Electron–phonon interaction and lattice thermal conductivity from metals to 2D Dirac crystals: a review
Bibliographic record
Abstract
Abstract Electron–phonon (e–ph) coupling governs electrical resistivity, hot-carrier cooling, heat flow, and critically, thermal transport in solids. Recent first-principles calculations now predict e–ph-limited thermal conductivity from d -band metals and wide-band-gap semiconductors to two-dimensional (2D) Dirac crystals without empirical parameters. In bulk metals, ab-initio lifetimes show that phonons, though secondary, still carry up to 40 % of the heat once e–ph scattering is included. We next survey coupled Boltzmann frameworks, exemplified by elphbolt , that capture mutual drag and ultrafast non-equilibrium in semiconductors; their results for Si, GaAs, and MoS 2 match the time-domain thermo-reflectance andisotope-controlled data within experimental error. For 2D Dirac crystals, mirror symmetry, scarrier density, strain, and finite size rearrange the scattering hierarchy: flexural (ZA) modes dominate pristine graphene yet become the main resistive branch in nanoribbons once σ h symmetry is broken. At low Fermi energies where E F ≪ k B T , the standard three-particle decay is partially cancelled, elevating-particle processes and necessitating dynamically screened, higher-order theory. Throughout, we identify the microscopic levers such as the electronic density of states, phonon frequency, deformation potential, and Fröhlich coupling, and show how doping, strain, or dielectric environment can tune e–ph damping. We conclude by outlining Open challenges such as: developing femtosecond-resolved, coupled e–ph solvers, solving the full mode-to-mode Peierls–Boltzmann equation with four-particle terms, embedding correlated-electron methods ( GW , dynamical mean-field theory, hybrid functionals) in e–ph workflows, implementing fully non-local, frequency-dependent screening for van-der-Waals stacks, and leveraging higher-order e–ph coupling and symmetry breaking to realize phononic thermal diodes and rectifiers. Solving these challenges will elevate e–ph theory from a diagnostic tool to a predictive, parameter-free platform that links symmetry, screening, and many-body effects to heat and charge transport in next-generation electronic, photonic, and thermoelectric devices.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".