From Novel Chemistry of Supercomposition Compounds to the Design of New Materials for Defense Applications
Abstract
Supercomposition compounds (SCC) refer to solid-state chemical substances in which the compositions of one or two elements (usually nonmetals) are exceedingly higher than the other components. Thanks to the advancement of computer simulation methods and high-pressure synthesis techniques, many new families of SCC are discovered recently, including metal superhydrides, clathrate borocarbides, clathrate silicides, metal poly-nitrides, etc. Many SCC materials show superior properties such as superhardness, super-high energy density, high Tc superconductivity, and superb electronic and optoelectronic properties, making them good candidate materials for many defense applications. In contrast to typical solid-state compounds, SCC typically consists of covalently bonded polyanion species and metal sublattices, and many maintain stability and functions after releasing the pressure. The chemical forces that stabilize these structures and the structure-property relations in these compounds are not well understood, which hinders their discovery and design. Different from traditional computational materials designs that are based on reverse engineering of a targeted property, we propose a forward search of SCC on a massive scale in conjunction with the comprehension of new chemical mechanisms and bonding features in these compounds. This approach does not focus on one specific property or one family of materials. Instead, it aims to search and design compounds with similar structural and chemical features altogether. By combining high throughput, crystal structure search and machine learning methods, this new computational materials design paradigm is a computationally most efficient approach to discovering new SCC materials in large numbers. The specific objectives of the project include: 1) Discover new SCC materials in large numbers, that show superb properties for defense applications such as superhardness, high energy density, high Tc superconductivity, etc. 2) Develop and implement high-throughput methods that can search new SCC materials with various structures, compositions, and constituent elements across the periodic table. 3) Understand the bonding features, the chemical interactions, and mechanisms that govern the stability of SCC and the structure-property relations in them. 4) Explore the border of chemistry in SCC, i.e. what polyanions can form in a certain pressure range and whether and how they can be maintained while releasing the pressure. 5) Develop a general Machine Learning method that can discover new compounds and chemistry by utilizing high-pressure results to expand the chemical space of matter. The project will enhance STEM education at CSUN in many aspects including 1) providing new opportunities of experiencing cutting-edge research; 2) offering a well-designed CMC course that provides hands-on practice; 3) giving career guidance and help for applying for graduate programs nationwide; 4) conducting outreach activities in local high schools and community colleges. In the past 7 years at CSUN, the PI published 60 papers in journals including Nat. Communs, Phys. Rev. Letts, JACS, Angewandte Chemie, etc.; was invited to write reviews by Nat. Rev. Chem., Commun Chem, etc.; reported by science media such as Scientific American, Science Blogs, Science Daily, etc.; obtained NSF CAREER award and the MRI grant; was invited to speak in Nature Conference, the APS March meeting, the Gordon Conference, etc.; mentored more than 25 undergraduate students and created a new Computational Chemistry course.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- May 24, 2023
- Source ID
- W911NF2310232
Entities
People
- Maosheng Miao
Organizations
- Army Contracting Command
- California State University, Northridge
- Office of the Secretary of Defense