This CAREER award supports an integrated computational research and education program to develop a rigorous formalism to predict phase stability and diffusion in multi-component crystalline solids susceptible to both order-disorder phenomena and structural phase transformations. The PI aims to generate a theoretical and computational framework for the first principles prediction of phase stability, diffusion and phase transformation kinetics in alloys, oxides and semi-conducting compounds that are simultaneously susceptible to order-disorder reactions as well as structural transformations. This will open the way to a truly first-principles prediction of the thermodynamic properties of most technologically important multi-component solids. The elucidation of the coupling between configurational and anharmonic vibrational excitations will pave the way for the discovery of new phase-change materials for memory storage, electrochemically activated shape-memory materials and thermoelectrics optimized to have a low thermal conductivity. The development of a first-principles formalism for substitutional diffusion will allow a rigorous characterization of diffusion during phase transformations of multi-component solids and lead to a better understanding of degradation mechanisms in hetero-structures used in structural and micro-electronics applications. An additional outcome of this effort will be implementing the computer modeling capabilities in the form of user-friendly software for the first-principles prediction of thermodynamic and kinetic properties to serve the goals of both research and education.
The project will connect existing computational power available to students with algorithms and modeling capabilities developed in the research effort to provide students with unprecedented access to user-friendly atomistic simulation software. Course development is undertaken to fulfill the educational benefit of this capability to solve real materials problems at the macroscopic length-scale. PI will be developing a novel undergraduate course that combines essential concepts and tools from solid-state physics with statistical mechanics with an emphasis on real materials. This course will cover the basics of solid-state physics and reinforce concepts by having students calculate and explore electronic properties with user-friendly ab initio electronic structure codes. The educational component will expand to cover elementary concepts of statistical mechanics and its role in coupling the Schroedinger equation to thermodynamics. This allows treatment of the partition function, thermodynamic averages and a statistical mechanical interpretation of entropy. To enhance the research experience students will apply the tools learned in class to a problem they design in material science.
NON-TECHNICAL SUMMARY: This CAREER award supports an integrated computational research and education program to develop a rigorous formalism to predict stability and dynamics of crystalline materials. This PI aims to generate a theoretical and computational framework for the prediction of stability, diffusion and dynamics in alloys and other compounds starting from only the knowledge of the identity of the constitutent atoms. This opens the way to true prediction of the thermodynamic properties of most technologically important multi-component solids. The research paves the way for the discovery of new materials for memory storage, electrochemically activated shape-memory materials and thermoelectrics.
An additional outcome of this effort will be software for the first-principles prediction of thermodynamic and kinetic properties to serve the goals of both research and education. Course development is undertaken to fulfill the educational benefit of this project. This includes developing a novel undergraduate course that combines essential concepts and tools from solid-state physics with statistical mechanics with an emphasis on real materials. This course will cover the basics of solid-state physics and reinforce concepts by having students calculate and explore electronic properties with user-friendly electronic structure codes. The educational component will expand to cover elementary concepts of statistical mechanics and its role in thermodynamics. To enhance the research experience students will apply the tools learned in class to a problem they design in material science.
The overarching objective of this research was to develop a theoretical and computational framework that connects the electronic structure of a material to its macroscopic properties arising from collective phenomena, with a focus on phase stability, diffusion and phase transformations in multi-component crystalline solids. The ability to predict materials properties from first principles is crucial to enable the computational design of new materials with minimal experimental input. A large class of technologically important high temperature materials cannot be adequately described and understood from first principles with current statistical mechanical methods and tools as many of these materials become mechanically unstable at low temperature, precluding the use conventional phonon theories. During the course of this project, we developed statistical mechanical methodology and accompanying software to enable a rigorous first-principles prediction of the thermodynamic and mechanical properties of high temperature materials that become mechanically unstable at low temperature. Applied to model a system that undergoes a structural phase transition with changing temperature, we were able to elucidate the character of the phase transition as well as the nature of the thermal excitations that stabilize the high temperature phase. The relation of macroscopic metrics of ionic transport in non-dilute multi-component crystalline solids to chemistry and crystal structure remains poorly understood and characterized. With support from this grant, we were able to develop new statistical mechanical methodology and software to predict multi-component diffusion coefficients in crystalline materials used in a wide-variety of technologies, ranging from electrodes for electrochemical energy storage and super alloys for high temperature aerospace applications. This grant has enabled the development of highly automated statistical mechanical computational tools to predict thermodynamic and kinetic properties of technologically important materials. Such tools facilitate the generation of new knowledge about the dependence of a wide variety of materials properties on chemistry and crystal structure. The development of new statistical mechanical methods to study diffusion and high temperature thermodynamic properties along with accompanying software tools to predict materials properties will greatly enhance the ability to design materials for high temperature and non-equilibrium applications from first principles, thereby potentially reducing the time and cost of the development of new materials. Areas where such tools will prove invaluable include the design of new (i) structural materials for aerospace applications and large-scale power generation plants, (ii) electrode and electrolyte materials for electrochemical energy storage, (iii) materials for thermoelectric applications and (iv) materials for shape memory applications. The activity within the context of this grant also led to the education and training of graduate students in computational materials science, a field that is increasingly recognized as invaluable in the design and rapid implementation of new materials.
