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Towards accelerated thermoelectric materials and process discovery

Towards accelerated thermoelectric materials and process discovery
Towards accelerated thermoelectric materials and process discovery
Thermoelectric materials have the ability to convert heat energy to electrical power and vice versa. While the thermodynamic upper limit is defined by the Carnot efficiency, the material figure of merit, zT, is far from this theoretical limit, typically limited by a complex interplay of non-equilibrium charge and phonon-scattering. Materials innovation is a slow, arduous process due to the complex correlations between crystal structure, microstructure engineering, and thermoelectric properties. Many physical concepts and materials have been unearthed in this path to discovery, supported ably by innovations in technology over many decades, revealing important material and transport descriptors. In this review, we look back at some case studies of inorganic thermoelectric materials employing a bird’s-eye view of complementary advancements in scientific concepts and technological advancements and conclude that most high values of zT have emerged from developed scientific models fueled by moderately mature technologies. On the basis of this conclusion, we then propose that the recent emergence of data-driven approaches and high-throughput experiments, encompassing synthesis as well as characterization, with machine learning guided inverse design is perfectly suited to provide an accelerated pathway toward the discovery of next-generation thermoelectric materials, potentially providing a feasible alternative source of energy for a sustainable future.
thermoelectric, accelerated discovery, high-throughput experiments, data-driven, machine learning
2574-0962
Recatala Gomez, Jose
d5cf1fe1-93a6-4dd0-a89c-c8f16fe6a056
Suwardi, Ady
cedecbc2-4d36-42a5-b16d-088a71a34558
Nandhakumar, Iris S.
e9850fe5-1152-4df8-8a26-ed44b5564b04
Abutaha, Anas
e8f3472c-02d2-462b-b6e6-0775101385f7
Hippalgaonkar, Kedar
3a01d862-0650-4f5d-9f3c-215fdf4d8ad9
Recatala Gomez, Jose
d5cf1fe1-93a6-4dd0-a89c-c8f16fe6a056
Suwardi, Ady
cedecbc2-4d36-42a5-b16d-088a71a34558
Nandhakumar, Iris S.
e9850fe5-1152-4df8-8a26-ed44b5564b04
Abutaha, Anas
e8f3472c-02d2-462b-b6e6-0775101385f7
Hippalgaonkar, Kedar
3a01d862-0650-4f5d-9f3c-215fdf4d8ad9

Recatala Gomez, Jose, Suwardi, Ady, Nandhakumar, Iris S., Abutaha, Anas and Hippalgaonkar, Kedar (2020) Towards accelerated thermoelectric materials and process discovery. ACS Applied Energy Materials. (doi:10.1021/acsaem.9b02222).

Record type: Article

Abstract

Thermoelectric materials have the ability to convert heat energy to electrical power and vice versa. While the thermodynamic upper limit is defined by the Carnot efficiency, the material figure of merit, zT, is far from this theoretical limit, typically limited by a complex interplay of non-equilibrium charge and phonon-scattering. Materials innovation is a slow, arduous process due to the complex correlations between crystal structure, microstructure engineering, and thermoelectric properties. Many physical concepts and materials have been unearthed in this path to discovery, supported ably by innovations in technology over many decades, revealing important material and transport descriptors. In this review, we look back at some case studies of inorganic thermoelectric materials employing a bird’s-eye view of complementary advancements in scientific concepts and technological advancements and conclude that most high values of zT have emerged from developed scientific models fueled by moderately mature technologies. On the basis of this conclusion, we then propose that the recent emergence of data-driven approaches and high-throughput experiments, encompassing synthesis as well as characterization, with machine learning guided inverse design is perfectly suited to provide an accelerated pathway toward the discovery of next-generation thermoelectric materials, potentially providing a feasible alternative source of energy for a sustainable future.

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ACS Applied Energy Materials Review_20200109 - Accepted Manuscript
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Accepted/In Press date: 13 January 2020
e-pub ahead of print date: 29 January 2020
Keywords: thermoelectric, accelerated discovery, high-throughput experiments, data-driven, machine learning

Identifiers

Local EPrints ID: 437977
URI: http://eprints.soton.ac.uk/id/eprint/437977
ISSN: 2574-0962
PURE UUID: 915613ca-7630-4875-a3d9-8dc66ac21713
ORCID for Iris S. Nandhakumar: ORCID iD orcid.org/0000-0002-9668-9126

Catalogue record

Date deposited: 25 Feb 2020 17:30
Last modified: 29 Jan 2021 05:01

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