CAREER: Data-driven design of graphene oxide for environmental applications enabled by natural language processing and machine learning techniques

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Graphene oxide (GO) has emerged as a promising functional nanomaterial and building block for various environmental technologies including antimicrobial coatings for drinking water filtration membranes, sorbents for the removal of pollutants from air and water, and photocatalysts for the removal and destruction of organic pollutants from contaminated water. However, the current approaches used to design, synthesize, and optimize GO-based nanomaterials for targeted environmental applications suffer from a lack of standardization leading to numerous trial and error runs with prohibitive material development costs. The overarching goal of this CAREER project is to explore the utilization of data-driven approaches to characterize and unravel critical correlations between the synthesis conditions of GO and the material properties that will enable the rationale design and development of GO-based water purification and environmental remediation technologies. To advance this goal, the Principal Investigator (PI) proposes to use natural language processing and machine learning techniques to 1) extract peer-reviewed information from hundreds of thousands of scientific papers devoted to GO synthesis, characterization, and applications, 2) structure this knowledge into robust datasets, and 3) leverage these datasets to develop and experimentally validate structure-property relationships between the synthesis conditions and properties of GO. The successful completion of this project will benefit society through the generation of new fundamental knowledge and the creation of curated datasets and associated computational tools to advance the design and development of GO-based environmental technologies. Additional benefits to society will be achieved through student education and training including the mentoring of a graduate student at the University of Florida.The design and synthesis of tailored graphene oxide (GO)-based functional nanomaterials for water purification and environmental remediation will require a knowledge of the relationships between the material synthesis input parameters and the resulting material properties. The structural, physicochemical, and functional surface/bulk properties of GO depend on several parameters including the nature and characteristics of the precursor graphite, the synthesis conditions, and the post-synthesis treatment protocols. In this CAREER project, the Principal Investigator (PI) proposes to combine natural language processing (NLP) with machine learning (ML) and targeted materials synthesis and characterization experiments to develop and validate structure-property relationships between the synthesis conditions and properties of GO to advance the rationale design and development of GO-based water purification and environmental remediation technologies. The specific objectives of the research are to: 1) use NLP tools (e.g., latent semantic analysis and named-entity recognition) to automatically extract relevant information about the synthesis of GO (e.g., synthesis conditions, precursor materials, and post-fabrication treatments) and the resulting material properties of GO (e.g., size of sheets, and various physical/chemical properties); 2) structure this information into datasets and use classical frequentist approaches and other data analysis techniques (e.g., principal component analysis, linear discriminant analysis, and T-distributed stochastic neighbor embedding) to find correlations between the synthesis input parameters and the resulting material properties of GO, and 3) validate these correlations using targeted experiments including material synthesis, characterization, and performance evaluation of the synthesized GO nanomaterials as antimicrobial coatings for drinking water filtration membranes and sorbents/photocatalysts for the removal and destruction of organic pollutants from contaminated water. The successful completion of this project has the potential for transformative impact through the generation of fundamental knowledge and structured datasets to advance the rationale design of GO-based nanomaterials for water purification and environmental remediation. To implement the educational and training goals of this CAREER project, the PI proposes to develop and teach a graduate course on data-driven design of nanomaterials with a focus on environmental applications. In addition, the PI plans to 1) recruit and mentor undergraduate students from underrepresented groups and 2) develop an online seminar series with the goal of contributing to the training of students at the University of Florida to work on important problems at the interface of materials sciences, data analysis, and environmental engineering.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.