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Monoclinic $\beta$-Ga$_2$O$_3$, a promising wide band gap semiconducting material, exhibits complex, anisotropic diffusional characteristics and mass transport behavior as a results of its low symmetry crystal structure. From first-principles calculations combined with master diffusion equations, we determine three-dimensional diffusion tensors for neutral ($\text{O}_{\text{i}}^{0}$) and 2- charged oxygen interstitials ($\text{O}_{\text{i}}^{2-}$). Systematic exploration of the configurational space identifies stable configurations in these two dominant charge states and their corresponding formation energies. By connecting every pair of low-energy configurations considering both interstitial or interstitialcy hops, we construct three-dimensional diffusion networks and evaluate hopping barriers of all transition pathways in networks. Combining the collection of (i) defect configurations and their formation energies and (ii) the hopping barriers that link them, we construct and solve the master diffusion equations for $\text{O}_{\text{i}}^{0}$ and $\text{O}_{\text{i}}^{2-}$ separately through the Onsager approach, resulting in respective three-dimensional diffusion tensors D$_{\text{O}_{\text{i}}}^{0}$ and D$_{\text{O}_{\text{i}}}^{2-}$. Both $\text{O}_{\text{i}}^{0}$ and $\text{O}_{\text{i}}^{2-}$ present the fastest diffusion along the $b$-axis, demonstrating significant anisotropy. The predicted self-diffusivities along [100] and [$\overline{2}01$] align well with previously reported values from isotopically labeled oxygen tracer experiments, highlighting the reliability of the approach in capturing complex diffusion mechanisms., Comment: 7 figures, 5 supplemental figures

The materials science community seeks to support the FAIR principles for computational simulation research. The MatCore Project was recently launched to address this need, with the goal of developing an overall metadata framework and accompanying guidelines. This paper reports on the MatCore goals and overall progress. Historical background context is provided, including a review of the principles underlying successful core metadata standards. The paper also presents selected MatCore examples and discusses future plans., Comment: 11 pages, 5 figures, Conference Paper

Incorporating group symmetries into neural networks has been a cornerstone of success in many AI-for-science applications. Diagonal groups of isometries, which describe the invariance under a simultaneous movement of multiple objects, arise naturally in many-body quantum problems. Despite their importance, diagonal groups have received relatively little attention, as they lack a natural choice of invariant maps except in special cases. We study different ways of incorporating diagonal invariance in neural network ans\"atze trained via variational Monte Carlo methods, and consider specifically data augmentation, group averaging and canonicalization. We show that, contrary to standard ML setups, in-training symmetrization destabilizes training and can lead to worse performance. Our theoretical and numerical results indicate that this unexpected behavior may arise from a unique computational-statistical tradeoff not found in standard ML analyses of symmetrization. Meanwhile, we demonstrate that post hoc averaging is less sensitive to such tradeoffs and emerges as a simple, flexible and effective method for improving neural network solvers.

B-gallium oxide (B-Ga2O3) is of high interest for power electronics because of its unique combination of melt growth, epitaxial growth, n-type dopability, ultrawide bandgap, and high critical field. Optimization of crystal growth processes to promote beneficial defects and suppress harmful ones requires accurate quantitative modelling of both native and impurity defects. Here we quantitatively model defect concentrations as a function of bulk crystal growth conditions and demonstrate the necessity of including effects such as bandgap temperature dependence, chemical potentials from thermochemistry, and defect vibrational entropy in modelling based on defect formation energies computed by density functional theory (DFT) with hybrid functionals. Without these contributions, grossly-erroneous and misleading predictions arise, e.g. that n-type doping attempts would be fully compensated by Ga vacancies. Including these effects reproduces the experimental facts that melt-grown Sn-doped B-Ga2O3 crystals are conductive with small compensation while annealing the same crystals in O2 at intermediate temperatures renders them insulating. To accomplish this modeling, we developed a comprehensive modelling framework (KROGER) based on calculated defect formation energies and flexible thermodynamic conditions. These capabilities allow KROGER to capture full and partial defect equilibria amongst native defects and impurities occurring during specific semiconductor growth or fabrication processes. We use KROGER to model 873 charge-states of 259 defects involving 19 elements in conditions representing bulk crystal growth by edge-fed growth (EFG) and annealing in O2. Our methodology is transferrable to a wide range of materials beyond B-Ga2O3. Integration of thermodynamic and first-principles modelling of point defects provides insight into optimization of point defect populations in growth and processing., Comment: 39 pages, 6 figures, references

Half-Heuslers are a promising family for thermoelectric (TE) applications, yet only a small fraction of their potential chemistries has been experimentally explored. In this work, we introduce a distinct computational high-throughput screening approach designed to identify underexplored yet promising material subspaces, and apply it to half-Heusler thermoelectrics. We analyze 1,126 half-Heuslers satisfying the $``$18 valence electron rule $''$, including 332 predicted to be semiconductors, using electronic structure calculations, semi-empirical transport models, and thermoelectric quality factor $\beta$. Unlike conventional filtering workflows, our approach employs statistical analysis of candidate material groups to uncover trends in their collective behavior, providing robust insights and minimizing reliance on uncertain predictions for individual compounds. Our findings link $n$-type performance to ultra-high mobility at conduction band edges and $p$-type performance to high band degeneracy. Statistical correlations reveal elemental subspaces associated with high $\beta$. We identify two primary (Y- and Zr-containing) and two secondary (Au- and Ir-containing) subspaces that reinforce key physical design principles, making them promising candidates for further exploration. These recommendations align with previous experimental results on yttrium pnictides. Inspired by these insights, we synthesize and characterize rare-earth gold stannides (REAuSn), finding Sc$_{0.5}$Lu$_{0.5}$AuSn to exhibit low thermal conductivity (0.9-2.3 Wm$^{-1}$K$^{-1}$ at 650 K). This work demonstrates alternative strategies for high throughput screening when using approximate but unbiased models, and offers predictive tools and design strategies for optimizing half-Heusler chemistries for TE performance.

This study introduces SLLMBO, an innovative framework leveraging large language models (LLMs) for hyperparameter optimization (HPO), incorporating dynamic search space adaptability, enhanced parameter space exploitation, and a novel LLM-tree-structured parzen estimator (LLM-TPE) sampler. By addressing limitations in recent fully LLM-based methods and traditional bayesian optimization (BO), SLLMBO achieves more robust optimization. This comprehensive benchmarking evaluates multiple LLMs, including GPT-3.5-Turbo, GPT-4o, Claude-Sonnet-3.5, and Gemini-1.5-Flash, extending prior work and establishing SLLMBO as the first framework to benchmark a diverse set of LLMs for HPO. By integrating LLMs' established strengths in parameter initialization with the exploitation abilities demonstrated in this study, alongside TPE's exploration capabilities, the LLM-TPE sampler achieves a balanced exploration-exploitation trade-off, reduces API costs, and mitigates premature early stoppings for more effective parameter searches. Across 14 tabular tasks in classification and regression, the LLM-TPE sampler outperformed fully LLM-based methods and achieved superior results over BO methods in 9 tasks. Testing early stopping in budget-constrained scenarios demonstrated competitive performance, indicating that LLM-based methods generally benefit from extended iterations for optimal results. This work lays the foundation for future research exploring open-source LLMs, reproducibility of LLM results in HPO, and benchmarking SLLMBO on complex datasets, such as image classification, segmentation, and machine translation.

With the increasing adoption of metal additive manufacturing (AM), researchers and practitioners are turning to data-driven approaches to optimise printing conditions. Cross-sectional images of melt tracks provide valuable information for tuning process parameters, developing parameter scaling data, and identifying defects. Here we present an image segmentation neural network that automatically identifies and measures melt track dimensions from a cross-section image. We use a U-Net architecture to train on a data set of 62 pre-labelled images obtained from different labs, machines, and materials coupled with image augmentation. When neural network hyperparameters such as batch size and learning rate are properly tuned, the learned model shows an accuracy for classification of over 99% and an F1 score over 90%. The neural network exhibits robustness when tested on images captured by various users, printed on different machines, and acquired using different microscopes. A post-processing module extracts the height and width of the melt pool, and the wetting angles. We discuss opportunities to improve model performance and avenues for transfer learning, such as extension to other AM processes such as directed energy deposition., Comment: 21 pages, 10 figures

The development of unbiased large language models is widely recognized as crucial, yet existing benchmarks fall short in detecting biases due to limited scope, contamination, and lack of a fairness baseline. SAGED(bias) is the first holistic benchmarking pipeline to address these problems. The pipeline encompasses five core stages: scraping materials, assembling benchmarks, generating responses, extracting numeric features, and diagnosing with disparity metrics. SAGED includes metrics for max disparity, such as impact ratio, and bias concentration, such as Max Z-scores. Noticing that metric tool bias and contextual bias in prompts can distort evaluation, SAGED implements counterfactual branching and baseline calibration for mitigation. For demonstration, we use SAGED on G20 Countries with popular 8b-level models including Gemma2, Llama3.1, Mistral, and Qwen2. With sentiment analysis, we find that while Mistral and Qwen2 show lower max disparity and higher bias concentration than Gemma2 and Llama3.1, all models are notably biased against countries like Russia and (except for Qwen2) China. With further experiments to have models role-playing U.S. presidents, we see bias amplifies and shifts in heterogeneous directions. Moreover, we see Qwen2 and Mistral not engage in role-playing, while Llama3.1 and Gemma2 role-play Trump notably more intensively than Biden and Harris, indicating role-playing performance bias in these models., Comment: COLING 2025 Main Conference Oral Presentation

The use of Large Language Models (LLMs) in hiring has led to legislative actions to protect vulnerable demographic groups. This paper presents a novel framework for benchmarking hierarchical gender hiring bias in Large Language Models (LLMs) for resume scoring, revealing significant issues of reverse gender hiring bias and overdebiasing. Our contributions are fourfold: Firstly, we introduce a new construct grounded in labour economics, legal principles, and critiques of current bias benchmarks: hiring bias can be categorized into two types: Level bias (difference in the average outcomes between demographic counterfactual groups) and Spread bias (difference in the variance of outcomes between demographic counterfactual groups); Level bias can be further subdivided into statistical bias (i.e. changing with non-demographic content) and taste-based bias (i.e. consistent regardless of non-demographic content). Secondly, the framework includes rigorous statistical and computational hiring bias metrics, such as Rank After Scoring (RAS), Rank-based Impact Ratio, Permutation Test, and Fixed Effects Model. Thirdly, we analyze gender hiring biases in ten state-of-the-art LLMs. Seven out of ten LLMs show significant biases against males in at least one industry. An industry-effect regression reveals that the healthcare industry is the most biased against males. Moreover, we found that the bias performance remains invariant with resume content for eight out of ten LLMs. This indicates that the bias performance measured in this paper might apply to other resume datasets with different resume qualities. Fourthly, we provide a user-friendly demo and resume dataset to support the adoption and practical use of the framework, which can be generalized to other social traits and tasks., Comment: EMNLP 2024 Findings Paper

Short-range order (SRO) alters the mechanical properties of technologically relevant structural materials such as medium/high entropy alloys and austenitic stainless steels. In this study, we present a generalized spin cluster expansion (CE) model and show that magnetism is a primary factor influencing the level of SRO present in austenitic Fe-Ni-Cr alloys. The spin CE consists of a chemical cluster expansion combined with an Ising model for Fe-Ni-Cr alloys. It explicitly accounts for local magnetic exchange interactions, thereby capturing the effects of finite temperature magnetism on SRO. Model parameters are obtained by fitting to a first-principles data set comprising both chemically and magnetically diverse FCC configurations. The magnitude of the magnetic exchange interactions are found to be comparable to the chemical interactions. Compared to a conventional implicit magnetism CE built from only magnetic ground state configurations, the spin CE shows improved performance on several experimental benchmarks over a broad spectrum of compositions, particularly at higher temperatures due to the explicit treatment of magnetic disorder. We find that SRO is strongly influenced by alloy Cr content, since Cr atoms prefer to align antiferromagnetically with nearest neighbors but become magnetically frustrated with increasing Cr concentration. We predict that increasing the Cr concentration in typical austenitic stainless steels promotes the formation of SRO and increases order-disorder transition temperatures. This study underscores the significance of considering magnetic interactions explicitly when exploring the thermodynamic properties of complex transition metal alloys. It also highlights guidelines for customizing SRO through adjustments of alloy composition.

Generative models have received significant attention in recent years for materials science applications, particularly in the area of inverse design for materials discovery. However, these models are usually assessed based on newly generated, unverified materials, which provide a narrow evaluation of a model's performance. Also, current efforts for inorganic materials have predominantly focused on small crystals, even though the capability to generate large disordered structures would significantly expand the applicability of generative modeling. In this work, we present the Disordered Materials & Interfaces Benchmark (Dismai-Bench), a generative model benchmark that uses datasets of disordered alloys, interfaces, and amorphous silicon (256-264 atoms per structure). Models are trained on each dataset independently, and evaluated through direct structural comparisons between training and generated structures. Benchmarking was performed on two graph diffusion models and two (coordinate-based) U-Net diffusion models. The graph models were found to significantly outperform the U-Net models due to the higher expressive power of graphs. While noise in the less expressive models can assist in discovering materials by facilitating exploration beyond the training distribution, these models face significant challenges when confronted with more complex structures. To further demonstrate the benefits of this benchmarking in the development process of a generative model, we considered the case of developing a point-cloud-based generative adversarial network (GAN) to generate low-energy disordered interfaces. We show that the best performing architecture, CryinGAN, outperforms the U-Net models, and is competitive against the graph models despite its lack of invariances and weaker expressive power. This work provides a new framework and insights to guide the development of future generative models.

The low symmetry of monoclinic $\beta$-Ga$_2$O$_3$ leads to elaborate intrinsic defects, such as Ga vacancies split amongst multiple lattice sites. These defects contribute to fast, anisotropic Ga diffusion, yet their complexity makes it challenging to understand dominant diffusion mechanisms. Here, we predict the 3D diffusivity tensors for Ga interstitials (Ga${_i^{3+}}$) and vacancies (V${_{Ga}^{3-}}$) via first principles and direct solution of the master diffusion equations. We first explore the maximum extent of configurationally complex ''$N$-split'' Ga interstitials and vacancies. With dominant low-energy defects identified, we enumerate all possible elementary hops connecting defect configurations to each other, including interstitialcy hops. Hopping barriers are obtained from nudged elastic band simulations. Finally, the comprehensive sets of (i) defect configurations and their energies and (ii) the hopping barriers that connect them are used to construct the master diffusion equations for both Ga${_i^{3+}}$ and V${_{Ga}^{3-}}$. The solution to these equations yields the Onsager transport coefficients, i.e. the components of the 3D diffusivity tensors $D_{{Ga}_i}$ and $D_{V_{Ga}}$ for Ga${_i^{3+}}$ and V${_{Ga}^{3-}}$, respectively. It further reveals the active diffusion paths along all crystallographic directions. We find that both Ga${_i^{3+}}$ and V${_{{Ga}}^{3-}}$ diffusion are fastest along the $c$-axis, due to 3-split defects that bridge neighboring unit cells along the $c$-axis and divert diffusing species around high-energy bottlenecks. Although isolated Ga${_i^{3+}}$ diffuse faster than isolated V${_{Ga}^{3-}}$, self-diffusion of Ga is predominantly mediated by V$_{Ga}^{3-}$ due to the higher V$_{Ga}^{3-}$ defect concentration under most thermodynamic environments.

In this study, a novel approach is demonstrated for converting calorimeter images from fast simulations to those akin to comprehensive full simulations, utilizing conditional Generative Adversarial Networks (GANs). The concept of pix2pix is tailored for CALPAGAN, where images from fast simulations serve as the basis(condition) for generating outputs that closely resemble those from detailed simulations. The findings indicate a strong correlation between the generated images and those from full simulations, especially in terms of key observables like jet transverse momentum distribution, jet mass, jet subjettiness, and jet girth. Additionally, the paper explores the efficacy of this method and its intrinsic limitations. This research marks a significant step towards exploring more efficient simulation methodologies in High Energy Particle Physics.

An uncertainty in studying twisted bilayer graphene (TBG) is the minimum energy geometry, which strongly affects the electronic structure. The minimum energy geometry is determined by the potential energy surface, which is dominated by van der Waals (vdW) interactions. In this work, large-scale diffusion quantum Monte Carlo (QMC) simulations are performed to evaluate the energy of bilayer graphene at various interlayer distances for four stacking registries. An accurate registry-dependent potential is fit to the QMC data and is used to describe interlayer interactions in the geometry of near-magic-angle TBG. The band structure for the optimized geometry is evaluated using the accurate local-environment tight-binding model. We find that compared to QMC, DFT-based vdW interactions can result in errors in the corrugation magnitude by a factor of 2 or more near the magic angle. The error in corrugation then propagates to the flat bands in twisted bilayer graphene, where the error in corrugation can affect the bandwidth by about 30\% and can change the nature and degeneracy of the flat bands.

Lack of rigorous reproducibility and validation are major hurdles for scientific development across many fields. Materials science in particular encompasses a variety of experimental and theoretical approaches that require careful benchmarking. Leaderboard efforts have been developed previously to mitigate these issues. However, a comprehensive comparison and benchmarking on an integrated platform with multiple data modalities with both perfect and defect materials data is still lacking. This work introduces JARVIS-Leaderboard, an open-source and community-driven platform that facilitates benchmarking and enhances reproducibility. The platform allows users to set up benchmarks with custom tasks and enables contributions in the form of dataset, code, and meta-data submissions. We cover the following materials design categories: Artificial Intelligence (AI), Electronic Structure (ES), Force-fields (FF), Quantum Computation (QC) and Experiments (EXP). For AI, we cover several types of input data, including atomic structures, atomistic images, spectra, and text. For ES, we consider multiple ES approaches, software packages, pseudopotentials, materials, and properties, comparing results to experiment. For FF, we compare multiple approaches for material property predictions. For QC, we benchmark Hamiltonian simulations using various quantum algorithms and circuits. Finally, for experiments, we use the inter-laboratory approach to establish benchmarks. There are 1281 contributions to 274 benchmarks using 152 methods with more than 8 million data-points, and the leaderboard is continuously expanding. The JARVIS-Leaderboard is available at the website: https://pages.nist.gov/jarvis_leaderboard

Data-driven approaches for material discovery and design have been accelerated by emerging efforts in machine learning. However, general representations of crystals to explore the vast material search space remain limited. We introduce a material discovery framework that uses natural language embeddings derived from language models as representations of compositional and structural features. The discovery framework consists of a joint scheme that first recalls relevant candidates, and next ranks the candidates based on multiple target properties. The contextual knowledge encoded in language representations conveys information about material properties and structures, enabling both representational similarity analysis for recall, and multi-task learning to share information across related properties. By applying the framework to thermoelectrics, we demonstrate diversified recommendations of prototype structures and identify under-studied high-performance material spaces. The recommended materials are corroborated by first-principles calculations and experiments, revealing novel materials with potential high performance. Our framework provides a task-agnostic means for effective material recommendation and can be applied to various material systems.

Hybrid lead halide perovskites (LHPs) are a class of semiconductor with novel properties that are distinctively governed by structural fluctuations. Diffraction experiments sensitive to average, long-range order reveal a cubic structure in the device-relevant, high-temperature phase. Local probes find additional short-range order with lower symmetry that may govern the structure-function relationships of LHPs. However, the dimensionality, participating atoms, and dynamics of this short-range order are unresolved, impeding our understanding of technologically relevant properties including long carrier lifetimes and facile halide migration. Here, we determine the true structure of two prototypical hybrid LHPs, CH$_3$NH$_3$PbI$_3$ and CH$_3$NH$_3$PbBr$_3$, using a combination of single-crystal X-ray and neutron diffuse scattering, neutron inelastic spectroscopy, and molecular dynamics simulations. The remarkable collective dynamics we found are not suggested by previous studies and consist of a network of local two-dimensional, circular pancake-like regions of dynamically tilting lead halide octahedra (lower symmetry) that induce longer range intermolecular correlations within the CH$_3$NH$_3^+$ sublattice. The dynamic local structure can introduce transient ferroelectric or antiferroelectric domains that increase charge carrier lifetimes, and strongly affect the halide migration, a poorly understood degradation mechanism. Our approach of co-analyzing single-crystal X-ray and neutron diffuse scattering data with MD simulations will provide unparalleled insights into the structure of hybrid materials and materials with engineered disorder.

Recent discoveries of materials with ultra-low thermal conductivity open a pathway to significant developments in the field of thermoelectricity. Here, we conduct a comparative study of three chemically similar antimonides to establish the root causes of their extraordinarily low thermal conductivity ($0.4-0.6$ Wm$^{-1}$K$^{-1}$ at 525 K). The materials of interest are: the unconventional type-XI clathrate K$_{58}$Zn$_{122}$Sb$_{207}$, the tunnel compound K$_{6.9}$Zn$_{21}$Sb$_{16}$, and the type-I clathrate K$_8$Zn$_{15.5}$Cu$_{2.5}$Sb$_{28}$ discovered herein. Calculations of the phonon dispersions show that the type-XI compound exhibits localized (i.e., rattling) phonon modes with unusually low frequencies that span the entire acoustic regime. In contrast, rattling in the type-I clathrate is observed only at higher frequencies, and no rattling modes are present in the tunnel structure. Modeling reveals that low-frequency rattling modes profoundly limit the acoustic scattering time; the scattering time of the type-XI clathrate is half that of the type-I clathrate and a quarter of the tunnel compound. For all three materials, the thermal conductivities are additionally suppressed by soft framework bonding that lowers the acoustic group velocities, and structural complexity that leads to diffusonic character of the optical modes. Understanding details of thermal transport in structurally complex materials will be crucial for developing the next generation of thermoelectrics.

While machine learning has emerged in recent years as a useful tool for rapid prediction of materials properties, generating sufficient data to reliably train models without overfitting is still impractical for many applications. Towards overcoming this limitation, we present a general framework for leveraging complementary information across different models and datasets for accurate prediction of data scarce materials properties. Our approach, based on a machine learning paradigm called mixture of experts, outperforms pairwise transfer learning on 16 of 19 materials property regression tasks, performing comparably on the remaining three. Unlike pairwise transfer learning, our framework automatically learns to combine information from multiple source tasks in a single training run, alleviating the need for brute-force experiments to determine which source task to transfer from. The approach also provides an interpretable, model-agnostic, and scalable mechanism to transfer information from an arbitrary number of models and datasets to any downstream property prediction task. We anticipate the performance of our framework will further improve as better model architectures, new pre-training tasks, and larger materials datasets are developed by the community.

A major hurdle in understanding the phase diagram of twisted bilayer graphene (TBLG) are the roles of lattice relaxation and electronic structure on isolated band flattening near magic twist angles. In this work, the authors develop an accurate local environment tight binding model (LETB) fit to tight binding parameters computed from $ab\ initio$ density functional theory (DFT) calculations across many atomic configurations. With the accurate parameterization, it is found that the magic angle shifts to slightly lower angles than often quoted, from around 1.05$^\circ$ to around 0.99$^\circ$, and that isolated flat bands appear for rigidly rotated graphene layers, with enhancement of the flat bands when the layers are allowed to distort. Study of the orbital localization supports the emergence of fragile topology in the isolated flat bands without the need for lattice relaxation.

The ability to control carrier concentration based on the extent of Cu solubility in the $\mathrm{Cu_{2x}Hg_{2-x}GeTe_4}$ alloy compound (where 0 $\leq$ x $\leq$ 1) makes $\mathrm{Cu_{2x}Hg_{2-x}GeTe_4}$ an interesting case study in the field of thermoelectrics. While Cu clearly plays a role in this process, it is unknown exactly how Cu incorporates into the $\mathrm{Cu_{2x}Hg_{2-x}GeTe_4}$ crystal structure and how this affects the carrier concentration. In this work, we use a combination of resonant energy X-ray diffraction (REXD) experiments and density functional theory (DFT) calculations to elucidate the nature of Cu incorporation into the $\mathrm{Cu_{2x}Hg_{2-x}GeTe_4}$ structure. REXD across the $\mathrm{Cu_k}$ edge facilitates the characterization of Cu incorporation in the $\mathrm{Cu_{2x}Hg_{2-x}GeTe_4}$ alloy and enables direct quantification of anti-site defects. We find that Cu substitutes for Hg at a 2:1 ratio, wherein Cu annihilates a vacancy and swaps with a Hg atom. DFT calculations confirm this result and further reveal that the incorporation of Cu occurs preferentially on one of the z = 1/4 or z = 3/4 planes before filling the other plane. Furthermore, the amount of $\mathrm{Cu_{Hg}}$ anti-site defects quantified by REXD was found to be directly proportional to the experimentally measured hole concentration, indicating that the $\mathrm{Cu_{Hg}}$ defects are the driving force for tuning carrier concentration in the $\mathrm{Cu_{2x}Hg_{2-x}GeTe_4}$ alloy. The link uncovered here between crystal structure, or more specifically anti-site defects, and carrier concentration can be extended to similar cation-disordered material systems and will aid the development of improved thermoelectric and other functional materials through defect engineering.

We develop a continuum dislocation description of twist and stretch moire superlattices in 2D material bilayers. The continuum formulation is based on the topological constraints introduced by the periodic dislocation network associated with the moire structure. The approach is based on solving analytically for the structural distortion and displacement fields that satisfy the topological constraints, and which minimize the total energy. The total energy is described by both the strain energy of each individual distorted layer, and a Peierls-Nabarro like interfacial contribution arising from stacking disregistry. The dislocation core emerges naturally within the formalism as a result of the competition between the two contributions. The approach presented here captures the structure and energetics of twist and stretch moire superlattices of dislocations with arbitrary direction and character, without assuming an analytical solution a priori, with no adjustable parameters, while accounting naturally for dislocation-dislocation image interactions. In comparisons to atomistic simulations using classical potentials, the maximum structure deviation is 6%, while the maximum line energy deviation is 0.019 eV/Angstrom. Several applications of our model are shown, including predicting the variation of structure with twist angle, and describing dislocation line tension and junction energies.

Many applications in different domains produce large amount of time series data. Making accurate forecasting is critical for many decision makers. Various time series forecasting methods exist which use linear and nonlinear models separately or combination of both. Studies show that combining of linear and nonlinear models can be effective to improve forecasting performance. However, some assumptions that those existing methods make, might restrict their performance in certain situations. We provide a new Autoregressive Integrated Moving Average (ARIMA)-Artificial Neural Network(ANN) hybrid method that work in a more general framework. Experimental results show that strategies for decomposing the original data and for combining linear and nonlinear models throughout the hybridization process are key factors in the forecasting performance of the methods. By using appropriate strategies, our hybrid method can be an effective way to improve forecasting accuracy obtained by traditional hybrid methods and also either of the individual methods used separately.

Many mixed ionic/electronic conductors (MIECs) applied in fuel cell electrodes can be considered as alloys between perovskite oxides and ordered oxygen vacancy compounds. For example, in the model MIEC (STF), low oxygen diffusion barrier exist in SrTiO3 lattice, when it has been mixed with SrFeO2.5 with intrinsic oxygen deficiency, the ionic conductivity can be greatly improved. Meanwhile, the electronic conductivity can be optimized by controlling the defect chemistry of the alloy. However, the configurational space is too large in such alloys so that it is difficult for direct atomic modeling, which hinders in-depth understanding and predictive modeling. In this work, we present a cluster expansion model to describe the energetics of the disordered SrTiO3/SrFeO2.5 alloy within the full solid solution composition space Sr(Ti1-x,Fex)O3-0.5x (0

Ion beam irradiation has recently emerged as a versatile approach to functional materials design. We show in this work that patterned defective regions generated by ion beam irradiation of silicon can create a phonon glass electron crystal (PGEC), a longstanding goal of thermoelectrics. By controlling the effective diameter of and spacing between the defective regions, molecular dynamics simulations suggest a reduction of the thermal conductivity by a factor of $\approx$20 is achievable. Boltzmann theory shows that the thermoelectric power factor remains largely intact in the damaged material. To facilitate the Boltzmann theory, we derive an analytical model for electron scattering with cylindrical defective regions based on partial wave analysis. Together we predict a figure of merit of ZT$\approx$0.5 or more at room temperature for optimally patterned geometries of these silicon metamaterials. These findings indicate that nanostructuring of patterned defective regions in crystalline materials is a viable approach to realize a PGEC, and ion beam irradiation could be a promising fabrication strategy., Comment: 21 pages, 4 figures

We present a generalized model to describe the lattice thermal conductivity of low-dimensional (low-D) and disordered systems. The model is a straightforward generalization of the Debye-Peierls and Allen-Feldman schemes to arbitrary dimensions, accounting for low-D effects such as differences in dispersion, density of states, and scattering. Similar in spirit to the Allen-Feldman approach, heat carriers are categorized according to their transporting capacity as propagons, diffusons, and locons. The results of the generalized model are compared to experimental results when available, and equilibrium molecular dynamics simulations otherwise. The results are in very good agreement with our analysis of phonon localization in disordered low-D systems, such as amorphous graphene and glassy diamond nanothreads. Several unique aspects of thermal transport in low-D and disordered systems, such as milder suppression of thermal conductivity and negligble diffuson contributions, are captured by the approach., Comment: 7 figures

We present an analysis of the polymorphic energy ordering and properties of the rock salt and zincblende structures of manganese oxide using fixed node diffusion Monte Carlo (DMC). Manganese oxide is a correlated, antiferromagnetic material that has proven to be challenging to model from first principles across a variety of approaches. Unlike conventional density functional theory and some hybrid functionals, fixed node diffusion Monte Carlo finds the rock salt structure to be more stable than the zincblende structure, and thus recovers the correct energy ordering. Analysis of the site-resolved charge fluctuations of the wave functions according to DMC and other electronic structure descriptions give insights into elements that are missing in other theories. While the calculated band gaps within DMC are in agreement with predictions that the zincblende polymorph has a lower band gap, the gaps themselves overestimate reported experimental values., Comment: 9 pages, 8 figures

The fixed node diffusion Monte Carlo (DMC) method has attracted interest in recent years as a way to calculate properties of solid materials with high accuracy. However, the framework for the calculation of properties such as total energies, atomization energies, and excited state energies is not yet fully established. Several outstanding questions remain as to the effect of pseudopotentials, the magnitude of the fixed node error, and the size of supercell finite size effects. Here, we consider in detail the semiconductors ZnSe and ZnO and carry out systematic studies to assess the magnitude of the energy differences arising from controlled and uncontrolled approximations in DMC. The former include time step errors and supercell finite size effects for ground and optically excited states, and the latter include pseudopotentials, the pseudopotential localization approximation, and the fixed node approximation. We find that for these compounds, the errors can be controlled to good precision using modern computational resources, and that quantum Monte Carlo calculations using Dirac-Fock pseudopotentials can offer good estimates of both cohesive energy and the gap of these systems. We do however observe differences in calculated optical gaps that arise when different pseudopotentials are used., Comment: 10 figures

We present a systematic assessment of the structural properties, the electronic density of states, the charge densities, and the phase stabilities of AgInSe$_2$ and AuInSe$_2$ using screened exchange hybrid density functional theory, and compare their properties to those of CuInSe$_2$. For AgInSe$_2$, hybrid density functional theory properly captures several experimentally measured properties, including the increase in the band gap and the change in the direction of the lattice distortion parameter $u$ in comparison to CuInSe$_2$. While the electronic properties of AuInSe$_2$ have not yet been experimentally characterized, we predict it to be a small gap ($\approx 0.15$ eV) semiconductor. We also present the phase stability of AgInSe$_2$ and AuInSe$_2$ according to screened-exchange density functional theory, and compare the results to predictions from conventional density functional theory, results tabulated from several online materials data repositories, and experiment (when available). In comparison to conventional density functional theory, the hybrid functional predicts phase stabilities of AgInSe$_2$ in better agreement with experiment: discrepancies in the calculated formation enthalpies are reduced by approximately a factor of three, from $\approx$ 0.20 eV/atom to $\approx$ 0.07 eV/atom, similar to the improvement observed for CuInSe$_2$. We further predict that AuInSe$_2$ is not a stable phase, and can only be present under non-equilibrium conditions., Comment: 8 figures

Reactive point processes (RPPs) are a new statistical model designed for predicting discrete events in time based on past history. RPPs were developed to handle an important problem within the domain of electrical grid reliability: short-term prediction of electrical grid failures ("manhole events"), including outages, fires, explosions and smoking manholes, which can cause threats to public safety and reliability of electrical service in cities. RPPs incorporate self-exciting, self-regulating and saturating components. The self-excitement occurs as a result of a past event, which causes a temporary rise in vulner ability to future events. The self-regulation occurs as a result of an external inspection which temporarily lowers vulnerability to future events. RPPs can saturate when too many events or inspections occur close together, which ensures that the probability of an event stays within a realistic range. Two of the operational challenges for power companies are (i) making continuous-time failure predictions, and (ii) cost/benefit analysis for decision making and proactive maintenance. RPPs are naturally suited for handling both of these challenges. We use the model to predict power-grid failures in Manhattan over a short-term horizon, and to provide a cost/benefit analysis of different proactive maintenance programs., Comment: Published at http://dx.doi.org/10.1214/14-AOAS789 in the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)

A continuum theory to describe periodic ripple formation in planar graphene/boron nitride superlattices is formulated. Due to the lattice mismatch between the two materials, it is shown that flat superlattices are unstable with respect to ripple formation of appropriate wavelengths. A competition between bending energy and transverse stretching energy gives rise to an optimal ripple wavelength that depends on the superlattice pitch. The optimal wavelengths predicted by the continuum theory are in good agreement with atomic scale total energy calculations previously reported in Nandwana and Ertekin, Nano Lett. 15 1468 (2015).

We present an approach to calculation of point defect optical and thermal ionization energies based on the highly accurate quantum Monte Carlo methods. The use of an inherently many-body theory that directly treats electron correlation offers many improvements over the typically-employed density functional theory Kohn-Sham description. In particular, the use of quantum Monte Carlo methods can help overcome the band gap problem and obviate the need for ad-hoc corrections. We demonstrate our approach to the calculation of the optical and thermal ionization energies of the F-center defect in magnesium oxide, and obtain excellent agreement with experimental and/or other high-accuracy computational results.

The problem of "approximating the crowd" is that of estimating the crowd's majority opinion by querying only a subset of it. Algorithms that approximate the crowd can intelligently stretch a limited budget for a crowdsourcing task. We present an algorithm, "CrowdSense," that works in an online fashion to dynamically sample subsets of labelers based on an exploration/exploitation criterion. The algorithm produces a weighted combination of a subset of the labelers' votes that approximates the crowd's opinion., Comment: Presented at Collective Intelligence conference, 2012 (arXiv:1204.2991)

Hyperdoping has emerged as a promising method for designing semiconductors with unique optical and electronic properties, although such properties currently lack a clear microscopic explanation. Combining computational and experimental evidence, we probe the origin of sub-band gap optical absorption and metallicity in Se-hyperdoped Si. We show that sub-band gap absorption arises from direct defect-to-conduction band transitions rather than free carrier absorption. Density functional theory predicts the Se-induced insulator-to-metal transition arises from merging of defect and conduction bands, at a concentration in excellent agreement with experiment. Quantum Monte Carlo calculations confirm the critical concentration, demonstrate that correlation is important to describing the transition accurately, and suggest that it is a classic impurity-driven Mott transition., Comment: 5 pages, 3 figures (PRL formatted)

Spatial phase inhomogeneity at the nano- to microscale is widely observed in strongly-correlated electron materials. The underlying mechanism and possibility of artificially controlling the phase inhomogeneity are still open questions of critical importance for both the phase transition physics and device applications. Lattice strain has been shown to cause the coexistence of metallic and insulating phases in the Mott insulator VO2. By continuously tuning strain over a wide range in single-crystal VO2 micro- and nanobeams, here we demonstrate the nucleation and manipulation of one-dimensionally ordered metal-insulator domain arrays along the beams. Mott transition is achieved in these beams at room temperature by active control of strain. The ability to engineer phase inhomogeneity with strain lends insight into correlated electron materials in general, and opens opportunities for designing and controlling the phase inhomogeneity of correlated electron materials for micro- and nanoscale device applications., Comment: 14 pages, 4 figures, with supplementary information

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