The efficient extraction of force constants (FCs) is crucial for the analysis of many thermodynamic materials properties. Approaches based on the systematic enumeration of finite differences scale poorly with system size and can rarely extend beyond third order when input data is obtained from first-principles calculations. Methods based on parameter fitting in the spirit of interatomic potentials, on the other hand, can extract FC parameters from semi-random configurations of high information density and advanced regularized regression methods can recover physical solutions from a limited amount of data. Here, the hiphive Python package, that enables the construction of force constant models up to arbitrary order is presented. hiphive exploits crystal symmetries to reduce the number of free parameters and then employs advanced machine learning algorithms to extract the force constants. Depending on the problem at hand, both over and underdetermined systems are handled efficiently. The FCs can be subsequently analyzed directly and or be used to carry out, for example, molecular dynamics simulations. The utility of this approach is demonstrated via several examples including ideal and defective monolayers of MoS 2 as well as bulk nickel.

The densification of integrated circuits requires thermal management strategies and high thermal conductivity materials1–3. Recent innovations include the development of materials with thermal conduction anisotropy, which can remove hotspots along the fast-axis direction and provide thermal insulation along the slow axis4,5. However, most artificially engineered thermal conductors have anisotropy ratios much smaller than those seen in naturally anisotropic materials. Here we report extremely anisotropic thermal conductors based on large-area van der Waals thin films with random interlayer rotations, which produce a room-temperature thermal anisotropy ratio close to 900 in MoS2, one of the highest ever reported. This is enabled by the interlayer rotations that impede the through-plane thermal transport, while the long-range intralayer crystallinity maintains high in-plane thermal conductivity. We measure ultralow thermal conductivities in the through-plane direction for MoS2 (57 ± 3 mW m−1 K−1) and WS2 (41 ± 3 mW m−1 K−1) films, and we quantitatively explain these values using molecular dynamics simulations that reveal one-dimensional glass-like thermal transport. Conversely, the in-plane thermal conductivity in these MoS2 films is close to the single-crystal value. Covering nanofabricated gold electrodes with our anisotropic films prevents overheating of the electrodes and blocks heat from reaching the device surface. Our work establishes interlayer rotation in crystalline layered materials as a new degree of freedom for engineering-directed heat transport in solid-state systems.

Alloy cluster expansions (CEs) provide an accurate and computationally efficient mapping of the potential energy surface of multi‐component systems that enables comprehensive sampling of the many‐dimensional configuration space. Here, integrated cluster expansion toolkit (ICET), a flexible, extensible, and computationally efficient software package, is introduced for the construction and sampling of CEs. ICET is largely written in Python for easy integration in comprehensive workflows, including first‐principles calculations for the generation of reference data and machine learning libraries for training and validation. The package enables training using a variety of linear regression algorithms with and without regularization, Bayesian regression, feature selection, and cross‐validation. It also provides complementary functionality for structure enumeration and mapping as well as data management and analysis. Potential applications are illustrated by two examples, including the computation of the phase diagram of a prototypical metallic alloy and the analysis of chemical ordering in an inorganic semiconductor.

Poly(ethylene oxide) is demonstrated to be a suitable matrix polymer for the solution‐doped conjugated polymer poly(3‐hexylthiophene). The polarity of the insulator combined with carefully chosen processing conditions permits the fabrication of tens of micrometer‐thick films that feature a fine distribution of the F4TCNQ dopant:semiconductor complex. Changes in electrical conductivity from 0.1 to 0.3 S cm−1 and Seebeck coefficient from 100 to 60 μV K−1 upon addition of the insulator correlate with an increase in doping efficiency from 20% to 40% for heavily doped ternary blends. An invariant bulk thermal conductivity of about 0.3 W m−1 K−1 gives rise to a thermoelectric Figure of merit ZT ∼ 10−4 that remains unaltered for an insulator content of more than 60 wt%. Free‐standing, mechanically robust tapes illustrate the versatility of the developed dopant:semiconductor:insulator ternary blends.

Linear models, such as force constant (FC) and cluster expansions, play a key role in physics and materials science. While they can in principle be parametrized using regression and feature selection approaches, the convergence behavior of these techniques, in particular with respect to thermodynamic properties is not well understood. Here, we therefore analyze the efficacy and efficiency of several state-of-the-art regression and feature selection methods, in particular in the context of FC extraction and the prediction of different thermodynamic properties. Generic feature selection algorithms such as recursive feature elimination with ordinary least-squares (OLS), automatic relevance determination regression, and the adaptive least absolute shrinkage and selection operator can yield physically sound models for systems with a modest number of degrees of freedom. For large unit cells with low symmetry and/or high-order expansions they come, however, with a non-negligible computational cost that can be more than two orders of magnitude higher than that of OLS. In such cases, OLS with cutoff selection provides a viable route as demonstrated here for both second-order FCs in large low-symmetry unit cells and high-order FCs in low-symmetry systems. While regression techniques are thus very powerful, they require well-tuned protocols. Here, the present work establishes guidelines for the design of protocols that are readily usable, e.g., in high-throughput and materials discovery schemes. Since the underlying algorithms are not specific to FC construction, the general conclusions drawn here also have a bearing on the construction of other linear models in physics and materials science.