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Researcher profile

Yang Sun

Yang Sun contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
cond-mat.mtrl-scicond-mat.supr-conArtificial Intelligenceastro-ph.GAnucl-thphysics.geo-phastro-ph.SRcond-mat.str-eleess.ASMachine LearningSoundastro-ph.COComputation and LanguageComputational Engineering, Finance, and ScienceComputer Visioncond-mat.stat-mechecon.THeess.SPEmerging TechnologiesInformation RetrievalInformation Theorymath.ITphysics.chem-phphysics.opticsSoftware Engineering

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Published work

28 published item(s)

preprint2026arXiv

Abundant Population of Broad H$α$ Emitters in the GOODS-N Field Revealed by CONGRESS, FRESCO, and JADES

We present a spectroscopic search for broad H$α$ emitters at z$\approx$3.7-6.5 in the GOODS-N field, utilizing JWST/NIRCam slitless spectroscopy from FRESCO and CONGRESS, complemented by JADES imaging. We identify 19 broad H$α$ emitters with FWHM$>$1000 km/s at z$\approx$4-5.5, including 9 new sources. The broad H$α$ luminosity function (LF) derived from our sample is consistent with those of other JWST-selected broad-line AGN reported in the literature. The black hole masses and AGN bolometric luminosities, inferred from the broad H$α$ components, indicate that most sources are accreting at ~10% of the Eddington limit. We derive their host stellar masses via SED fitting and find higher $M_{BH}/M_{*}$ ratios relative to the local $M_{BH}-M_{*}$ relations, consistent with previous studies. We find that 42% of the sample do not satisfy the widely-used color selection criteria for Little Red Dots (LRDs), with the majority of these sources lacking the characteristic steep rest-optical red slope, indicating that the LRD selection is highly incomplete when selecting AGN galaxies. A comparison of the average SEDs between our sample and LRDs selected in the same field reveals that the steep red slopes observed in some LRDs are likely due to line-boosting effects as previously suggested. Furthermore, we find that 68% of color-selected LRDs with H$α$ detections in the NIRCam/Grism spectra do not exhibit broad-line features. While the limited sensitivity of the grism spectra may hinder the detection of broad-line components in faint sources, our findings still highlight the enigmatic nature of the LRD population.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Agentic Discovery of Exchange-Correlation Density Functionals

The development of accurate exchange-correlation (XC) functionals remains a longstanding challenge in density functional theory (DFT). The vast majority of XC functionals have been hand designed by human researchers combining physical insight, exact constraints, and empirical fitting. Recent advances in large language models enable a systematic, automated alternative to this human-driven design loop. This report presents an agentic search system in which an LLM proposes structured functional-form changes guided by evolutionary history. The system attempts to improve functional performance through an iterative plan-execute-summarize loop, where improvements are measurable by optimizing functional parameters against a standard thermochemistry dataset, then evaluating performance on a held-out subset. The strongest discovered functional, SAFS26-a (Seed Agentic Functional Search 2026), improves upon the gold-standard ωB97M-V baseline by ~9%. These results also surface a cautionary lesson for AI-assisted science: models powerful enough to discover genuine improvements are equally capable of exploiting unphysical shortcuts to game the benchmark; domain expertise translated into explicitly enforced constraints remains essential to keeping results scientifically grounded.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

LFD: Layer Fused Decoding to Exploit External Knowledge in Retrieval-Augmented Generation

Retrieval-augmented generation (RAG) incorporates external knowledge into large language models (LLMs), improving their adaptability to downstream tasks and enabling information updates. Surprisingly, recent empirical evidence demonstrates that injecting noise into retrieved relevant documents paradoxically facilitates exploitation of external knowledge and improves generation quality. Although counterintuitive and challenging to apply in practice, this phenomenon enables granular control and rigorous analysis of how LLMs integrate external knowledge. Therefore, in this paper, we intervene on noise injection and establish a layer-specific functional demarcation within the LLM: shallow layers specialize in local context modeling, intermediate layers focus on integrating long-range external factual knowledge, and deeper layers primarily rely on parametric internal knowledge. Building on this insight, we propose Layer Fused Decoding (LFD), a simple decoding strategy that directly combines representations from an intermediate layer with final-layer decoding outputs to fully exploit the external factual knowledge. To identify the optimal intermediate layer, we introduce an internal knowledge score (IKS) criterion that selects the layer with the lowest IKS value in the latter half of layers. Experimental results across multiple benchmarks demonstrate that LFD helps RAG systems more effectively surface retrieved context knowledge with minimal cost.

0 citations0 reviewsNo code linkedOpen access
preprint2025arXiv

Ab initio superionic-liquid phase diagram of Fe1-xOx under Earth's inner core conditions

The superionic state is a phase of matter in which liquid-like ionic mobility coexists with a solid crystalline lattice. Recently identified in Earth's inner core (IC), this state has attracted considerable attention for its unique kinetic behavior and geophysical implications. However, the ab initio phase diagram describing the equilibrium between the superionic phase and the liquid solution under core conditions remains largely unexplored. Here, we present a thermodynamic approach to compute the Gibbs free energy and construct the ab initio superionic-liquid phase diagram for the Fe1-xOx system under IC conditions. We find that oxygen forms superionic states in both hcp and bcc Fe phases, with a pronounced influence on cooperative diffusion of iron in the bcc lattice. The stability fields of these superionic phases are sensitive to oxygen stoichiometry. The presence of superionic states leads to a higher oxygen concentration in the IC than previously estimated. Our work establishes a framework for investigating superionic-liquid equilibria under extreme conditions.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

A deep machine learning potential for atomistic simulation of Fe-Si-O systems under Earth's outer core conditions

Using artificial neural-network machine learning (ANN-ML) to generate interatomic potentials has been demonstrated to be a promising approach to address the long-standing challenge of accuracy versus efficiency in molecular dynamics (MD) simulations. Here, taking the Fe-Si-O system as a prototype, we show that accurate and transferable ANN-ML potentials can be developed for reliable MD simulations of materials at high-pressure and high-temperature conditions of the Earth's outer core. The ANN-ML potential for Fe-Si-O system is trained by fitting to the energies and forces of related binaries and ternary liquid structures at high pressures and temperatures obtained by first-principles calculations based on density functional theory (DFT). We show that the generated ANN-ML potential describes well the structure and dynamics of liquid phases of this complex system. The efficient ANN-ML potential with DFT accuracy provides a promising scheme for accurate atomistic simulations of structures and dynamics of complex Fe-Si-O system in the Earth's outer core.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Electron-phonon coupling strength from ab initio frozen-phonon approach

We propose a fast method for high-throughput screening of potential superconducting materials. The method is based on calculating metallic screening of zone-center phonon modes, which provides an accurate estimate for the electron-phonon coupling strength. This method is complementary to the recently proposed Rigid Muffin Tin (RMT) method, which amounts to integrating the electron-phonon coupling over the entire Brillouin zone (as opposed to the zone center), but in a relatively inferior approximation. We illustrate the use of this method by applying it to MgB$_\text{2}$, where the high-temperature superconductivity is known to be driven largely by the zone-center modes, and compare it to a sister compound AlB$_\text{2}$. We further illustrate the usage of this descriptor by screening a large number of binary hydrides, for which accurate first-principle calculations of electron-phonon coupling have been recently published. Together with the RMT descriptor, this method opens a way to perform initial high-throughput screening in search of conventional superconductors via machine learning or data mining.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Feature Learning and Ensemble Pre-Tasks Based Self-Supervised Speech Denoising and Dereverberation

Self-supervised learning (SSL) achieves great success in monaural speech enhancement, while the accuracy of the target speech estimation, particularly for unseen speakers, remains inadequate with existing pre-tasks. As speech signal contains multi-faceted information including speaker identity, paralinguistics, and spoken content, the latent representation for speech enhancement becomes a tough task. In this paper, we study the effectiveness of each feature which is commonly used in speech enhancement and exploit the feature combination in the SSL case. Besides, we propose an ensemble training strategy. The latent representation of the clean speech signal is learned, meanwhile, the dereverberated mask and the estimated ratio mask are exploited to denoise and dereverberate the mixture. The latent representation learning and the masks estimation are considered as two pre-tasks in the training stage. In addition, to study the effectiveness between the pre-tasks, we compare different training routines to train the model and further refine the performance. The NOISEX and DAPS corpora are used to evaluate the efficacy of the proposed method, which also outperforms the state-of-the-art methods.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Heat Conduction Plate Layout Optimization using Physics-driven Convolutional Neural Networks

The layout optimization of the heat conduction is essential during design in engineering, especially for thermal sensible products. When the optimization algorithm iteratively evaluates different loading cases, the traditional numerical simulation methods used usually lead to a substantial computational cost. To effectively reduce the computational effort, data-driven approaches are used to train a surrogate model as a mapping between the prescribed external loads and various geometry. However, the existing model are trained by data-driven methods which requires intensive training samples that from numerical simulations and not really effectively solve the problem. Choosing the steady heat conduction problems as examples, this paper proposes a Physics-driven Convolutional Neural Networks (PD-CNN) method to infer the physical field solutions for random varied loading cases. After that, the Particle Swarm Optimization (PSO) algorithm is used to optimize the sizes and the positions of the hole masks in the prescribed design domain, and the average temperature value of the entire heat conduction field is minimized, and the goal of minimizing heat transfer is achieved. Compared with the existing data-driven approaches, the proposed PD-CNN optimization framework not only predict field solutions that are highly consistent with conventional simulation results, but also generate the solution space with without any pre-obtained training data.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

High-throughput screening of strong electron-phonon couplings in ternary metal diborides

We perform a high-throughput screening on phonon-mediated superconductivity in ternary metal diboride structure with alkali, alkaline earth, and transition metals. We find 17 ground states and 78 low-energy metastable phases. From fast calculations of zone-center electron-phonon coupling, 43 compounds are revealed to show electron-phonon coupling strength higher than that of MgB2. An anti-correlation between energetic stability and electron-phonon coupling strength is identified. We suggest two phases, i.e., Li3ZrB8 and Ca3YB8, to be synthesized, which show reasonable energetic stability and superconducting critical temperature.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

LawBreaker: An Approach for Specifying Traffic Laws and Fuzzing Autonomous Vehicles

Autonomous driving systems (ADSs) must be tested thoroughly before they can be deployed in autonomous vehicles. High-fidelity simulators allow them to be tested against diverse scenarios, including those that are difficult to recreate in real-world testing grounds. While previous approaches have shown that test cases can be generated automatically, they tend to focus on weak oracles (e.g. reaching the destination without collisions) without assessing whether the journey itself was undertaken safely and satisfied the law. In this work, we propose LawBreaker, an automated framework for testing ADSs against real-world traffic laws, which is designed to be compatible with different scenario description languages. LawBreaker provides a rich driver-oriented specification language for describing traffic laws, and a fuzzing engine that searches for different ways of violating them by maximising specification coverage. To evaluate our approach, we implemented it for Apollo+LGSVL and specified the traffic laws of China. LawBreaker was able to find 14 violations of these laws, including 173 test cases that caused accidents.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Model-Based and Graph-Based Priors for Group Testing

The goal of the group testing problem is to identify a set of defective items within a larger set of items, using suitably-designed tests whose outcomes indicate whether any defective item is present. In this paper, we study how the number of tests can be significantly decreased by leveraging the structural dependencies between the items, i.e., by incorporating prior information. To do so, we pursue two different perspectives: (i) As a generalization of the uniform combinatorial prior, we consider the case that the defective set is uniform over a \emph{subset} of all possible sets of a given size, and study how this impacts the information-theoretic limits on the number of tests for approximate recovery; (ii) As a generalization of the i.i.d.~prior, we introduce a new class of priors based on the Ising model, where the associated graph represents interactions between items. We show that this naturally leads to an Integer Quadratic Program decoder, which can be converted to an Integer Linear Program and/or relaxed to a non-integer variant for improved computational complexity, while maintaining strong empirical recovery performance.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Neuromorphic computing using wavelength-division multiplexing

Optical neural networks (ONNs), or optical neuromorphic hardware accelerators, have the potential to dramatically enhance the computing power and energy efficiency of mainstream electronic processors, due to their ultralarge bandwidths of up to 10s of terahertz together with their analog architecture that avoids the need for reading and writing data back and forth. Different multiplexing techniques have been employed to demonstrate ONNs, amongst which wavelength division multiplexing (WDM) techniques make sufficient use of the unique advantages of optics in terms of broad bandwidths. Here, we review recent advances in WDM based ONNs, focusing on methods that use integrated microcombs to implement ONNs. We present results for human image processing using an optical convolution accelerator operating at 11 Tera operations per second. The open challenges and limitations of ONNs that need to be addressed for future applications are also discussed.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Structure and motifs of iron oxides from 1 to 3 TPa

Iron oxides are fundamental components of planet-forming materials. Understanding the Fe-O system's behavior and properties under high pressure can help us identify many new phases and states possible in exoplanetary interiors, especially terrestrial ones. Using the adaptive genetic algorithm (AGA), we investigate the structure of iron oxides for a wide range of stoichiometries ($0.25\leq x_O \leq 0.8$) at 1, 2, and 3 TPa. Five unreported ground-state structures with Fe$_2$O, FeO, Fe$_3$O$_5$, FeO$_2$, and FeO$_4$ compositions are identified. The calculated density of states (DOS) suggests that, except for FeO$_4$, all phases are metallic, but their carrier densities decrease with increasing pressure and oxygen content. The cluster alignment analysis of stable and metastable phases shows that several motifs may co-exist in a structure of iron oxides with low O content. In contrast, most iron oxides with high O content adopt a simple BCC motif at TPa pressures. Our results provide a crystal structure database of iron oxides for modeling and understanding the interiors of exoplanets.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Thermodynamics of spin crossover in ferropericlase: an improved LDA+$U_{sc}$ calculation

We present LDA+$U_{sc}$ calculations of high-spin (HS) and low-spin (LS) states in ferropericlase (fp) with an iron concentration of 18.75$\%$. The Hubbard parameter $U$ is determined self-consistently with structures optimized at arbitrary pressures. We confirm a strong dependence of $U$ on the pressure and spin state. Static calculations confirm that the antiferromagnetic configuration is more stable than the ferromagnetic one in the HS state, consistent with low-temperature measurements. Phonon calculations guarantee the dynamical stability of HS and LS states throughout the pressure range of the Earth mantle. Compression curves for HS and LS states agree well with experiments. Using a non-ideal mixing model for the HS to LS states solid solution, we obtain a crossover starting at $\sim$45 GPa at room temperature and considerably broader than previous results. The spin-crossover phase diagram is calculated, including vibrational, magnetic, electronic, and non-ideal HS-LS entropic contributions. Our results suggest the mixed-spin state predominates in fp in most of the lower mantle.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Two-stage Fall Events Classification with Human Skeleton Data

Fall detection and classification become an imper- ative problem for healthcare applications particularity with the increasingly ageing population. Currently, most of the fall clas- sification algorithms provide binary fall or no-fall classification. For better healthcare, it is thus not enough to do binary fall classification but to extend it to multiple fall events classification. In this work, we utilize the privacy mitigating human skeleton data for multiple fall events classification. The skeleton features are extracted from the original RGB images to not only mitigate the personal privacy, but also to reduce the impact of the dynamic illuminations. The proposed fall events classification method is divided into two stages. In the first stage, the model is trained to achieve the binary classification to filter out the no-fall events. Then, in the second stage, the deep neural network (DNN) model is trained to further classify the five types of fall events. In order to confirm the efficiency of the proposed method, the experiments on the UP-Fall dataset outperform the state-of-the-art.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Two-step nucleation of the Earth's inner core

It has long been assumed the Earth's solid inner core started to grow when molten iron cooled to its melting point. However, the nucleation mechanism, which is a necessary step of crystallization, has not been well understood. Recent studies found it requires an unrealistic degree of undercooling to nucleate the stable hexagonal close-packed (hcp) phase of iron, which can never be reached under the actual Earth's core conditions. This contradiction leads to the inner core nucleation paradox [1]. Here, using a persistent-embryo method and molecular dynamics simulations, we demonstrate that the metastable body-centered cubic (bcc) phase of iron has a much higher nucleation rate than the hcp phase under inner-core conditions. Thus, the bcc nucleation is likely to be the first step of inner core formation instead of direct nucleation of the hcp phase. This mechanism reduces the required undercooling of iron nucleation, which provides a key factor to solve the inner-core nucleation paradox. The two-step nucleation scenario of the inner core also opens a new avenue for understanding the structure and anisotropy of the present inner core.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Validations and Corrections of the SFD and Planck Reddening Maps Based on LAMOST and Gaia Data

Precise correction of dust reddening is fundamental to obtain the intrinsic parameters of celestial objects. The Schlegel et al. (SFD) and the Planck 2D extinction maps are widely used for the reddening correction. In this work, using accurate reddening determinations of about two million stars from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) data release 5 (DR5) and Gaia DR2, we check and calibrate the SFD and Planck maps in the middle and high Galactic latitudes. The maps show similar precision in reddening correction. We find small yet significant spatially dependent biases for the four maps, which are similar between the SFD and Planck2014-R maps, and between the Planck2014-Tau and Planck2019-Tau maps. The biases show a clear dependence on the dust temperature and extinction for the SFD and Planck2014-R maps. While those of the Planck2014-Tau and Planck2019-Tau maps have a weak dependence on the dust temperature, they both strongly depend on the dust spectral index. Finally, we present corrections of the SFD and Planck extinction maps within the LAMOST footprint, along with empirical relations for corrections outside the LAMOST footprint. Our results provide important clues for the further improvement of the Galactic all-sky extinction maps and lay an significant foundation for the accurate extinction correction in the era of precision astronomy.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Description of $^{93}$Nb stellar electron-capture rates by the Projected Shell Model

Capture of electrons by nuclei is an important process in stellar environments where excited nuclear states are thermally populated. However, accurate treatment for excited configurations in electron capture (EC) rates has been an unsolved problem for medium-heavy and heavy nuclei. In this work, we take the $^{93}$Nb $\rightarrow$ $^{93}$Zr EC rates as the example to introduce the Projected-Shell-Model (PSM) in which excited configurations are explicitly included as multi-quasiparticle states. Applying the prevalent assumption that the parent nucleus always stays in its ground state in stellar conditions, we critically compare the obtained PSM results with the recently-measured Gamow-Teller transition data, and with the previous calculations by the conventional shell model and the quasiparticle random-phase approximation. We discuss important ingredients that are required in theoretical models used for stellar EC calculations, and demonstrate effects of the explicit inclusion of excited nuclear states in EC rate calculations, especially when both electron density and environment temperature are high.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Self-Supervised Learning based Monaural Speech Enhancement with Multi-Task Pre-Training

In self-supervised learning, it is challenging to reduce the gap between the enhancement performance on the estimated and target speech signals with existed pre-tasks. In this paper, we propose a multi-task pre-training method to improve the speech enhancement performance with self-supervised learning. Within the pre-training autoencoder (PAE), only a limited set of clean speech signals are required to learn their latent representations. Meanwhile, to solve the limitation of single pre-task, the proposed masking module exploits the dereverberated mask and estimated ratio mask to denoise the mixture as the second pre-task. Different from the PAE, where the target speech signals are estimated, the downstream task autoencoder (DAE) utilizes a large number of unlabeled and unseen reverberant mixtures to generate the estimated mixtures. The trained DAE is shared by the learned representations and masks. Experimental results on a benchmark dataset demonstrate that the proposed method outperforms the state-of-the-art approaches.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Structural Interventions in Networks

Two types of interventions are commonly implemented in networks: characteristic intervention, which influences individuals' intrinsic incentives, and structural intervention, which targets the social links among individuals. In this paper we provide a general framework to evaluate the distinct equilibrium effects of both types of interventions. We identify a hidden equivalence between a structural intervention and an endogenously determined characteristic intervention. Compared with existing approaches in the literature, the perspective from such an equivalence provides several advantages in the analysis of interventions that target network structure. We present a wide range of applications of our theory, including identifying the most wanted criminal(s) in delinquent networks and targeting the key connector for isolated communities.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

A Generic Network Compression Framework for Sequential Recommender Systems

Sequential recommender systems (SRS) have become the key technology in capturing user's dynamic interests and generating high-quality recommendations. Current state-of-the-art sequential recommender models are typically based on a sandwich-structured deep neural network, where one or more middle (hidden) layers are placed between the input embedding layer and output softmax layer. In general, these models require a large number of parameters (such as using a large embedding dimension or a deep network architecture) to obtain their optimal performance. Despite the effectiveness, at some point, further increasing model size may be harder for model deployment in resource-constraint devices, resulting in longer responding time and larger memory footprint. To resolve the issues, we propose a compressed sequential recommendation framework, termed as CpRec, where two generic model shrinking techniques are employed. Specifically, we first propose a block-wise adaptive decomposition to approximate the input and softmax matrices by exploiting the fact that items in SRS obey a long-tailed distribution. To reduce the parameters of the middle layers, we introduce three layer-wise parameter sharing schemes. We instantiate CpRec using deep convolutional neural network with dilated kernels given consideration to both recommendation accuracy and efficiency. By the extensive ablation studies, we demonstrate that the proposed CpRec can achieve up to 4$\sim$8 times compression rates in real-world SRS datasets. Meanwhile, CpRec is faster during training\inference, and in most cases outperforms its uncompressed counterpart.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Fermion dynamical symmetry and strongly-correlated electrons: a comprehensive model of high-temperature superconductivity

We review application of the SU(4) model of strongly-correlated electrons to cuprate and iron-based superconductors. A minimal self-consistent generalization of BCS theory to incorporate antiferromagnetism on an equal footing with pairing and strong Coulomb repulsion is found to account systematically for the major features of high-temperature superconductivity, with microscopic details of the parent compounds entering only parametrically. This provides a systematic procedure to separate essential from peripheral, suggesting that many features exhibited by the high-$T\tsub c$ data set are of interest in their own right but are not central to the superconducting mechanism. More generally, we propose that the surprisingly broad range of conventional and unconventional superconducting and superfluid behavior observed across many fields of physics results from the systematic appearance of similar algebraic structures for the emergent effective Hamiltonians, even though the microscopic Hamiltonians of the corresponding parent states may differ radically from each other.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Intrinsic Color Indices of Early-Type Dwarf Stars

Early-type stars are short lived and scarce in comparison with other types. Based on the recently released catalogs of early type stars from the largest LAMOST spectroscopic survey, the intrinsic colors of the stars with effective temperature up to 32,000\,K are determined for the bands from ultraviolet to infrared by using the blue-edge method. Analytic relations are derived for the intrinsic color index with the effective temperature for the \emph{WISE}, 2MASS, \emph{Gaia}, APASS, SDSS, Pan-STARRS1, and \emph{GALEX} bands. The results are generally consistent with previous works. In addition, the intrinsic colors of O-type dwarfs and OB supergiants are roughly estimated.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

LDA+Usc calculations of phase relations in FeO

Using the LDA+$\textit{U}_\text{sc}$ method, we present calculations phase relations of iron monoxides involving five polytypes in multiple spin-state configurations. The Hubbard parameter $U$ is determined self-consistently simultaneously with the occupation matrix and structures at arbitrary pressures. The Hubbard parameter strongly depends on pressure, structure, and spin state. Comparison with experimental structural data indicates the LDA+$\textit{U}_\text{sc}$ can predict structure, compression curves, phase relations, and transition pressures very well for the insulating B1 and iB8 states. However, it requires additional calculations using the Mermin functional that includes the electronic entropic contribution to the free energy to obtain an nB8 metallic state and a consistent iB8 to nB8 insulator-to-metal transition pressure.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

The Superconducting Critical Temperature

Two principles govern the critical temperature for superconducting transitions: (1)~intrinsic strength of the pair coupling and (2)~effect of the many-body environment on the efficiency of that coupling. Most discussions take into account only the first but we argue that the properties of unconventional superconductors are governed more often by the second, through dynamical symmetry relating normal and superconducting states. Differentiating these effects is essential to charting a path to the highest-temperature superconductors.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Theoretical prediction of a highly responsive material: Spin fluctuations and superconductivity in FeNiB2 system

By analyzing Fe-Ni-B compositional diagram we predict an energetically and dynamically stable FeNiB2 compound. This system belongs to the class of highly responsive state of material, as it is very sensitive to the external perturbations. This state is also characterized by a high level of spin fluctuations which strongly influence possible magnetic long- and short-range orders. Furthermore, we demonstrate that these antiferromagnetically dominating fluctuations could lead to the appearance of spin mediated superconductivity. The obtained results suggest a promising avenue for the search of strong spin fluctuation systems and related superconductors.

0 citations0 reviewsNo code linkedOpen access
preprint2018arXiv

Spatially-correlated Site Occupancy in the Nonstoichiometric Meta-stable ε-Al60Sm11 Phase during Devitrification of Al-10.2 at.% Sm Glasses

A metastable ε-Al60Sm11 phase appears during the initial devitrification of as-quenched Al-10.2 at.% Sm glasses. The ε phase is nonstoichiometric in nature since Al occupation is observed on the 16f Sm lattice sites. Scanning transmission electron microscopic images reveal profound spatial correlation of Sm content on these sites, which cannot be explained by the "average crystal" description from Rietveld analysis of diffraction data. Thermodynamically favorable configurations, established by Monte Carlo (MC) simulations based on a cluster-expansion model, also give qualitatively different correlation functions from experimental observations. On the other hand, molecular dynamics simulations of the growth of ε-Al60Sm11 in undercooled liquid show that when the diffusion range of Sm is limited to ~ 4 Å, the correlation function of the as-grown crystal structure agrees well with that of the STEM images. Our results show that kinetic effects, especially the limited diffusivity of Sm atoms plays the fundamental role in determining the nonstoichiometric site occupancies of the ε-Al60Sm11 phase during the crystallization process.

0 citations0 reviewsNo code linkedOpen access
preprint2017arXiv

A self-contained algorithm for determination of solid-liquid equilibria in an alloy system

We describe a self-contained procedure to evaluate the free energy of liquid and solid phases of an alloy system. The free energy of a single-element solid phase is calculated with thermodynamic integration using the Einstein crystal as the reference system. Then, free energy difference between the solid and liquid phases is calculated by Gibbs-Duhem integration. The central part of our method is the construction of a reversible alchemical path connecting a pure liquid and a liquid alloy to calculate the mixing enthalpy and entropy. We have applied the method to calculate the free energy of solid and liquid phases in the Al-Sm system. The driving force for fcc-Al nucleation in Al-Sm liquid and the melting curve for fcc-Al and Al3Sm are also calculated.

0 citations0 reviewsNo code linkedOpen access