计算材料建模、分析和设计方面的实践经验，并通过指导本科生和研究生少数民族学生来促进有色人种学生的招聘和成功。

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.