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We propose an effective strategy to significantly enhance the thermoelectric power factor (PF) of a series of 2D semimetals and semiconductors by driving them towards a topological phase transition (TPT). Employing first-principles calculations with explicit consideration of electron-phonon interactions, we analyze the electronic transport properties of germanene across the TPT by applying hydrogenation and biaxial strain. We reveal that the nontrivial semimetal phase, hydrogenated germanene with 8% bi- axial strain, achieves a considerable fourfold PF enhancement, attributed to the highly asymmetric electronic structure and semimetallic nature of the nontrivial phase. We extend the strategy to another two representative 2D materials—stanene and HgSe— and observe a similar trend, with a marked sixfold and fivefold increase in PF, respectively. The wide selection of functional groups, universal applicability of biaxial strain, and broad spectrum of 2D semimetals and semiconductors render our approach highly promising for designing novel 2D materials with superior thermoelectric performance. 

Rhenium chalcohalide cluster compounds are a photoluminescent family of mixed-anion chalcohalide cluster materials. Here we report the new material Rb6Re6S8I8, which crystallizes in the cubic space group Fm m and contains isolated [Re6S8I6]4− clusters. Rb6Re6S8I8 has a band gap of 2.06(5) eV and an ionization energy of 5.51(3) eV, and exhibits broad photoluminescence (PL) ranging from 1.01 eV to 2.12 eV. The room-temperature PL exhibits a PL quantum yield of 42.7% and a PL lifetime of 77 μs (99 μs at 77 K). Rb6Re6S8I8 is found to be soluble in multiple polar solvents including N,N-dimethylformamide, which enables solution processing of the material into films with thickness under 150 nm. Light-emitting diodes based on films of Rb6Re6S8I8 were fabricated, demonstrating the potential for this family of materials in optoelectronic devices. 

The growth of layered 2D compounds is a key ingredient in finding new phenomena in quantum materials, optoelectronics, and energy conversion. Here, we report SnP₂Se₆, a van der Waals chiral (R3 space group) semiconductor with an indirect bandgap of 1.36 to 1.41 electron volts. Exfoliated SnP₂Se₆flakes are integrated into high-performance field-effect transistors with electron mobilities >100 cm²/Vs and on/off ratios >10⁶at room temperature. Upon excitation at a wavelength of 515.6 nanometer, SnP₂Se₆phototransistors show high gain (>4 × 10⁴) at low intensity (≈10⁻⁶W/cm²) and fast photoresponse (< 5 microsecond) with concurrent gain of ≈52.9 at high intensity (≈56.6 mW/cm²) at a gate voltage of 60 V across 300-nm-thick SiO₂dielectric layer. The combination of high carrier mobility and the non-centrosymmetric crystal structure results in a strong intrinsic bulk photovoltaic effect; under local excitation at normal incidence at 532 nm, short circuit currents exceed 8 mA/cm²at 20.6 W/cm². 

Abstract Deep learning (DL) is one of the fastest-growing topics in materials data science, with rapidly emerging applications spanning atomistic, image-based, spectral, and textual data modalities. DL allows analysis of unstructured data and automated identification of features. The recent development of large materials databases has fueled the application of DL methods in atomistic prediction in particular. In contrast, advances in image and spectral data have largely leveraged synthetic data enabled by high-quality forward models as well as by generative unsupervised DL methods. In this article, we present a high-level overview of deep learning methods followed by a detailed discussion of recent developments of deep learning in atomistic simulation, materials imaging, spectral analysis, and natural language processing. For each modality we discuss applications involving both theoretical and experimental data, typical modeling approaches with their strengths and limitations, and relevant publicly available software and datasets. We conclude the review with a discussion of recent cross-cutting work related to uncertainty quantification in this field and a brief perspective on limitations, challenges, and potential growth areas for DL methods in materials science. 

Abstract The development of efficient thermal energy management devices such as thermoelectrics and barrier coatings often relies on compounds having low lattice thermal conductivity (κ_l). Here, we present the computational discovery of a large family of 628 thermodynamically stable quaternary chalcogenides, AMM′Q₃(A = alkali/alkaline earth/post-transition metals; M/M′ = transition metals, lanthanides; Q = chalcogens) using high-throughput density functional theory (DFT) calculations. We validate the presence of lowκ_lin these materials by calculatingκ_lof several predicted stable compounds using the Peierls–Boltzmann transport equation. Our analysis reveals that the lowκ_loriginates from the presence of either a strong lattice anharmonicity that enhances the phonon-scatterings or rattler cations that lead to multiple scattering channels in their crystal structures. Our thermoelectric calculations indicate that some of the predicted semiconductors may possess high energy conversion efficiency with their figure-of-merits exceeding 1 near 600 K. Our predictions suggest experimental research opportunities in the synthesis and characterization of these stable, lowκ_lcompounds. 

The Open Databases Integration for Materials Design (OPTIMADE) application programming interface (API) empowers users with holistic access to a growing federation of databases, enhancing the accessibility and discoverability of materials and chemical data. Since the first release of the OPTIMADE specification (v1.0), the API has undergone significant development, leading to the v1.2 release, and has underpinned multiple scientific studies. In this work, we highlight the latest features of the API format, accompanying software tools, and provide an update on the implementation of OPTIMADE in contributing materials databases. We end by providing several use cases that demonstrate the utility of the OPTIMADE API in materials research that continue to drive its ongoing development.