Materials Informatics for Synthesis, Optimization and Scale-Up of Functional Materials

Thomas Han (16-ERD-019)

Project Description

The importance of discovery of novel materials has recently been emphasized with the launching of the Materials Genome Initiative by the White House in 2011. The goal of the initiative is to deploy advanced materials twice as fast, at a fraction of the cost, by integrating computational and experimental tools, digital data, and collaborative networks. New materials with unique properties are synthesized in the hundreds (if not in the thousands) every year by researchers around the globe. However, advanced materials produced with discovery research and development often do not transition to industry for application integration, partly because of scaling the synthesis process to the large-scale quantities required by industry. One reason why an experimentalist cannot reproduce high-quality materials at the larger scale is because of lack of understanding of the critical reaction parameters involved in the synthesis. In a given synthesis of a material, there are a number of reaction parameters, including specific chemicals, chemical concentrations, temperatures, additives, reaction times, and solvents, just to name a few. It is often difficult and time-consuming to experimentally pinpoint the most important parameters to obtain the desired results. If we can discover the most relevant critical reaction parameters from existing literature using computational and data-processing techniques, and verify their veracity with experimental validation, we will have made a significant leap in the field of materials synthesis and materials informatics. We propose to successfully transition academic research and development processes to pre-pilot plant scales by understanding the fundamental principles in materials synthesis. We intend to do this by extracting and experimentally verifying correlations between processing, structure, and function in the existing literature by integrating automated information extraction, machine learning, data analytics, and experimental materials synthesis and characterization (see figure).

We will leverage LLNL's expertise in advanced materials synthesis and computational processes to develop a new capability that will allow efficient scaling and production of advanced materials to meet current and future demands. We intend, as an example, to address solution-based synthesis of metal, metal oxides, and magnetic materials. More specifically, we will address the shape and size evolution of these materials from the nanometer to micrometer scales. Typically, transitioning a synthesis from a small to a larger scale is often difficult because of the precipitous drop in the quality of the materials as the scale of synthesis increases. The successful completion of our research effort will provide a science-based material synthesis approach to scale-up high-quality materials from a wealth of small-scale materials synthesis literature, which will have a significant impact in numerous applications. In particular, we expect to develop information- and observation-driven processes to synthesize materials with high quality and quantity to meet the demands of both the internal and external community. The research work we perform may also lead to discovery of new synthesis processes that have not yet been explored.

Mission Relevance

Our research effort will enhance current Laboratory capabilities in the core competency of advanced materials and manufacturing by addressing our limited capability to synthesize large quantities of advanced materials to supply multiple programs and external commercial and government entities. Products we generate during this research can impact several mission focus areas including stockpile stewardship science, by providing materials relevant to weapon systems, energy and climate security by producing magnetic materials to improve energy applications, and the National Ignition Facility by providing materials relevant to laser target development. Computational and informatics processes we will develop support the core competency in high-performance computing, simulation, and data science.

FY16 Accomplishments and Results

In FY16 we initiated the development of an algorithm for information extraction from peer-reviewed journals to extract researched, existing relevant information regarding synthesis of novel materials, focusing on entities that will provide necessary chemicals to successfully synthsize the materials. In addition, we developed a method to present peer-reviewed papers in semantic graph formats.

We are leveraging livermore's core competencies to build a new materials structure knowledge base and application programming interfaces to analyze, query, discover, and optimize processes to quickly deploy novel advanced materials.
We are leveraging Livermore's core competencies to build a new materials structure knowledge base and application programming interfaces to analyze, query, discover, and optimize processes to quickly deploy novel advanced materials.