(Invited) Exploring Thermoelectric Materials Using Predictive Theoretical Modeling
Bibliographic record
Abstract
An important challenge facing society involves reducing our energy consumption and shifting away from fossil fuels and towards sustainable, abundant sources of power that do not harm the environment. Waste heat is a tremendous unutilized energy source, accounting for roughly 60% of the energy humans produce, that if we could only partially recover would have a major impact. Thermoelectrics (TE) provide a solution as materials than can convert heat into electrical power. A major goal is to enhance the TE conversion efficiency, characterized by the figure-of-merit ZT, which depends on the material TE properties: Seebeck coefficient, electrical conductivity and electronic/lattice thermal conductivities. Increasing ZT is critical for the advancement of TE technologies, but difficult since the TE properties are interrelated. To discover more high-ZT materials, it is important to explore and design new materials with unique properties tailored for TE conversion. In the past decade, there have been several breakthroughs in this direction, including nanostructured alloys, resonant impurity states, superlattice structures, all-scale defect engineering, and ultra-low thermal conductivity materials. These, like most advances, were experimentally led and based on trial-and-error, which can be expensive and time-consuming. To help guide experimental efforts and expedite discoveries, there is an opportunity to explore emerging materials using predictive theoretical modeling. In this talk, I will present recent work focused on the development and application of a first principles approach for TE parameter predictions. I will describe our adopted framework, which combines density functional theory (DFT) and the Boltzmann transport equation (BTE). DFT is employed to extract the full electron and phonon dispersions, and to compute the detailed electron-phonon and phonon-phonon lifetimes, the dominant scattering mechanisms for electrons and phonons, respectively. These material properties serve as input for the BTE, which is solved for the TE transport coefficients. With this DFT-based approach, all TE parameters are obtained predictively, including Seebeck coefficient, electrical conductivity and electronic/lattice thermal conductivities, as a function of temperature and carrier concentration. Recent results on select materials, for example SnSe, will be presented to demonstrate the approach and to illustrate how we can gain physical insight. This work will help guide experimental breakthroughs by theoretically exploring and identifying new TE materials with enhanced properties.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.008 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".