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The melting temperature is important for materials design because of its relationship with thermal stability, synthesis, and processing conditions. Current empirical and computational melting point estimation techniques are limited in scope, computational feasibility, or interpretability. We report the development of a machine learning methodology for predicting melting temperatures of binary ionic solid materials. We evaluated different machine-learning models trained on a data set of the melting points of 476 non-metallic crystalline binary compounds, using materials embeddings constructed from elemental properties and density-functional theory calculations as model inputs. A direct supervised-learning approach yields a mean absolute error of around 180~K but suffers from low interpretability. We find that the fidelity of predictions can further be improved by introducing an additional unsupervised-learning step that first classifies the materials before the melting-point regression. Not only does this two-step model exhibit improved accuracy, but the approach also provides a level of interpretability with insights into feature importance and different types of melting that depend on the specific atomic bonding inside a material. Motivated by this finding, we used a symbolic learning approach to find interpretable physical models for the melting temperature, which recovered the best-performing features from both prior models and provided additional interpretability., Comment: 21 pages and 3 figures for the main manuscript, and 11 pages and 15 figures of supplementary information

The phase transition layered Li$_{0.5}$NiO$_2$ to spinel Li(NiO$_2$)$_2$ is a potential degradation pathway in LiNiO$_2$-based lithium-ion battery cathodes. We investigated the mechanism of this phase transformation from first principles. Consistent with experimental observations reported in the literature, our results indicate a high energy barrier for the transformation due to high defect-formation energies, a complex charge-transfer mechanism, and electronic frustration. Our results suggest that partially inverse spinel phases are unlikely to form for Li$_{0.5}$NiO$_2$, a qualitative difference from the chemically similar Li$_{0.5}$MnO$_2$, in which the transformation occurs at room temperature. We show that Ni and Li atoms migrate concertedly towards their respective spinel sites for the layered-to-spinel transformation to occur. We investigated the charge ordering in layered phases along the LiNiO$_2$-NiO$_2$ composition line, finding a pronounced impact of the symmetry and space group on the layered-to-spinel transition in Li$_{0.5}$NiO$_2$. Finally, we evaluated the relative stability of different spinel space groups, finding that previously reported experimental observations are consistent with a temperature-averaged structure rather than the 0 Kelvin ground-state structure of Li(NiO$_2$)$_2$ spinel., Comment: 29 pages and 8 figures in the main manuscript, and 15 pages and 7 figures of supplementary information

Although LiNiO$_2$ is chemically similar to LiCoO$_2$ and offers a nearly identical theoretical capacity, LiNiO$_2$ and related Co-free Ni-rich cathode materials suffer from degradation during electrochemical cycling that has prevented practical use in Li-ion batteries. The observed capacity decay of LiNiO$_2$ has been attributed to the formation of structural defects via Li/Ni cation mixing that reduces cyclability and leads to poor capacity retention. Herein, we investigate the kinetics and thermodynamics of Li/Ni mixing in ideal LiNiO$_2$ and off-stoichiometric Li$_{1-z}$Ni$_{1+z}$O$_2$. We find that ideal LiNiO$_2$ is stable against cation mixing with similar characteristics as LiCoO$_2$. Li/Ni mixing is promoted by extra Ni in the Li layers that cannot be avoided in synthesis. Our study elucidates the crucial role of extra Ni atoms on Li sites in the cation mixing mechanism, an insight that can inform the development of Co-free cathode materials., Comment: Supplemental information included at the end of the PDF file

Local atomic environment descriptors (LAEDs) are used in the materials science and chemistry communities, for example, for the development of machine learning interatomic potentials. Despite the fact that LAEDs have been extensively studied and benchmarked for various applications, global structure descriptors (GSDs), i.e., descriptors for entire molecules or crystal structures, have been mostly developed independently based on other approaches. Here, we propose a systematically improvable methodology for constructing a space of representations of GSDs from LAEDs by incorporating statistical information and information about chemical elements. We apply the method to construct GSDs of varying complexity for lithium thiophosphate structures that are of interest as solid electrolytes and use an information-theoretic approach to obtain an optimally compressed GSD. Finally, we report the performance of the compressed GSD for energy prediction tasks., Comment: 19 pages, 1 main figure, 8 supplementary figures, 5 supplementary tables, published at ICLR 2023 ML4Materials workshop

Electrochemical degradation of solid electrolytes is a major roadblock in the development of solid-state batteries, and the formed solid-solid interphase (SSI) plays a key role in the performance of solid-state batteries. In this study, by combining experimental X-ray absorption spectroscopy (XAS) measurements, first-principles simulations, and unsupervised machine learning, we have unraveled the atomic-scale oxidative degradation mechanisms of sulfide electrolytes at the interface using the baseline Li3PS4 (LPS) electrolyte as a model system. The degradation begins with a decrease of Li neighbor affinity to S atoms upon initial delithiation, followed by the formation of S-S bonds as the PS4 tetrahedron deforms. After the first delithiation cycle, the PS4 motifs become strongly distorted and PS3 motifs start to form. Spectral fingerprints of the local structural evolution are identified, which correspond to the main peak broadening and the peak shifting to a higher energy by about 2.5 eV in P K-edge XAS and a new peak emerging at 2473 eV in S K-edge XAS during delithiation. The spectral fingerprints serve as a proxy for the electrochemical stability of phosphorus sulfide solid electrolytes beyond LPS, as demonstrated in argyrodite Li6PS5Cl. We observed that the strong distortion and destruction of PS4 tetrahedra and the formation of S-S bonds are correlated with an increased interfacial impedance. To the best of our knowledge, this study showcases the first atomic-scale insights into the oxidative degradation mechanism of the LPS electrolyte, which can provide guidance for controlling macroscopic reactions through microstructural engineering and, more generally, can advance the rational design of sulfide electrolytes.

Solid ionic conductors are essential components of batteries and fuel cells. In many cases, ionic conduction through crystalline materials with substitutional disorder can be modeled with atomic-scale lattice model percolation simulations. The ionic percolation theory reviewed in this chapter describes the percolation behavior of diffusion pathways, identifies the fraction of lattice sites that contributes to ionic conduction, and quantifies the tortuosity of diffusion networks. These quantities can be related to the bulk diffusivity and capacity of intercalation battery electrodes. We discuss applications to lithium-ion battery cathodes in the disordered rocksalt and related crystal structures, showing how the diffusion pathways and their tortuosity vary with the Li content short-range order. All examples are based on the free and open-source Dribble simulation software., Comment: book chapter, 20 pages, 6 figures

X-ray absorption spectroscopy (XAS) is a premier technique for materials characterization, providing key information about the local chemical environment of the absorber atom. In this work, we develop a database of sulfur K-edge XAS spectra of crystalline and amorphous lithium thiophosphate materials based on the atomic structures reported in Chem. Mater., 34, 6702 (2022). The XAS database is based on simulations using the excited electron and core-hole pseudopotential approach implemented in the Vienna Ab initio Simulation Package. Our database contains 2681 S K-edge XAS spectra for 66 crystalline and glassy structure models, making it the largest collection of first-principles computational XAS spectra for glass/ceramic lithium thiophosphates to date. This database can be used to correlate S spectral features with distinct S species based on their local coordination and short-range ordering in sulfide-based solid electrolytes. The data is openly distributed via the Materials Cloud, allowing researchers to access it for free and use it for further analysis, such as spectral fingerprinting, matching with experiments, and developing machine learning models., Comment: 11 pages, 3 figures, 1 table

Heterovalent substitution of toxic lead is an increasingly popular design strategy to obtain environmentally sustainable variants of the exciting material class of halide perovskites. Perovskite nanocrystals (NCs) obtained through solution-based methods exhibit exceedingly high optical quality. Unfortunately, most of these synthesis routes still require reaction under inert gas and at very high temperatures. Herein we present a novel synthesis routine for lead-free double perovskite NCs. We combine hot injection and ligand-assisted reprecipitation (LARP) methods to achieve a low-temperature and ambient atmosphere-based synthesis for manganese-doped Cs_{2}NaBiCl_{6} NCs. Mn incorporation is critical for the otherwise non-emissive material, with a 9:1 Bi:Mn precursor ratio maximizing the bright orange photoluminescence (PL) and quantum yield (QY). Higher temperatures slightly increased the material's performance, yet NCs synthesized at room temperature were still emissive, highlighting the versatility of the synthetic approach. Furthermore, the NCs show excellent long-term stability in ambient conditions, facilitating additional investigations and energy-related applications.

With the demand for renewable energy and efficient devices rapidly increasing, a need arises to find and optimize novel (nano)materials. This can be an extremely tedious process, often relying significantly on trial and error. Machine learning has emerged recently as a powerful alternative; however, most approaches require a substantial amount of data points, i.e., syntheses. Here, we merge three machine-learning models with Bayesian Optimization and are able to dramatically improve the quality of CsPbBr3 nanoplatelets (NPLs) using only approximately 200 total syntheses. The algorithm can predict the resulting PL emission maxima of the NPL dispersions based on the precursor ratios, which lead to previously unobtainable 7 and 8 ML NPLs. Aided by heuristic knowledge, the algorithm should be easily applicable to other nanocrystal syntheses and significantly help to identify interesting compositions and rapidly improve their quality.

Outstanding optoelectronic properties and a facile synthesis render halide perovskite nanocrystals (NCs) a promising material for nanostructure-based devices. However, the commercialization is hindered mainly by the lack of NC stability under ambient conditions and inefficient charge carrier injection. Here, we investigate solutions to both problems, employing methylammonium lead bromide (MAPbBr_3) NCs encapsulated in diblock copolymer core-shell micelles of tunable size. We confirm that the shell does not prohibit energy transfer, as FRET efficiencies between these NCs and 2D CsPbBr_3 nanoplatelets (NPLs) reach 73.6%. This value strongly correlates to the micelle size, with thicker shells displaying significantly reduced FRET efficiencies. Those high efficiencies come with a price, as the thinnest shells protect the encapsulated NCs less from environmentally induced degradation. Finding the sweet spot between efficiency and protection could lead to the realization of tailored energy funnels with enhanced carrier densities for high-power perovskite NC-based optoelectronic applications.

Two-dimensional halide perovskite nanoplatelets (NPLs) have exceptional light-emitting properties, including wide spectral tunability, ultrafast radiative decays, high quantum yields (QY), and oriented emission. To realize efficient devices, it is imperative to understand how exciton transport progresses in NPL thin films. Due to the high binding energies of electron-hole pairs, excitons are generally considered the dominant species responsible for carrier transfer. We employ spatially and temporally resolved optical microscopy to map exciton diffusion in perovskite nanocrystal (NC) thin films between 15 {\deg}C and 50 {\deg}C. At room temperature (RT), we find the diffusion length to be inversely correlated to the thickness of the nanocrystals (NCs). With increasing temperatures, exciton diffusion declines for all NC films, but at different rates. This leads to specific temperature turnover points, at which thinner NPLs exhibit higher diffusion lengths. We attribute this anomalous diffusion behavior to the coexistence of excitons and free electron hole-pairs inside the individual NCs within our temperature range. The organic ligand shell surrounding the NCs prevents charge transfer. Accordingly, any time an electron-hole pair spends in the unbound state reduces the FRET-mediated inter-NC transfer rates and consequently the overall diffusion. These results clarify how exciton diffusion progresses in strongly confined halide perovskite NC films, emphasizing critical considerations for optoelectronic devices., Comment: accepted in ACS Nano (final version pre-acceptance)

Lithium thiophosphates (LPS) with the composition (Li$_2$S)$_x$(P$_2$S$_5$)$_{1-x}$ are among the most promising prospective electrolyte materials for solid-state batteries (SSBs), owing to their superionic conductivity at room temperature ($>10^{-3}$ S cm$^{-1}$), soft mechanical properties, and low grain boundary resistance. Several glass-ceramic (gc) LPS with different compositions and good Li conductivity have been previously reported, but the relationship between composition, atomic structure, stability, and Li conductivity remains unclear due to the challenges in characterizing non-crystalline phases in experiments or simulations. Here, we mapped the LPS phase diagram by combining first principles and artificial intelligence (AI) methods, integrating density functional theory, artificial neural network potentials, genetic-algorithm sampling, and ab initio molecular dynamics simulations. By means of an unsupervised structure-similarity analysis, the glassy/ceramic phases were correlated with the local structural motifs in the known LPS crystal structures, showing that the energetically most favorable Li environment varies with the composition. Based on the discovered trends in the LPS phase diagram, we propose a candidate solid-state electrolyte composition, (Li$_{2}$S)$_{x}$(P$_{2}$S$_{5}$)$_{1-x}$ ($x\sim{}0.725$), that exhibits high ionic conductivity ($>10^{-2}$ S cm$^{-1}$) in our simulations, thereby demonstrating a general design strategy for amorphous or glassy/ceramic solid electrolytes with enhanced conductivity and stability., Comment: 37 pages, 9 figures, 1 table, 2 supporting figures

Halide perovskite nanocrystals (NCs) have emerged as a promising material for applications ranging from light-emitting diodes (LEDs) to solar cells and photodetectors. Still, several issues impede the realization of the nanocrystals' full potential, most notably their susceptibility to degradation from environmental stress. This work demonstrates highly stable perovskite nanocrystals (NCs) with quantum yields as high as 95 % by exploiting a ligand-assisted copolymer nanoreactor-based synthesis. The organic ligands thereby serve a dual function by enhancing the uptake of precursors and passivating the NCs. The polymer micelles and ligands thus form a double protection system, shielding the encapsulated NCs from water-, heat- and UV-light-induced degradation. We demonstrate the optoelectronic integrability by incorporating the perovskite NCs as spectrally pure downconverters on top of a deep-blue-emitting organic LED. These results establish a way of stabilizing perovskite NCs for optoelectronics while retaining their excellent optical properties.

Nickel-based layered oxides offer an attractive platform for the development of energy-dense cobalt-free cathodes for lithium-ion batteries but suffer from degradation via oxygen gas release during electrochemical cycling. While such degradation has previously been characterized phenomenologically with experiments, an atomic-scale understanding of the reactions that take place at the cathode surface has been lacking. Here, we develop a first-principles methodology for the prediction of the surface reconstructions of intercalation electrode particles as a function of the temperature and state of charge. We report the surface phase diagrams of the LiNiO2(001) and (104) surfaces and identify surface structures that are likely visited during the first charge and discharge. Our calculations indicate that both surfaces experience oxygen loss during the first charge, resulting in irreversible changes to the surface structures. At the end of charge, the surface Ni atoms migrate into tetrahedral sites, from which they further migrate into Li vacancies during discharge, leading to Li/Ni mixed discharged surface phases. Further, the impact of the temperature and voltage range during cycling on the charge/discharge mechanism is discussed. The present study thus provides insight into the initial stages of cathode surface degradation and lies the foundation for the computational design of cathode materials that are stable against oxygen release., Comment: 18 pages, 14 figures

Semiconductor nanoplatelets (NPLs), with their large exciton binding energy, narrow photoluminescence (PL), and absence of dielectric screening for photons emitted normal to the NPL surface, could be expected to become the fastest luminophores amongst all colloidal nanostructures. However, super-fast emission is suppressed by a dark (optically passive) exciton ground state, substantially split from a higher-lying bright (optically active) state. Here, the exciton fine structure in 2-8 monolayer (ML) thick Cs_{n-1}Pb_nBr_{3n+1} NPLs is revealed by merging temperature-resolved PL spectra and time-resolved PL decay with an effective mass modeling taking quantum confinement and dielectric confinement anisotropy into account. This approach exposes a thickness-dependent bright-dark exciton splitting reaching 32.3meV for the 2ML NPLs. The model also reveals a 5-16 meV splitting of the bright exciton states with transition dipoles polarized parallel and perpendicular to the NPL surfaces, the order of which is reversed for the thinnest NPLs, as confirmed by TR-PL measurements. Accordingly, the individual bright states must be taken into account, while the dark exciton state strongly affects the optical properties of the thinnest NPLs even at room temperature. Significantly, the derived model can be generalized for any isotropically or anisotropically confined nanostructure.

We present the probability distribution of the systematic errors in the most accurate, high-latency version of the reconstructed dimensionless strain $h$, at the Hanford and Livingston LIGO detectors, used for gravitational-wave astrophysical analysis, including parameter estimation, in the last five months of the third observing run (O3B). This work extends the results presented in Sun et. al (2020) [1] for the first six months of the third observing run (O3A). The complex-valued, frequency-dependent, and slowly time-varying systematic error (excursion from unity magnitude and zero phase) in O3B generally remains at a consistent level as in O3A, yet changes of detector configurations in O3B have introduced a non-negligible change in the frequency dependence of the error, leading to larger excursions from unity at some frequencies and/or during some observational periods; in some other periods the excursions are smaller than those in O3A. For O3B, the upper limit on the systematic error and associated uncertainty is 11.29% in magnitude and 9.18 deg in phase (68% confidence interval) in the most sensitive frequency band 20-2000 Hz. The systematic error alone is estimated at levels of < 2% in magnitude and $\lesssim 4$ deg in phase. These errors and uncertainties are dominated by the imperfect modeling of the frequency dependence of the detector response functions rather than the uncertainty in the absolute reference, the photon calibrators.

Semi-local DFT methods exhibit significant errors for the phase diagrams of transition-metal oxides that are caused by an incorrect description of molecular oxygen and the large self-interaction error in materials with strongly localized electronic orbitals. Empirical and semiempirical corrections based on the DFT+U method can reduce these errors, but the parameterization and validation of the correction terms remains an on-going challenge. We develop a systematic methodology to determine the parameters and to statistically assess the results by considering thermochemical data across a set of transition metal compounds. We consider three interconnected levels of correction terms: (1) a constant oxygen binding correction, (2) Hubbard-U correction, and (3) DFT/DFT+U compatibility correction. The parameterization is expressed as a unified optimization problem. We demonstrate this approach for 3d transition metal oxides, considering a target set of binary and ternary oxides. With a total of 37 measured formation enthalpies taken from the literature, the dataset is augmented by the reaction energies of 1,710 unique reactions that were derived from the formation energies by systematic enumeration. To ensure a balanced dataset across the available data, the reactions were grouped by their similarity using clustering and suitably weighted. The parameterization is validated using leave-one-out cross-validation (CV), a standard technique for the validation of statistical models. We apply the methodology to the SCAN density functional. Based on the CV score, the error of binary (ternary) oxide formation energies is reduced by 40% (75%) to 0.10 (0.03) eV/atom. The method and tools demonstrated here can be applied to other classes of materials or to parameterize the corrections to optimize DFT+U performance for other target physical properties., Comment: 49 pages, 8 figures, 1 table, 6 SI figure, 5 SI tables

Recent advances in machine-learning interatomic potentials have enabled the efficient modeling of complex atomistic systems with an accuracy that is comparable to that of conventional quantum mechanics based methods. At the same time, the construction of new machine-learning potentials can seem a daunting task, as it involves data-science techniques that are not yet common in chemistry and materials science. Here, we provide a tutorial-style overview of strategies and best practices for the construction of artificial neural network (ANN) potentials. We illustrate the most important aspects of (i) data collection, (ii) model selection, (iii) training and validation, and (iv) testing and refinement of ANN potentials on the basis of practical examples. Current research in the areas of active learning and delta learning are also discussed in the context of ANN potentials. This tutorial review aims at equipping computational chemists and materials scientists with the required background knowledge for ANN potential construction and application, with the intention to accelerate the adoption of the method, so that it can facilitate exciting research that would otherwise be challenging with conventional strategies., Comment: 19 pages, 11 figures

Small, highly absorbing points are randomly present on the surfaces of the main interferometer optics in Advanced LIGO. The resulting nano-meter scale thermo-elastic deformations and substrate lenses from these micron-scale absorbers significantly reduces the sensitivity of the interferometer directly though a reduction in the power-recycling gain and indirect interactions with the feedback control system. We review the expected surface deformation from point absorbers and provide a pedagogical description of the impact on power build-up in second generation gravitational wave detectors (dual-recycled Fabry-Perot Michelson interferometers). This analysis predicts that the power-dependent reduction in interferometer performance will significantly degrade maximum stored power by up to 50% and hence, limit GW sensitivity, but suggests system wide corrections that can be implemented in current and future GW detectors. This is particularly pressing given that future GW detectors call for an order of magnitude more stored power than currently used in Advanced LIGO in Observing Run 3. We briefly review strategies to mitigate the effects of point absorbers in current and future GW wave detectors to maximize the success of these enterprises., Comment: 49 pages, 16 figures. -V2: typographical errors in equations B9 and B10 were corrected (stray exponent of "h" was removed). Caption of Figure 9 was corrected to indicate that 40mW was used for absorption in the model, not 10mW as incorrectly indicated in V1

Exciton-exciton annihilation (EEA) and Auger recombination are detrimental processes occurring in semiconductor optoelectronic devices at high carrier densities. Despite constituting one of the main obstacles for realizing lasing in semiconductor nanocrystals (NCs), the dependencies on NC size are not fully understood, especially for those with both weakly and strongly confined dimensions. Here, we use differential transmission spectroscopy to investigate the dependence of EEA on the physical dimensions of thickness-controlled 2D halide perovskite nanoplatelets (NPls). We find the EEA lifetimes to be extremely short on the order of 7-60 ps. Moreover, they are strongly determined by the NPl thickness with a power-law dependence according to {\tau}_2~d^5.3. Additional measurements show that the EEA lifetimes also increase for NPls with larger lateral dimensions. These results show that a precise control of the physical dimensions is critical for deciphering the fundamental laws governing the process especially in 1D and 2D NCs.

Despite showing great promise for optoelectronics, the commercialization of halide perovskite nanostructure-based devices is hampered by inefficient electrical excitation and strong exciton binding energies. While transport of excitons in an energy-tailored system via F\"orster resonance energy transfer (FRET) could be an efficient alternative, halide ion migration makes the realization of cascaded structures difficult. Here, we show how these could be obtained by exploiting the pronounced quantum confinement effect in two dimensional CsPbBr3 based nanoplatelets (NPls). In thin films of NPls of two predetermined thicknesses, we observe an enhanced acceptor photoluminescence (PL) emission and a decreased donor PL lifetime. This indicates a FRET-mediated process, benefitted by the structural parameters of the NPls. We determine corresponding transfer rates up to k_FRET=0.99 ns^-1 and efficiencies of nearly \eta_FRET=70%. We also show FRET to occur between perovskite NPls of other thicknesses. Consequently, this strategy could lead to tailored, energy cascade nanostructures for improved optoelectronic devices.

Metal halide perovskites represent a flourishing area of research, driven by both their potential application in photo-voltaics and optoelectronics, and for the fundamental science underpinning their unique optoelectronic properties. The advent of colloidal methods for the synthesis of halide perovskite nanocrystals has brought to the attention inter-esting aspects of this new type of materials, above all their defect-tolerance. This review aims to provide an updated survey of this fast-moving field, with a main focus on their colloidal synthesis. We examine the chemistry and the ca-pability of different colloidal synthetic routes to control the shape, size and optical properties of the resulting nano-crystals. We also provide an up to date overview of their post-synthesis transformations, and summarize the various so-lution processes aimed at fabricating halide perovskite-based nanocomposites. We then review the fundamental optical properties of halide perovskite nanocrystals, by focusing on their linear optical properties, on the effects of quantum confinement and, then, on the current knowledge of their exciton binding energies. We also discuss the emergence of non-linear phenomena such as multiphoton absorption, biexcitons and carrier multiplication. At last, we provide an outlook in the field, with the most cogent open questions and possible future directions.

Perovskite nanoplatelets (NPls) hold great promise for light-emitting applications, having achieved high photoluminescence quantum efficiencies (PLQEs) approaching unity in the blue wavelength range, where other metal-halide perovskites have typically been ineffective. However, the external quantum efficiency (EQE) of blue-emitting NPl light-emitting diodes (LEDs) have only reached 0.12%, with typical values well below 0.1%. In this work, we show that the performance of NPl LEDs is primarily hindered by a poor electronic interface between the emitter and hole-injector. Through Kelvin Probe and X-ray photoemission spectroscopy measurements, we reveal that the NPls have remarkably deep ionization potentials (>=6.5 eV), leading to large barriers for hole injection, as well as substantial non-radiative decay at the interface between the emitter and hole-injector. We find that an effective way to reduce these non-radiative losses is by using poly(triarylamine) interlayers. This results in an increase in the EQE of our blue LEDs emitting at 464 nm wavelength to 0.3%. We find that our results can be generalized to thicker sky-blue-emitting NPls, where we increase the EQE to 0.55% using the poly(triarylamine) interlayer. Our work also identifies the key challenges for further efficiency increases.

Recently, layered hybrid perovskites have been attracting huge interest due to a wide range of possible chemical compositions and the resulting tunability of the materials properties. In this study, we investigate the effect of the chain length of the organic ligands on the optical properties of stacks of two dimensional perovskite layers consisting of alkylammonium lead iodide (CnH2n+1NH3)2PbI4 with n=4-18. Photoluminescence and absorption spectroscopy reveal a blueshift with increasing chain length n including a jump of 110 meV between the n=10 and n=12 ligands due to a change in octahedral tilting. Using Xray diffraction (XRD) we determine the crystal structure and find the octahedral tilting to be the main cause of this blueshift. However, for very short chain lengths additional effects further reduce the transition energy. Results of effective mass approximation model calculations show good agreement between the expected reduction of transition energy and measured photoluminescence emission wavelength for these samples. This highlights how octahedral tilting plays a major role in determining the optical bandgap and suggests that miniband formation plays only a minor role in this material.

For high-speed optoelectronic applications relying on fast relaxation or energy transfer mechanisms, understanding of carrier relaxation and recombination dynamics is critical. Here, we compare the differences in photoexcited carrier dynamics in 2D and quasi-3D colloidal methylammonium lead iodide perovskite nanoplatelets via differential transmission spectroscopy. We find that the cooling of excited electron-hole pairs by phonon emission progresses much faster and is intensity-independent in the 2D-case. This is due to the low dielectric surrounding of the thin perovskite layers, for which the Fr\"ohlich interaction is screened less efficiently leading to higher and less density dependent carrier-phonon scattering rates. In addition, rapid dissipation of heat into the surrounding occurs due to the high surface-to-volume ratio. Furthermore, we observe a sub-picosecond dissociation of resonantly excited 1s excitons in the quasi-3D case, an effect which is suppressed in the 2D nanoplatelets due to their large exciton binding energies. The results highlight the importance of the surrounding environment of the inorganic nanoplatelets on their relaxation dynamics. Moreover, this 2D material with relaxation times in the sub-picosecond regime shows great potential for realizing devices such as photodetectors or all-optical switches operating at THz frequencies.

The easily tunable emission of halide perovskite nanocrystals throughout the visible spectrum makes them an extremely promising material for light-emitting applications. Whereas high quantum yields and long-term colloidal stability have already been achieved for nanocrystals emitting in the red and green spectral range, the blue region currently lags behind, with low quantum yields, broad emission profiles and insufficient colloidal stability. In this work, we present a facile synthetic approach for obtaining two-dimensional CsPbBr3 nanoplatelets with monolayer-precise control over their thickness, resulting in sharp photoluminescence and electroluminescence peaks with a tunable emission wavelength between 432 and 497 nm due to quantum confinement. Subsequent addition of a PbBr2-ligand solution repairs surface defects likely stemming from bromide and lead vacancies in a sub-ensemble of weakly emissive nanoplatelets. The overall photoluminescence quantum yield of the blue-emissive colloidal dispersions is consequently enhanced up to a value of 73+-2 %. Transient optical spectroscopy measurements focusing on the excitonic resonances further confirm the proposed repair process. Additionally, the high stability of these nanoplatelets in films and to prolonged UV light exposure is shown.

Semipermeable silica membranes are attractive as protective coatings for metal electrocatalysts such as platinum but their impact on the catalytic properties has not been fully understood. Here, we develop a first principles formalism to investigate how silica membranes interact with the surface of platinum metal electrocatalysts to develop a better understanding of the membrane-metal interplay. By generalizing the concept of Pourbaix diagrams to electrochemical solid-solid interfaces, we establish which bonds are formed between the SiO$_2$ membrane and the Pt(111) surface in aqueous electrolytes for different pH values and potential biases. We find that the membrane termination changes as a function of the pH and the potential, which affects the adhesion strength and the energy requirements for partial membrane detachment, controlling the Pt surface area that is accessible for reactant species. The charge-transfer between the Pt surface and the SiO$_2$ membrane is also pH and potential dependent and results in changes of the Pt surface d-band states, which are known to correlate with catalytic activity. Our analysis reveals the complex response of a buried interface to the electrochemical environment and identifies trends that are expected to apply also to other membrane-coated electrocatalysts., Comment: 19 pages, 15 figures (including appendix)

The raw outputs of the detectors within the Advanced Laser Interferometer Gravitational-Wave Observatory need to be calibrated in order to produce the estimate of the dimensionless strain used for astrophysical analyses. The two detectors have been upgraded since the second observing run and finished the year-long third observing run. Understanding, accounting, and/or compensating for the complex-valued response of each part of the upgraded detectors improves the overall accuracy of the estimated detector response to gravitational waves. We describe improved understanding and methods used to quantify the response of each detector, with a dedicated effort to define all places where systematic error plays a role. We use the detectors as they stand in the first half (six months) of the third observing run to demonstrate how each identified systematic error impacts the estimated strain and constrain the statistical uncertainty therein. For this time period, we estimate the upper limit on systematic error and associated uncertainty to be $< 7\%$ in magnitude and $< 4$ deg in phase ($68\%$ confidence interval) in the most sensitive frequency band 20-2000 Hz. The systematic error alone is estimated at levels of $< 2\%$ in magnitude and $< 2$ deg in phase.

Artificial neural network (ANN) potentials enable the efficient large-scale atomistic modeling of complex materials with near first-principles accuracy. For molecular dynamics simulations, accurate energies and interatomic forces are a prerequisite, but training ANN potentials simultaneously on energies and forces from electronic structure calculations is computationally demanding. Here, we introduce an efficient alternative method for the training of ANN potentials on energy and force information based on an extrapolation of the total energy via a Taylor expansion. By translating the force information to approximate energies, the quadratic scaling with the number of atoms exhibited by conventional force-training methods can be avoided, which enables the training on reference data sets containing complex atomic structures. We demonstrate for different materials systems, clusters of water molecules, bulk liquid water, and a lithium transition-metal oxide, that the proposed force-training approach provides substantial improvements over schemes that train on energies only. Including force information for training reduces the size of the reference data sets required for ANN potential construction, increases the transferability of the potential, and generally improves the force prediction accuracy. For a set of water clusters, the Taylor-expansion approach achieves around 50% of the force error improvement compared to the explicit training on all force components, at a much smaller computational cost. The alternative force training approach thus simplifies the construction of general ANN potentials for the prediction of accurate energies and interatomic forces for diverse types of materials, as demonstrated here for water and a transition-metal oxide., Comment: 19 pages, 13 figures

Elucidating the underlying principles behind band gap engineering is paramount for the successful implementation of semiconductors in photonic and optoelectronic devices. Recently it has been shown that the band gap of a wide and direct band gap semiconductor, such as ZnO, can be modified upon co-crystallization with amino acids, with the role of the biomolecules remaining unclear. Here, by probing and modeling the light emitting properties of ZnO-amino acid co-crystals, we identify the amino acids role on this band gap modulation and demonstrate their effective chirality transfer to the inter-band excitations in ZnO. Our 3D quantum model suggests that the strong band edge emission blue shift in the co-crystals can be explained by a quasi-periodic distribution of amino acid potential barriers within the ZnO crystal lattice. Overall, our findings indicate that biomolecule co-crystallization can be used as a truly bio-inspired means to induce chiral quantum confinement effects in quasi-bulk semiconductors.

Halide perovskite nanocrystals (NCs) have shown impressive advances, exhibiting optical properties that outpace conventional semiconductor NCs, such as near-unity quantum yields and ultrafast radiative decay rates. Nevertheless, the NCs suffer even more from stability problems at ambient conditions and due to moisture than their bulk counterparts. Herein, we report a strategy of employing polymer micelles as nanoreactors for the synthesis of methylammonium lead trihalide perovskite NCs. Encapsulated by this polymer shell, the NCs display strong stability against water degradation and halide ion migration. Thin films comprising these NCs exhibit a more than 15-fold increase in lifespan in comparison to unprotected NCs in ambient conditions and even survive over 75 days of complete immersion in water. Furthermore, the NCs, which exhibit quantum yields of up to 63% and tunability of the emission wavelength throughout the visible range, show no signs of halide ion exchange. Additionally, heterostructures of MAPI and MAPBr NC layers exhibit efficient F\"orster resonance energy transfer (FRET), revealing a strategy for optoelectronic integration., Comment: This document is the unedited Author version of a Submitted Work that was subsequently accepted for publication in Nano Letters, copyright American Chemical Society after peer review. To access the final edited and published work see http://pubs.acs.org/articlesonrequest/AOR-W59yZfkIX7sG6Xuvp8NE

Nanostructured Si is the most promising high-capacity anode material to substantially increase the energy density of Li-ion batteries. Among the remaining challenges is its low rate capability as compared to conventional materials. To understand better what controls the diffusion of Li in the amorphous Li-Si alloy, we use a novel machine-learning potential trained on more than 40,000 ab-initio calculations and nanosecond-scale molecular dynamics simulations, to visualize for the first time the delithiation of entire LiSi nanoparticles. Our results show that the Si host is not static but undergoes a dynamic rearrangement from isolated atoms, to chains, and clusters, with the Li diffusion strongly governed by this Si rearrangement. We find that the Li diffusivity is highest when Si segregates into clusters, so that Li diffusion proceeds via hopping between the Si clusters. The average size of Si clusters and the concentration range over which Si clustering occurs can thus function as design criteria for the development of rate-improved anodes based on modified Si., Comment: 31 pages, 3 main figures, 14 supplementary figures, 1 main table, 1 supplementary table