RESUMEN
Accurate and explainable artificial-intelligence (AI) models are promising tools for accelerating the discovery of new materials. Recently, symbolic regression has become an increasingly popular tool for explainable AI because it yields models that are relatively simple analytical descriptions of target properties. Due to its deterministic nature, the sure-independence screening and sparsifying operator (SISSO) method is a particularly promising approach for this application. Here, we describe the new advancements of the SISSO algorithm, as implemented into SISSO++, a C++ code with Python bindings. We introduce a new representation of the mathematical expressions found by SISSO. This is a first step toward introducing "grammar" rules into the feature creation step. Importantly, by introducing a controlled nonlinear optimization to the feature creation step, we expand the range of possible descriptors found by the methodology. Finally, we introduce refinements to the solver algorithms for both regression and classification, which drastically increase the reliability and efficiency of SISSO. For all these improvements to the basic SISSO algorithm, we not only illustrate their potential impact but also fully detail how they operate both mathematically and computationally.
RESUMEN
The anharmonicity of atomic motion limits the thermal conductivity in crystalline solids. However, a microscopic understanding of the mechanisms active in strong thermal insulators is lacking. In this Letter, we classify 465 experimentally known materials with respect to their anharmonicity and perform fully anharmonic ab initio Green-Kubo calculations for 58 of them, finding 28 thermal insulators with κ<10 W/mK including 6 with ultralow κâ²1 W/mK. Our analysis reveals that the underlying strong anharmonic dynamics is driven by the exploration of metastable intrinsic defect geometries. This is at variance with the frequently applied perturbative approach, in which the dynamics is assumed to evolve around a single stable geometry.
RESUMEN
Symbolic regression identifies nonlinear, analytical expressions relating materials properties and key physical parameters. However, the pool of expressions grows rapidly with complexity, compromising its efficiency. We tackle this challenge hierarchically: identified expressions are used as inputs for further obtaining more complex expressions. Crucially, this framework can transfer knowledge among properties, as demonstrated using the sure-independence-screening-and-sparsifying-operator approach to identify expressions for lattice constant and cohesive energy, which are then used to model the bulk modulus of ABO_{3} perovskites.
RESUMEN
Optically coupling quantum emitters to nanoparticles provides the foundation for many plasmonic applications. Including quantum mechanical effects within the calculations can be crucial for designing new devices, but classical approximations are sometimes sufficient. Comprehending how the classical and quantum mechanical descriptions of quantum emitters alter their calculated optical response will lead to a better understanding of how to design devices. Here, we describe how the semiclassical Maxwell-Liouville method can be used to calculate the optical response from inhomogeneously broadened states. After describing the Maxwell-Liouville algorithm, we use the method to study the photon echoes from quantum dots and compare the results against analytical models. We then modify the quantum dot's state distribution to match a PbS 850 nm quantum dot's absorption spectra to see how the complete quasi-band structure affects their coupling to gold nanoislands. Finally, we compare the results with previously published work to demonstrate where the complete quantum dot description is necessary.
