High-throughput Screening Of Materials Research Articles

Topological charge-2 triply degenerate point: Theory and high-throughput material screening

Topological charge-2 triply degenerate point: Theory and high-throughput material screening

Structure to Property: Chemical Element Embeddings for Predicting Electronic Properties of Crystals.

We present a new general-purpose machine learning model that is able to predict a variety of crystal properties, including Fermi level energy and band gap, as well as spectral ones such as electronic densities of states. The model is based on atomic representations that enable it to effectively capture complex information about each atom and its surrounding environment in a crystal. The accuracy achieved for band gaps exceeds results previously published. By design, our model is not restricted to the electronic properties discussed here but can be extended to fit diverse chemical descriptors. Its advantages are (a) its low computational requirements, making it an efficient tool for high-throughput screening of materials; and (b) the simplicity and flexibility of its architecture, facilitating implementation and interpretation, especially for researchers in the field of computational chemistry.

High-Throughput Compatibility Screening of Materials for SF6-Alternative Insulation.

With the annual global electricity production exceeding 30,000 TWh, the safe transmission of electric power has been heavily relying on SF6, the most potent industrial greenhouse gas. While promising SF6 alternatives have been proposed, their compatibilities with materials used in gas-insulated equipment (GIE) must be thoroughly studied. This is particularly true as the emerging SF6 alternatives generally leverage their relatively higher reactivity to achieve lower global warming potentials (GWPs). Here, a high-throughput compatibility screening of common GIE materials was conducted with a representative SF6 alternative, namely, C4F7N (2,3,3,3-tetrafluoro-2-(trifluoromethyl)propanenitrile)/CO2 gas mixtures. In this screening, the insulation performance of C4F7N/CO2 gas mixtures, as an indicator of the C4F7N/materials compatibility level, was periodically monitored during the thermal aging with tens of materials from SF6-insulated GIE, including desiccants/adsorbents, rubber, plastics, composites, ceramics, metals, etc. The identification of incompatible materials and the follow-up mechanism studies suggested that the acidity of materials represents the primary cause for C4F7N/materials incompatibility when C4F7N/CO2 gas mixtures are used as a drop-in replacement solution for existing SF6-insulated apparatuses. Mitigation strategies tackling the acidity of materials were then proposed and validated. Additionally, the amphoteric characteristics of C4F7N were briefly discussed. This work provides insight into the materials incompatibility of SF6 alternatives, along with validated mitigation strategies, for the selection and design of materials used in future eco-friendly GIE.

Unlocking Single Particle Anisotropy in Real-Time for Photoelectrochemistry Processes at the Nanoscale.

The key to rationally and rapidly designing high-performance materials is the monitoring and comprehension of dynamic processes within individual particles in real-time, particularly to gain insight into the anisotropy of nanoparticles. The intrinsic property of nanoparticles typically varies from one crystal facet to the next under realistic working conditions. Here, we introduce the operando collision electrochemistry to resolve the single silver nanoprisms (Ag NPs) anisotropy in photoelectrochemistry. We directly identify the effect of anisotropy on the plasmonic-assisted electrochemistry at the single NP/electrolyte interface. The statistical collision frequency shows that heterogeneous diffusion coefficient among crystal facets facilitates Ag NPs to undergo direction-dependent mass transfer toward the gold ultramicroelectrode. Subsequently, the current amplitudes of transient events indicate that anisotropy enables variations in dynamic interfacial electron transfer behaviors during photothermal processes. The results presented here demonstrate that the measurement precision of collision electrochemistry can be extended to the sub-nanoparticle level, highlighting the potential for high-throughput material screening with comprehensive kinetics information at the nanoscale.

Machine learning based screening of organic frameworks for separation of CF4/N2, C2F6/N2, and SF6/N2

Through molecular simulation, feature analysis and extraction, as well as the modeling and optimization of machine learning algorithms, we have developed exceptional regression prediction models for the high-throughput screening of organic framework materials in CF4/N2, C2F6/N2, and SF6/N2 separation. The Grand Canonical Ensemble Monte Carlo method was employed to simulate the adsorption behavior of these three gas mixtures in 603 organic framework materials at room temperature 298 K and different pressures, constructing a dataset suitable for subsequent machine learning studies. We analyzed the impact of six common structural features of adsorbents (PLD, LCD, Density, ASA, AVF, and AV) on separation performance, determining the optimal ranges of each structural feature for adsorption separation in different gas mixtures. Additionally, we introduced two custom descriptors (AVG_SIG, AVG_SQRT_EPS) to describe adsorbent force field parameters, revealing their significant correlation with adsorption separation performance. Using pressure, adsorbent structural features, and custom descriptors as features, and TSQ value representing adsorption separation performance as the target, we applied eight machine learning models based on linear regression (MLR, RR), decision tree (DT, RF, GBDT, XGBoost), and neural network (MLP, GN) principles to model and predict on three datasets. Results indicated that simple models struggle to reliably predict adsorption separation performance, while structurally complex machine models demonstrate significant potential. We utilized the Harris Hawks Optimization (HHO) algorithm to perform hyperparameter optimization on multiple machine learning models and introduced improvements to the GN network. The optimized models, especially XGBoost and GN, exhibited outstanding performance, significantly enhancing the accuracy of predicting adsorption separation.

First-Principles Simulation and Materials Screening for Spin-Orbit Torque in 2D van der Waals Heterostructures.

Recent advancements in spin-orbit torque (SOT) technology in two-dimensional van der Waals (2D vdW) materials have not only pushed spintronic devices to their atomic limits but have also unveiled unconventional torques and novel spin-switching mechanisms. The vast diversity of SOT observed in numerous 2D vdW materials necessitates a screening strategy to identify optimal materials for torque device performance. However, such a strategy has yet to be established. To address this critical issue, a combination of density functional theory and non-equilibrium Green's function is employed to calculate the SOT in various 2D vdW bilayer heterostructures. This leads to the discovery of three high SOT systems: WTe2/CrSe2, MoTe2/VS2, and NbSe2/CrSe2. Furthermore, a figure of merit that allows for rapid and efficient estimation of SOT is proposed, enabling high-throughput screening of optimal materials and devices for SOT applications in the future.

Multimodal learning of heat capacity based on transformers and crystallography pretraining

Thermal properties of materials are essential to many applications of thermal electronic devices. Density functional theory (DFT) has shown capability in obtaining an accurate calculation. However, the expensive computational cost limits the application of the DFT method for high-throughput screening of materials. Recently, machine learning models, especially graph neural networks (GNNs), have demonstrated high accuracy in many material properties’ prediction, such as bandgap and formation energy, but fail to accurately predict heat capacity(CV) due to the limitation in capturing crystallographic features. In our study, we have implemented the material informatics transformer (MatInFormer) framework, which has been pretrained on lattice reconstruction tasks. This approach has shown proficiency in capturing essential crystallographic features. By concatenating these features with human-designed descriptors, we achieved a mean absolute error of 4.893 and 4.505 J/(mol K) in our predictions. Our findings underscore the efficacy of the MatInFormer framework in leveraging crystallography, augmented with additional information processing capabilities.

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.

A machine learning approach for determining temperature-dependent bandgap of metal oxides utilizing Allen–Heine–Cardona theory and O’Donnell model parameterization

To evaluate the high temperature sensing properties of metal oxide and perovskite materials suitable for use in combustion environments, it is necessary to understand the temperature dependence of their bandgaps. Although such temperature-driven changes can be calculated via the Allen–Heine–Cardona (AHC) theory, which assesses electron–phonon coupling for the bandgap correction at given temperatures, this approach is computationally demanding. Another approach to predict bandgap temperature-dependence is the O’Donnell model, which uses analytical expressions with multiple fitting parameters that require bandgap information at 0 K. This work employs data-driven Gaussian process regression (GPR) to predict the parameters employed in the O’Donnell model from a set of physical features. We use a sample of 54 metal oxides for which density functional theory has been performed to calculate the bandgap at 0 K, and the AHC calculations have been carried out to determine the shift in the bandgap at non-zero temperatures. As the AHC calculations are impractical for high-throughput screening of materials, the developed GPR model attempts to alleviate this issue by predicting the O'Donnell parameters purely from physical features. To mitigate the reliability issues arising from the very small size of the dataset, we apply a Bayesian technique to improve the generalizability of the data-driven models as well as quantify the uncertainty associated with the predictions. The method captures well the overall trend of the O’Donnell parameters with respect to a reduced feature set obtained by transforming the available physical features. Quantifying the associated uncertainty helps us understand the reliability of the predictions of the O’Donnell parameters and, therefore, the bandgap as a function of temperature for any novel material.

High-Throughput Screening of Sulfur Reduction Reaction Catalysts Utilizing Electronic Fingerprint Similarity.

The catalytic performance is determined by the electronic structure near the Fermi level. This study presents an effective and simple screening descriptor, i.e., the one-dimensional density of states (1D-DOS) fingerprint similarity, to identify potential catalysts for the sulfur reduction reaction (SRR) in lithium-sulfur batteries. The Δ1D-DOS in relation to the benchmark W2CS2 was calculated. This method effectively distinguishes and identifies 30 potential candidates for the SRR from 420 types of MXenes. Further analysis of the Gibbs free energy profiles reveals that MXene candidates exhibit promising thermodynamic properties for SRR, with the protocol achieving an accuracy rate exceeding 93%. Based on the crystal orbital Hamilton population (COHP) and differential charge analysis, it is confirmed that the Δ1D-DOS could effectively differentiate the interaction between MXenes and lithium polysulfide (LiPS) intermediates. This study underscores the importance of the electronic fingerprint in catalytic performance and thus may pave a new way for future high-throughput material screening for energy storage applications.

Sampling-accelerated prediction of phonon scattering rates for converged thermal conductivity and radiative properties

The prediction of thermal conductivity and radiative properties is crucial. However, computing phonon scattering, especially for four-phonon scattering, could be prohibitively expensive, and the thermal conductivity for silicon after considering four-phonon scattering is significantly under-predicted and not converged in the literature. Here we propose a method to estimate scattering rates from a small sample of scattering processes using maximum likelihood estimation. The calculation of scattering rates and associated thermal conductivity and radiative properties are dramatically accelerated by three to four orders of magnitude. This allows us to use an unprecedented q-mesh (discretized grid in the reciprocal space) of 32 × 32 × 32 for calculating four-phonon scattering of silicon and achieve a converged thermal conductivity value that agrees much better with experiments. The accuracy and efficiency of our approach make it ideal for the high-throughput screening of materials for thermal and optical applications.

Predicting Two-Photon Absorption Spectra of Octupolar Molecules: A Deep-Learning Approach Based Exclusively on Molecular Structures.

Octupolar molecules possessing a strong two-photon response are vital for numerous advanced applications. However, accurately predicting their two-photon absorption (TPA) spectra requires high-precision quantum chemical calculations, which are computationally expensive due to repeated simulations of molecular excited-state properties. To address this challenge, we introduce a deep learning approach capable of rapidly and accurately forecasting TPA spectra for octupolar molecules. By leveraging the geometric structure as an initial descriptor, we employ a graph neural network to predict the maximum two-photon transition wavelength and cross-section. Our model demonstrates a mean absolute percentage error of less than 4% compared to time-dependent density-functional theory calculations, effectively reproducing experimental observations. Notably, this deep learning technique is nearly 100 000 times faster than comparable quantum calculations, making it an efficient and cost-effective tool for simulating TPA properties of octupolar molecules. Furthermore, this method holds great promise for the high-throughput screening of exceptional TPA materials.

From computational screening to the synthesis of a promising OER catalyst.

The search for new materials can be laborious and expensive. Given the challenges that mankind faces today concerning the climate change crisis, the need to accelerate materials discovery for applications like water-splitting could be very relevant for a renewable economy. In this work, we introduce a computational framework to predict the activity of oxygen evolution reaction (OER) catalysts, in order to accelerate the discovery of materials that can facilitate water splitting. We use this framework to screen 6155 ternary-phase spinel oxides and have isolated 33 candidates which are predicted to have potentially high OER activity. We have also trained a machine learning model to predict the binding energies of the *O, *OH and *OOH intermediates calculated within this workflow to gain a deeper understanding of the relationship between electronic structure descriptors and OER activity. Out of the 33 candidates predicted to have high OER activity, we have synthesized three compounds and characterized them using linear sweep voltammetry to gauge their performance in OER. From these three catalyst materials, we have identified a new material, Co2.5Ga0.5O4, that is competitive with benchmark OER catalysts in the literature with a low overpotential of 220 mV at 10 mA cm-2 and a Tafel slope at 56.0 mV dec-1. Given the vast size of chemical space as well as the success of this technique to date, we believe that further application of this computational framework based on the high-throughput virtual screening of materials can lead to the discovery of additional novel, high-performing OER catalysts.

High throughput screening of semiconductors with low lattice thermal transport induced by long-range interactions.

Semiconductors with long-range interactions (LRI) due to resonant bonding exhibit delocalized electronic states and low lattice thermal conductivity, contributing to the efficiency of heat-to-electricity conversion. Here, we build a descriptor for high-throughput screening of LRI materials from the second-order interaction force constants. We identify 75 semiconducting candidates from the binary compounds in the MatHub-3d database that contain LRI. By analyzing the bonding properties of LRI atoms, we classify LRI in materials into two categories: type I and type II. In the structural unit of type I LRI, the atoms have strong bond connections, while a weak bond exists between the two groups in the structural unit of type II LRI. We have identified atypical type I LRI formed by Sb-Sb and Mg-Mg pairs in the emerging thermoelectric material Mg3Sb2, resulting in the softening of TA1 phonons and large anharmonicity. For type II LRI, the LRI of Ge-Ge and Se-Se pairs in R3m-GeSe can cross different layers. Moreover, we observe a combination of type II LRI and rattling effect in BaSe2 to restrict thermal transport. This work is of great significance for understanding the relationship between LRI and thermal transport properties, and for designing new LRI-induced low lattice thermal conductivity materials.

High-throughput screening of stable layered anode materials A2TMO3Cl for chloride-ion batteries

A vacuum cleaner-like “TM–O–A–Cl” configuration simultaneously retains Cl-storage/binding capacity in A2TMO3Cl.

High-throughput computational materials screening of transition metal peroxides.

Semiconductor materials of abnormal stoichiometric ratio often exhibit unique properties, yet it is still a challenge to determine the structures of such materials in an efficient way. Herein, we propose a method for structurally biased screening according to the coordination numbers and the numbers of Wyckoff positions, balancing the atom local environment and the global symmetry of structures. Based on first-principles calculations, we have predicted two metastable peroxides P21/c-ScO2 and Pmmn-TiO3 with more than six coordination points. For these two structures, the most stable intrinsic defect is the oxygen vacancy (VO) at the peroxide anion (O2-2), which induces the absence of antibonding orbital formed by O2-2 near the valence band maximum. With the introduction of VO, the decrease of coordination numbers leads to charge recombination, and results in the appearance of an ordered phase TiO2.5 with stronger Ti-O orbital hybridization. The proposed method presents a promising and feasible approach for the screening of novel compounds.

Phase-Change Memory from Molecular Tellurides.

Phase-change memory (PCM) is an emerging memory technology based on the resistance contrast between the crystalline and amorphous states of a material. Further development and realization of PCM as a mainstream memory technology rely on innovative materials and inexpensive fabrication methods. Here, we propose a generalizable and scalable solution-processing approach to synthesize phase-change telluride inks in order to meet demands for high-throughput material screening, increased energy efficiency, and advanced device architectures. Bulk tellurides, such as Sb2Te3, GeTe, Sc2Te3, and TiTe2, are dissolved and purified to obtain inks of molecular metal telluride complexes. This allowed us to unlock a wide range of solution-processed ternary tellurides by the simple mixing of binary inks. We demonstrate accurate and quantitative composition control, including prototype materials (Ge-Sb-Te) and emerging rare-earth-metal telluride-doped materials (Sc-Sb-Te). Spin-coating and annealing convert ink formulations into high-quality, phase-pure telluride films with preferred orientation along the (00l) direction. Deposition engineering of liquid tellurides enables thickness-tunable films, infilling of nanoscale vias, and film preparation on flexible substrates. Finally, we demonstrate cyclable and non-volatile prototype memory devices, achieving performance indicators such as resistance contrast and low reset energy on par with state-of-the-art sputtered PCM layers.

Transition Temperature for Spin-Crossover Materials with the Mean Value Ensemble Hubbard-U Correction.

Calculation of transition temperatures T1/2 for thermally driven spin-crossover in condensed phases is challenging, even with sophisticated state-of-the-art density functional approximations. The first issue is the accuracy of the adiabatic crossover energy difference ΔEHL between the low- and high-spin states of the bistable metal-organic complexes. The other is the proper inclusion of entropic contributions to the Gibbs free energy from the electronic and vibrational degrees of freedom. We discuss the effects of treatments of both contributions upon the calculation of thermochemical properties for a set of 20 spin-crossover materials using a Hubbard-U correction obtained from a reference ensemble spin-state. The U values obtained from a simplest bimolecular representation may overcorrect, somewhat, the ΔEHL values, hence giving somewhat excessive reduction of the T1/2 results with respect to their U = 0 values in the crystalline phase. We discuss the origins of the discrepancies by analyzing different sources of uncertainties. By use of a first-coordination-sphere approximation and the assumption that vibrational contributions from the outermost atoms in a metal-organic complex are similar in both low- and high-spin states, we achieve T1/2 results with the low-cost, widely used PBE generalized gradient density functional approximation comparable to those from the more costly, more sophisticated r2SCAN meta-generalized gradient approximation. The procedure is promising for use in high-throughput materials screening, because it combines rather low computational effort requirements with freedom from user manipulation of parameters.

New Family of Interstitial Oxygen Ion Conductor Discovered By High-Throughput Computational Screening

Discovery and design of highly efficient oxygen-active materials which react with, absorb, and transport oxygen is essential for fuel cells and related applications. Currently, the predominant families of oxygen-active materials used in commercial devices are perovskites and fluorites, which diffuse oxygen via a vacancy mechanism and only have adequate oxygen diffusivity and surface exchange at high temperatures (e.g., ~800 ˚C), impeding their use at lower temperatures desirable for more cost-effective and long-lasting device operation. The development of alternative electrolyte/electrode materials with high oxygen ionic conductivity at low temperatures (e.g., room temperature to 400 °C) is of great interest. Interstitial oxygen ion conductors typically have significantly lower migration barriers than even state-of-the-art vacancy-mediated oxygen conductors, which can provide orders of magnitude kinetic enhancement at low temperatures. In addition, the interstitial oxygen defects typically become more thermodynamically favorable as the temperature is lowered, potentially providing an additional boost to the oxygen conductivity at low temperatures from a higher mediating defect concentration. The synergistic combination of low migration barriers and thermodynamically favorable interstitial oxygen content at low temperatures provides the tantalizing possibility that interstitial oxygen conductors may lead to transformative performance improvements in oxygen-active materials.In this work, we developed an approach to discover new interstitial-mediated oxygen conductors by performing high-throughput computational screening of the 34k oxide materials from the Materials Project database and discovered a new structural family of interstitial oxygen diffusers, perrierite-type oxide La4Mn5Si4O22+ 𝜹 (LMS). We used a hierarchy of screening criteria including the geometric free space, thermodynamic stability, synthesizability, redox-active elements, and presence of short diffusion pathways. This approach, based largely on simple, physically intuitive properties, quickly winnowed the field of 34k oxides down to just 345 oxides, an approximate 99% reduction in the search space considered, with essentially no significant computational cost. From this reduced set of promising materials, ab initio simulations were performed to calculate the interstitial oxygen formation energy and migration energy. Our screening has yielded several material families to date, among which the A4B5C4O22+ 𝜹, with La4Mn5Si4O22+ 𝜹 (LMS) as a representative material, was selected for investigation with higher-fidelity DFT hybrid functional calculations and experimental validations. The calculated formation energy of interstitial oxygen is -0.11 eV at a concentration of 2.3% under air condition, indicating that LMS is predicted to be oxygen hyperstoichiometric with an expected composition of La4Mn5Si4O22+0.5 in air. Two separate interstitial and interstitialcy (“kick-out”) diffusion mechanisms are revealed by ab initio molecular dynamics (AIMD) simulation. For the interstitial diffusion pathway, the moves through the channel in between the zigzag-arranged sorosilicate Si2O7 groups, with a barrier of 0.69 eV. For the interstitialcy diffusion pathway, the moves along the corner-sharing Si2O7-MnO2-Si2O7 framework by kicking out the lattice oxygen, with a barrier of 0.74 eV. These results align well with the experimental findings of stable interstitial oxygen content and high oxygen mobility in LMS (Please see a separate poster from Md Sariful Sheikhfor experimental results). This work provides a systematic approach to screening a large number of prospective materials and opens a new avenue of finding promising interstitial oxide ion conducting materials for potential applications in a range of fast oxygen ion conduction-based technologies.

High-Throughput Screening of High-Performance Thermoelectric Materials with Gibbs Free Energy and Electronegativity.

Thermoelectric (TE) materials are an important class of energy materials that can directly convert thermal energy into electrical energy. Screening high-performance thermoelectric materials and improving their TE properties are important goals of TE materials research. Based on the objective relationship among the molar Gibbs free energy (Gm), the chemical potential, the Fermi level, the electronegativity (X) and the TE property of a material, a new method for screening TE materials with high throughput is proposed. This method requires no experiments and no first principle or Ab initio calculation. It only needs to find or calculate the molar Gibbs free energy and electronegativity of the material. Here, by calculating a variety of typical and atypical TE materials, it is found that the molar Gibbs free energy of Bi2Te3 and Sb2Te3 from 298 to 600 K (Gm = -130.20~-248.82 kJ/mol) and the electronegativity of Bi2Te3 and Sb2Te3 and PbTe (X = 1.80~2.21) can be used as criteria to judge the potential of materials to become high-performance TE materials. For good TE compounds, Gm and X are required to meet the corresponding standards at the same time. By taking Gm = -130.20~-248.82 kJ/mol and X = 1.80~2.21 as screening criteria for high performance TE materials, it is found that the Gm and X of all 15 typical TE materials and 9 widely studied TE materials meet the requirement very well, except for the X of Mg2Si, and 64 pure substances are screened as potential TE materials from 102 atypical TE materials. In addition, with reference to their electronegativity, 44 pure substances are selected directly from a thermochemical data book as potential high-performance TE materials. A particular finding is that several carbides, such as Be2C, CaC2, BaC2, SmC2, TaC and NbC, may have certain TE properties. Because the Gm and X of pure substances can be easily found in thermochemical data books and calculated using the X of pure elements, respectively, the Gm and X of materials can be used as good high-throughput screening criteria for predicting TE properties.