| Abstract Scope |
High-throughput first-principles calculations have transformed materials discovery by enabling large-scale predictions of phase stability, metastability, and structure-property relationships. However, many finite-temperature properties require an additional lattice dynamical layer beyond 0 K energetics and harmonic phonons. In this talk, I will discuss our recent efforts to develop scalable first-principles and machine-learning workflows for anharmonic phonons, vibrational thermodynamics, and lattice thermal transport. The framework combines high-throughput force-constant calculations, self-consistent phonon renormalization, multi-phonon interactions, and off-diagonal transport contributions to assess dynamical stability and thermal transport across large materials spaces. I will highlight how anharmonicity reshapes phonon spectra, stabilizes or destabilizes candidate compounds at finite temperature, and controls thermal conductivity in strongly anharmonic materials. Finally, I will discuss emerging directions that integrate machine-learning potentials and beyond-quasiparticle spectral functions toward autonomous finite-temperature materials discovery. |