High-throughput Calculations Research Articles

Surface Engineering of the Mechanical Properties of Molecular Crystals via an Atomistic Model for Computing the Facet Stress Response of Solids.

Dynamic molecular crystals are an emerging class of crystalline materials that can respond to mechanical stress by dissipating internal strain in a number of ways. Given the serendipitous nature of the discovery of such crystals, progress in the field requires advances in computational methods for the accurate and high-throughput computation of the nanomechanical properties of crystals on specific facets which are exposed to mechanical stress. Here, we develop and apply a new atomistic model for computing the surface elastic moduli of crystals on any set of facets of interest using dispersion-corrected density functional theory (DFT-D) methods. The model was benchmarked against a total of 24 reported nanoindentation measurements from a diverse set of molecular crystals and was found to be generally reliable. Using only the experimental crystal structure of the dietary supplement, L-aspartic acid, the model was subsequently applied under blind test conditions, to correctly predict the growth morphology, facet and nanomechanical properties of L-aspartic acid to within the accuracy of the measured elastic stiffness of the crystal, 24.53±0.56 GPa. This work paves the way for the computational design and experimental realization of other functional molecular crystals with tailor-made mechanical properties.

Screening of low-friction two-dimensional materials from high-throughput calculations using lubricating figure of merit

Two-dimensional materials are excellent lubricants with inherent advantages. However, superlubricity has been reported for only a few of these materials. Unfortunately, other promising two-dimentional (2D) materials with different physical properties cannot be discovered or applied in production; thus, energy consumption can be greatly reduced. Here, we carry out high-throughput calculations for 1,475 2D materials and screen for low-friction materials. To set a standard, we propose, for the first time, a geometry-independent lubricating figure of merit based on the conditions for stick-slip transition and our theory of Moiré friction. For the efficient calculation of this figure of merit, an innovative approach was developed based on an improved registry index model. Through calculations, 340 materials were found to have a figure of merit lower than 10−3. Eventually, a small set of 21 materials with a figure of merit lower than 10−4 were screened out. These materials can provide diverse choices for various applications. In addition, the efficient computational approach demonstrated in this work can be used to study other stacking-dependent properties.

Na in diamond: high spin defects revealed by the ADAQ high-throughput computational database

Color centers in diamond are at the forefront of the second quantum revolution. A handful of defects are in use, and finding ones with all the desired properties for quantum applications is arduous. By using high-throughput calculations, we screen 21,607 defects in diamond and collect the results in the ADAQ database. Upon exploring this database, we find not only the known defects but also several unexplored defects. Specifically, defects containing sodium stand out as particularly relevant because of their high spins and predicted improved optical properties compared to the NV center. Hence, we studied these in detail, employing high-accuracy theoretical calculations. The single sodium substitutional (NaC) has various charge states with spin ranging from 0.5 to 1.5, ZPL in the near-infrared, and a high Debye-Waller factor, making it ideal for biological quantum applications. The sodium vacancy (NaV) has a ZPL in the visible region and a potential rare spin-2 ground state. Our results show sodium implantation yields many interesting spin defects that are valuable additions to the arsenal of point defects in diamond studied for quantum applications.

Charge govern ion-selective mechanism of YX2 desalination pore using high-throughput computations and machine learning

Energy-efficient and low-cost desalination of water continues to be a problem that society is facing. With the advances in nanotechnology, a variety of nanopore membranes have emerged for desalination. In particular, YX2 nanopores (YW, Mo, etc. and X = S, Se, Te, etc.) have been considered as promising membranes for water purification. In this work, we used a combination of high-throughput molecular dynamics simulations and machine learning to reveal the nanopore mass transfer mechanism and rationally design the YX2 nanopore. The pore size and the charge of the pore mouth were found to be the main factors affecting water permeation and salt rejection, as discovered through the machine learning study. The physical-chemical analysis provides us with a deep understanding of the charge-governed mechanism on the desalination performance of YX2 nanopores. It revealed that YX2 nanopores with a conical shape and minimized pore charges could be ideal candidates for excellent desalination performance. Based on this, we designed the YX2 pore with a specific charge distribution, which could achieve high water permeation with high salt rejection. Therefore, the combination of machine learning and high-throughput computation methods could aid in designing materials with excellent performance for desalination.

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.

Active learning driven discovery of novel alloyed catalysts for selective ammonia oxidation

Alloy catalysts design faces the contradiction of small sample and huge screen space, especially in rational design with multi-objective and multi-constraints. Here, we conduct the active learning enabled innovative catalyst screening in alloy space composed of transition metal elements. First, the small dataset and large search space are constructed based on knowledge informed feature group. Then, the surrogate model based on Gaussian process regression are trained for predicting catalytic activity and N2 selectivity descriptors for selective catalytic oxidation of ammonia (NH3-SCO) process, merely based on the small dataset. Furthermore, coupling pareto solution and Bayesian optimization, the active learning driven material searching are performed in the huge alloy spaces with more than 1000 configurations. After 3 iterations with high throughput calculation on 30 alloyed configurations and further theoretical validation based on microkinetic and stability analysis, 9 ensemble configurations are considered as innovative alloy catalysts of NH3-SCO with reactivity and selectivity trade-off. Our study provides the reliable framework for catalyst design with multi-objective and multi-constraints when facing with small sample data and huge searching space and gives the specific guidance for rational design of NH3−SCO alloy catalysts.

Differentiable and accelerated spherical harmonic and Wigner transforms

Many areas of science and engineering encounter data defined on spherical manifolds. Modelling and analysis of spherical data often necessitates spherical harmonic transforms, at high degrees, and increasingly requires efficient computation of gradients for machine learning or other differentiable programming tasks. We develop novel algorithmic structures for accelerated and differentiable computation of generalised Fourier transforms on the sphere S2 and rotation group SO(3), i.e. spherical harmonic and Wigner transforms, respectively. We present a recursive algorithm for the calculation of Wigner d-functions that is both stable to high harmonic degrees and extremely parallelisable. By tightly coupling this with separable spherical transforms, we obtain algorithms that exhibit an extremely parallelisable structure that is well-suited for the high throughput computing of modern hardware accelerators (e.g. GPUs). We also develop a hybrid automatic and manual differentiation approach so that gradients can be computed efficiently. Our algorithms are implemented within the JAX differentiable programming framework in the S2FFT▪ software code. Numerous samplings of the sphere are supported, including equiangular and HEALPix sampling. Computational errors are at the order of machine precision for spherical samplings that admit a sampling theorem. When benchmarked against alternative C codes we observe up to a 400-fold acceleration. Furthermore, when distributing over multiple GPUs we achieve very close to optimal linear scaling with increasing number of GPUs due to the highly parallelised and balanced nature of our algorithms. Provided access to sufficiently many GPUs our transforms thus exhibit an unprecedented effective linear time complexity.

High-Throughput screening of metal nitrides for electrochemical nitrogen reduction

Metal nitrides have garnered considerable attention in the context of nitrogen reduction reactions (NRR) due to their rich lattice nitrogen and abundant nitrogen vacancies to activate N2. However, the identification of suitable metal nitrides with superior stability, activity, and selectivity (vs. hydrogen evolution reaction, HER) for electrochemical NRR under rigorous isotope labeling experiments remains a perplexing challenge in experiments. In this study, we implemented a systematic screening protocol to assess the electrochemical stability and efficiency of various metal nitrides for NRR by high-throughput density functional theory calculations, aiming to search out promising metal nitride catalysts. Through a set of predetermined criteria, a subset of 6 candidates emerges as the most promising metal nitrides for NRR from a pool of 668 metal nitrides sourced from the Materials Project Database, all of which catalyze NRR following the Mars-van Krevelen pathway. Importantly, based on the analysis of these candidates, the unique structural property (i.e., four(or three)-coordinate lattice nitrogen) and energy descriptor (i.e., the formation energy of the lattice nitrogen vacancy is around 2.1 eV) of potential metal nitrides for electrochemical NRR were extracted, which could be used as the direction to rationally design metal nitride catalyst. This research not only serves as a valuable guide for experimental investigations into potential metal nitrides for NRR, but also provides an efficient method for high-throughput screening of potential catalysts for other electrochemical reactions.

Combined Machine Learning and High-Throughput Calculations Predict Heyd-Scuseria-Ernzerhof Band Gap of 2D Materials and Potential MoSi2N4 Heterostructures.

We present a novel target-driven methodology devised to predict the Heyd-Scuseria-Ernzerhof (HSE) band gap of two-dimensional (2D) materials leveraging the comprehensive C2DB database. This innovative approach integrates machine learning and density functional theory (DFT) calculations to predict the HSE band gap, conduction band minimum (CBM), and valence band maximum (VBM) of 2176 types of 2D materials. Subsequently, we collected a comprehensive data set comprising 3539 types of 2D materials, each characterized by its HSE band gaps, CBM, and VBM. Considering the lattice disparities between MoSi2N4 (MSN) and 2D materials, our analysis predicted 766 potential MSN/2D heterostructures. These heterostructures are further categorized into four distinct types based on the relative positions of their CBM and VBM: Type I encompasses 230 variants, Type II comprises 244 configurations, Type III consists of 284 permutations, and 0 band gap comprises 8 types.

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 discovery of metal oxides with high thermoelectric performance via interpretable feature engineering on small data

In this work, we have proposed a data-driven screening framework combining the interpretable machine learning with high-throughput calculations to identify a series of metal oxides that exhibit both high-temperature tolerance and high power factors. Aiming at the problem of weak generalization ability of small data with power factors at high temperatures, we employ symbolic regression for feature creation which enhances the robustness of the model while preserving the physical meaning of features. 33 candidate metal oxides are finally targeted for high-temperature thermoelectric applications from a pool of 48,694 compounds in the Materials Project database. The Boltzmann transport theory is utilized to perform electrical transport properties calculations at 1,000 K. The relaxation time is approximated by employing constant electron-phonon coupling based on the deformation potential theory. Considering band degeneracy, the electron group velocity is obtained using the momentum matrix element method, yielding 28 materials with power factors greater than 50 μWcm−1K−2. The high-throughput framework we proposed is instrumental in the selection of metal oxides for high-temperature thermoelectric applications. Furthermore, our data-driven analysis and transport calculation suggest that metal oxides rich in elements such as cerium (Ce), tin (Sn), and lead (Pb) tend to exhibit high power factors at high temperatures.

Wenzhou TE: A First-Principle-Calculated Thermoelectric Materials Database.

Since the implementation of the Materials Genome Project by the Obama administration in the United States, the development of various computational materials' databases has fundamentally expanded the choice of industries such as materials and energy. In the field of thermoelectric materials, the thermoelectric figure of merit (ZT) quantifies the performance of the material. From the viewpoint of calculations for vast materials, the ZT values are not easily obtained due to their computational complexity. Here, we show how to build a database of thermoelectric materials based on first-principle calculations for the electronic and heat transport of materials. Firstly, the initial structures are classified according to the values of bandgap and other basic properties using the clustering algorithm K-means in machine learning, and high-throughput first principle calculations are carried out for narrow-bandgap semiconductors which exhibit a potential thermoelectric application. The present framework of calculations mainly includes a deformation potential module, an electrical transport performance module, a mechanical and a thermodynamic properties module. We have also set up a search webpage for the calculated database of thermoelectric materials, providing search facilities and the ability to view the related physical properties of materials. Our work may inspire the construction of more computational databases of first-principle thermoelectric materials and accelerate research progress in the field of thermoelectrics.

Modulating single-atom sulfur-vacancy defect in MoS2-x catalysts to boost cathode redox kinetics for vanadium flow batteries

Vanadium flow batteries (VFBs) have great potential for application in energy storage systems. However, the sluggish cathode redox kinetics still greatly restricts their operation at high current densities. Herein, we boost cathode redox chemistry by modulating single-atom sulfur-vacancy (S-vacancy) defect of MoS2-x in-situ grown on carbon felts via a facile chemical etching method. Firstly, the optimized S-vacancy concentration is figured out via high throughput calculations based on d-band center theory. By precisely controlling etching duration, we achieve a tailored S-vacancy concentration, leading to highly dispersed S-vacancies, increased specific surface area, and improved hydrophilicity. Electrochemical characterizations demonstrate that optimized S-vacancy state can significantly facilitate the VO2+/VO2+ kinetics. Moreover, analysis of electron density difference and integrated crystal orbital Hamiltonian group further reveals that dispersed S-vacancy distribution also contribute to efficient surface electronic structure and enhanced adsorption process. Benefiting from enhanced VO2+/VO2+ kinetics, VFB single cell achieves a superior EE of 78.73 % at 300 mA cm−2 and is able to last for 500 cycles without decay. This work demonstrates the promising potential of single-atom S-vacancies catalysts in the fabrication of flow battery electrodes and more importantly sheds light on the fundamental modulation essence of d-band center in MoS2-x towards enhanced cathode redox kinetics.

Screening of Complex Layered Chalcogenide Structures as High-Performance Thermoelectrics by High-Throughput Calculations

Thermoelectric materials have drawn much attention over the last two decades due to the increase in global energy demand. However, designing efficient thermoelectrics reveals itself as a tough task for their properties (Seebeck coefficient, electrical conductivity, thermal conductivity) are mutually opposed. Hence, most recently, new design approaches have appeared, among which high-throughput methods have been implemented either experimentally or computationally. In this work, a high-throughput computer program has been designed to generate over 4000 structures based on a small set of complex layered chalcogenide compounds taken from the mAIVBVI nA2VB3VI homologous series, where AIV is Ge, AV is Sb and BVI is Te. The computer-generated structures have been investigated using density-functional theory methods, and the electronic and transport properties have been calculated. It has been found, using the quantum theory of atoms in molecules and crystals, that a wide variety of bond types constitutes the bonding network of the structures. All the structures are found to have negative formation energies. Among the obtained final structures, 43 are found with a wide band gap energy (&gt;0.25 eV), 358 with semi-conductor/metal characteristics, and 731 with metallic characteristics. The transport properties calculations, using the Boltzmann equation, reveal that two p-type and 86 n-type structures are potentially promising compounds for thermoelectric applications.

Phase stability and mechanical property trends for MAB phases by high-throughput ab initio calculations

MAB phases (MABs) are atomically-thin laminates of ceramic/metallic-like layers, having made a breakthrough in the development of 2D materials. Though offering a vast chemical and phase space, relatively few MABs have been synthesised. To guide experiments, we perform high-throughput ab initio screening of MABs that combine group 4–7 transition metals (M); Al, Si, Ga, Ge, or In (A); and boron (B) focusing on their phase stability trends and mechanical properties. Considering the 1:1:1, 2:1:1, 2:1:2, 3:1:2, 3:1:3, and 3:1:4 M:A:B ratios and 10 phase prototypes, synthesisability of a single-phase compound for each elemental combination is estimated through formation energy spectra of competing dynamically stable MABs. Based on the volumetric proximity of energetically-close phases, we identify systems in which volume-changing deformations may facilitate transformation toughening. Subsequently, chemistry- and phase-structure-related trends in the elastic stiffness and ductility are predicted using elastic-constants-based descriptors. The analysis of directional Cauchy pressures and Young's moduli allows comparing mechanical response parallel and normal to M–B/A layers. The suggested promising MABs include Nb3AlB4, Cr2SiB2, Mn2SiB2 or the already synthesised MoAlB.

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.

Pooled multicolour tagging for visualizing subcellular protein dynamics

Imaging-based methods are widely used for studying the subcellular localization of proteins in living cells. While routine for individual proteins, global monitoring of protein dynamics following perturbation typically relies on arrayed panels of fluorescently tagged cell lines, limiting throughput and scalability. Here, we describe a strategy that combines high-throughput microscopy, computer vision and machine learning to detect perturbation-induced changes in multicolour tagged visual proteomics cell (vpCell) pools. We use genome-wide and cancer-focused intron-targeting sgRNA libraries to generate vpCell pools and a large, arrayed collection of clones each expressing two different endogenously tagged fluorescent proteins. Individual clones can be identified in vpCell pools by image analysis using the localization patterns and expression level of the tagged proteins as visual barcodes, enabling simultaneous live-cell monitoring of large sets of proteins. To demonstrate broad applicability and scale, we test the effects of antiproliferative compounds on a pool with cancer-related proteins, on which we identify widespread protein localization changes and new inhibitors of the nuclear import/export machinery. The time-resolved characterization of changes in subcellular localization and abundance of proteins upon perturbation in a pooled format highlights the power of the vpCell approach for drug discovery and mechanism-of-action studies.

High-throughput design of three-dimensional carbon allotropes with Pmna space group

284 carbon allotropes with Pmna space group (No. 53) are proposed based on high-throughput calculations and density functional theory. Out of 14,285 initially identified candidates, 284 carbon allotropes are confirmed by structure optimization, removal of repetitive structures, calculation of relative enthalpies, and verification of the mechanical and thermal stabilities. Among them, 135 are metals, 55 are direct band gap semiconductors (in 15 cases with a band gap between 1.0 and 1.5 eV), 46 have three-dimensional conductive channels, 32 are superhard, and 3 are type-I Dirac semimetals.

The Egyptian national HPC grid (EN-HPCG): open-source Slurm implementation from cluster to grid approach

Recently, Egypt has recognized the pivotal role of High Performance Computing in advancing science and innovation. Additionally, Egypt realizes the importance of collaboration between different institutions and universities to consolidate their own computational and data resources into a unified platform to serve different disciplines (e.g., scientific, industrial, governmental). Otherwise, additional resources would be needed to be purchased with the associated cost, effort, and time difficulties (e.g., setup, administration, maintenance, etc.). Thus, this paper delves into the architecture and capabilities of the EN-HPCG grid using two different workload management systems: (i) Slurm (Open-Source) and (ii) PBS Pro (Licensed). This paper compares the performance of the grid between Slurm and PBS Pro in specific high-throughput computing (HTC) applications using the NAS Grid parallel benchmark (NGB) to determine which workload manager is more suitable for EN-HPCG. The evaluation includes grid-level performance metrics such as throughput, and the number of tasks completed as a function of time. Also, the presented methodology aims to assist potential partners in their decision-making process to join the EN-HPCG grid, with a focus on the site speed-up metric. Our results showed that, unless an open-source solution without cost and license problems is an obligation (in which case, Slurm is the viable solution), then it is not advisable to integrate a cluster with high-speed hardware with a cluster possessing outdated hardware when using the Slurm scheduler. In contrast, the PBS Pro scheduler takes into account online decision-making in a dynamic environment using a unified grid.

High-throughput hybrid-functional DFT calculations of bandgaps and formation energies and multifidelity learning with uncertainty quantification

High-throughput hybrid-functional DFT calculations of bandgaps and formation energies and multifidelity learning with uncertainty quantification