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High-throughput study and machine learning on MAX and MAB phases: new materials and fingerprints of superior lattice thermal conductivities by Shaohan Li; Zening Yang; Rasoul Khaledialidusti; Shuai Lin; Jin Yu; Mohammad Khazaei; Jing Zhang; Litao Sun; Xin Li; Weiwei Sun is a Engineering article available to read on EtoBox.
What is High-throughput study and machine learning on MAX and MAB phases: new materials and fingerprints of superior lattice thermal conductivities about?
The ternary ceramic MAX and recently emerging MAB phases have exhibited a combination of properties of metals and ceramics, excellent mechanical properties along with high-damage tolerance. Thermal conductivity as one of the fundamental properties is closely associated with operating conditions, which could serve as an essential indicator of thermo-functional applications. Upon performing a high-throughput density functional theory calculation on M2AX (X = B, C, or N) and M2AB2 phases in a wide compositional space, a great number of new materials are stable. Several ultra-low/high lattice thermal conductors are identified. Combined with the machine-learning, the underlying origins of approaching superior lattice thermal conductivity are unraveled and verified to be self-consistent by reverse comparison. Towards the “truly” stable materials by including reaction enthalpies, about seventy materials are retained, in which Zr2SnC and Nb2SnB having ultralow κ ph (< 2 W (m K)−1) are successfully synthesized and further characterized. A new continent of ceramics with superior lattice thermal conductivities is thus reported, and we tend to lay foundations of the lattice thermal conductivit
Who reads High-throughput study and machine learning on MAX and MAB phases: new materials and fingerprints of superior lattice thermal conductivities?
It is typically read by researchers, students, and practitioners in Engineering.
- Author
- Shaohan Li; Zening Yang; Rasoul Khaledialidusti; Shuai Lin; Jin Yu; Mohammad Khazaei; Jing Zhang; Litao Sun; Xin Li; Weiwei Sun
- Publisher
- Elsevier BV
- Published
- 2023
- Language
- EN
- Field
- Engineering (Physical Sciences)