Electrical conductivity from first principles

Die zuverlässige Berechnung der elektrischen Leitfähigkeit vieler Materialien aus ersten Prinzipien erfordert die Berücksichtigung der anharmonischen Gitterdynamik. Der ab initio Kubo-Greenwood (KG)-Ansatz, der die KG-Leitfähigkeitsformel und die ab initio-Molekulardynamik kombiniert, scheint vielversprechend zu sein, da er die Anharmonizität des Gitters auf natürliche Weise berücksichtigt. Seine Anwendung auf kristalline Materialien hat jedoch bisher nur wenig Beachtung gefunden. Diese Arbeit beschreibt den KG-Ansatz und stellt eine numerische Implementierung dieses Ansatzes für den harmonischen Kristall Si und den anharmonischen Kristall SnSe vor. Die Fallstudie für Si zeigt erhebliche numerische Schwierigkeiten bei den KG-Berechnungen auf. Insbesondere behindert die erforderliche dichte k-Punkt-Abtastung die Konvergenz in Superzellengröße und macht die Berechnungen nur innerhalb der (semi-)lokalen Dichtefunktionaltheorie (DFT) durchführbar. Außerdem führt die notwendige Einführung eines Verbreiterungsparameters (η) zu einer erheblichen Unsicherheit bei der Bestimmung der Leitfähigkeit. Um diese Probleme zu lösen, werden rechnerisch effiziente Strategien diskutiert, darunter: (i) der "Scherenoperator"-Ansatz zur Korrektur des DFT-Bandlückenproblems; (ii) das "Optimal-η-Schema" zur Wahl eines geeigneten Wertes von η; und (iii) die Finite-Size-Scaling-Methode zur Ableitung der Leitfähigkeit in der thermodynamischen Grenze. Es wird festgestellt, dass die KG-Berechnungen mit diesen Strategien Leitfähigkeiten in angemessener Übereinstimmung mit den Experimenten ergeben. Der Vergleich mit früheren ab initio Boltzmann-Transportberechnungen zeigt jedoch, dass das η-Problem und die Frage der Konvergenz in Superzellengröße weiter verbesserte Konzepte erfordern. Die Fallstudie für SnSe zeigt sehr ähnliche numerische Schwierigkeiten wie im Fall von Si. Es werden Einblicke in die Auswirkung der Anharmonizität auf die Konvergenz der Superzellengröße gegeben.Reliable first-principles calculation of the electrical conductivity in many materials requires accounting for the anharmonic lattice dynamics. The ab initio Kubo-Greenwood (KG) approach, which combines the KG conductivity formula and ab initio molecular dynamics, appears to be promising because it naturally includes lattice anharmonicity. However, its application to crystalline materials has so far received very little attention. This thesis describes the KG approach and presents a numerical implementation of this approach for the harmonic crystal Si and the anharmonic crystal SnSe. The case study for Si identifies considerable numerical difficulties in the KG calculations. In particular, the dense k-point sampling required hinders supercell-size convergence and makes the calculations only feasible within (semi)local density-functional theory (DFT). Besides, the necessary introduction of a broadening parameter (η) introduces a significant uncertainty in determining the conductivity. To address these issues, computationally efficient strategies are discussed, including: (i) the "scissor operator" approach to correct the DFT band-gap problem; (ii) the "optimal-η scheme" to choose an appropriate value of η; and (iii) the finite-size scaling method to deduce the conductivity in the thermodynamic limit. It is found that with these strategies, the KG calculations yield conductivities in reasonable agreement with experiment. Yet, comparison with previous ab initio Boltzmann transport calculations shows that the η problem and the issue of supercell-size convergence still require improved concepts. The case study for SnSe shows very similar numerical difficulties as in the case of Si. Insights into the effect of anharmonicity on the supercell-size convergence are provided

First-principles study of intrinsic and hydrogen point defects in the earth-abundant photovoltaic absorber Zn3P2

Zinc phosphide (Zn3P2) has had a long history of scientific interest largely because of its potential for earth-abundant photovoltaics. To realize high-efficiency Zn3P2 solar cells, it is critical to understand and control point defects in this material. Using hybrid functional calculations, we assess the energetics and electronic behavior of intrinsic point defects and hydrogen impurities in Zn3P2. All intrinsic defects are found to act as compensating centers in p-type Zn3P2 and have deep levels in the band gap, except for zinc vacancies which are shallow acceptors and can act as a source of doping. Our work highlights that zinc vacancies rather than phosphorus interstitials are likely to be the main source of p-type doping in as-grown Zn3P2. We also show that Zn-poor and P-rich growth conditions, which are usually used for enhancing p-type conductivity of Zn3P2, will facilitate the formation of certain deep-level defects (P_Zn and P_i) which might be detrimental to solar cell efficiency. For hydrogen impurities, which are frequently present in the growth environment of Zn3P2, we study interstitial hydrogen and hydrogen complexes with vacancies. The results suggest small but beneficial effects of hydrogen on the electrical properties of Zn3P2

Multiagent Bidirectionally-Coordinated Nets: Emergence of Human-level Coordination in Learning to Play StarCraft Combat Games

Many artificial intelligence (AI) applications often require multiple intelligent agents to work in a collaborative effort. Efficient learning for intra-agent communication and coordination is an indispensable step towards general AI. In this paper, we take StarCraft combat game as a case study, where the task is to coordinate multiple agents as a team to defeat their enemies. To maintain a scalable yet effective communication protocol, we introduce a Multiagent Bidirectionally-Coordinated Network (BiCNet ['bIknet]) with a vectorised extension of actor-critic formulation. We show that BiCNet can handle different types of combats with arbitrary numbers of AI agents for both sides. Our analysis demonstrates that without any supervisions such as human demonstrations or labelled data, BiCNet could learn various types of advanced coordination strategies that have been commonly used by experienced game players. In our experiments, we evaluate our approach against multiple baselines under different scenarios; it shows state-of-the-art performance, and possesses potential values for large-scale real-world applications.Comment: 10 pages, 10 figures. Previously as title: "Multiagent Bidirectionally-Coordinated Nets for Learning to Play StarCraft Combat Games", Mar 201

Large Language Model for Multi-objective Evolutionary Optimization

Multiobjective evolutionary algorithms (MOEAs) are major methods for solving multiobjective optimization problems (MOPs). Many MOEAs have been proposed in the past decades, of which the search operators need a carefully handcrafted design with domain knowledge. Recently, some attempts have been made to replace the manually designed operators in MOEAs with learning-based operators (e.g., neural network models). However, much effort is still required for designing and training such models, and the learned operators might not generalize well on new problems. To tackle the above challenges, this work investigates a novel approach that leverages the powerful large language model (LLM) to design MOEA operators. With proper prompt engineering, we successfully let a general LLM serve as a black-box search operator for decomposition-based MOEA (MOEA/D) in a zero-shot manner. In addition, by learning from the LLM behavior, we further design an explicit white-box operator with randomness and propose a new version of decomposition-based MOEA, termed MOEA/D-LO. Experimental studies on different test benchmarks show that our proposed method can achieve competitive performance with widely used MOEAs. It is also promising to see the operator only learned from a few instances can have robust generalization performance on unseen problems with quite different patterns and settings. The results reveal the potential benefits of using pre-trained LLMs in the design of MOEAs

Multicharged optical vortices induced in a dissipative atomic vapor system

We investigate numerically the dynamics of optical vortex beams carrying different topological charges, launched in a dissipative three level ladder type nonlinear atomic vapor. We impose the electromagnetically induced transparency (EIT) condition on the medium. Linear, cubic, and quintic susceptibilities, considered simultaneously with the dressing effect, are included in the analysis. Generally, the beams slowly expand during propagation and new vortices are induced, commonly appearing in oppositely charged pairs. We demonstrate that not only the form and the topological charge of the incident beam, but also its growing size in the medium greatly affect the formation and evolution of vortices. We formulate common rules for finding the number of induced vortices and the corresponding rotation directions, stemming from the initial conditions of various incident beams, as well as from the dynamical aspects of their propagation. The net topological charge of the vortex is conserved during propagation, as it should be, but the total number of charges is not necessarily same as the initial number, because of the complex nature of the system. When the EIT condition is lifted, an enhancement region of beam dynamics if reached, in which the dynamics and the expansion of the beam greatly accelerate. In the end, we discuss the liquid like behavior of light evolution in this dissipative system and propose a potential experimental scheme for observing such a behavior.Comment: 9 pages, 6 figures, 3 table

Analysis of necroptosis-related prognostic genes and immune infiltration in idiopathic pulmonary fibrosis

BackgroundIPF is an undetermined, progressive lung disease. Necroptosis is a type of programmed apoptosis, which involved in the pathogenesis of lung diseases like COPD and ARDS. However, necroptosis in IPF have not been adequately studied. This study aimed to investigate the necroptosis in IPF and the relationship between necroptosis and immune infiltration, to construct a prognostic prediction model of IPF based on necroptosis-related genes.MethodsGSE110147 was downloaded from the GEO database and utilized to analyze the expression of necroptosis-related differentially expressed genes (NRDEGs). Then NRDEGs were used to construct protein-protein interaction (PPI) networks in the STRING database, and Cytoscape software was used to identify and visualize hub genes. Necroptosis-related prognosticgenes were explored in GSE70866, and a prognostic prediction model was constructed. The ImmuCellAI algorithm was utilized to analyze the landscape of immune infiltration in GSE110147. The single-cell RNA sequencing dataset GSE122960 was used to explore the association between necroptosis and type II alveolar epithelial cells (AT II) in IPF. The GSE213001 and GSE93606 were used for external validation. The expression of prognostic genes was quantified using RT-qPCRin the IPF A549 cell model, and was further verified by western blotting in the bleomycin-induced pulmonary fibrosis mouse model.ResultsIt was observed that necroptosis-related signaling pathways were abundantly enriched in IPF. 29 NRDEGs were screened, of which 12 showed consistent expression trends in GSE213001. Spearman correlation analysis showed that the expression of NRDEGs was positively correlated with the infiltration of proinflammatory immune cells, and negatively correlated with the infiltration of anti-inflammatory immune cells. NRDEGs, including MLKL, were highly expressed in AT II of fibrotic lung tissue. A necroptosis-related prediction model was constructed based on 4 NRDEGsby the cox stepwise regression. In the validation dataset GSE93606, the prognostic prediction model showed good applicability. The verification results of RT-qPCR and western blotting showed the reliability of most of the conclusions.ConclusionsThis study revealed that necroptosis existed in IPF and might occur in AT II. Necroptosis was associated with immune infiltration, suggesting that necroptosis of AT II might involve in IPF by activating immune infiltration and immune response

Discovery of the Zintl-phosphide BaCd2_{2}P2_{2} as a long carrier lifetime and stable solar absorber

Thin-film photovoltaics offers a path to significantly decarbonize our energy production. Unfortunately, current materials commercialized or under development as thin-film solar cell absorbers are far from optimal as they show either low power conversion efficiency or issues with earth-abundance and stability. Entirely new and disruptive materials platforms are rarely discovered as the search for new solar absorbers is traditionally slow and serendipitous. Here, we use first principles high-throughput screening to accelerate this process. We identify new solar absorbers among known inorganic compounds using considerations on band gap, carrier transport, optical absorption but also on intrinsic defects which can strongly limit the carrier lifetime and ultimately the solar cell efficiency. Screening about 40,000 materials, we discover the Zintl-phosphide BaCd$_{2}$P$_{2}$ as a potential high-efficiency solar absorber. Follow-up experimental work confirms the predicted promises of BaCd$_{2}$P$_{2}$ highlighting an optimal band gap for visible absorption, bright photoluminescence, and long carrier lifetime of up to 30 ns even for unoptimized powder samples. Importantly, BaCd$_{2}$P$_{2}$ does not contain any critical elements and is highly stable in air and water. Our work opens an avenue for a new family of stable, earth-abundant, high-performance Zintl-based solar absorbers. It also demonstrates how recent advances in first principles computation can accelerate the search of photovoltaic materials by combining high-throughput screening with experiment