CAREER: Advancing Atomic-Level Understanding of Kinetically Driven Solid-Solid Phase Transitions from First Principles and Machine Learning

University Of Maine, Orono ME

Investigators

Abstract

NONTECHNICAL SUMMARY This CAREER award supports theoretical and computational research to advance the fundamental understanding of solid-solid phase transitions. Most materials have several different stable crystalline structures, each with a characteristic set of physical, chemical, and mechanical properties. Carbon, which can form graphite (flaky, black material used in pencils) or diamond (hard, colorless gemstone) structures, is a well-known example. Solid-solid transitions that occur between different crystalline forms of the same compound are ubiquitous and important phenomena. They can lead to a wide variety of technologically important applications such as diamond and steel production, synthesis of ceramic materials, thermal energy harvesting and storage, rewritable optical data storage, and nonvolatile electronic memories. Historically, considerable progress has been made in understanding solid-solid transitions from thermodynamics concerning the relative phase stability (the “driving force” for the phase transition), regardless of transition paths between the initial and final structures. However, the kinetics that dictates whether or not the transition can occur in practice under given environmental conditions and which path the transition likely takes place remain poorly understood. This project will advance the atomic-level understanding of kinetics underlying solid-solid transitions without using empirical data and develop an advanced artificial intelligence method for the fast and accurate prediction of kinetic barriers that control solid-solid transition in various environments. The data and methods acquired will be broadly disseminated to the scientific community and the general public through open-source distributions and publications. Education and outreach activities are integrated in this project with the goal to inspire and develop a diverse, globally competitive next generation STEM workforce in computational materials science that will benefit the State of Maine as well as the nation. The research team will (i) develop a “kinetics-driven phase-change materials by design” module for high school students in collaboration with the Maine Center for Research in STEM Education, (ii) develop an advanced courses in “computational materials physics and modeling” for seniors and graduate students in science and engineering departments at the University of Maine, (iii) expand the partnership between the University of Maine and Oak Ridge National Laboratory to provide students the opportunity to take advantage of facilities and computational resources in the national laboratory to expand their experiences beyond the traditional university setting, and (iv) create a summer research fellowship program to provide opportunities for talented undergraduates majoring in science, engineering, and mathematics to conduct computational materials research. TECHNICAL SUMMARY This CAREER award supports theoretical and computational research to advance atomic level understanding of solid-solid phase transitions. Solid-solid phase transitions are ubiquitous phenomena that play key roles in diverse technologies across physics, chemistry, biology, materials science and engineering. Despite having been studied for over a century, the fundamental understanding of phase transition kinetics remains largely qualitative or phenomenological; the atomistic mechanism of such transition processes and design rules for controlling kinetics are still crucially missing. This project will advance atomic-level understanding of kinetics of solid-solid phase transitions using a combined method of modern first-principles electronic structure theory calculations, quantitative chemical bond analysis, and machine learning. The specific objectives are to (i) identify physical principles and structural motifs that control kinetic barriers of polymorphic transitions from first principles, and (ii) develop a bottom-up physics-driven machine learning method for the fast and accurate prediction of transition barriers. The study will be carried on a set of select well-known phase-transition materials that are technologically important for energy and electronic applications. The research will accelerate the design and discovery of new functional phase-change materials where kinetics is essential. Education and outreach activities are integrated in this project with the goal to inspire and develop a diverse, globally competitive next-generation STEM workforce in computational materials science that will benefit the State of Maine as well as the nation. The research team will (i) develop a “kinetics-driven phase-change materials by design” module for high school students in collaboration with the Maine Center for Research in STEM Education, (ii) develop an advanced courses in “computational materials physics and modeling” for seniors and graduate students in science and engineering departments at the University of Maine, (iii) expand the partnership between the University of Maine and Oak Ridge National Laboratory to provide students the opportunity to take advantage of facilities and computational resources in the national laboratory to expand their experiences beyond the traditional university setting, and (iv) create a summer research fellowship program to provide opportunities for talented undergraduates majoring in science, engineering, and mathematics to conduct computational materials research. This project is jointly funded by the Division of Materials Research through the Condensed Matter and Materials Theory program, and the Established Program to Stimulate Competitive Research (EPSCoR). 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.