Searcharxiv⌕ Search

arXiv · 2610.25477

NEP-kappa 2.0: A unified framework for phonon transport and nonequilibrium thermalization with machine-learning interatomic potentials

Abstract

We present NEP-kappa 2.0, a unified framework that integrates density-functional theory, material-specific machine-learning interatomic potentials, and pretrained force models for advanced phonon calculations. A common workflow enables harmonic lattice dynamics, quasi-harmonic and stochastic self-consistent phonon treatments, three- and four-phonon transport, Wigner population--coherence transport, and time-dependent phonon dynamics. Applications to 3C-SiC, BAs, and Cs$_2$AgInCl$_6$ demonstrate consistent force-model comparisons, finite-temperature renormalization, higher-order scattering, and intermode coherence. Beyond steady-state transport, mode-resolved simulations of selectively excited 3C-SiC reveal that half of the injected optical-phonon energy reaches acoustic modes within approximately 3.75 ps, primarily through one-optical--two-acoustic scattering. Acoustic branches exhibit response times differing by a factor of five, with competing scattering channels producing a transient energy overshoot. Channel-resolved entropy production and persistent nonthermal occupations distinguish interbranch energy redistribution from intrabranch thermalization. The framework connects equilibrium phonon transport with microscopic nonequilibrium energy dynamics.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Shixian Liu, Bin Liu, Fei Yin, Gang Wang, Yiming Dong, Yangjun Qin, Alexandra V. Kosyakova, Qiang Xian, Alexander A. Barinov, Ke Xu, Gang Zhang. 2026-10-18. NEP-kappa 2.0: A unified framework for phonon transport and nonequilibrium thermalization with machine-learning interatomic potentials. https://arxiv.org/abs/2610.25477

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Effective Atom Theory: Gradient-Driven Materials Design

We introduce Effective Atom Theory (EAT), which embeds discrete atomic identities into a continuous differentiable space. During optimization, sites pass through temporary effective mixtures, then are driven to fully discrete, physically realizable atoms. This enables gradient-based optimization in DFT and MLIPs, yielding orders-of-magnitude acceleration over discrete methods. EAT recovers the exact ground state of NaCl in a 128 atom cell, reconstructs the Cu-Pd phase diagram, optimizes high-entropy oxides for oxygen evolution, and discovers bonding topologies as demonstrated for SrTiO3.

cond-mat.mtrl-sci↗

Fully Automated Wannier Disentanglement via Projectability Selection in Gaussian and Augmented Plane-Wave Frameworks

Constructing the frozen manifold for entangled bands requires balancing exact reproduction of the target bands with sufficient variational freedom to build a well-localized subspace from the broader Bloch pool. We present a workflow that systematically determines the energy ceiling for a target number of selected Wannier projections, freezing every Bloch state below this threshold across the sampled k-mesh. By measuring projectability, the spectral weight of each Bloch state projected onto the target localized basis, the workflow detects missing conduction channels and quantifies discrepancies between orbital character and energy-based freezing. In comparisons where we controlled the extent of the energy ceiling, freezing all sub-ceiling states is generally favored over projectability-based frozen states. Of the seven materials we tested, freezing all sub-ceiling states lowers the band error on four from between 11 meV and 1.2 eV to 18 meV or less, and matches the projectability-based choice to within about 2 meV on the remaining three. In each of the four, projectability-based frozen states give the lower spread, showing that spread alone cannot identify the better model. With the frozen states held fixed, the workflow minimizes the gauge-invariant spread, constructs the gauge from density-matrix columns on atomic orbitals, and passes an isolated set of bands to Wannier90 for localization. The workflow is implemented in two programs for seamless high-throughput Wannierization with WIEN2k (wien2wannier) and CRYSTAL (LCAO2Wannier). Using a single set of calibrated defaults, the CRYSTAL implementation with analytic overlaps produces models that reproduce the reference bands of five cells, from bulk h-BN in a joint valence-and-conduction model to a magnetic framework with 1424 orbitals. The WIEN2k implementation produces eight such models from seven calculations.

cond-mat.mtrl-sci↗

Learning the Geometry of Materials Space with Trained Neural Tangent Kernels

Neural networks trained to predict materials properties learn representations whose physical content can be difficult to extract. We use the neural tangent kernel (NTK), evaluated at trained parameters, to recover the geometry learned by a network that predicts superconducting transition temperatures from elemental composition alone. Kernel ridge regression with the trained NTK closely reproduces the network's predictive performance and substantially outperforms regression with the initial kernel. Spectral analysis and multidimensional scaling reveal a geometry dominated by a small number of directions. Clustering and local neighborhood analysis organize compounds into recognizable superconducting families and connect materials with related chemical and correlated-electron properties despite substantial differences in $T_c$. These results establish trained tangent kernels as practical probes of learned materials representations, connecting property prediction with the exploration of materials space.

cond-mat.mtrl-sci↗