Abstract
We design an advanced machine-learning (ML) model based on crystal graph convolutional neural network that is insensitive to volumes (i.e., scale) of the input crystal structures to discover novel quaternary chalcogenides, AMM′Q3 (A/M/M' = alkali, alkaline earth, post-transition metals, lanthanides, and Q = chalcogens). These compounds are shown to possess ultralow lattice thermal conductivity (κl), a desired requirement for thermal-barrier coatings and thermoelectrics. Upon screening the thermodynamic stability of ~1 million compounds using the ML model iteratively and performing density-functional theory (DFT) calculations for a small fraction of compounds, we discover 99 compounds that are validated to be stable in DFT. Taking several DFT-stable compounds, we calculate their κl using Peierls–Boltzmann transport equation, which reveals ultralow κl (<2 Wm−1K−1 at room temperature) due to their soft elasticity and strong phonon anharmonicity. Our work demonstrates the high efficiency of scale-invariant ML model in predicting novel compounds and presents experimental-research opportunities with these new compounds.
Original language | English (US) |
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Article number | 48 |
Journal | npj Computational Materials |
Volume | 8 |
Issue number | 1 |
DOIs | |
State | Published - Dec 2022 |
Funding
The authors acknowledge support from the U.S. Department of Energy under Contract No. DE-SC0014520 (thermal-conductivity calculations), National Institute of Standards and Technology as part of the Center for Hierarchical Materials Design (CHiMaD) under the Award 70NANB19H005 by U.S. Department of Commerce (HT-DFT calculations), the Toyota Research Institute through the Accelerated Materials Design and Discovery program (machine learning and lattice dynamics), and the National Science Foundation through the MRSEC program (NSF-DMR 1720139) at the Materials Research Center (phase stability). We acknowledge the computing resources provided by (a) the National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility operated under Contract No. DE-AC02-05CH11231, (b) Quest High-Performance Computing Facility at Northwestern University, which is jointly supported by the Office of the Provost, the Office for Research, and Northwestern University Information Technology, and (c) the Extreme Science and Engineering Discovery Environment (National Science Foundation Contract ACI-1548562).
ASJC Scopus subject areas
- Modeling and Simulation
- General Materials Science
- Mechanics of Materials
- Computer Science Applications