English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Journal Article

Accelerating materials-space exploration for thermal insulators by mapping materials properties via artificial intelligence

MPS-Authors
/persons/resource/persons237953

Purcell,  Thomas
NOMAD, Fritz Haber Institute, Max Planck Society;

/persons/resource/persons22064

Scheffler,  Matthias       
NOMAD, Fritz Haber Institute, Max Planck Society;

/persons/resource/persons21549

Ghiringhelli,  Luca M.
NOMAD, Fritz Haber Institute, Max Planck Society;

/persons/resource/persons21413

Carbogno,  Christian
NOMAD, Fritz Haber Institute, Max Planck Society;

External Resource
No external resources are shared
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)

s41524-023-01063-y.pdf
(Publisher version), 2MB

Supplementary Material (public)
There is no public supplementary material available
Citation

Purcell, T., Scheffler, M., Ghiringhelli, L. M., & Carbogno, C. (2023). Accelerating materials-space exploration for thermal insulators by mapping materials properties via artificial intelligence. npj Computational Materials, 9: 112. doi:10.1038/s41524-023-01063-y.


Cite as: https://hdl.handle.net/21.11116/0000-000D-8156-0
Abstract
Reliable artificial-intelligence models have the potential to accelerate the discovery of materials with optimal properties for various applications, including superconductivity, catalysis, and thermoelectricity. Advancements in this field are often hindered by the scarcity and quality of available data and the significant effort required to acquire new data. For such applications, reliable surrogate models that help guide materials space exploration using easily accessible materials properties are urgently needed. Here, we present a general, data-driven framework that provides quantitative predictions as well as qualitative rules for steering data creation for all datasets via a combination of symbolic regression and sensitivity analysis. We demonstrate the power of the framework by generating an accurate analytic model for the lattice thermal conductivity using only 75 experimentally measured values. By extracting the most influential material properties from this model, we are then able to hierarchically screen 732 materials and find 80 ultra-insulating materials.