High-throughput Screening Of Materials Research Articles

High-throughput computational screening of adsorbents and membrane materials for acetylene capture

Adsorption-based and membrane-based separation technologies have attracted much attention in acetylene purification due to their low energy consumption and environmental friendliness. Herein, through high-throughput Monte Carlo simulations, we sweep through a total of 5039 materials available in crystal database and find that as many as 165 of them are potential acetylene-type adsorbents. Based on the first-principles force field, three structures with open metal sites were determined to have high adsorption capacity, surpassing that of CuBTC. An adsorption mode map of the trapped acetylene is depicted. Then, high-throughput molecular dynamics simulations were carried out to calculate the diffusion behavior of selected membrane materials and the Robeson Upper Bound for C2H2/CO2 was plotted. The diffusion path reveals the transition of the confined space of acetylene molecule from anion pillar to A-type channel and then to B-type channel in nanopores in a simple cubic topology. Moreover, data-driven exploration reveals that the H, O, and F atoms in the pores are preferential adsorption sites. As demonstration, the extreme gradient boosting model can accurately predict the loading of C2H2, and the decision tree model was used to show an optimal selection path (RE/T > 0.118, PLD ≤6.831 Å, GSA > 2724.7 m2/g) for the permeability of membrane materials.

Advances in Photovoltaic Technologies from Atomic to Device Scale

The question of how energy resources can be efficiently used is likewise of fundamental and technological interest. In this opinion, we give a brief overview on developments of harvesting solar energy across different length scales and address some strategies to tackle economic and ecological challenges, in particular with a view to sustainability and toward a circular economy. On the mesoscopic scale, the emergence of thermodynamic laws in open quantum systems is of central importance and how they can be employed for efficient quantum thermal machines and batteries. The broad tunability of band gaps in quantum dot systems makes them attractive for hybrid photovoltaic devices. Complementary, machine learning-aided band gap engineering and the high-throughput screening of novel materials assist with improving absorption characteristics. On the device scale, hybrid concepts of optical control via metasurfaces enable a multitude of functionalities such as a directed re-emission of embedded photoluminescent materials or field enhancement effects from nanostructures. Advanced techniques in computational nanophotonics concern a topology optimization of nanostructured layers together with multiobjective optimization toward specific light management tasks. On the industrial level, modern manufacturers explore 3D printing and flexible solar cell platforms obtained from roll-to-roll technologies. The remote control of solar parks through applications via the Internet of Things opens up new strategies to expand to difficult terrain where human interaction is only required to a limited extent.

Machine Learning and High Throughput Synthesis Acceleration of the Discovery of Alkaline Electrolyte Oxygen Evolution Reaction Electrocatalysts

Accelerating the development and discovery of new catalysts is vital for advancing many electrochemical energy conversion technologies (EECT) required to achieve a sustainable future utilizing carbon-free fuel, a circular economy, and to meet the grand energy challenges of the 21st century. The oxygen evolution reaction (OER) is at the heart of many EECT such as water and carbon dioxide electrolyzers, fuel cells, and metal-oxygen batteries. The sluggish kinetics of oxygen electrocatalysis, resulting high overpotential necessary to attain practical current densities, and the high cost of the state-of-the-art OER platinum group metal (PGM) and precious metals catalysts (i.e., IrO2 and RuO2) limit the cost-effective implementation and development of several promising electrolysis technologies.1-3 The development of alternative PGM-free OER catalysts, with comparable or superior activity and durability to the PGM catalysts and derived from earth-abundant materials has thus been an active research area for decades.The application of perovskite oxides as PGM-free electrocatalysts for the OER in alkaline environments has seen significant research interest in the last decade, with tri-metallic and tetra-metallic compounds showing activities comparable to PGM-based catalysts.4,5 The chemical space of these compounds is exceptionally large, yet the development of new perovskite oxides with high OER performance (activity and durability) has been limited and often discovered through trial and error, a time and cost inefficient route that restricted the discovery of more advanced materials. Recent advances in high-performance computing, machine learning (ML), and high throughput material synthesis and screening technologies have enabled high-throughput catalyst design and discovery.4-10 This presentation will describe how the machine learning and high throughput synthesis technologies worked synergistically to accelerate the discovery of alkaline oxygen evolution reaction electrocatalysts. The role of ML in accelerating the materials synthesis and the role of high throughput synthesis in optimizing the ML model predictions will be discussed.AcknowledgmentsThis work was supported by the U.S. Department of Energy, Advanced Research Projects Agency-Energy (ARPA-E) under the DIFFERENTIATE program. This work was authored in part by Argonne National Laboratory, a U.S. Department of Energy (DOE) Office of Science laboratory operated for DOE by UChicago Argonne, LLC under contract no. DE-AC02-06CH11357.References Katsounaros, Ioannis, Serhiy Cherevko, Aleksandar R. Zeradjanin, and Karl JJ Mayrhofer. "Oxygen electrochemistry as a cornerstone for sustainable energy conversion." Angewandte Chemie International Edition53, no. 1 (2014): 102-121.Lee, Youngmin, Jin Suntivich, Kevin J. May, Erin E. Perry, and Yang Shao-Horn. "Synthesis and activities of rutile IrO2 and RuO2 nanoparticles for oxygen evolution in acid and alkaline solutions." The journal of physical chemistry letters3, no. 3 (2012): 399-404.Cherevko, S. et al. Oxygen and hydrogen evolution reactions on Ru, RuO2, Ir, and IrO2 thin film electrodes in acidic and alkaline electrolytes: A comparative study on activity and stability. Today 262, 170–180 (2016).Nahar, Lamia, Ahmed A. Farghaly, Richard J. Alan Esteves, and Indika U. Arachchige. "Shape controlled synthesis of Au/Ag/Pd nanoalloys and their oxidation-induced self-assembly into electrocatalytically active aerogel monoliths." Chemistry of Materials29, no. 18 (2017): 7704-7715.Farghaly, Ahmed A., Rezaul K. Khan, and Maryanne M. Collinson. "Biofouling-resistant platinum bimetallic alloys." ACS applied materials & interfaces10, no. 25 (2018): 21103-21112.Khan, Rezaul K., Ahmed A. Farghaly, Tiago A. Silva, Dexian Ye, and Maryanne M. Collinson. "Gold-Nanoparticle-Decorated Titanium Nitride Electrodes Prepared by Glancing-Angle Deposition for Sensing Applications." ACS Applied Nano Materials2, no. 3 (2019): 1562-1569.Farghaly, Ahmed A., Mai Lam, Christopher J. Freeman, Badharinadh Uppalapati, and Maryanne M. Collinson. "Potentiometric measurements in biofouling solutions: comparison of nanoporous gold to planar gold." Journal of The Electrochemical Society163, no. 4 (2015): H3083.Suntivich, Jin, Kevin J. May, Hubert A. Gasteiger, John B. Goodenough, and Yang Shao-Horn. "A perovskite oxide optimized for oxygen evolution catalysis from molecular orbital principles." Science334, no. 6061 (2011): 1383-1385.Hwang, Jonathan, Zhenxing Feng, Nenian Charles, Xiao Renshaw Wang, Dongkyu Lee, Kelsey A. Stoerzinger, Sokseiha Muy et al. "Tuning perovskite oxides by strain: electronic structure, properties, and functions in (electro) catalysis and ferroelectricity." Materials Today31 (2019): 100-118.Gómez-Bombarelli, Rafael, Jennifer N. Wei, David Duvenaud, José Miguel Hernández-Lobato, Benjamín Sánchez-Lengeling, Dennis Sheberla, Jorge Aguilera-Iparraguirre, Timothy D. Hirzel, Ryan P. Adams, and Alán Aspuru-Guzik. "Automatic chemical design using a data-driven continuous representation of molecules." ACS central science4, no. 2 (2018): 268-276.

Design of Organic-Inorganic Hybrid Heterostructured Semiconductors via High-Throughput Materials Screening for Optoelectronic Applications.

Organic-inorganic hybrid semiconductors, of which organometal halide perovskites are representative examples, have drawn significant research interest as promising candidates for next-generation optoelectronic applications. This interest is mainly ascribed to the emergent optoelectronic properties of the hybrid semiconductors that are distinct from those of their purely inorganic and organic counterparts as well as different material fabrication strategies and the other material (e.g., mechanical) properties that combine the advantages of both. Herein, we present a high-throughput first-principles material screening study of the hybrid heterostructured semiconductors (HHSs) that differ entirely from organometal halide perovskite hybrid ion-substituting semiconductors. HHSs crystallize as superlattice structures composed of inorganic tetrahedrally coordinated semiconductor sublayers and organic sublayers made of bidentate chain-like molecules. By changing the composition (e.g., IV, III-V, II-VI, I-III-VI2 semiconductor) and polymorph (e.g., wurtzite and zinc-blende) of the inorganic components, the type of organic molecules (e.g., ethylenediamine, ethylene glycol, and ethanedithiol), and the thickness of the composing layers across 234 candidate HHSs, we investigated their thermodynamic, electronic structure, and optoelectronic properties. Thermodynamic stability analysis indicates the existence of 96 stable HHSs beyond the ZnTe/ZnSe-based ones synthesized experimentally. The electronic structure and optoelectronic properties of HHSs can be modulated over a wide range by manipulating their structural variants. A machine learning approach was further applied to the high-throughput calculated data to identify the critical descriptors determining thermodynamic stability and electronic band gap. Our results indicate promising prospects and provide valuable guidance for the rational design of organic-inorganic hybrid heterostructured semiconductors for potential optoelectronic applications.

High-throughput screening of stable and efficient double inorganic halide perovskite materials by DFT

Perovskite solar cells have become the most promising third-generation solar cells because of their superior physical–chemical properties and high photoelectric conversion efficiency. However, the current obstacles to commercialization of perovskite solar cells are their poor stability and harmful elements. How to find high-efficiency, high-stability and non-toxic perovskite materials from thousands of possible perovskite crystals is the key to solve this problem. In this paper, the inorganic halide double perovskite A2BX6 and its crystal structure are considered, and the data mining algorithm in informatics is introduced into the high-throughput computing data to analyze various elements in nature to study the perovskite materials that can meet the requirements of high performance. The photoelectric conversion properties and stability of 42 inorganic double perovskite materials are studied based on density functional theory (DFT). The results show that the tolerance factors of 39 crystals are between 0.8 and 1.10, indicating that these crystals have stable perovskite structure. In addition, the dielectric function, PDOS, elastic modulus, shear modulus and poison’s ratio of these crystals are analyzed. According to the above theoretical simulation results, three candidate materials for ideal light absorption are presented. This can provide a theoretical basis for the industrial application of perovskite solar cells.

(Invited) Detailed Analysis of Highly-Efficient Emission in Organic Light-Emitting Diodes: The Dynamic Exciton Effect

Thermally activated delayed fluorescence (TADF) is an excellent way to convert originally dark triplet excitons into light.1 To realize the conversion, minimization of the energy gap between S1 and T1, called ΔE ST, has been effective.1,2 Actually, reverse intersystem crossing (RISC) becomes possible by the small ΔE ST.1-4 However, the rate constant of RISC (k RISC) is relatively small compared to those of competing processes, such as radiative and non-radiative decays, resulting that RISC is the rate-limiting process in TADF systems. Therefore, the acceleration of RISC is a key factor to obtain further excellent TADF-based organic light-emitting diodes (OLEDs).The small k RISC originates from small spin-orbit coupling (SOC). The S1 and T1 of TADF molecules tend to have charge transfer (CT) character, because the HOMO and LUMO are well-separated to make the ΔE ST small. The intersystem crossing (ISC) and RISC between CT-type S1 (1CT) and CT-type T1 (3CT) are spin-forbidden.5 This problem can be solved by intervening locally excited (LE) state(s) between 1CT and 3CT. Recently, we successfully achieved very fast RISC with a k RISC exceeding 107 s-1 by a new material design concept, named “tilted face-to-face alignment with optimal distance” (tFFO)6-8 by realizing the excellent energy level matching of 1CT, 3CT, and 3LE with sufficient SOC between 1CT and 3LE.In the presentation, I will first talk about detailed analysis of a TADF molecule, MA-TA.8-10 The performance is very high; the experimentally-obtained photoluminescence quantum yield (PLQY) and maximum external quantum efficiency (EQE) were 100% and 23.9%, respectively. The results seem to be contradictory to the above, because donor and acceptor (D-A) segments are perpendicular with each other resulting that both the S1 and T1 are CT-type, and higher-lying 3LE states cannot participate in the RISC process. Also, highly PLQY of 100% cannot be expected from the perpendicular structure. Our detailed quantum chemical analysis revealed that the dynamic or static D-A fluctuation was the key trigger of both the fast RISC and high PLQY. Such direct 3CT→1CT RISC-based OLEDs without using any 3LEs have an advantage that various kinds of hosts having different polarities can be used without sacrificing the device performance, that is, high EQE and blue-shifted emission can be realized simultaneously.Secondly, I will talk about the development of a high-throughput material screening method by quantitatively predicting rate constants of all relevant electronic transitions. TADF molecules have been designed through the calculations of ΔE ST and oscillator strength to realize effective RISC and following radiative decay simultaneously. However, in addition to ΔE ST, SOC is also another key factor to control k RISC as described above. Not only radiative but also non-radiative decays should be considered to obtain high PLQYs. We here propose a new method to predict TADF performance more precisely.11 Our method based on the Fermi golden rule enables us to theoretically predict relevant rate constants for all types of electronic transitions in an emitter molecule with reasonable computational cost. We applied the method to benzophenone; the calculated rate constants quantitatively agreed with the experimental ones. We are now extending this method to various TADF molecules and singlet fission systems.12-14 We express sincere thanks to my group members. This work was supported by JSPS KAKENHI Grant Numbers JP20H05840 (Grant-in-Aid for Transformative Research Areas, “Dynamic Exciton”). Computation time and NMR measurements were supported by the international Joint Usage/Research Centre at the Institute for Chemical Research, Kyoto University, Japan.1 H. Uoyama, K. Goushi, K. Shizu, H. Nomura & C. Adachi Nature 492, 234 (2012).2 H. Kaji et al. Nat. Commun. 6, 8476 (2015).3 Z. Yang et al. Chem. Soc. Rev. 46, 915 (2017).4 M. Y. Wong & E. Zysman-Colman Adv. Mater. 29, 1605444 (2017).5 M. A. El-Sayed J. Chem. Phys. 38, 2834 (1963).6 Y. Wada, H. Nakagawa, S. Matsumoto, Y. Wakisaka & H. Kaji Nat. Photon. 14, 643 (2020).7 Y. Kusakabe, Y. Wada, H. Nakagawa, K. Shizu & H. Kaji Front. Chem. 8, 530 (2020).8 H. Imahori, Y. Kobori & H. Kaji Acc. Chem. Res. 2, 501 (2021).9 Y. Wada, Y. Wakisaka & H. Kaji ChemPhysChem 22, 625 (2021).10 Y. Wada, K. Shizu & H. Kaji J. Phys. Chem. A 125, 4534 (2021).11 K. Shizu & H. Kaji J. Phys. Chem. A 125, 9000 (2021).12 K. Shizu, C. Adachi & H. Kaji J. Phys. Chem. A 124, 3641 (2020).13 K. Shizu, C. Adachi & H. Kaji Bull. Chem. Soc. Jpn. 93, 1305 (2020).14 K. Shizu, C. Adachi & H. Kaji ACS Omega 6, 2638 (2021).

Operando Optical Tracking of Single-Particle Ion Dynamics in Batteries

In order to advance lithium-ion battery technologies, particularly fast charging abilities, it is important to understand the non-equilibrium processes occurring in functioning materials under realistic conditions, in real time, and on the nano- to meso-scale. Currently, operando imaging of lithium-ion dynamics requires sophisticated synchrotron X-ray or electron microscopy methods. These techniques do not lend themselves to high-throughput material screening, and often fail to capture the behaviour of individual particles in batteries charging faster than 2C. In this work, we introduce a laboratory-based optical interferometric scattering microscope to resolve lithium-ion transport in battery materials at the single-particle level, and apply it to follow rapid cycling of the archetypical cathode material Li x CoO2. We directly visualise the insulator-metal, solid solution and lithium ordering phase transitions and determine rates of lithium insertion and removal from individual particles, identifying different mechanisms on charge vs. discharge. Finally, we capture the real-time formation of domain boundaries between different crystal orientations associated with the monoclinic lattice distortion at Li0.5CoO2. The high throughput nature of our methodology allows many particles to be sampled across the electrode and enables exploration of the role of defects, morphologies and cycling rate on battery degradation. The imaging concept is general and may be applied to study any active electrode material, and more broadly, any system where the transport of ions is associated with electronic or structural changes. Due to its straightforward lab-based implementation, we hope that this methodology will become an indispensable tool for high-throughput material discovery and mechanistic studies, to complement existing synchrotron-based methodologies. References Merryweather, A.J., Schnedermann, C., Jacquet, Q. et al. Operando optical tracking of single-particle ion dynamics in batteries. Nature 594, 522–528 (2021).

Identifying Key Descriptors for the Single-Atom Catalyzed CO Oxidation

Identifying Key Descriptors for the Single-Atom Catalyzed CO Oxidation

Dependence of local atomic structure on piezoelectric properties of PbZr1−xTixO3 materials

ABSTRACT Lead zirconate titanate (PZT, ) is a piezoelectric ceramic which can be used for several applications such as actuators, sensors, and microelectronic devices. Depending on its composition, PZT can exhibit rhombohedral, orthorhombic (tetragonal) phases, with the piezoelectric charge constant () key to evaluating its piezoelectric properties. In this study, () was calculated using density functional perturbation theory (DFPT) based on first-principles methods. First, we reveal that the rhombohedral structure is stable for Zr-rich compositions. Second, extending the study to and (PSZT), we evaluated the values of both theoretically and experimentally to investigate how they are affected by doping. The microscopic movements of individual atoms within the optimized crystal were analyzed to investigate the correlations between the structural characteristics and . The results show that the relative positions of Ti and Zr (B-site) atoms and the quadratic elongation in the PZT octahedron depend strongly on the piezoelectric charge constant , which itself depends on the Ti content. The local atomic structural parameters described in this study can be used as a descriptor for the high-throughput screening of PZT materials.

Machine Learning for the Discovery, Design, and Engineering of Materials

Machine learning (ML) has become a part of the fabric of high-throughput screening and computational discovery of materials. Despite its increasingly central role, challenges remain in fully realizing the promise of ML. This is especially true for the practical acceleration of the engineering of robust materials and the development of design strategies that surpass trial and error or high-throughput screening alone. Depending on the quantity being predicted and the experimental data available, ML can either outperform physics-based models, be used to accelerate such models, or be integrated with them to improve their performance. We cover recent advances in algorithms and in their application that are starting to make inroads toward (a) the discovery of new materials through large-scale enumerative screening, (b) the design of materials through identification of rules and principles that govern materials properties, and (c) the engineering of practical materials by satisfying multiple objectives. We conclude with opportunities for further advancement to realize ML as a widespread tool for practical computational materials design.

A High-Throughput Computational Study on the Stability of Ni- and Ti-Doped Zr2Fe Alloys

Zr2Fe alloys have been widely used in fusion energy and hydrogen energy for hydrogen storage. However, disproportionation reactions occur easily in Zr-based alloys at medium and high temperatures, which greatly reduces the storage capacity of the alloys, and is not conducive to repeated cycle applications. The doping of Zr-based alloys with appropriate transition metal elements has been found to significantly improve their H storage properties and prevent hydrogen disproportionation. A convenient approach is required to efficiently predict the desirable doped structures that are physically stable with optimal properties. In this paper, based on the MatCloud High-Throughput Material Integrated Computing Platform (MatCloud), an automated process algorithm was established to solve the disproportionation reaction of Zr2Fe. Rather than testing the doping materials one by one, such high-throughput material screening is effective in reducing the computational time. The structural stability of modified Zr2Fe alloys, with different doping elements and doping concentrations, is systematically studied. The results indicate that the maximum doping concentration of Ni-doped Zr2Fe is 33 at%, and beyond this doping concentration, Zr2(Fe1−xNix) phases become unstable. While Ti doping Zr2Fe will form a new phase, the overall hydrogen absorption capacity may have been affected by the decrease in the phase content of Zr2Fe in the main phase. The present study can shed valuable light on the design of high-performance Zr-based alloys for fusion energy and hydrogen storage.

High Throughput Calculation and Characteristic Structure Selection of Cu-Cr-Sn Copper Alloy Based on Matclould Platform

In this paper, high-throughput crystal structure modeling and high-throughput screening optimization algorithm are used to optimize the performance parameters such as characteristic microstructure type, generation conditions, strength and conductivity. The theoretical criterion of multi parameter collaborative optimization and control of key properties of matrix and second phase is established, based on which the optimal composition range of matrix and second phase is determined, which guides high-throughput experimental verification and material screening, and provides basic data for material composition design and structure control. Realize 102 secondary high-throughput parallel computing and screening based on matcloud platform, and calculate and screen the number of candidate material samples ≧106 and other related technologies. Through the functions of online generation, submission and job monitoring of first principle high-throughput jobs, all computing processes are realized automatically, and the computing results are extracted, standardized processed and stored in the database according to certain rules. In this project, the prediction model and process design platform of material composition structure process performance relationship are established according to the dynamic selection parameters of copper alloy structure, pseudopotential recommendation, k-point file generated according to calculation tasks and structure, and the correlation of some other main parameters.

First-principles high-throughput screening of bulk piezo-photocatalytic materials for sunlight-driven hydrogen production

A high-throughput screening of piezo-photocatalytic materials based on first-principles calculations and a simple electrostatic model is presented that identifies new bulk compounds able to catalyse the water splitting reaction under sunlight.

Combining crystal graphs and domain knowledge in machine learning to predict metal-organic frameworks performance in methane adsorption

A novel method capable of efficiently predicting the methane adsorption uptakes by metal-organic frameworks (MOFs) is proposed, which is built on the crystal graph convolutional neural network algorithm. Our method considers certain key physical features of MOFs along with information on secondary building units. Because the general force field fails to reproduce methane–MOF interactions involving the open metal site (OMS) adequately, the new force field parameters for methane are refined specifically for the OMS MOFs using a generic algorithm, which is particularly useful in constructing a more accurate dataset via the high-throughput grand canonical Monte Carlo. Extensive analyses demonstrate that the Pearson correlation coefficient and mean average error can reach as high as 0.973 and 9.98 cm3/cm3 for the newly developed algorithm, respectively. Furthermore, this model acquires information from the adsorption volume as embedding representations. This allows for the application of transfer learning by fine-tuning a pretrained graph network for the different properties. Finally, we predicted the methane adsorption volumes by all the MOFs in a 137953 hypothetical database at 298 K and 65 bar within hours. It is believed that this new method could be a useful tool in the early stages of high-throughput virtual screening of novel porous materials for gas adsorption or separation.

High-throughput computational screening of nanoporous materials in targeted applications

Given the large number of known and hypothetical nanoporous materials, high-throughput computational screening is an efficient method to identify the current best-performing materials and to guide the design of future materials.

Effective high-throughput screening of two-dimensional layered materials for potential lithium-ion battery anodes.

Lithium-ion batteries (LIBs) are considered the promising next-generation advanced energy storage devices. It is very important to quickly screen out ideal anode materials for LIBs with excellent performance. In this work, an effective procedure is designed for the high-throughput screening of the three kinds of LIB anode materials from 131 613 inorganic compounds in the Materials Project database. The high throughput screen procedure was not only reliable but was also easily realized. Three ideal anode materials were obtained by considering remarkable thermodynamic stability, Li capacity larger than 372 mA h g-1, band gap smaller than 1.0 eV, and two-dimensional constraint. Furthermore, open-circuit voltage, volume expansion ratio, and the diffusion energy barrier were calculated by the DFT-D corrected density functional method. We believe that our high throughput screen procedure can effectively and accurately search for other kinds of anode materials, which can strongly support the theoretical basis for experimental research.

Measurement of the single crystal elasticity matrix of polycrystalline materials

Many engineering metals are polycrystalline, as such the elasticity, crystalline orientation and grain distribution are cardinal factors in determining the physical properties of the material. The grain distribution can be measured using a number of different techniques and the orientation by a subset of these (electron back scatter diffraction, spatially resolved acoustic spectroscopy). These measurements are routinely deployed in materials development. However, the elasticity remains a more difficult parameter to measure and is rarely measured because the existing techniques are slow and cumbersome, with most current techniques requiring the laborious growth or destructive isolation of single crystals. In this work we present a technique that can determine the elasticity, crystalline orientation and grain distribution in a fast and easy measurement. The technique utilises SRAS imaging to provide the raw measurement of single grain velocity surfaces, this is input to a novel inverse solver that mitigates the problem of the inversion being very ill-conditioned, by simultaneously solving for multiple uniquely orientated grains at once in a brute-force approach. This allows simultaneous determination of the elastic constants and crystallographic orientation. Furthermore, this technique has the potential to work on polycrystalline materials with minimal preparation and is capable of high accuracy, with the potential to realise errors in the determination of elastic constants values of less than 1 GPa (∼1). In this work we demonstrate good agreement with EBSD (<6∘ disagreement on average for all Euler angles) and determine elastic constants in line with existing single-crystal values, with an expected accuracy of better than 4 GPa. Experimental results are presented for pure α-Ti (hexagonal), Ni and the more exotic Ni-base alloy CMSX-4 (both cubic). With the proposed method, once the initial measurement has been made, subsequent measurements of the elasticity on the same sample can be made rapidly so that the elasticity can be measured in real time, opening the possibility that on-line measurement of elasticity can be used to monitor processes and enable high-throughput materials screening. The current instrumentation approach is applicable to materials with grain sizes down to 50 µm, with the possibility of improving this to grain sizes of ∼5 µm. Further modifications to instrumentation and acoustic velocity calculation will facilitate greater accuracy in the determination of elastic constants.

High-throughput computational material screening of the cycloalkane-based two-dimensional Dion–Jacobson halide perovskites for optoelectronics

Two-dimensional (2D) layered perovskites have emerged as potential alternates to traditional three-dimensional (3D) analogs to solve the stability issue of perovskite solar cells. In recent years, many efforts have been spent on manipulating the interlayer organic spacing cation to improve the photovoltaic properties of Dion–Jacobson (DJ) perovskites. In this work, a serious of cycloalkane (CA) molecules were selected as the organic spacing cation in 2D DJ perovskites, which can widely manipulate the optoelectronic properties of the DJ perovskites. The underlying relationship between the CA interlayer molecules and the crystal structures, thermodynamic stabilities, and electronic properties of 58 DJ perovskites has been investigated by using automatic high-throughput workflow cooperated with density-functional (DFT) calculations. We found that these CA-based DJ perovskites are all thermodynamic stable. The sizes of the cycloalkane molecules can influence the degree of inorganic framework distortion and further tune the bandgaps with a wide range of 0.9–2.1 eV. These findings indicate the cycloalkane molecules are suitable as spacing cation in 2D DJ perovskites and provide a useful guidance in designing novel 2D DJ perovskites for optoelectronic applications.

Iterative peak-fitting of frequency-domain data via deep convolution neural networks

High-throughput material screening for the discovery and design of novel functional materials requires automatized analyses of theoretical and experimental data. Here we study the subject of human-free analyses of one-dimensional spectroscopic data, {\it e.g.} in the frequency domain, via employing deep convolution neural network. Specifically, we trained various deep convolution neural network and benchmarked their performance in decomposing one-dimensional noisy data into multiple nonorthogonal peaks in an iterative manner, after which a conventional basin-hopping algorithm was applied to further reduce residual fitting error. Among six different network architectures, a variant of "Squeeze-and-excitation" network (SENet) structure that we first propose in this study shows the best performance. Dependency of training performance with respect to the choice of the loss function is also discussed. We conclude by applying our modified SENet model to experimental photoemission spectra of graphene, MoS$_2$, and WS$_2$ and address its potential applications and limitations.

Rationalization of Nonlinear Adsorption Energy-Strain Relations and Brønsted-Evans-Polanyi and Transition State Scaling Relationships under Strain.

Scaling relations play a vital role in high-throughput screening of catalytic materials, and more and more attention is being paid to strain-based regulation of catalytic performance. Here we investigated the variation of several energetics, including adsorption energies in the initial state, transition state, and final state, reaction energies, and energy barriers with strain, by studying CO, BH, NH, CH, and NO adsorption and dissociation on M(111) (M = Cu, Ag, Ni, Pd, or Pt) surfaces. We show that energy barriers, reaction energies, and adsorption energies can vary either linearly or nonlinearly (quadratically) with strain. Systems with stronger adsorbate-substrate interaction and weaker atom-atom interaction in substrates are more likely to exhibit nonlinear relations. The well-known Brønsted-Evans-Polanyi relationships and transition state scaling relationships under strain were also examined, and both of them can be nonlinear under strain, in principle. The observed nonlinear relationships were satisfactorily rationalized with the equations derived from Mechanics of Solids.