This CAREER award supports computational and theoretical research and education aimed at advancing rational materials design. The PI aims to develop new computational tools for the prediction and characterization of crystal structure of materials, one of the most fundamental problems of materials science, and to investigate low-temperature compounds in alloys of strategic importance for modern technology. Although phase diagrams of alloys contain more information than the stable compounds, any rational theoretical material development begins from the knowledge of the "available" stable structures. Binary phase diagrams have long been studied experimentally, but the existence of phases stable only at low temperature in important systems remains relatively unexplored due to the very long holding times needed to form such phases. Normal experimental methods fail to find such low temperature phases, but computational techniques are well adapted to the prediction of their compositions, structures, and stabilities. The prediction of crystal structures has usually been approached by direct simulation methods. However, a global optimization over all possible atomic configurations is not possible with these methods, and so, only a small subset of possible structures can be sampled. This research approaches the problem with methods that can represent a wider range of structures and genetic algorithms. Local atomic environments are considered as building blocks of structure prototypes, and genetic recombination and mutation concepts are transferred to alloy theory, for the purpose of creating algorithms for global minimization of configuration energies. The two goals of the proposed research, related to alloy theory and characterization are (1) developing techniques for ab initio prediction of compounds in alloys, based on genetic algorithms acting on local atomic environments, and (2) using these methods to identify novel intermetallic compounds structures in alloys with strategic applications, such as automotive and aerospace materials, marine structural applications, nanotechnology, medical applications, catalysis, and energy conversion. The educational activity includes recruiting students with disabilities and students from underrepresented populations and involving them in the research. The PI, in collaboration with other faculty at Duke University, will establish two courses covering the subjects of electronic structure, ligand, alloys and compounds theory which will expose senior undergraduate students and first year graduate students to concepts and techniques necessary for materials design, and educate a diverse cadre of students well versed in this new methodology. While improvements on the "architectural barriers" that separate persons with disabilities from regular university life have been made, there still exists a considerable barrier that prevents the acceptance and inclusion of students with disabilities. The PI aims to provide a summer research experience for a hearing impaired student in each year of funding, to encourage greater interchange and interaction, and overcome barriers.
NON-TECHNICAL SUMMARY:
This CAREER award supports computational and theoretical research and education aimed at advancing rational materials design. The PI will develop new computational tools with aim of advancing the ability to predict the structure of crystalline materials starting from only the constituent atoms. The PI's methods will use an algorithm inspired by biological evolution to explore a much larger set of candidate structures than traditional methods can include. The PI plans to apply these methods to enhance our understanding of existing materials that are particularly promising for various applications, including automotive and aerospace materials (strong lightweight magnesium alloys), marine structural applications (novel titanium alloys), nanotechnology (metallic nanoparticles for nanotubes growth), medical applications (tantalum based implants alloys), catalysis (phase stability of platinum-group nanoparticles), and energy conversion (alloys for fuel cells). The education component includes recruiting students with disabilities and students from underrepresented populations and involving them in the research. The PI, in collaboration with other faculty at Duke University, will establish two courses covering the subjects of electronic structure, ligand, alloys and compounds theory which will expose senior undergraduate students and first year graduate students to concepts and techniques necessary for materials design, and educate a diverse cadre of students well versed in this new methodology. While improvements on the "architectural barriers" that separate persons with disabilities from regular university life have been made, there still exists a considerable barrier that prevents the acceptance and inclusion of students with disabilities. The PI aims to provide a summer research experience for a hearing impaired student in each year of funding, to encourage greater interchange and interaction, and overcome barriers.
" supported one student during various periods of his PhD program, toward the completion of the degree. Several articles were written and published with this this support, mostly in the field of alloy theory and high-throughput computational materials design which is an emerging area of materials science. By combining advanced thermodynamic and electronic-structure methods with intelligent data mining and database construction, and exploiting the power of current supercomputer architectures, scientists generate, manage and analyze enormous data repositories for the discovery of novel materials. An important outreach was the creation of the aflowlib.org repository of quantum calculations. It is an online project and it comprises more than 550,000+ thermodynamic, magnetic and electronic structure entries. The database follows the philosophy of the Materials Genome Initiative. The funding also helped further development of the" AFLOW: project: an automatic framework for high-throughput materials discovery". In the figure we show an example of AFLOW+AFLOWLIB project: "High-throughput analysis of binary intermetallics". Top?left triangle: ordering tendency of binary mixtures for elements ordered by Pettifor’s chemical scale. Grey circles indicate no ordering, whereas darker blue circles indicate increasing capability to form stable compounds. Bottom right triangle: comparison ?of HT versus experimental results. Green and grey circles denote agreement between calculation and experimental data on the existence (green) or absence (grey) of compounds. Purple (red) triangles indicate disagreement of HT predictions of compound absence (existence) versus experimental existence (absence). Yellow triangles indicate that data is unavailable for comparison.
