Advancing Atomic-level Understanding and Prediction of Kinetics of Solid-Solid Phase Transitions from First Principles and Machine Learning

The University Of Central Florida Board Of Trustees, Orlando FL

Investigators

Abstract

NONTECHNICAL SUMMARY This award supports theoretical and computational research aimed at advancing the fundamental understanding of solid-solid phase transitions. A solid-solid phase transition refers to a change in the arrangement of atoms or molecules within a solid material, resulting in a different crystalline structure. These transitions are prevalent in various materials and can occur due to changes in temperature, pressure, or other external factors. A typical example is the transition between graphite (a flaky, black material used in pencils) and diamond (a hard, colorless gemstone). Solid-state transitions are of significant interest in materials science, as 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 a thermodynamic perspective, concerning the relative phase stability, regardless of transition paths between the initial and final structures. However, the kinetics that dictate whether or not the transition can occur in practice under given environmental conditions and which path the transition is likely to take remain poorly understood. This project will advance the atomic-level understanding of the kinetics underlying solid-solid transitions without using empirical data and develop an advanced artificial intelligence method for the fast and accurate prediction of kinetic-related physical properties. 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 into this project with the goal of inspiring and developing a diverse, globally competitive next-generation STEM workforce in computational materials science that will benefit the State of Florida as well as the nation. The research team will (i) develop a new course on “materials modeling and simulation” for seniors and/or graduate students across all science and engineering departments at the University of Central Florida (UCF) — a federally designated Hispanic Serving Institution, (ii) create a summer research fellowship program to provide opportunities for talented undergraduates majoring in science, engineering, and mathematics to conduct computational materials research, and (iii) foster and expand graduate and postdoc research and education collaborations with UCF’s multiple interdisciplinary research centers focused on “materials” for applications in environmental, energy, and biomedical sectors. TECHNICAL SUMMARY Solid-solid phase transitions are ubiquitous phenomena that play pivotal roles in a myriad of technologies spanning physics, chemistry, biology, materials science and engineering. Despite more than a century of study, the fundamental understanding of phase transition kinetics remains predominantly qualitative or phenomenological. The atomistic mechanism underlying these transitions and the essential design rules for controlling kinetics are still crucially elusive. This project will advance the atomic-level understanding of kinetics in 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 the 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 rapid and accurate prediction of transition barriers. The study will be carried out 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 into this project with the goal of inspiring and developing a diverse, globally competitive next-generation STEM workforce in computational materials science that will benefit the State of Florida as well as the nation. The research team will (i) develop a new course on “materials modeling and simulation” for seniors and/or graduate students across all science and engineering departments at the University of Central Florida (UCF) — a federally designated Hispanic Serving Institution, (ii) create a summer research fellowship program to provide opportunities for talented undergraduates majoring in science, engineering, and mathematics to conduct computational materials research, and (iii) foster and expand graduate and postdoc research and education collaborations with UCF’s multiple interdisciplinary research centers focused on “materials” for applications in environmental, energy, and biomedical sectors. 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.