NSF Award
2334275
NONTECHNICAL SUMMARY:<br/>This award is made on an EAGER proposal. It supports progress on a project advanced at the SSMCDAT 2023 Datathon held at Lehigh University. This EAGER award supports research aimed at advancing our understanding of how to synthesize solid inorganic materials, which serve as the cornerstone for novel functional devices to meet societal needs for clean energy, environmental sustainability, and human welfare. Nowadays, many new inorganic materials have been virtually designed using computers, but they have rarely been converted into real applications because of missing knowledge of how to synthesize them in practice. In this project, a team of interdisciplinary researchers with expertise in materials science and computer science will develop a data-driven framework to predict the best ways to synthesize these computationally designed materials. The team will utilize deep learning algorithms of artificial intelligence to efficiently predict the free energies of the material to be synthesized and their possible precursors, considering the effects of temperature, pressure, and chemical compositions of the materials. With these predictions, the team will assess the feasibility of the chemical reactions associated with synthesis and correspondingly identify the optimal reaction conditions. To validate and improve their computational predictions, the researchers will also conduct laboratory experiments where they will closely monitor the synthesis process as it happens using a real time characterization technique. To demonstrate the effectiveness of their approach, the researchers will focus on successfully synthesizing a group of novel inorganic compounds known as transition metal oxynitrides, which are promising for energy conversion applications but not yet fully realized experimentally. Through this project, the team aims to bridge the gap between computational design and practical synthesis, unlocking new possibilities for novel functional materials that can benefit society.<br/><br/>This award also supports activities to make the field of science and engineering more inclusive and diverse. It will broaden the participation of women and young girls in these fields, especially in countries in eastern Africa. It will also promote research involvement among undergraduate students from underrepresented backgrounds. Moreover, the researchers will develop new educational materials to teach data-driven materials science to undergraduate students at Drexel University. <br/><br/>TECHNICAL SUMMARY:<br/>This award is made on an EAGER proposal. It supports progress on a project advanced at the SSMCDAT 2023 Datathon held at Lehigh University. This EAGER award supports research aimed at advancing scientific knowledge in the synthesizability of solid-state inorganic materials, especially those that have been computationally designed but not yet realized experimentally. The research will develop a data-driven framework, integrating deep learning algorithms, computational thermodynamic modeling, and validation experiments to efficiently predict synthesis pathways and optimal conditions. Utilizing deep learning algorithms, the framework will predict temperature-dependent Gibbs free energy functions for various compound stoichiometries, based on the relationship between thermochemical properties of inorganic compounds and their electronic structures. With the predictions of Gibbs free energy functions for the compound to be synthesized and its various possible precursors, the framework will then model the thermodynamics of each candidate synthesis reaction in the context of the CALculation of PHAse Diagram (CALPHAD) method. Suitable precursors and synthesis conditions will be rationally identified by assessing the dependence of reaction spontaneity on the key controlling parameters of practical synthesis. Experimental understanding of the reaction pathway and its dependence on reaction conditions obtained through an in situ synthesis approach will be used to validate and improve modeling predictions. The team will focus on successfully synthesizing a group of computationally designed transition metal oxynitrides, highly interesting for energy conversion applications but not yet fully realized experimentally, as a demonstration of the framework's capabilities. Through this project, the team aims to bridge the gap between computational design and practical synthesis, unlocking new possibilities for novel functional materials that can benefit society.<br/><br/>This award also supports various education and outreach activities that will 1) expand the involvement of women and young girls in the fields of science and engineering, particularly in countries in eastern Africa; 2) promote research participation among underrepresented undergraduate students; and 3) develop new learning modules that incorporate data-driven materials science in the undergraduate curriculum at Drexel University.<br/><br/>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.
