Predicting Synthesis and Synthesizability

Our research group is interested in resolving outstanding fundamental scientific problems that impede the computational materials design process. Our immediate goal is to develop a more quantitative understanding of materials synthesis-structure relationships; specifically, to elucidate and predict crystallization pathways through metastable phases during materials formation. Our work involves developing new thermodynamic frameworks and density functional theory (DFT)-based tools to investigate phase equilibria at the nanoscale, competitive nucleation, and constrained equilibria. We also use data-mining techniques to design new technological materials, and to map relationships across the structural and thermodynamic landscape of known materials.

Our primary research focus is on developing new quantitative and predictive theories of inorganic materials synthesis. This effort was born out of a realization that the computational materials discovery pipeline is no longer bottlenecked by the identification of promising new materials, but rather, by the difficulty of synthesizing predicted compounds in the laboratory. The ability to predict how materials form, and under which conditions, is the final step required to ‘close-the-loop’ in the computational materials discovery and design process.

 

Targeted Synthesis

DFT is often used to predict novel materials with exciting properties. However, even when DFT can predict what compounds to make, how do we synthesize these newly predicted materials in a lab? Developing a predictive theory of materials synthesis requires a better understanding of metastable phases—which often appear as kinetic byproducts during materials formation. We derive theoretical frameworks to predict non-equilibrium crystallization pathways, helping chemists to navigate through the thermodynamic and kinetic energy landscape towards the synthesis of novel target materials.

 

Exploratory Synthesis

In the modern age of data science, there is more catalogued and query-able materials data than ever before. We employ high-throughput computational materials discovery techniques to survey uncharted chemical spaces for novel synthesizable materials, constructing large stability maps to help guide exploratory synthesis. Using data-mining and machine-learning algorithms, we aim to explain the complex interplay between chemistry, composition, and electronic structure in governing large-scale stability trends across broad materials spaces.

 

Nucleation and Growth

Crystallization from a supersaturated solution frequently proceeds through a series of transient, metastable phases prior to the formation of the lowest-energy, stable polymorph. This phenomenon, popularly referred to as “Ostwald’s Rule of Stages”, occurs in all classes of materials, from inorganic minerals and functional technological materials to organic crystals and biological protein crystals. Despite there being a lower thermodynamic driving force for the formation of a metastable polymorph, a metastable phase can still dominate the kinetics of crystallization if it has a lower nucleation barrier than the equilibrium phase. Our group aims to unify classical thermodynamics, nucleation, diffusion, and crystal growth theories to predict the Temperature-Time-Transformation (TTT) transformation pathways for compounds during materials synthesis.

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