High-throughput alloy design of superior thermoelectric materials (Allotherm)

• Schrade, Matthias; Xing, Wen; Thorshaug, Knut & Belle, Branson (2022). Centimeter-Sized Monolayer CVD Graphene with High Power Factor for Scalable Thermoelectric Applications. ACS Applied Electronic Materials. ISSN 2637-6113. 4(4), p. 1506–1510. doi: 10.1021/acsaelm.1c00995. Full text in Research Archive Show summary

  Among the many extraordinary qualities of graphene, its thermoelectric properties have attracted significant interest, for example, in active cooling applications. Here, we report on the thermoelectric transport properties of centimeter-sized monolayer CVD graphene, electrostatically controlled by a high-capacity ionic gel. The power factor reaches 7 and 5.4 mW m–1 K–2 for hole and electron conduction, respectively, similar to earlier reports obtained for microdevices despite our devices being over 2 orders of magnitude larger. On the basis of these results, we propose nonvolatile ferroelectric polarization as a scalable technology for graphene-based thermoelectric applications.

• Tranås, Rasmus André; Løvvik, Ole Martin & Berland, Kristian (2022). Attaining Low Lattice Thermal Conductivity in Half-Heusler Sublattice Solid Solutions: Which Substitution Site Is Most Effective? Electronic Materials. 3(1), p. 1–14. doi: 10.3390/electronicmat3010001. Full text in Research Archive
• Berland, Kristian; Løvvik, Ole Martin & Tranås, Rasmus André (2021). Discarded gems: Thermoelectric performance of materials with band gap emerging at the hybrid-functional level. Applied Physics Letters. ISSN 0003-6951. 119(8). doi: 10.1063/5.0058685. Full text in Research Archive Show summary

  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 be incorrectly predicted metallic. In a more accurate treatment, such materials may instead appear as low band gap materials and could 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 MATERIALS PROJECT 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 MgSc2Hg and Li2CaSi to exhibit promising thermoelectric properties. These findings underline the potential of reassessing band gaps and band structures of compounds to identify additional potential thermoelectric materials.

• Tranås, Rasmus André; Løvvik, Ole Martin; Tomic, Oliver & Berland, Kristian (2021). Lattice thermal conductivity of half-Heuslers with density functional theory and machine learning: Enhancing predictivity by active sampling with principal component analysis. Computational Materials Science. ISSN 0927-0256. 202. doi: 10.1016/j.commatsci.2021.110938. Full text in Research Archive Show summary

  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.

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• Løvvik, Ole Martin (2024). Lyddemping i tømmervegger, holdbarhet av hamptau og den høyeste mulige temperaturen. [Radio]. Intervju i Abels tårn, NRK P2.
• Tranås, Rasmus André; Løvvik, Ole Martin & Berland, Kristian (2023). Screening of low lattice thermal conductivity materials using active learning.
• Løvvik, Ole Martin (2023). Abels tårns årskavalkade – nye materialer og maskinlæring. [Radio]. Intervju i Abels tårn, NRK P2.
• Løvvik, Ole Martin (2023). Plast som leder strøm og andre nye materialer for det grønne skiftet. [Radio]. Intervju i Ekko, NRK P2.
• Løvvik, Ole Martin (2023). Storsatsing på utvikling av materialer med automatisering og kunstig intelligens. [Radio]. Intervju i Abels tårn.
• Løvvik, Ole Martin (2023). Norwegian efforts towards a materials acceleration platform: combining high-throughput experiments, modelling, and machine learning/artificial intelligence.
• Tranås, Rasmus André; Løvvik, Ole Martin & Berland, Kristian (2022). Material Informatics: Machine Learning with Active Sampling for Small Training Sets.
• Tranås, Rasmus André; Løvvik, Ole Martin & Berland, Kristian (2022). Finding Low Lattice Thermal Conductivity Compounds in Materials Space: Machine Learning with Active Sampling .
• Løvvik, Ole Martin (2022). Bør du lukke døra til komfyren når den er ferdig med å steke? [TV]. Intervju i Praktisk Info med Jon Almaas, TV Norge.
• Løvvik, Ole Martin (2022). Thermoelectrics of alloys and intermetallic materials: potential benefits and challenges of using SPS.
• Løvvik, Ole Martin (2022). Hvordan få strøm fra varme – uten bevegelige deler?
• Løvvik, Ole Martin; Rahman, Jamil Ur; Finstad, Terje Gunnar; Gunnæs, Anette Eleonora & Almeida Carvalho, Patricia (2022). Thermoelectric properties of multiple component (high entropy) silicides and antimonides.
• Løvvik, Ole Martin; Schrade, Matthias; Grimenes, Øven Andreas; Tranås, Rasmus André & Berland, Kristian (2022). Predictive screening for thermoelectric properties using atomic-scale simulations and machine learning tools.
• Tranås, Rasmus André; Berland, Kristian; Tomic, Oliver & Løvvik, Ole Martin (2022). Discovering Energy Materials: Low Thermal Conductivity Thermoelectric Compounds and Barocaloric Compounds.
• Tranås, Rasmus André; Løvvik, Ole Martin & Berland, Kristian (2022). Alloying leads to drastic reduction of lattice thermal conductivity of half-Heusler compounds.
• Tranås, Rasmus André; Løvvik, Ole Martin; Tomic, Oliver & Berland, Kristian (2021). Active sample selection for finding low lattice thermal conductivity materials using machine learning .
• Tranås, Rasmus André; Berland, Kristian; Løvvik, Ole Martin & Tomic, Oliver (2021). Fingerprints of low lattice thermal conductivity compounds: half-Heusler case study and beyond.
• Løvvik, Ole Martin (2021). Casting Materials Acceleration Platform and the accelerated search for thermoelectric materials.
• Løvvik, Ole Martin (2021). Screening of materials for thermoelectric conversion of heat into electricity.
• Løvvik, Ole Martin (2021). Vi skal utvikle materialer for fornybar energi ti ganger raskere enn i dag. Apollon : Forskningsmagasin for Universitetet i Oslo. ISSN 0803-6926. 4/2021, p. 21–22.

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