Quantum spectral thermodynamics and active learning enable million-scale exploration of high-entropy ceramics

Jie Sun; Yiheng Shen

Home/Archive/arXiv

arXiv · 2610.06467

Quantum spectral thermodynamics and active learning enable million-scale exploration of high-entropy ceramics

Jie Sun·Yiheng Shen

View original source ↗Read source document ↗Export metadata ↓

Abstract

Understanding phase stability and navigating vast compositional spaces in multicomponent solids remain central challenges in solid-state chemistry. Here, we develop a quantum spectral thermodynamic framework connecting interaction-induced phonon spectral broadening to free energy, alongside an uncertainty-guided active-learning workflow that explores 7.7 million high-entropy ceramic configurations at density-functional-theory fidelity, achieving a $10^5$-fold acceleration. We show that phonon self-energy effects arising from chemical disorder provide an intrinsic vibrational contribution to thermodynamic stabilization beyond ideal configurational entropy, compensating unfavorable mixing enthalpies and suppressing phase separation. Across the chemical space, we uncover a robust ~12 at.% solute threshold separating strengthening and softening regimes, associated with the filling of metal-carbon antibonding states. Chemical disorder further enables an unusual combination of high-temperature mechanical stiffness and low thermal conductivity, together with anomalous temperature-dependent lattice heat transport. This work establishes a quantum spectral foundation for connecting many-body interactions to thermodynamics and phase stability, while providing a scalable framework for exploring previously inaccessible multicomponent chemical spaces.

Explore related subjects

cond-mat.mtrl-sci

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Jie Sun, Yiheng Shen. 2026-10-05. Quantum spectral thermodynamics and active learning enable million-scale exploration of high-entropy ceramics. https://arxiv.org/abs/2610.06467

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

KEEP EXPLORING

Related papers

arXiv2026

Probabilistic calibration of crystal plasticity material models with synthetic global and local data

Calibrating full-field crystal plasticity (CP) models using global stress-strain curves alone often produces non-unique parametrizations: multiple parameter sets can predict the same global behavior but different local, grain-scale behavior. Incorporating local calibration data can mitigate uniqueness issues, but this typically requires specialized experiments like high-energy X-ray diffraction microscopy. The computational expense of full-field simulations also prevents uncertainty quantification with sampling-based probabilistic calibration algorithms like Markov chain Monte Carlo. In this study, a two-stage probabilistic calibration procedure is developed that uses both global and local data and balances the efficiency of a surrogate model with the accuracy of a full-field CP model. To enable tractable Bayesian inference via sampling from the full-field model, an efficient, parallelized sequential Monte Carlo (SMC) algorithm is used to perform the calibration, which involves more than 100,000 CP simulations. Synthetic measurement data is used in the calibration to assess uncertainty and accuracy of the parameter posterior distributions relative to a "ground-truth" simulation with a microstructure representative of Inconel 718. The two-stage calibration is shown to be robust to limited and noisy local data. Posterior distributions 'and computational costs are compared between the two-stage calibration and a one-stage calibration using only the full-field model, which is also enabled by SMC, as well as an efficient but biased one-stage calibration using a surrogate model. Overall, including local measurement data is shown to reduce parameter uncertainty, while joint distributions of the calibrated parameters highlight important considerations in choosing constitutive models and calibration data, including challenges resulting from correlated parameters.

cond-mat.mtrl-sci↗

arXiv2026

Altermagnetism in MnF$_2$: Band Splitting and Its Physical Consequences

MnF$_2$ is widely regarded as a candidate altermagnet, but the magnitude and implications of its altermagnetic band splitting remain debated. Using electronic-structure calculations, we construct minimal models that capture the magnetic and electronic properties of MnF$_2$. These models show that the parameters governing the chiral magnon splitting and the spin splitting of the electronic bands are relatively small. Moreover, the electronic system lies in the strong-coupling regime, where most magnetic properties are controlled by the ratio $t/U$ between the characteristic hopping amplitude $t$ and the large on-site Coulomb repulsion $U$. Consequently, all exchange interactions scale as $1/U$, so a small altermagnetic hopping $δt$ produces only a proportionally small exchange term. Upon doping, the altermagnetic contribution to the anomalous Hall effect is likewise suppressed, being smaller than the conventional (non-altermagnetic) contribution by a factor of order $δt/U$. In contrast, the behavior of the conductivity tensor $\hatσ(ω)$ at $\hbar ω\sim U$ differs qualitatively, because $δt$ enters the energies of interband optical transitions \emph{directly} rather than through the reduced ratio $δt/U$. This contribution strongly reshapes $\hatσ(ω)$, leading to a dramatic enhancement of the magneto-optical response.

cond-mat.mtrl-sci↗

arXiv2026

An Extensible Agentic Framework for Expert-Level Automation of Atomistic Simulations

Traditionally, atomistic simulation has been constrained by the computational scaling limits of ab initio methods and the parameterization overhead of empirical force fields. The recent emergence of universal machine learning interatomic potentials has significantly mitigated these bottlenecks, offering near-quantum accuracy and generalizability across diverse chemical spaces at a fraction of the computational cost. However, this shift has relocated the bottleneck to the human dimension: time-consuming mechanical processes, such as input preparation and data analysis, now dominate the research lifecycle. We introduce Paimon, a Platform for Agentic Integration in Materials Optimization and Nanoscale-simulations. Through hundreds of trials on an expert-level liquid electrolyte simulation, we show that Paimon substantially improves the reliability of agentic workflows by suppressing silent errors: plausible yet physically incorrect results. Paimon accommodates a new capability without modifying the agents already present, which we demonstrate by integrating an externally developed scientific agent. As an agent harness for atomistic simulations, Paimon affords researchers a continuous, science-centric workflow throughout the entire simulation lifecycle.

cond-mat.mtrl-sci↗