Quantifying charge-state transition energy levels of impurities in semiconductors is critical to understanding and engineering their optoelectronic properties for applications ranging from solar photovoltaics to infra...
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Quantifying charge-state transition energy levels of impurities in semiconductors is critical to understanding and engineering their optoelectronic properties for applications ranging from solar photovoltaics to infrared lasers. While these transition levels can be measured and calculated accurately, such efforts are time-consuming and more rapid prediction methods would be beneficial. Here, we significantly reduce the time typically required to predict impurity transition levels using multi-fidelity datasets and a machine learning approach employing features based on elemental properties and impurity positions. We use transition levels obtained from low-fidelity (i.e., local-density approximation or generalized gradient approximation) density functional theory (DFT) calculations, corrected using a recently proposed modified band alignment scheme, which well-approximates transition levels from high-fidelity DFT (i.e., hybrid HSE06). The model fit to the large multi-fidelity database shows improved accuracy compared to the models trained on the more limited high-fidelity values. Crucially, in our approach, when using the multi-fidelity data, high-fidelity values are not required for model training, significantly reducing the computational cost required for training the model. Our machine learning model of transition levels has a root mean squared (mean absolute) error of 0.36 (0.27) eV vs high-fidelity hybrid functional values when averaged over 14 semiconductor systems from the II-VI and III-V families. As a guide for use on other systems, we assessed the model on simulated data to show the expected accuracy level as a function of bandgap for new materials of interest. Finally, we use the model to predict a complete space of impurity charge-state transition levels in all zinc blende III-V and II-VI systems. Published under an exclusive license by AIP Publishing.
We derive an algorithm for computing the total differentials of multi-loop integrals expressed as one-fold integrals of multiple polylogarithms, which can involve square roots of polynomials up to degree four and may ...
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In this article, we proposed a decoupling method that uses neural networks to approximate the solution $u$ and the spectral method to approximate another solution $p$ for solving the partial differential equations who...
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We propose a delay-agnostic asynchronous coordinate update algorithm (DEGAS) for computing operator fixed points, with applications to asynchronous optimization. DEGAS includes novel asynchronous variants of ADMM and ...
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Given a sequence of related modules Mn defined over a sequence of related polynomial rings, one may ask how to simultaneously compute a finite Gröbner basis for each Mn. Furthermore, one may ask how to simultaneo...
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Whereas electromagnetic surface waves are confined to a planar interface between two media, line waves exist at the one–dimensional interface between three materials. Here we derive a non–local integral equation for...
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The computation of the normaliser of a permutation group in the full symmetric group is an important and hard problem in computational group theory. This article reports on an algorithm that builds a descending chain ...
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Many theories of scientific and technological progress imagine science as an iterative, developmental process periodically interrupted by innovations which disrupt and restructure the status quo. Due to the immense so...
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Many theories of scientific and technological progress imagine science as an iterative, developmental process periodically interrupted by innovations which disrupt and restructure the status quo. Due to the immense societal value created by these disruptive scientific and technological innovations, accurately operationalizing this perspective into quantifiable terms represents a key challenge for researchers seeking to understand the history and mechanisms underlying scientific and technological progress. Researchers in this area have recently proposed a number of quantitative measures that seek to quantify the extent to which works in science and technology are disruptive with respect to their scientific context. While these disruption measures show promise in their ability to quantify potentially disruptive works of science and technology, their definitions are bespoke to the science of science and lack a broader theoretical framework, obscuring their interrelationships and limiting their adoption within broader network science paradigms. We propose a mathematical framework for conceptualizing and measuring disruptive scientific contributions within citation networks through the lens of network centrality, and formally relate the CD Index disruption measure and its variants to betweenness centrality. By reinterpreting disruption through the lens of centrality, we unify a number of existing citation-based disruption measures while simultaneously providing natural generalizations which enjoy empirical and computational efficiencies. We validate these theoretical observations by computing a variety of disruption measures on real citation data and find that computing these centrality-based disruption measures over ego networks of increasing radius results in better discernment of future award-winning scientific innovations relative to conventional disruption metrics which rely on local citation context alone. This work extends the theoretical foundations and potential
The program AutoEFT is described. It allows one to generate Effective Field Theories (EFTs) from a given set of fields and symmetries. Allowed fields include scalars, spinors, gauge bosons, and gravitons. The symmetri...
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We give a robust characterization of Nash equilibrium by postulating coherent behavior across varying games: Nash equilibrium is the only solution concept that satisfies consequentialism, consistency, and rationality....
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