Feeds — 2026-03-23

Introduces numerically exact diagrammatic Monte Carlo polaron calculations grounded in first-principles electron–phonon interactions, directly advancing EPW-based polaron physics in real materials like SrTiO3 and TiO2.

Identifies moiré optical phonons strongly coupled to flat bands via an effective Holstein model at the moiré scale, directly relevant to the researcher's work on moiré phonons and electron-phonon coupling in twisted graphene.

Shows that moiré phonons—including the phason—become infrared-active upon doping via interband electron-phonon matrix elements, intersecting moiré physics, optical response, and flat-band superconductivity interests simultaneously.

Uses time-resolved EUV momentum microscopy and DFPT to show CDW fluctuations in 1T-TiSe2 are electron-phonon-driven rather than excitonic, directly addressing the researcher's CDW and first-principles phonon interests.

Comprehensive review unifying effective-Hamiltonian and ab initio polaron frameworks including exciton polarons and self-trapped excitons within a single field-theoretic language, directly relevant to EPW polaron calculations.

The definitive modern review of DFPT + Wannier-interpolation electron-phonon methodology underpinning EPW, superconductivity, transport, and polaron calculations throughout this researcher's work.

Essential reference for the Wannier interpolation framework underlying EPW, BerkeleyGW, and tight-binding Hamiltonians used throughout the researcher's computational toolkit.

Landmark ab initio GW self-energy paper establishing quasiparticle band-structure calculations foundational to BerkeleyGW and all GW/BSE work in the profile.

Primary EPW code reference describing Wannier-based electron-phonon interpolation for superconductivity, transport, and polaron calculations—core methodology for this researcher.

Reference implementation paper for BerkeleyGW, the GW/BSE code central to this researcher's quasiparticle band structures and excitonic optical response calculations.

Bridges AI-assisted DFT automation with moiré structural relaxation in MX2 TMDs—an unexpected convergence of the researcher's ML/automation interests with large-scale moiré phonon calculations normally intractable by direct DFT.

Equivariant graph neural networks that predict electron-phonon coupling matrix elements at DFT accuracy orders of magnitude faster—could transform EPW-based polaron and superconductivity workflows the researcher relies on.