Understanding thermal transport in ${\mathrm{Al}}_x{\mathrm{Ga}}_{1-x}\mathrm{N}$ alloys is critical for the reliable thermal management of high-power electronic devices. However, theoretical predictions of lattice thermal conductivity ${\kappa }_{\ell }$ based on the traditional Abele’s virtual crystal approximation (VCA) significantly deviate from experimental measurements, and these discrepancies have long been attributed to finite-size effects. Unlike the traditional VCA, which homogenizes atomic masses and bonding environments, we explicitly incorporate both mass disorder and interatomic force constant (IFC) disorder by directly performing Boltzmann transport equation calculations on large special quasirandom structures, aiming to uncover their impact on lattice thermal transport. Our results show that mass disorder plays a dominant role in suppressing ${\kappa }_{\ell }$ by significantly enhancing the anharmonic scattering of low-frequency phonons, whereas the heat-carrying capacity of high-frequency phonons is completely eliminated due to substantially reduced group velocities and strong harmonic scattering. In comparison, IFC disorder mainly increases anharmonic scattering rates but has negligible influence on the group velocities of low-frequency phonons. When both disorder types are accounted for, the predicted thermal conductivities align closely with experimental observations. This indicates that the previously reported overestimations from VCA are not due to finite-size effects. We also find that the alloy scattering model based on perturbation theory significantly overestimates the harmonic scattering rates of high-frequency phonons, despite their negligible contribution to thermal conductivity. These findings highlight the necessity of atomistic disorder modeling in wide-bandgap alloys and provide critical insights for thermal design in ${\mathrm{Al}}_x{\mathrm{Ga}}_{1-x}\mathrm{N}$-based electronics.

Wind-farm power spectra display distinct regimes that reflect how turbines collectively interact with the atmosphere. At low frequencies (approximately ${10}^{-4}$ Hz and below), turbine power outputs remain correlated, whereas at high frequencies (approximately ${10}^{-2}$ Hz and above), they decorrelate, smoothing wind-farm power fluctuations. Existing models typically capture only one end of this spectrum and lack a unified description across the transitional range (approximately ${10}^{-3}$ Hz). Here we introduce a dynamic sweeping model that predicts wind-farm power spectra across all scales. Slow mesoscale variations are represented as a time-dependent sweeping velocity that advects boundary-layer turbulence through the wind farm, while small-scale turbulence is modeled with use of the random sweeping framework. The model shows that the three spectral regimes originate from interactions between mesoscale weather systems, with timescales of about 15 min to hours, and microscale turbulence evolving over seconds to minutes. Large-eddy simulations and field data confirm that the model captures the mesoscale, transitional, and turbulence-dominated regimes, providing a physically consistent foundation for predicting wind power variability and supporting reliable grid integration.

Energy costs of information processing are growing exponentially. Bit erasure is a key problem in this energy-information nexus, and a number of seminal relationships have been deduced regarding the relationship between thermodynamic costs and memory storage. To continue making progress in the modern era, however, requires confronting thermodynamic costs in realistic physical systems, which operate away from equilibrium. Here, we explore the thermodynamic costs of bit erasure in a complementary metal-oxide-semiconductor model of two types of random-access memory. We find dynamic random-access memory dissipates the least amount of energy when operated in the quasistatic limit, where errors are also minimized. By contrast, static random-access memory is most efficiently operated in finite time due to the energy required to maintain the state of the bit. We demonstrate a numerically robust optimization scheme using mean field theory and automatic differentiation, finding optimal protocols compatible with electrical engineering insights. These results provide a framework for operating realistic circuits in thermodynamically advantageous ways.

Graphite, the standard commercial anode for $\mathrm{Li}$-ion batteries, is thermodynamically incompatible with $\mathrm{Na}$-ion batteries, leading researchers to search for alternative C-based structures (e.g., hard carbon, expanded graphite). In a simplified picture, the main idea of such a search relies on identifying disordered C structures with a large interlayer spacing and distribution of local structural motifs (e.g., pores) with target electrochemical properties. Such an exploration is typically done via trial-and-error experimentation, which often does not allow precise understanding of the role of interlayer distance and even $\mathrm{Na}\text{−}\mathrm{or}$ $\mathrm{Li}$-ion intercalation in the electrochemical performance. Motivated by this, using density-functional theory and cluster expansion, we establish a structure-property relationship for $\mathrm{Li}$ and $\mathrm{Na}$ intercalation across a range of graphite interlayer spacings and stacking arrangements. We show that $\mathrm{Na}$ intercalation becomes thermodynamically possible at high concentrations once the interlayer spacing exceeds 4.21 Å even without a change in interlayer spacing. Conversely, $\mathrm{Li}$ intercalation has a narrow optimal window, with maximum capacity (close to the commercial limit) at approximately 3.75 Å, while larger spacings (e.g., 4.58 Å) quickly reduce $\mathrm{Li}$ storage capacity. We also find that AA-stacked domains consistently offer stronger ion bonding and higher voltages than AB-stacked domains for both ions. Our results, thus, not only explain the role of metal-ion intercalation in the electrochemical performance, but also clarify the fundamental design trade-offs for expanded C anodes, offering practical targets and interlayer distance ranges for the independent optimization of the next generation of negative electrodes for metal-ion batteries.

Precise control over electronic coupling at nanoscale interfaces is critical for designing materials with tunable charge-transfer behavior and catalytic function. Heterostructures with locally coherent interfaces provide a platform for interrogating interfacial charge redistribution in coupled material systems. Here, we report ${\mathrm{Cu}}_2\mathrm{S}/{\mathrm{Mo}\mathrm{S}}_2$ heterostructures exhibiting nanoscale crystallographic alignment, which are synthesized through a rapid thermal transformation pathway. We employed electrochemical reduction reactions to probe interfacial charge transfer, revealing shifts in product distribution attributable to modified interfacial energetics, even in the absence of optimized catalytic performance. The observed formate Faradaic efficiency suggests that interfacial electronic modulation in the heterostructure shifts product selectivity towards formate, highlighting how interface-driven electronic modulation can direct reaction pathways and influence product selectivity. Optimizing catalyst loading, architecture, and reactor configuration will be critical for future improvement. Structural and compositional integrity were confirmed through powder x-ray diffraction, x-ray photoelectron spectroscopy, and high-resolution transmission electron microscopy. Electron transfer between ${\mathrm{Cu}}_2\mathrm{S}$ and ${\mathrm{Mo}\mathrm{S}}_2$ domains was further evaluated by electrochemical impedance spectroscopy, while selected-area electron diffraction revealed local crystallographic alignment consistent with a local epitaxial relationship at the ${\mathrm{Cu}}_2\mathrm{S}/{\mathrm{Mo}\mathrm{S}}_2$ heterointerface. To illustrate the broader applicability of this approach, a $\mathrm{Zn}\mathrm{S}/{\mathrm{Mo}\mathrm{S}}_2$ heterostructure was also synthesized using the same microwave strategy, confirming the generalizability of interfacial engineering principles across metal sulfide-${\mathrm{Mo}\mathrm{S}}_2$ systems. Collectively, these findings demonstrate that controlled local epitaxial alignment serves as an effective design principle for tuning interfacial energetics and catalytic reactivity in complex heterostructure materials.

Polymerized Y-type nonfullerene acceptors (PY-NFAs) have shown great promise for efficient and stable organic solar cells (OSCs), yet their excited-state dynamics and structure-photophysics-performance relationships remain unclear. Here, we investigate a series of PY-NFAs and distinguish between intrachain excitons (intra-Ex) and interchain excitons (inter-Ex) formed in the solid state using cryogenic transient absorption (TA) spectroscopy at 100 K. We identify the emergence of trapped intra-Ex in the PY-NFA films from approximately 100 ps onwards after photoexcitation. By comparing this low-temperature TA signal among different PY-NFA films, we uncover a strong correlation between the trapped intra-Ex signature and the performance of these PY-NFAs in donor-acceptor blend OSC devices. This correlation indicates that the low-temperature TA response provides a quantitative measure of structural disorder in the PY-NFA aggregates and in the corresponding donor-acceptor blends. Correlating these spectroscopic results with molecular packing and morphological data, we reveal that high-performance PY-NFAs simultaneously exhibit (1) extended backbone conjugation and large intrachain exciton bandwidths and (2) uniform, oriented interchain packing despite moderately increased π-π stacking distances. These findings provide new design rules for PY-NFAs and offer deeper insight into how structural disorder and exciton dynamics govern OSC performance.

We describe the correlation structure of wind-power fluctuations in a farm of 80 turbines, sampled over 5 years. We report the presence of universal, collective, and nonlinear correlations, responsible for the excess persistency and intermittency of farm-aggregated power output. A first cross-correlation analysis of turbine production reveals a dynamical scaling transition (à la Family-Vicszek) from local decoherence to large-scale turbulence-driven scaling, and responsible for the geographical smoothing effect, previously reported beyond farm scale [M. M. Bandi, Spectrum of wind-power fluctuations, Phys. Rev. Lett. 118, 028301 (2017)]. A second bivariate analysis shows the long-range correlation of non-Gaussian features, responsible for their amplification in total farm output. These findings provide important insights into wind energy, the variability of which directly results from atmospheric turbulent forcing. By characterizing the multivariate and intermittent structure of fluctuations, our results advance the current understanding of wind-power variability and provide valuable priors for grid management and storage optimization, thereby supporting improved integration of wind energy into modern power systems.

We address the question of the optimal broadband absorption of waves in an open dissipative system and its key implications for solar cells. We develop a general framework for absorption induced by multiple overlapping resonances, based on quasinormal modes and radiative and nonradiative decay rates. We demonstrate that the upper bounds on broadband absorption in a slab of thickness $d$ take the simple form $A=1-\exp (-F\alpha d)$, where $\alpha$ is the absorption coefficient and $F$ is the path enhancement factor. We apply these results to sunlight absorption in photovoltaics and answer the long-standing debate on the best light-trapping strategy in solar cells. For angle-independent absorption enhanced by scattering, we derive the isotropic upper bound $F=4n^2$ (where $n$ is the refractive index), extending the well-known Lambertian (or Yablonovitch) limit beyond the ray-optics and weak-absorption regimes. For angle-restricted illumination, we show that $F$ can be further increased up to $8\pi n^2/\sqrt{3}$ using multiresonant absorption induced by periodical patterning. These results have a general scope in the field of wave physics and open new opportunities to maximize absorption, detection, and attenuation of electromagnetic or mechanical waves in ultrathin devices.

Temperature-dependent transport data, including diffusion coefficients and ionic conductivities, are routinely analyzed by fitting empirical models such as the Arrhenius equation. These fitted models yield parameters such as the activation energy, and can be used to extrapolate to temperatures outside the measured range. Researchers frequently face challenges in this analysis: quantifying the uncertainty of fitted parameters, assessing whether the data quality is sufficient to support a particular empirical model, and using these models to predict behavior at temperatures outside the measured range. Bayesian methods offer a coherent framework that addresses all of these challenges. This tutorial introduces the use of Bayesian methods for analyzing temperature-dependent transport data, covering parameter estimation, model selection, and extrapolation with uncertainty propagation, with illustrative examples from molecular dynamics simulations of superionic materials.

Lignin pyrolysis generates oxygenated aromatics that can serve as renewable biofuel precursors. Anisole and substituted anisoles are widely used as model compounds to probe processes related to their production and use. We present a comparative study of anisole and 4-vinylanisole thermal decomposition using vacuum-ultraviolet photoionization mass spectrometry in a silicon carbide microreactor from 300 to 1300 K. For both molecular species, the initial decomposition pathway is $\mathrm{O}—{\mathrm{CH}}_3$ bond cleavage. The vinyl substituent lowers the bond dissociation energy, shifting decomposition to lower temperatures for 4-vinylanisole. Anisole pyrolysis produces hydrogen-atom, methyl, propargyl, phenoxy, and cyclopentadienyl radicals, phenol, cyclopentadiene, cyclopentadienyl dimer, methylcyclopentadiene, $\mathrm{CO}$, acetylene, and benzene via methylcyclopentadiene. The products of 4-vinylanisole pyrolysis include hydrogen-atom, methyl, propargyl, fulvenallenyl, vinylphenoxy, vinylcyclopentadienyl, and cyclopentadienyl radicals, vinylphenol, vinylcyclopentadiene, dimethylfulvene, fulvenallene, vinylacetylene, $\mathrm{CO}$, and acetylene, but no benzene. Vinyl substitution thus facilitates resonance-stabilized radical (RSR) formation at temperatures lower than the cracking temperature of anisole. Whereas 4-vinylanisole decomposes at a lower temperature than anisole, both species produce significant RSR products that facilitate aromatic growth and gas-to-condensed-phase hydrocarbon conversion, in contrast to their methyl-substituted analog. These new insights into RSR formation are applicable to lignin pyrolysis and may be useful for limiting char or tar formation during biofuel production and for developing predictive models of emissions from smoldering and propagating flaming wildfires.

Ergotropy provides a fundamental measure of the extractable work from a quantum system and, consequently, of the maximal useful energy, or charge, stored within it. Understanding how this quantity can be manipulated and transformed efficiently is crucial for advancing quantum energy management technologies. Here, we introduce and formalize the concepts of isoergotropic states and ergotropy-preserving operations, which reorganize the internal structure of ergotropy while keeping its total value unchanged. These ideas are illustrated for both discrete (two-level systems) and continuous-variable systems (single-mode Gaussian states). In each case, we show how ergotropy-preserving operations redistribute the respective coherent-incoherent and displacement-squeezing components. We further examine the thermodynamic exchanges accompanying ergotropy-preserving operations, including variations in energy and entropy, and demonstrate that these transformations can be dynamically implemented through standard beam-splitter-type interactions with an auxiliary system. Finally, we discuss the practical implications of isoergotropic states and operations in optimizing charging protocols and mitigating charge loss in open quantum batteries.

Nonreciprocal quantum batteries (QBs) leverage engineered environments to impose unidirectional energy flow, achieving a fourfold enhancement in energy storage over conventional designs. In this work, we establish thermal-reservoir engineering as a powerful tool for controlling and optimizing the performance of QBs. We demonstrate that a thermal-reservoir-driven nonreciprocal QB achieves significantly enhanced charging, surpassing its vacuum-reservoir counterpart. Crucially, the nonreciprocal ergotropy is thermally invariant and coincides with the vacuum-reservoir energy, despite the temperature-dependent rise in total stored energy. This reveals a “thermally shielded” work potential, where nonreciprocity effectively decouples energy storage from energy quality, ensuring robust work extraction despite the thermal environment. Consequently, engineered thermal reservoirs emerge as a key resource for developing high-performance, noise-resilient quantum batteries.

Usable magnesium batteries are promising alternatives to lithium technology. The currently most widely used battery type based on lithium is reaching its cost-reduction limit due to the relative rarity of the precursors and environmental side effects. While other ions, such as sodium, have also been investigated for use in batteries, magnesium stands out due to its several advantages, including high abundance and high theoretical capacity. While most of the current magnesium batteries contain liquid electrolytes, solid-state electrolytes (SSEs) are especially of interest due to their potential advantages over the liquid ones, including enhanced safety, higher energy density, wider electrochemical stability window and operative temperature range, resistance to side reactions and degradation, and simplified battery-pack design. However, $\mathrm{Mg}$-based SSEs have their own difficulties, most notably the lack of ionic conductivity. Only a few $\mathrm{Mg}$-ion conductors can achieve superionic conductivity at room temperature with a low activation energy. Thus, researchers continually search for inorganic solids that can conduct $\mathrm{Mg}$ ions better while having no other issues, such as high electronic conductivity or electrochemical instability. With a wide range of materials that have already been investigated and a vast array with potential that remains untapped, it is essential to keep updated on this rapidly expanding field. This minireview aims to identify emerging $\mathrm{Mg}$-ion conductors that can serve as promising candidates for SSEs, as well as to review methods for enhancing the relevant properties of established materials. We highlight the new materials and methods investigated in the last 2–3 years that are yet to be covered in a review. An exciting new area of emerging research is highlighted, utilizing machine learning and high-throughput materials screening and calculations to identify promising inorganic $\mathrm{Mg}$-ion conductors from materials databases.

Methods of elucidating the mechanisms of fast-ion conduction in solid-state materials are pivotal for advancements in energy technologies such as batteries, fuel cells, sensors, and supercapacitors. In this study, we examine the ionic conduction pathways in single crystal ${\mathrm{Sr}\mathrm{Cl}}_2$, which is a fast-ion conductor above 900 K, using four-dimensional quasielastic neutron scattering (4D-QENS). We explore both coherent and incoherent neutron scattering at temperatures above the transition temperature into the superionic phase to explore the correlated motion of hopping anions. Refinements of the incoherent QENS yield residence times and jump probabilities between lattice sites in good agreement with previous studies, confirming that ionic hopping along nearest-neighbor directions is the most probable conduction pathway. However, the coherent QENS reveals evidence of de Gennes narrowing, indicating the importance of ionic correlations in the conduction mechanism. This highlights the need for improvements both in the theory of ionic transport in fluorite compounds and the modeling of coherent 4D-QENS in single crystals.

Halocarbons have a vast array of industrial applications and non-negligible environmental impact. Their structures, properties, and thermochemistry are often modeled with the use of density functional theory (DFT) calculations. Here we analyze the DFT-calculated gas-phase formation enthalpies of 56 halocarbons containing various amounts of $\mathrm{F}$, $\mathrm{Cl}$, $\mathrm{Br}$, and $\mathrm{I}$ using a group-additivity approach and seven exchange-correlation functionals (Perdew-Burke-Ernzerhof, Perdew-Wang 91, revised Perdew-Burke-Ernzerhof, Bayesian error estimation functional with van der Waals corrections, Tao-Perdew-Staroverov-Scuseria, Becke 3-parameter Lee-Yang-Parr, and Heyd-Scuseria-Ernzerhof 2006). Carbon—halogen bonds are found to introduce significant errors in the formation enthalpies, which are proportional to the electronegativity of the elements. We devised two correction methods based on the number and type of functional groups in each molecule in the reactants and products of the formation reactions. Both methods lower the mean absolute errors from approximately 0.35 eV to approximately 0.05 eV. Finally, the corrections were tested in an independent set of more than 150 reactions, and high accuracy was found without relying on error cancellation.

Mechanical and elastic properties of materials are among the most fundamental quantities for many engineering and industrial applications. Here, we present a formulation that is efficient and accurate for calculating the elastic and bending rigidity tensors of crystalline solids, leveraging interatomic force constants and long-wavelength perturbation theory. Crucially, in the long-wavelength limit, lattice vibrations induce macroscopic electric fields, which further couple with the propagation of elastic waves, and a separate treatment on the long-range electrostatic interactions is thereby required to obtain elastic properties under the appropriate electrical boundary conditions. A cluster expansion of the charge-density response and dielectric screening function in the long-wavelength limit has been developed to efficiently extract multipole and dielectric tensors of arbitrarily high order. We implement the proposed method in a first-principles framework and perform extensive validations on silicon, $\mathrm{Na}\mathrm{Cl}$, $\mathrm{Ga}\mathrm{As}$ and rhombohedral ${\mathrm{Ba}\mathrm{Ti}\mathrm{O}}_3$ as well as monolayer graphene, hexagonal $\mathrm{BN}$, ${\mathrm{Mo}\mathrm{S}}_2$, and $\mathrm{In}\mathrm{Se}$, obtaining good to excellent agreement with other theoretical approaches and experimental measurements. Notably, we establish that multipolar interactions up to at least octupoles are necessary to obtain the accurate short-circuit elastic tensor of bulk materials, while higher orders beyond octupole interactions are required to converge the bending rigidity tensor of two-dimensional crystals. The present approach greatly simplifies the calculations of bending rigidities and will enable the automated characterization of the mechanical properties of novel functional materials.

The design of materials for electrochemical energy conversion is complicated by a vast search space of candidate materials and multifaceted property requirements: multicarrier conductivity, stability, and catalytic activity are all necessary but rarely intersect. Although self-driving laboratories are rapidly rising to address such material optimization problems, the required infrastructure for integrated, large-scale robotic facilities can be cost-prohibitive. Here we develop and evaluate a closed-loop measurement system for efficient screening of proton-conducting oxide electrodes for ceramic fuel cells and electrolyzers, building on top of an existing benchtop instrument and integrating techniques for rapid impedance measurement and automated analysis. This system exemplifies a “minimum viable” self-driving implementation that can deliver substantial benefits with relatively simple infrastructure. Combinatorial thin-film microelectrode libraries are characterized with a recently developed joint time-domain and frequency-domain impedance measurement technique, which provides an order-of-magnitude acceleration relative to conventional impedance spectroscopy. The distribution of relaxation times is extracted from impedance data and analyzed without human intervention. These results feed an active learning and Bayesian optimization process that learns to predict electrochemical impedance as a function of material composition, measurement temperature, oxygen partial pressure, and electrical bias, which further reduces the screening time by tenfold with optimized experimental sequences. We apply this system to $\mathrm{Ba}(\mathrm{Co},\mathrm{Fe},\mathrm{Zr},\mathrm{Y}){\mathrm{O}}_{3-\delta }$ combinatorial libraries and evaluate its effectiveness for learning material property trends and optimizing expensive-to-evaluate properties such as activation energy. This offers insights into key methodological aspects of practical autonomous experimentation, including surrogate model validation, cost-aware acquisition functions, and high-throughput data interpretation. Our results demonstrate the efficacy of the system for rapidly gathering information, but also highlight real-world experimental challenges of thin-film degradation and numerical instability in surrogate models.

The nuclear spin-isomer (ortho-para) composition of molecular hydrogen strongly influences the safety and efficiency of cryogenic hydrogen handling, as the energy of ortho-para conversion exceeds the enthalpy of vaporization of liquid hydrogen, generating boil-off losses. Accurate in situ measurement of spin-isomer composition at cryogenic conditions is therefore essential for liquefaction process design, spin-isomer catalyst characterization, and custody transfer. Here we demonstrate a microwave cavity resonator as a novel sensor for real-time quantification of hydrogen ortho-para composition. The method exploits contrast in the dielectric permittivity of the nuclear-spin isomers, enabling precise tracking of natural ortho-para conversion dynamics in solid (10.0 K), liquid (16.6–19.9 K), and supercritical (40.4 K) hydrogen. Measurement uncertainties averaged 0.007 mol/mol across all phases. During conversion, the resonant frequencies followed a second-order kinetic model consistent with theoretical predictions and prior studies, with final equilibrium compositions independently validated by Raman spectroscopy. The determined conversion rate constants agree with the empirical correlation of a previous study, while also revealing the potential errors introduced from measuring natural conversion kinetics over a limited concentration range. Furthermore, our results identify significant discrepancies between experimental dielectric data and current predictive models, underscoring the need to refine hydrogen property models at cryogenic conditions. This work establishes microwave cavity resonators as a powerful tool for ortho-para hydrogen sensing at cryogenic conditions, with applications spanning industrial liquid hydrogen process monitoring, hydrogen isomer conversion catalyst development, and high-energy physics.

The electric double layer (EDL) that forms at the interface between metals and ionic solutions is at the heart of various energy technologies. Recent experimental data have challenged our traditional understanding of the EDL charging behavior, which is based on mean-field Gouy-Chapman-Stern-type (GCS) models. In this article, we present a classical theory for the EDL, derived from first-principles statistical mechanics, that accounts for electron-ion correlation effects using the method of image charges and systematically extends beyond the mean-field level. Such electron-ion correlations introduce an additional interaction between the metal surface and electrolyte ions, significantly altering the EDL structure. Our theory, valid in the limit of dilute electrolyte solutions and weakly charged metal surfaces, achieves quantitative agreement with experimental capacitance data across a wide range of electrode materials and electrolyte solvents, and thus resolves long-standing questions on the origin of discrepancies to GCS predictions. Thereby, the framework conceptually unifies the processes of double-layer charging and ion adsorption, which are typically considered as distinct phenomena, but are shown to be manifestations of the same fundamental electrostatic principles.

High-entropy materials offer a promising avenue for thermoelectric materials discovery, design, and optimization. However, the large chemical spaces that need to be explored hamper their development. In this work, a large family of high-entropy skutterudites is explored as promising thermoelectric materials. Their potential synthesizability is screened and rationalized using the disordered enthalpy-entropy descriptor through high-throughput density functional theory calculations. In the case of high-entropy skutterudites, the thermodynamic density of states and the entropy gain parameter appear to be key factors for their stabilization. Electronic band structure analyses not only show a reduction in the band gap, which enhances carrier concentration and electrical conductivity, but also a band convergence phenomenon for some specific compositions, which is related to the “cocktail effect.” Analyzing atom-projected band structures shows how band convergence is due to the simultaneous presence of $\mathrm{Fe}$, $\mathrm{Ni}$, and $\mathrm{Co}$ in the compound. The presence of $\mathrm{Rh}$ or $\mathrm{Ir}$, while not contributing to this band-convergence effect, can be directly linked to an increase in the system’s entropy, which enhances the thermodynamic stability of these materials. Transport properties are computed for the most promising compositions, and their dynamical, mechanical, and thermal stability are addressed. Our results demonstrate that these types of compounds open new avenues, not only to enhance thermoelectric efficiency but also to reduce costs by utilizing more abundant elements and also improving their durability.