High-throughput Density Functional Theory Calculations Research Articles

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.

Artificial Intelligence Driving Materials Discovery? Perspective on the Article: Scaling Deep Learning for Materials Discovery.

The discovery of new crystalline inorganic compounds-novel compositions of matter within known structure types, or even compounds with completely new crystal structures-constitutes an important goal of solid-state and materials chemistry. Some fractions of new compounds can eventually lead to new structural and functional materials that enhance the efficiency of existing technologies or even enable completely new technologies. Materials researchers eagerly welcome new approaches to the discovery of new compounds, especially those that offer the promise of accelerated success. The recent report from a group of scientists at Google who employ a combination of existing data sets, high-throughput density functional theory calculations of structural stability, and the tools of artificial intelligence and machine learning (AI/ML) to propose new compounds is an exciting advance. We examine the claims of this work here, unfortunately finding scant evidence for compounds that fulfill the trifecta of novelty, credibility, and utility. While the methods adopted in this work appear to hold promise, there is clearly a great need to incorporate domain expertise in materials synthesis and crystallography.

High-throughput thermoelectric materials screening by deep convolutional neural network with fused orbital field matrix and composition descriptors

Thermoelectric materials harvest waste heat and convert it into reusable electricity. Thermoelectrics are also widely used in inverse ways such as refrigerators and cooling electronics. However, most popular and known thermoelectric materials to date were proposed and found by intuition, mostly through experiments. Unfortunately, it is extremely time and resource consuming to synthesize and measure the thermoelectric properties through trial-and-error experiments. Here, we develop a convolutional neural network (CNN) classification model that utilizes the fused orbital field matrix and composition descriptors to screen a large pool of materials to discover new thermoelectric candidates with power factor higher than 10 μW/cm K2. The model used our own data generated by high-throughput density functional theory calculations coupled with ab initio scattering and transport package to obtain electronic transport properties without assuming constant relaxation time of electrons, which ensures more reliable electronic transport properties calculations than previous studies. The classification model was also compared to some traditional machine learning algorithms such as gradient boosting and random forest. We deployed the classification model on 3465 cubic dynamically stable structures with non-zero bandgap screened from Open Quantum Materials Database. We identified many high-performance thermoelectric materials with ZT > 1 or close to 1 across a wide temperature range from 300 to 700 K and for both n- and p-type doping with different doping concentrations. Moreover, our feature importance and maximal information coefficient analysis demonstrates two previously unreported material descriptors, namely, mean melting temperature and low average deviation of electronegativity, that are strongly correlated with power factor and thus provide a new route for quickly screening potential thermoelectrics with high success rate. Our deep CNN model with fused orbital field matrix and composition descriptors is very promising for screening high power factor thermoelectrics from large-scale hypothetical structures.

High-throughput screening of N-doped curved graphene-loaded transition metal single-atom catalysts for nitrogen reduction reaction

Nitrogen reduction reaction (NRR) is necessary as an environmentally friendly and sustainable method for producing NH3 in the context of the energy crisis and increasing environmental concerns. Efficient catalysts play an important role in the NRR reaction. In this work, a high-curvature tetracoordinate transition metal (TM) nitrogen-doped graphene single-atom catalyst was designed and five candidate catalysts with excellent performances were selected from 406 models through a combination of high-throughput screening and density functional theory calculations. Using seven different coordination types MC4, MC3N1, MC2N2-1, MC2N2-2, MC2N2-3, MC1N3 and MN4, of which VN4 (side-on) with four N-atom coordination has the lowest limiting potential of 0.31 V and the competing Hydrogen Evolution Reaction (HER) was suppressed. This work provides guidance for the development of efficient NRR catalysts.

Realizing a high OER activity in new single-atom catalysts formed by introducing TMNx (x = 3 and 4) units into carbon nanotubes using high-throughput calculations.

Exploring highly efficient electrocatalysts for the oxygen evolution reaction (OER) is of great significance for hydrogen production through water splitting. By means of high-throughput density functional theory (DFT) calculations, we investigated the OER catalytic activity of a series of one-dimensional carbon nanotube (CNT)-based systems containing TMN4 or TMN3 functional units. Through the screening of 3d/4d/5d transition metals (TMs) from Group IVB to Group VIII, eight newly obtained TMNx@CNT (x = 3 and 4) systems were found to exhibit excellent OER activity, with very low overpotentials in the range 0.29-0.51 V, where the Co, Rh, Ir, Ti, Fe, and Ru atoms could be used as active sites. It was found that under the framework of TMN3@CNTs, the pre-adsorption of some species from water dissociation on the relevant TM sites (TM = Ti, Fe, and Ru) could lead to a high OER catalytic activity, which was different from the general situation where OER reactions directly occur on the clean surfaces of the remaining systems with Co/Rh/Ir metal centers. Moreover, the catalytic mechanisms were analyzed in detail. This work can be conducive to obtaining low-cost and high-performance OER single-atom electrocatalysts based on excellent CNT nanomaterials.

Development of machine learning interatomic potential for zinc

The HCP structure of zinc (Zn) exhibits an anomalous c/a ratio, a factor that traditional empirical potentials find challenging to reproduce accurately, thus impeding thorough investigations of its property and behavior via realistic atomistic simulations. The advent of machine learning interatomic potentials in recent years has ushered in new opportunities for the development of reliable Zn potentials. In this study, we construct a large set of atomic configurations composed of Zn atoms and calculate their energy, atomic forces, and stress tensor through high-throughput density functional theory calculations. Based on the calculated data, a moment tensor potential for Zn is developed, whose fidelity is then thoroughly examined by comprehensive comparison to existing potentials from the literature, density function theory calculations, and/or experimental measurements. It is found that the constructed potential can accurately reproduce the c/a ratio of HCP Zn, the relative stability of different crystal phases, the elastic properties, and defect properties of Zn. It can also describe reliably the structure, thermodynamic, and kinetic properties of Zn melt. This potential therefore offers a reliable tool for further exploration of the property and behavior of Zn at the atomic scale.

New insight into biodegradation mechanism of phenylurea herbicides by cytochrome P450 enzymes: Successive N-demethylation mechanism

Phenylurea herbicides (PUHs) present one of the most important herbicides, which have cause serious effects on ecological environment and humans. Nowadays enzyme strategy shows great advantages in degradation of PUHs. Here density functional theory (DFT), quantitative structure − activity relationship (QSAR) and quantum mechanics/molecular mechanics (QM/MM) approaches are used to investigate the degradation mechanism of PUHs catalyzed by P450 enzymes. Two successive N-demethylation pathways are identified and two hydrogen abstraction (H-abstraction) reaction pathways are identified as the rate-determining step through high-throughput DFT calculations. The Boltzmann-weighted average energy barrier of the second H-abstraction pathway (19.95 kcal/mol) is higher than that of the first H-abstraction pathway (16.80 kcal/mol). Two QSAR models are established to predict the energy barriers of the two H-abstraction pathways based on the quantum chemical descriptors and mordred molecular descriptors. The determination coefficient (R2) values of QSAR models are > 0.9, which reveal that the established QSAR models have great predictive capability. QM/MM calculations indicate that human P450 enzymes are more efficient in degradation of PUHs than crop and weed P450 enzymes. Correlations between energy barriers and key structural/charge parameters are revealed and key parameters that have influence on degradation efficiency of PUHs are identified. This study provides lateral insights into the biodegradation strategy and removal method of PUHs and valuable information for designing or engineering of highly efficient degradation enzymes and genetically modified crops.

A computational approach for mapping electrochemical activity of multi-principal element alloys

Multi principal element alloys (MPEAs) comprise an atypical class of metal alloys. MPEAs have been demonstrated to possess several exceptional properties, including, as most relevant to the present study a high corrosion resistance. In the context of MPEA design, the vast number of potential alloying elements and the staggering number of elemental combinations favours a computational alloy design approach. In order to computationally assess the prospective corrosion performance of MPEA, an approach was developed in this study. A density functional theory (DFT) – based Monte Carlo method was used for the development of MPEA ‘structure’; with the AlCrTiV alloy used as a model. High-throughput DFT calculations were performed to create training datasets for surface activity/selectivity towards different adsorbate species: O2-, Cl- and H+. Machine-learning (ML) with combined representation was then utilised to predict the adsorption and vacancy energies as descriptors for surface activity/selectivity. The capability of the combined computational methods of MC, DFT and ML, as a virtual electrochemical performance simulator for MPEAs was established and may be useful in exploring other MPEAs.

Quantitative prediction of corrosion resistance in ferrous alloys: A first-principles study of coupled doping of alloying elements

The corrosion resistance of ferrous alloys is greatly influenced by the presence of alloying elements, however, predicting the coupling effects of these elements remains a challenge. In the present paper, the effects of various alloying elements on the corrosion resistance of iron matrix material were investigated using high-throughput density functional theory (DFT) calculations. This work explores the dissolution behavior of metal and alloy surfaces and established correlations between calculated parameters and experimentally measurable values to predict polarization curves and simulate corrosion behavior. The results showed that doping with Cu, P, Ni, Al, Mn, Co, Si, Ti, and Zn on the Fe (110) surface can reduce the corrosion current density of the iron matrix. For Fe-Cu and Fe-Mn steels, coupling P-Ti in Fe-Cu steel and P-Si in Fe-Mn steel can significantly improve their corrosion resistance. The developed model can quantitatively predict material properties and efficiently screen compositions to design corrosion-resistant alloys.

Multiobjective Solid Electrolyte Design of Tetragonal and Cubic Inverse-Perovskites for All-Solid-State Lithium-Ion Batteries by High-Throughput Density Functional Theory Calculations and AI-Driven Methods

Multiobjective Solid Electrolyte Design of Tetragonal and Cubic Inverse-Perovskites for All-Solid-State Lithium-Ion Batteries by High-Throughput Density Functional Theory Calculations and AI-Driven Methods

High-throughput design of Co-based magnetic Heusler compounds

High-throughput (HTP) density functional theory (DFT) calculations are carried out on Co-based Heusler alloys, i.e., Co2YZ and X2CoZ (where X, Y, and Z range from lithium (Li) to bismuth (Bi) excluding inert gas and 4f rare-earth elements) in both cubic and tetragonal structures of the regular and inverse Heusler phases. After evaluating the thermodynamic stabilities based on the formation energy (ΔEf) and the distance to the convex hull (ΔEHD), we validate the HTP calculations with 65 experimentally known cases and further predict 158 novel compounds with ΔEHD smaller than 50 meV/atom, in which 117 compounds exhibit finite magnetization larger than 1 μB/f.u.. The novel Co-based Heuslers predicted by our HTP work also includes some all-d-metal compounds. In contrast to the conventional Heuslers with dominating pd hybridization, the all-d-metal Heuslers consisting of mainly dd hybridization cannot be properly interpreted by Burch’s rule. The Slater–Pauling rule, on the other hand, is found to well describe the relationship between the intrinsic magnetic properties, including the total spin moment and Curie temperature, and the number of valence electrons. Furthermore, magneto-crystalline anisotropy is calculated for stable tetragonal compounds, providing useful information for potential applications in the fields of permanent magnets and magnetic recording materials.

Accelerating the Discovery of Hybrid Perovskites with Targeted Band Gaps via Interpretable Machine Learning.

The band gap of hybrid organic-inorganic perovskites (HOIPs) is a key factor affecting the light absorption characteristics and thus the performance of perovskite solar cells (PSCs). However, band gap engineering, using experimental trial and error and high-throughput density functional theory calculations, is blind and costly. Therefore, it is critical to statistically identify the multiple factors influencing band gaps and to rationally design perovskites with targeted band gaps. This study combined feature engineering, the gradient-boosted regression tree (GBRT) algorithm, and the genetic algorithm-based symbolic regression (GASR) algorithm to develop an interpretable machine learning (ML) strategy for predicting the band gap of HOIPs accurately and quantitatively interpreting the factors affecting the band gap. Seven best physical features were selected to construct a GBRT model with a root-mean-square error of less than 0.060 eV, and the most important feature is the electronegativity difference between the B-site and the X-site (χB-X). Further, a mathematical formula (Eg = χB-X2 + 0.881χB-X) was constructed with GASR for a quantitative interpretation of the band gap influence patterns. According to the ML model, the HOIP MA0.23FA0.02Cs0.75Pb0.59Sn0.41Br0.24I2.76 was obtained, with a suitable band gap of 1.39 eV. Our proposed interpretable ML strategy provides an effective approach for developing HOIP structures with targeted band gaps, which can also be applied to other material fields.

Rational Modulation of Single Atom Coordination Microenvironments in a BCN Monolayer for Multifunctional Electrocatalysis.

Single-atom (SA) catalysts (SACs) have demonstrated outstanding catalytic performances toward plenty of relevant electrochemical reactions. Nevertheless, controlling the coordination microenvironment of catalytically active SAs to further enhance their catalytic oerformences has remained elusive up to now. Herein, a systematic investigation of 20 transition metal atoms that are coordinated with 20 different microenvironments in a boroncarbon-nitride monolayer (BCN) is conducted using high-throughput density functional theory calculations. The experimentally synthesized ternary BCN monolayer contains carbon, nitrogen, and boron atoms in its 2D network, thus providing a lot of new coordination environments than those of the current Cx Ny nanoplatforms. By exploring the structural/electrochemical stability, catalytic activity, selectivity, and electronic properties of 400 (20 × 20) TM-BCN moieties, it is discovered that specific SA coordination environments can achieve superior stability and selectivity for different electrocatalytic reactions. Moreover, a universal descriptor to accelerate the experimental process toward the synthesis of BCN-SACs is reported. These findings not only provide useful guidance for the synthesis of efficient multifunctional BCN-SACs but also will immediately benefit researchers by levering up their understanding of the mechanistic effects of SA coordination microenvironments on electrocatalytic reactions.

Combined High-Throughput DFT and ML Screening of Transition Metal Nitrides for Electrochemical CO2 Reduction

The electrochemical reduction of CO2 (CO2RR) using renewable electricity has the potential to reduce atmospheric CO2 levels while producing valuable chemicals and fuels. However, the practical implementation of this technology is limited by the activity, selectivity, and stability of catalyst materials. In this study, we employ high-throughput density functional theory (DFT) calculations to screen ∼800 transition metal nitrides and identify potential catalysts for CO2RR. The stability and activity of the screened materials were thoroughly evaluated via thermodynamic analysis, revealing Co, Cr, and Ti transition metal nitrides as the most promising candidates. Additionally, we conduct a feature importance analysis using machine learning (ML) regression models for binding energy prediction and determine the primary factors influencing the stability of catalysts. We show that the group number of metals has a significant impact on the binding energy of *OH and thus on the stability of the catalysts. We anticipate that this combined approach of high-throughput DFT screening and design strategy derived from ML regression analysis could effectively lead to the discovery of improved energy materials.

Quantifying uncertainty in high-throughput density functional theory: A comparison of AFLOW, Materials Project, and OQMD

A central challenge in high-throughput density functional theory (HT-DFT) calculations is selecting a combination of input parameters and postprocessing techniques that can be used across all materials classes, while also managing accuracy-cost tradeoffs. To investigate the effects of these parameter choices, we consolidate three large HT-DFT databases: Automatic-FLOW (AFLOW), the Materials Project (MP), and the Open Quantum Materials Database (OQMD), and compare reported properties across each pair of databases for materials calculated using the same initial crystal structure. We find that HT-DFT formation energies and volumes are generally more reproducible than band gaps and total magnetizations; for instance, a notable fraction of records disagree on whether a material is metallic (up to 7%) or magnetic (up to 15%). The variance between calculated properties is as high as 0.105 eV/atom (median relative absolute difference, or MRAD, of 6%) for formation energy, 0.65 ${\AA{}}^{3}/\text{atom}$ (MRAD of 4%) for volume, 0.21 eV (MRAD of 9%) for band gap, and $0.15\phantom{\rule{0.16em}{0ex}}{\ensuremath{\mu}}_{\mathrm{B}}/\text{formula unit}$ (MRAD of 8%) for total magnetization, comparable to the differences between DFT and experiment. We trace some of the larger discrepancies to choices involving pseudopotentials, the $\mathrm{DFT}+U$ formalism, and elemental reference states, and argue that further standardization of HT-DFT would be beneficial to reproducibility.

Accelerated search for new ferroelectric materials

We report the development of a combined machine learning and high-throughput density functional theory (DFT) framework to accelerate the search for new ferroelectric materials. The framework can predict potential ferroelectric compounds using only elemental composition as input. A series of machine-learning algorithms initially predict the possible stable and insulating stoichiometries with polar crystal structures, necessary for ferroelectricity, within a given chemical composition space. A classification model then predicts the point groups of these stoichiometries. A subsequent series of high-throughput DFT calculations finds the lowest-energy crystal structure within the point group. As a final step, nonpolar parent structures are identified using group theory considerations, and the values of the spontaneous polarization are calculated using DFT. By predicting the crystal structures and the polarization values, this method provides a powerful tool to explore new ferroelectric materials beyond those in existing databases.

A map of single-phase high-entropy alloys

High-entropy alloys have exhibited unusual materials properties. The stability of equimolar single-phase solid solution of five or more elements is supposedly rare and identifying the existence of such alloys has been challenging because of the vast chemical space of possible combinations. Herein, based on high-throughput density-functional theory calculations, we construct a chemical map of single-phase equimolar high-entropy alloys by investigating over 658,000 equimolar quinary alloys through a binary regular solid-solution model. We identify 30,201 potential single-phase equimolar alloys (5% of the possible combinations) forming mainly in body-centered cubic structures. We unveil the chemistries that are likely to form high-entropy alloys, and identify the complex interplay among mixing enthalpy, intermetallics formation, and melting point that drives the formation of these solid solutions. We demonstrate the power of our method by predicting the existence of two new high-entropy alloys, i.e. the body-centered cubic AlCoMnNiV and the face-centered cubic CoFeMnNiZn, which are successfully synthesized.

Local phase structure evolution and ferroelectric response in novel Bi-layered structure relaxor BaBi2Ta2O9 ceramic

It is of importance to discover new type ferroelectric materials with enhanced functionalities to meet various demand of applications, here we prepared the BaBi2Ta2O9 ceramic with orthogonal structure and oxygen octahedral rotation. This type of material had been screened out to be a candidate ferroelectric previously based on high-throughput first-principles density functional theory (DFT) calculations. The relationship between local phase structure and ferroelectric properties of this system is investigated by dielectric spectrum. Low-temperature relaxor behavior is described by using the Curie-Weiss law and the Vogel-Fulcher relationship. The polarization response and size of Polar nanoregions (PNRs) are described using the phenomenological statistical model. The P(E) loops imply that long-range-order ferroelectric is destroyed formation frozen randomly interacting PNRs. Element segregation and lattice distortion are directly observed using TEM, which is associated with the substitution of multiple atoms for the one cation occupation.

High-Throughput DFT-Based Discovery of Next Generation Two-Dimensional (2D) Superconductors.

High-throughput density functional theory (DFT) calculations allow for a systematic search for conventional superconductors. With the recent interest in two-dimensional (2D) superconductors, we used a high-throughput workflow to screen over 1000 2D materials in the JARVIS-DFT database and performed electron-phonon coupling calculations, using the McMillan-Allen-Dynes formula to calculate the superconducting transition temperature (Tc) for 165 of them. Of these 165 materials, we identify 34 dynamically stable structures with transition temperatures above 5 K, including materials such as W2N3, NbO2, ZrBrO, TiClO, NaSn2S4, Mg2B4C2, and the previously unreported Mg2B4N2 (Tc = 21.8 K). Finally, we performed experiments to determine the Tc of selected layered superconductors (2H-NbSe2, 2H-NbS2, ZrSiS, FeSe) and discuss the measured results within the context of our DFT results. We aim that the outcome of this workflow can guide future computational and experimental studies of new and emerging 2D superconductors by providing a roadmap of high-throughput DFT data.

Conventional High-Temperature Superconductivity in Metallic, Covalently Bonded, Binary-Guest C-B Clathrates.

Inspired by the synthesis of XB3C3 (X = Sr, La) compounds in the bipartite sodalite clathrate structure, density functional theory (DFT) calculations are performed on members of this family containing up to two different metal atoms. A DFT-chemical pressure analysis on systems with X = Mg, Ca, Sr, Ba reveals that the size of the metal cation, which can be tuned to stabilize the B-C framework, is key for their ambient-pressure dynamic stability. High-throughput density functional theory calculations on 105 Pm3̅ symmetry XYB6C6 binary-guest compounds (where X, Y are electropositive metal atoms) find 22 that are dynamically stable at 1 atm, expanding the number of potentially synthesizable phases by 19 (18 metals and 1 insulator). The density of states at the Fermi level and superconducting critical temperature, Tc, can be tuned by changing the average oxidation state of the metal atoms, with Tc being highest for an average valence of +1.5. KPbB6C6, with an ambient-pressure Eliashberg Tc of 88 K, is predicted to possess the highest Tc among the studied Pm3̅n XB3C3 or Pm3̅ XYB6C6 phases, and calculations suggest it may be synthesized using high-pressure high-temperature techniques and then quenched to ambient conditions.