CAREER: Teaching Machines to Design Self-Assembling Materials

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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.