arXiv subjects

Rasmus Tranås

Publications and source records attributed to Rasmus Tranås.

3 recordsLinked to original sources

arXiv2023

Lattice Thermal Conductivity from First Principles and Active Learning with Gaussian Process Regression

The lattice thermal conductivity ($κ_{\ell}$) is a key materials property in power electronics, thermal barriers, and thermoelectric devices. Identifying a wide pool of compounds with low $κ_{\ell}$ is particularly important in the development of materials with high thermoelectric efficiency. The present study contributed to this with a reliable machine learning (ML) model based on a training set consisting of 268 cubic compounds. For those, $κ_{\ell}$ was calculated from first principles using the temperature-dependent effective potential (TDEP) method based on forces and phonons calculated by density functional theory (DFT). 238 of these were preselected and used to train an initial ML model employing Gaussian process regression (GPR). The model was then improved with active learning (AL) by selecting the 30 compounds with the highest GPR uncertainty as new members of an expanded training set. This was used to predict $κ_{\ell}$ of the 1574 cubic compounds in the \textsc{Materials Project} (MP) database with a validation R2-score of 0.81 and Spearman correlation of 0.93. Out of these, 27 compounds were predicted to have very low values of $κ_{\ell}$ ($\leq 1.3$ at 300~K), which was verified by DFT calculations. Some of these have not previously been reported in the literature, suggesting further investigations of their electronic thermoelectric properties.

cond-mat.mtrl-sci↗

arXiv2021

Lattice thermal conductivity of half-Heuslers with density functional theory and machine learning: Enhancing predictivity by active sampling with principal component analysis

Low lattice thermal conductivity is essential for high thermoelectric performance of a material. Lattice thermal conductivity is often computed using density functional theory (DFT), typically at a high computational cost. Training machine learning models to predict lattice thermal conductivity could offer an effective procedure to identify low lattice thermal conductivity compounds. However, in doing so, we must face the fact that such compounds can be quite rare and distinct from those in a typical training set. This distinctness can be problematic as standard machine learning methods are inaccurate when predicting properties of compounds with features differing significantly from those in the training set. By computing the lattice thermal conductivity of 122 half-Heusler compounds, using the temperature-dependent effective potential method, we generate a data set to explore this issue. We first show how random forest regression can fail to identify low lattice thermal conductivity compounds with random selection of training data. Next, we show how active selection of training data using feature and principal component analysis can be used to improve model performance and the ability to identify low lattice thermal conductivity compounds. Lastly, we find that active learning without the use of DFT-based features can be viable as a quicker way of selecting samples.

cond-mat.mtrl-sci↗

arXiv2021

Discarded gems: Thermoelectric performance of materials with band gap emerging at the hybrid-functional level

A finite electronic band gap is a standard filter in high-throughput screening of materials using density functional theory (DFT). However, because of the systematic underestimation of band gaps in standard DFT approximations, a number of compounds may incorrectly be predicted metallic. In a more accurate treatment, such materials may instead appear as low band gap materials and could e.g. have good thermoelectric properties if suitable doping is feasible. To explore this possibility, we performed hybrid functional calculations on 1093 cubic materials listed in the MaterialsProjects database with four atoms in the primitive unit cell, spin-neutral ground state, and a formation energy within 0.3 eV of the convex hull. Out of these materials, we identified eight compounds for which a finite band gap emerges. Evaluating electronic and thermal transport properties of these compounds, we found the compositions MgSc2 Hg and Li2 CaSi to exhibit promising thermoelectric properties. These findings underline the potential of reassessing band gaps and band structures of compounds to indentify additional potential thermoelectric materials.

cond-mat.mtrl-sci↗