High-throughput First-principles Research Articles

Effect of alloying elements on zinc-induced liquid metal embrittlement in steels: A first-principles study

Liquid metal embrittlement (LME) induced by Zn melt in galvanized steels during spot-welding poses a significant challenge to the advancement of the automotive industry. A critical strategy to address this challenge is identifying promising alloying elements capable of mitigating LME. High-throughput first-principles calculations were employed to investigate the segregation behavior of 22 common alloying elements at the Σ5(310) grain boundary of iron and their impact on the grain boundary energy. The results indicate that volume distortion upon doping of the alloying elements predominantly influences their segregation behavior, while the weak bonding between Zn and Fe is responsible for the grain boundary embrittlement. Exploration of solute interaction between Zn and the other alloying element in the Fe grain boundary model reveals that the attraction between metallic doping elements and Zn correlates with both the elastic effects and the magnetic properties of the dopant, while the repulsion between non-metallic elements and Zn is primarily governed by the elastic effects. Separating the grain boundary strengthening energy into mechanical and chemical contributions shows that they correlate linearly with variations in volume of the doping site and changes in bond energy for bonds associated with the doping site at the grain boundary, respectively. Accordingly, the strengthening effect of the doping elements can be estimated by evaluating the induced changes in local volume and bond energies on grain boundaries. Furthermore, potential alloying elements to mitigate the Zn-induced LME are screened and discussed, with B and Mo being the most promising candidates.

Anisotropic materials with abnormal Poisson's ratios and acoustic velocities.

Isotropic materials are required to adhere to various mechanical principles due to their limited thermal stability. For instance, it is essential for Poisson's ratio to be within the range of -1 to 0.5, and the longitudinal wave velocity must exceed the transverse wave velocity. Nevertheless, perfect crystals, as anisotropic materials, have the ability to defy conventional rules. Through the integration of high-throughput processes and first-principles calculations, a comprehensive exploration of known materials was conducted, resulting in the establishment of a database featuring an extreme anisotropic mechanism. This included the identification of abnormal Poisson's ratios (with the directional Poisson's ratio ranging from -3.00 to 3.67), the discovery of extreme negative linear compressibility, the determination of the upper and lower limits of the sound velocity, and other associated properties. Several materials with abnormal Poisson's ratios (<-1 or >0.5) were listed, and their peculiar mechanical behavior, wherein the volume decreased counterintuitively with uniaxial tension, was discussed. Finally, this study focused on the velocities of longitudinal and transverse waves, with specific emphasis on materials exhibiting transverse wave velocities that exceeded the longitudinal wave velocities.

High-throughput first-principles study on the effect of Ni segregation on the formation of Cr-depleted zone and corrosion resistance of S32205 duplex stainless steels

High-throughput first-principles study on the effect of Ni segregation on the formation of Cr-depleted zone and corrosion resistance of S32205 duplex stainless steels

A substitutional quantum defect in WS2 discovered by high-throughput computational screening and fabricated by site-selective STM manipulation

Point defects in two-dimensional materials are of key interest for quantum information science. However, the parameter space of possible defects is immense, making the identification of high-performance quantum defects very challenging. Here, we perform high-throughput (HT) first-principles computational screening to search for promising quantum defects within WS2, which present localized levels in the band gap that can lead to bright optical transitions in the visible or telecom regime. Our computed database spans more than 700 charged defects formed through substitution on the tungsten or sulfur site. We found that sulfur substitutions enable the most promising quantum defects. We computationally identify the neutral cobalt substitution to sulfur (CoS0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${}_{{{{{{{{\\rm{S}}}}}}}}}^{0}$$\\end{document}) and fabricate it with scanning tunneling microscopy (STM). The CoS0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${}_{{{{{{{{\\rm{S}}}}}}}}}^{0}$$\\end{document} electronic structure measured by STM agrees with first principles and showcases an attractive quantum defect. Our work shows how HT computational screening and nanoscale synthesis routes can be combined to design promising quantum defects.

Theoretical Study of the Competition Mechanism of Alloying Elements in L12-(Nix1Crx2Cox3)3Al Precipitates

The impact of variations in the content of single alloying element on the properties of alloy materials has been extensively discussed, but the influence of this change on the content of multiple alloying elements in the alloy materials has been disregarded, as the performances of alloy materials should be determined by the collective influence of multiple alloying elements. To address the aforementioned issue, the present study conducted a comprehensive investigation into the impact of variations in the content of alloying elements, namely Ni, Cr, and Co, on the structural and mechanical properties of L12-(Nix1Crx2Cox3)3Al precipitates using the high-throughput first-principles calculations and the partial least squares (PLS) regression, and the competitive mechanism among these three elements was elucidated. The findings demonstrate that the same alloying element may exhibit opposite effects in both single element analysis and comprehensive multi-element analysis, for example, the effect of Ni element on elastic constant C11, and the influence of Cr element on Vickers hardness and yield strength. The reason for this is that the impact of the content of other two alloying elements is ignored in the single element analysis. Meanwhile, the Co element demonstrates a significant competitive advantage in the comparative analysis of three alloying elements for different physical properties. Therefore, the methodology proposed in this study will facilitate the elucidation of competition mechanisms among different alloy elements and offer a more robust guidance for experimental preparation.

Band Alignment of Oxides by Learnable Structural-Descriptor-Aided Neural Network and Transfer Learning.

The band alignment of semiconductors, insulators, and dielectrics is relevant to diverse material properties and device structures utilizing their surfaces and interfaces. In particular, the ionization potential and electron affinity are fundamental quantities that describe surface-dependent band-edge positions with respect to the vacuum level. Their accurate and systematic determination, however, demands elaborate experiments or simulations for well-characterized surfaces. Here, we report machine learning for the band alignment of nonmetallic oxides using a high-throughput first-principles calculation data set containing about 3000 oxide surfaces. Our neural network accurately predicts the band positions for relaxed surfaces of binary oxides simply by using the information on bulk structures and surface termination planes. Moreover, we extend the model to naturally include multiple-cation effects and transfer it to ternary oxides. The present approach enables the band alignment of a vast number of solid surfaces, thereby opening the way to a systematic understanding and materials screening.

Chemical-substitution-driven giant anomalous Hall and Nernst effects in magnetic cubic Heusler compounds

Chemical substitution efficiently optimizes the physical properties of Heusler compounds, especially their anomalous transport properties, including anomalous Hall conductivity (AHC) and anomalous Nernst conductivity (ANC). This study systematically investigates the effect of chemical substitution on AHC and ANC in 1493 magnetic cubic Heusler compounds using high-throughput first-principles calculations. Notable trends emerge in Co- and Rh-based compounds, where chemical substitution effectively enhances the AHC and ANC. Intriguingly, certain chemically substituted candidates exhibit outstanding enhancement in AHCs and ANCs, such as (Co0.8Ni0.2)2FeSn with considerable AHC and ANC values of −2567.78 Scm−1 and 8.27 Am−1K−1, respectively, and (Rh0.8Ru0.2)2MnIn with an AHC of 1950.49 Scm−1. In particular, an extraordinary ANC of 8.57 Am−1K−1 is identified exclusively in Rh2Co0.7Fe0.3In, nearly double the maximum value of 4.36 Am−1K−1 observed in the stoichiometric Rh2CoIn. A comprehensive band structure analysis underscores that the notable enhancement in ANC arises from the creation and modification of the energy-dependent nodal lines through chemical substitution. This mechanism generates a robust Berry curvature, resulting in significant ANCs. These findings emphasize the pivotal role of chemical substitution in engineering high-performance materials, thereby expanding the horizons of transport property optimization within Heusler compounds.

Tuning the Surface Stability and Li/Na Storage of MXenes by Controlling the Surface Termination Coverage.

2D transition metal carbides and/or nitrides, MXenes, are a class of widely studied materials with great potential for energy storage applications. The control of surface chemistry is an effective approach for preparing novel MXenes and modifying their electrochemical properties. However, an in-depth and systematic atomic-scale study of the effect of surface termination on MXene stability and electrochemical performance is scarce and thus is highly desired. Here, through high-throughput first-principles calculations, 28 stable chalcogen-functionalized M2 CTz (M = V, Nb, and Ta, T = S, Se, and Te) under different chemical environments are identified. The reduction of termination coverage improves electrical conductivity but weakens in-plane stiffness. Intriguingly, based on charge transfer mechanism, the diffusion barrier of lithium/sodium atoms on the M2 CTz exhibits a volcano-like relationship with termination coverage, and the ion diffusion channel formed in half termination coverage greatly accelerates lithium ion diffusion and returns to or exceeds sodium ion diffusion rate at full termination coverage. V2 CSe2 /Nb2 CSz not only displays the large lithium/sodium capacity (592/409-466 mAhg-1 ) but also exhibits low barrier energy and open-circuit voltage, suggesting a promising candidate anode material for lithium/sodium-ion batteries. These findings provide insights into the design and fabrication of MXenes and tuning the electrochemical performance of MXenes by controlling termination coverage.

Tunable magnetic anisotropy of Os-Ru dimer on 2D transition metal chalcogenides substrates

The precise manipulation of atoms enables the creation of distinct materials from the bottom up to construct devices with breakthrough performance, especially in the field of quantum technologies. A large magnetic anisotropy energy (MAE) is important to realize bit storage of information in magnetic memory devices. As the smallest magnetic nanostructure, substrate-supported transition metal dimers are potential atomic-scale storage medium to obtain large MAEs. Using high-throughput first-principles calculations, we have performed a systematic investigation of the MAE of 76 heterodimensional systems consisting of zero-dimensional Os-Ru dimer and experimentally synthesized two-dimensional transition metal dichalcogenides (TMDs). Huge MAEs in the range of 102.09–247.69 meV were found in 13 of these heterodimensional systems. In particular, the Os-Ru@T-ZrSe2 with the largest MAE of 247.69 meV corresponds to a theoretical blocking temperature (67 K) in terms of a relaxation time of 10 years and a storage density of 281 Tb·inch−2. The underlying mechanism for the significant enhancement of MAE is attributed to the rearrangement of the in-plane molecular orbitals near the Fermi level, which is closed relative to the electron transfer capability between the Os-Ru dimer and the TMD substrates. In addition, we have also constructed a heat map for TMD-supported Os-Ru dimer, showing the degree of correlation between MAEs and feature descriptors. Our work not only suggests an effective way to improve MAE of transition metal dimers but also extracts relatively simple rules for substrate selection.

Lead-Free Double Perovskites: A Review of the Structural, Optoelectronic, Mechanical, and Thermoelectric Properties Derived from First-Principles Calculations, and Materials Design Applicable for Pedagogical Purposes

Metal halide perovskite materials have shown significant advancements in their application as light absorbers in perovskite solar cells, with power conversion efficiencies reaching 27%. However, lead-based perovskites pose a concern due to their toxicity and stability issues in moisture, UV radiation, and heat. This has led to a pressing need to explore substitute materials that do not contain lead but maintain the remarkable characteristics of lead-based perovskites. This review article focuses on halide double perovskites characterised by the A2B’B”X6 composition, highlighting their structural, optical, thermoelectric, and mechanical capabilities. Additionally, the review evaluates several materials databases to investigate materials suitable for high-throughput first-principles calculations integrated inside density functional theory. The review aims to identify novel perovskite materials, offer a thorough evaluation of the potential benefits and drawbacks associated with this class of materials, and, from the pedagogical standpoint, discover effective instructional frameworks.

Superalkali halide perovskites with suitable direct band gaps for photovoltaic applications.

The construction of superalkali halide perovskites has attracted attention for the development of new photovoltaic materials, but stable superalkalis have not been found until now. Herein, to construct new three-dimensional superalkali halide perovskites with a MI3 frame (M = Sn and Pb), a new Li(H2O)3+ superalkali cation is designed and selected based on low vertical ionization potential, suitable tolerance factor, small ionic radius and large dissociation energy. High-throughput first-principles calculations show that superalkalis with lower vertical ionization potentials exhibit stronger interactions with the MI3 frame. The normal and cubic Li(H2O)3MI3 perovskites and cubic Li(H2O)4PbI3 perovskites have direct band gaps, s-p and p-p electron transitions, effective carrier masses of less than 0.45me and exciton binding energies of less than 291 meV. Moreover, the cubic Li(H2O)3PbI3 perovskite with a direct band gap of 1.40 eV can in theory show a power conversion efficiency of 33.49%. These results strongly suggest that superalkali cations with large dissociation energy can be used to develop stable superalkali perovskites for photovoltaic applications.

Transition Metal Functionalized C30N12S6 as High-Performance Trifunctional Catalysts with Integrated Descriptors toward Hydrogen Evolution, Oxygen Evolution, and Oxygen Reduction Reactions: A Case of High-Throughput First-Principles Screening within the Framework of TM-N2@C30N10S6.

In energy conversion and storage technologies, the design of highly efficient trifunctional electrocatalysts integrating with the high hydrogen evolution reaction (HER) and oxygen evolution/reduction reaction (OER/ORR) activities is highly desirable. Herein, utilizing first-principles computations, a novel periodically ordered macropore C30N12S6 monolayer was proposed, and the stability analysis attests to its good stability. Single transition metal (TM) atom anchored onto this newly proposed C30N12S6 monolayer to form single-atom catalysts, as achieved by TM-N2@C30N10S6, among which the Co-N2@C30N10S6 is the most promising multifunctional catalyst toward HER/OER/ORR with low overpotential of 0.01/0.59/0.3 V; meanwhile, the Rh-N2@C30N10S6 can be used as a bifunctional OER/ORR catalyst with low overpotential of 0.37/0.44 V, overmatching the landmark Pt (111) and IrO2/RuO2 catalysts. Particularly, the TM-d orbital in TM@CNS is remarkably hybridized with the O-p orbital of oxygenated intermediates, so that the lone electrons initially located at the antibonding orbital pair up and fill the downward bonding orbital, allowing OH* to be suitably adsorbed on TM@CNS, enhancing the catalytic performance. The relevant attributes, such as good stabilities and metallic features, ensured their applications in ambient conditions. Moreover, multilevel descriptors were constructed to clarify the origin of activity on TM@CNS, such as ΔGOH* (Gibbs free energy of OH*), εd (d-band center), COHP (crystal orbital Hamilton population), Nd/Nd+s (number of d/d + s electrons) and φ (descriptor), among which the filling of outer d-electrons of TM atom significantly affects the value of ΔGOH* that can determine the overpotential and, thus, become a key descriptor.

Exploring magnetism of lead-free halide double perovskites: A high-throughput first-principles study

Exploring magnetism of lead-free halide double perovskites: A high-throughput first-principles study

Advancing thermoelectric materials discovery through semi-supervised learning and high-throughput calculations

The data-driven machine learning technique is widely used to assist in accelerating the design of thermoelectric materials. In this study, we proposed a positive and unlabeled learning (PU learning) method, a semi-supervised learning, to train a classifier to distinguish the positive samples from the unlabeled samples, in which the positive class was labeled by matching the formulas in our dataset with the published article titles. The probabilities that the unlabeled materials belong to the positive class were predicted by PU learning, and 40 candidate thermoelectric materials were determined. The transport properties were calculated by high-throughput first-principles calculations, among which 8 p-type and 12 n-type materials have the maximum theoretical zT values greater than 1. Specifically, a series of AX2 binary compounds, (Cd/Zn)(GaTe2)2 ternary compounds, and Cs(Dy/Ho/Tb)2Ag3Te5 quaternary compounds deserve further investigations in the future.

Doping engineering of monolayer MSe (M = Ga, In) by high-throughput first-principles calculations

Monolayer gallium selenide (GaSe) and indium selenide (InSe) have demonstrated promise in applications of ultra-thin electronics and optoelectronics. Based on high-throughput first-principles calculations, we investigate the stability and electronic and magnetic properties of substitutionally doped monolayer GaSe and InSe with the aim of broadening their application prospects. Most non-metal dopants from Group A can only be p-type carriers, whereas metals can be both p- and n-type carriers. In addition, considerable magnetism is predicted in GaSe doped with transition metals from periods four to six in groups ⅣB–VIIIB10, with relatively high binding energies, indicating high potential for spintronic applications. Our findings provide theoretical support for experimentally extending the utilization of metal chalcogenides, such as GaSe and InSe, in spintronic, electronic and optoelectronic applications.

Systematic Search for Stabilizing Dopants in ZrO₂ and HfO₂ Using First-Principles Calculations

In this study, we performed a systematic search for dopants to stabilize the tetragonal structure of HfO2 and ZrO2 by using high-throughput first-principles calculations. By exhaustively exploring all possible impurity configurations for over 12,000 systems, we obtained the expected most stable doping status for various dopants at different doping levels. The stabilization effect of dopants are investigated and compared. The results show that Si or Ge significantly stabilize the tetragonal phase.

Double transition metal MBenes: A family of 2D MXenes with tunable magnetic properties

The emergence of two-dimensional (2D) magnetic materials is critical to the development of nanoscale spintronic devices. However, the lack of intrinsic magnetism limits their applications. In this work, we propose a universal strategy to develop experimentally feasible 2D MBenes with intrinsic magnetism. Using high-throughput first-principles calculations, 546 kinds of ordered double transition metal MBenes with chemical formula (M′2/3M″1/3)2B2 (M′ and M″ = Sc, Y, Ti, Zr, Hf, V, Nb, Ta, Cr, Mo, W, Mn, Fe, and Co) were developed and studied in detail. Among them, we have identified 21 (M′2/3M″1/3)2B2 with robust magnetic ordering and good structural stability, including 15 ferromagnetic metals, 1 ferromagnetic half-metal, 3 antiferromagnetic metals, and 2 antiferromagnetic semiconductors. Remarkably, (Cr2/3Zr1/3)2B2 is a ferromagnetic half-metal with a high Curie temperature of 610 K and a strong out-plane magnetic anisotropy energy of 305 μeV per Cr atom. (Mn2/3Sc1/3)2B2 and (Fe2/3Nb1/3)2B2 are antiferromagnetic semiconductors with Néel temperatures of 257 and 462 K, respectively. These excellent magnetic properties make the double transition metal MBenes desirable for 2D spintronics.

Exploration of Solid Solutions and the Strengthening of Aluminum Substrates by Alloying Atoms: Machine Learning Accelerated Density Functional Theory Calculations.

In this paper, we studied the effects of a series of alloying atoms on the stability and micromechanical properties of aluminum alloy using a machine learning accelerated first-principles approach. In our preliminary work, high-throughput first-principles calculations were explored and the solution energy and theoretical stress of atomically doped aluminum substrates were extracted as basic data. By comparing five different algorithms, we found that the Catboost model had the lowest RMSE (0.24) and lowest MAPE (6.34), and this was used as the final prediction model to predict the solid solution strengthening of the aluminum matrix by the elements. Calculations show that alloying atoms such as K, Na, Y and Tl are difficult to dissolve in the aluminum matrix, whereas alloy atoms like Sc, Cu, B, Zr, Ni, Ti, Nb, V, Cr, Mn, Mo, and W exerted a strengthening influence. Theoretical studies on solid solutions and the strengthening effect of various alloy atoms in an aluminum matrix can offer theoretical guidance for the subsequent selection of suitable alloy elements. The theoretical investigation of alloy atoms in an aluminum matrix unveils the fundamental aspects of the solution strengthening effect, contributing significantly to the expedited development of new aluminum alloys.

High-throughput identification of spin-photon interfaces in silicon.

Color centers in host semiconductors are prime candidates as spin-photon interfaces for quantum applications. Finding an optimal spin-photon interface in silicon would move quantum information technologies toward a mature semiconducting host. However, the space of possible charged defects is vast, making the identification of candidates from experiments alone extremely challenging. Here, we use high-throughput first-principles computational screening to identify spin-photon interfaces among more than 1000 charged defects in silicon. The use of a single-shot hybrid functional approach is critical in enabling the screening of many quantum defects with a reasonable accuracy. We identify three promising spin-photon interfaces as potential bright emitters in the telecom band: [Formula: see text], [Formula: see text], and [Formula: see text]. These candidates are excited through defect-bound excitons, stressing the importance of such defects in silicon for telecom band operations. Our work paves the way to further large-scale computational screening for quantum defects in semiconductors.

Machine Learning-Aided High-Throughput First-Principles Calculations to Predict the Formation Energy of μ Phase.

The μ phase is a type of hard and brittle constituent that exists in high-temperature alloys. The formation energy is a crucial thermochemical datum, and the accurate calculation of the formation energy of the μ phase contributes to the material design of high-temperature alloys. Traditional first-principles calculations demand significant computational time and resources. In this study, an innovative machine learning (ML)-based approach to accurately predict the formation energy of the μ phase is proposed. This approach involves the utilization of six algorithms and two model evaluation methods to construct the ML models. Leveraging a comprehensive data set containing 1036 binary configurations of the μ phase, the model trained using a 10-fold cross-validation technique, and the multilayer perceptron (MLP) algorithm achieves a mean absolute error (MAE) of 23.906 meV/atom. To validate its generalization performance, the trained model is further validated on 900 ternary configurations, resulting in an MAE of 32.754 meV/atom. Compared with solely using traditional first-principles calculations, our approach significantly reduces the computational time by at least 52%. Moreover, the ML model exhibits exceptional accuracy in predicting the lattice parameters of the μ phase. The MAE values for the a and c parameters are 0.024 and 0.214 Å, respectively, corresponding to low error rates of only 0.479 and 0.578%. Additionally, the ML model was utilized to accurately predict the formation energy of all of the possible ternary configurations. To enhance accessibility to the formation energy data of the μ phase, a user-friendly graphical user interface (GUI) was developed, ensuring convenient usability for researchers and practitioners.