Accelerating Thermoelectric Materials Discovery via Dopability Predictions
The project’s ultimate objective is to build a robust and accurate dopability recommendation engine to overcome the dopability bottleneck in thermoelectric materials discovery. The recommendation engine will use materials informatics to enable high-throughput predictions of dopability, relying only on quantities that are inexpensive to calculate, experimental measurements, and known structural/chemical features as inputs. It will thus allow dopability screening of thousands of compounds. First, an accurate training set will be built for the recommendation engine containing native defect formation enthalpies and structural/chemical descriptors from a diverse array of thermoelectric-relevant compounds. Whereas prior dopant studies focused on single compounds, a new, automated calculation infrastructure will be leveraged that allows the rapid creation of an extensive training set, initially containing approximately 30 compounds but growing to over 100 during the project. Experimental charge transport and local dopant structure measurements will validate the training set. Second, the prediction engine will be trained on the data to extract patterns and correlations, and ultimately identify robust descriptors of dopability. Initially, the engine will predict if `killer' defects limit the available dopant range. The engine will ultimately grow to suggest specific extrinsic dopants for compounds that pass this initial screening. Together, this combination of accurate predictions of intrinsic transport properties (prior DMREF) and dopability (proposed DMREF) is expected to accelerate the discovery process for thermoelectric materials.
Publications
Research Highlights
Defect & Dopant Predictions for Thermoelectric Materials
https://dmref.org/files/13bc53a2-6be6-4e57-855e-ff7d538df0c1
3/20/2018
Predicting Anisotropic Performance in Thermoelectrics
Eric Toberer, Vladan Stevanovic
2/15/2019
Combining Experiment and Computation to Control Doping in Thermoelectric Materials
Elif Ertekin (UIUC), Eric Toberer (CO Schl. Mines), and Michael Toney (Stanford U.)
1/15/2021
Resolving Order in Ternary Semiconductors via Resonant X-ray Diffraction
Eric Toberer (Colorado School of Mines) and Michael Toney (Stanford U.)
12/12/2022
VTAnDeM: A Python Toolkit for Simultaneously Visualizing Phase Stability, Defect Energetics, and Carrier Concentration
Elif Ertikin (University of Illinois)
1/1/2023
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