Thermodynamics, Electronic Structure, and Vibrational Properties of Sn n (S1-x Se x ) m Solid Solutions for Energy Applications

Synthetic Strategies toward High Entropy Materials: Atoms-to-Lattices for Maximum Disorder

High-entropy materials are a nascent class of materials that exploit a high configurational entropy to stabilize multiple elements in a single crystal lattice and to yield unique physical properties for applications in energy storage, catalysis, and thermoelectric energy conversion. Initially, the synthesis of these materials was conducted by approaches requiring high temperatures and long synthetic time scales. However, successful homogeneous mixing of elements at the atomic level within the lattice remains challenging, especially for the synthesis of nanomaterials. The use of atom-up synthetic approaches to build crystal lattices atom by atom, rather than the top-down alteration of extant crystalline lattices, could lead to faster, lower-temperature, and more sustainable approaches to obtaining high entropy materials. In this Perspective, we discuss some of these state-of-the-art atom-up synthetic approaches to high entropy materials and contrast them with more traditional approaches.

Block sparsity promoting algorithm for efficient construction of cluster expansion models for multicomponent alloys

Multicomponent alloys are gaining significance as drivers of technological breakthroughs especially in structural and energy storage materials. The vast configuration space of these materials prohibit computational modeling using first-principles based methods alone. The cluster expansion (CE) method is the most widely used tool for modeling configurational disorder in alloys. CE relies on machine learning algorithms to train Hamiltonians and uses first-principles calculated data as training sets. In this paper we present a new compressive sensing-based algorithm for the efficient construction of CE Hamiltonians of multicomponent alloys. Our algorithm constructs highly sparse and physically reasonable models from a carefully selected small training set of alloy structures. Compared to conventional fitting algorithms, the algorithm achieves more than 50% reduction in the training set size. The resultant sparse models can sample the configuration space at least 3 × faster. We demonstrate this algorithm on 4 different alloy systems, namely Ag-Au, Ag-Au-Cu, Ag-Au-Cu-Pd and (Ge,Sn)(S,Se,Te).The sparse CE models for these alloys can rapidly reproduce known ground state orderings and order-disorder transitions. Our method can truly enable high-throughput multicomponent alloy thermodynamics by reducing the cost associated with model construction and configuration sampling.

Accuracy of Hybrid Functionals with Non-Self-Consistent Kohn-Sham Orbitals for Predicting the Properties of Semiconductors

Accurately modeling the electronic structure of materials is a persistent challenge to high-throughput screening. A promising means of balancing accuracy against computational cost is non-self-consistent calculations with hybrid density-functional theory, where the electronic band energies are evaluated using a hybrid functional from orbitals obtained with a less demanding (semi)local functional. We have quantified the performance of this technique for predicting the physical properties of 16 tetrahedral semiconductors with bandgaps from 0.2 to 5.5 eV. Provided the base functional predicts a nonmetallic electronic structure, bandgaps within 5% of the PBE0 and HSE06 gaps can be obtained with an order of magnitude reduction in computing time. The positions of the valence and conduction band extrema and the Fermi level are well reproduced, enabling calculation of the band dispersion, density of states, and dielectric properties using Fermi's Golden Rule. While the error in the non-self-consistent total energies is ∼50 meV atom^-1, the energy-volume curves are reproduced accurately enough to obtain the equilibrium volume and bulk modulus with minimal error. We also test the dielectric-dependent scPBE0 functional and obtain bandgaps and dielectric constants to within 2.5% of the self-consistent results, which amounts to a significant improvement over self-consistent PBE0 for a similar computational cost. We identify cases where the non-self-consistent approach is expected to perform poorly and demonstrate that partial self-consistency provides a practical and efficient workaround. Finally, we perform proof-of-concept calculations on CoO and NiO to demonstrate the applicability of the technique to strongly correlated open-shell transition-metal oxides.