The Division of Materials Research and the Chemistry Division contribute funds to this award. It supports theoretical and computational research, computational tool development, and education in support of the prediction of new materials and their properties by computer simulation. Materials scientists can model the "building blocks" of new materials, from atoms to molecules to nanoparticles, and solve those models on fast computers. Although much work is still needed to improve the fidelity of models with additional physics, inform the models with measurements from experiments, and develop faster algorithms on modern architectures to solve these models, the paradigm of predictive materials simulation is well in hand. Predictive materials simulation can be thought of as a "forward-type" simulation, where one essentially runs computer experiments from initial conditions for a given system and observes the result.
Materials scientists are now at the threshold of a paradigm shift in materials simulation, where it is becoming feasible to design for materials structure and properties, rather than simply predicting them based on physical models. This use of molecular simulation for the "inverse problem" approach to design materials - that is, the determination of attributes of building blocks such that those building blocks will self-assemble into materials with specific structural, optical, mechanical, thermodynamic or other properties - would represent a major leap forward.
Moving into an era of inverse materials design requires new computational tools capable of performing simulations that flip the usual forward-type simulations on their head. These tools must be scientifically valid, robust, accessible and easy to use, and should exploit the fastest available hardware. This project will develop computational tools, share them broadly with an existing and rapidly growing user base, and apply them to the important problems of nanoparticle self-assembly and colloidal crystallization. The new tools will run efficiently on laptops, desktops, and even massive GPU (graphics processing units) supercomputers, thereby serving multiple user types. The approaches and tools developed under this award are transferable and will be of immediate and even broader interest to the materials, engineering, and chemistry communities interested in inverse design.
The Division of Materials Research and the Chemistry Division contribute funds to this award. It supports theoretical and computational research, computational tool development, and education in support of the prediction of new materials and their properties by computer simulation. Missing in today's computational materials research ecosystem is inverse-type predictive software; that is, software that enables materials design starting from desired properties. In "inverse problem" simulations for design, a target materials property, behavior, and/or structure, is specified and the building blocks and thermodynamic conditions that will achieve that target are predicted. Inverse design methods may still require molecular dynamics, Monte Carlo, or other workhorse algorithms, but the entire conceptual approach is flipped on its head. The PI and her group recently developed an inverse design algorithm called "digital alchemy." The algorithm is grounded rigorously in statistical thermodynamics, and is based on the idea of generalized thermodynamic ensembles. They have developed a way to simulate materials in an "alchemical ensemble," where particle attributes like shape or interparticle interactions are treated as thermodynamic variables, and change during the course of the simulation, sampling many millions of possibilities in minutes. In these simulations, a target structure is defined, and the simulation finds the optimal set of building block attributes that will self-assemble the target structure. They have applied digital alchemy to the design of nanoparticle and colloidal particle shapes for target, one-component colloidal crystal structures, including some very complex ones. Under this project, the PI will fully develop digital alchemy into a method that can treat other, experimentally relevant particle attributes and implement the method into the publicly available, open source, particle simulation toolkit, HOOMD-blue. HOOMD-blue will be able to "discover" building block attributes and thermodynamic conditions optimized for a target structure, target properties, and/or target behavior. The research team will demonstrate the use of this new inverse design capability of HOOMD-blue to design multicomponent colloidal crystals, colloidal crystals of patchy particles, and reconfigurable colloidal crystals. The overarching goals of this project are to provide open-source software capable of inverse materials design to the materials community, and demonstrate the power and possibilities of GPU-enabled inverse design simulations via several scientific use cases relevant to nanoparticle self-assembly and colloidal crystallization.
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.
