Inorganic Crystal Structure Database Research Articles

Screening Nitrides with High Debye Temperatures as Nonlinear Optical Materials

Nitrides, in which P in pnictides is substituted by N, are proposed as nonlinear optical (NLO) materials. Herein, 34 asymmetric nitrides collected in the Inorganic Crystal Structure Database (ICSD) are evaluated and predicted by first-principles calculations. As a result, three Si-nitrides possessing wide band gaps and large NLO coefficients are screened out: BeSiN2 (6.66 eV, d24 = −1.09 pm/V), LiSi2N3 (6.51 eV, d15 = −1.38 pm/V), and LaSi3N5 (4.73 eV, d14 = −0.98 pm/V). In addition, LiGaS2-like structure MoZn3N4 is a promising infrared NLO material with a large NLO coefficient d24 = −16.70 pm/V, which is 1.2 times that of the infrared benchmark AgGaS2 (d36 = 13.4 pm/V); meanwhile, its band gap of 3.05 eV is better than that of AgGaS2 (2.73 eV). Moreover, MoZn3N4 with a higher Debye temperature of 795.5 K, which suggests higher sound speeds and longer phonon lifetimes, thus should exhibit higher thermal conductivity. These nitrides represent a new exploration family of NLO materials.

Catalogue of flat-band stoichiometric materials.

Topological electronic flattened bands near or at the Fermi level are a promising route towards unconventional superconductivity and correlated insulating states. However, the related experiments are mostly limited to engineered materials, such as moiré systems1-3. Here we present a catalogue of the naturally occuring three-dimensional stoichiometric materials with flat bands around the Fermi level. We consider 55,206 materials from the Inorganic Crystal Structure Database catalogued using the Topological Quantum Chemistry website4,5, which provides their structural parameters, space group, band structure, density of states and topological characterization. We combine several direct signatures and properties of band flatness with a high-throughput analysis of all crystal structures. In particular, we identify materials hosting line-graph or bipartite sublattices-in either two or three dimensions-that probably lead to flat bands. From this trove of information, we create the Materials Flatband Database website, a powerful search engine for future theoretical and experimental studies. We use the database to extract a curated list of 2,379 high-quality flat-band materials, from which we identify 345 promising candidates that potentially host flat bands with charge centres that are not strongly localized on the atomic sites. We showcase five representative materials and provide a theoretical explanation for the origin of their flat bands close to the Fermi energy using the S-matrix method introduced in a parallel work6.

The effect of alumina addition on the synthesis of zeolite from fly ash by hydrothermal method

ZSM-5 and analcime were successfully synthesized from coal fly ash using hydrothermal method in alkaline solution. The fly ash used was taken from the Paiton PLTU, one of the largest steam power plants in Indonesia. The fly ash was purified using sulphuric acid for 30 minutes and it was found that it has 17.7 wt.% of Si content and 2.79 wt.% of Al content. The zeolite synthesis was carried out in a hydrothermal reactor at 160 oC and 3 bar for 48 hours both with and without alumina addition. Subsequently, the zeolite produced was then calcined and characterized using X-ray diffraction (XRD) analysis. The results showed that ZSM-5 crystals were obtained from the hydrothermal process without alumina addition. On the other hand, the addition of alumina has a large impact on the hydrothermal process, which formed analcime crystals as the final product. Furthermore, the ZSM-5 and analcime produced have great correspondence to ICSD (Inorganic Crystal Structure Database) standards (no. 61010 for ZSM-5 and no. 34877 for analcime).

The Phosphorus Bond, or the Phosphorus-Centered Pnictogen Bond: The Covalently Bound Phosphorus Atom in Molecular Entities and Crystals as a Pnictogen Bond Donor.

The phosphorus bond in chemical systems, which is an inter- or intramolecular noncovalent interaction, occurs when there is evidence of a net attractive interaction between an electrophilic region associated with a covalently or coordinately bonded phosphorus atom in a molecular entity and a nucleophile in another, or the same, molecular entity. It is the second member of the family of pnictogen bonds, formed by the second member of the pnictogen family of the periodic table. In this overview, we provide the reader with a snapshot of the nature, and possible occurrences, of phosphorus-centered pnictogen bonding in illustrative chemical crystal systems drawn from the ICSD (Inorganic Crystal Structure Database) and CSD (Cambridge Structural Database) databases, some of which date back to the latter part of the last century. The illustrative systems discussed are expected to assist as a guide to researchers in rationalizing phosphorus-centered pnictogen bonding in the rational design of molecular complexes, crystals, and materials and their subsequent characterization.

A general method for searching for homometric structures.

The discovery of the homometry of X-ray diffraction dates back to the 1930s and shows the ambiguity in structure characterization purely from diffraction technologies. Since then, there has only been limited discussion on this topic with reference to several real structures and an efficient method for finding homometric structures for a known three-dimensional structure is still lacking today. In this work, a method was developed to create a set of structures with the same interatomic distances as an original structure, which can then be identified as powder X-ray diffraction or single-crystal X-ray diffraction homometric structures. This method was applied to the Inorganic Crystal Structure Database (ICSD) and homometric structures for 37 reported structures were found. Also it was found that homometric structures are encountered frequently in solving crystal structures when building initial models with only heavy atoms, which indicates that special caution is needed in interpreting XRD data with heavy-atom methods.

Linear unit BN2: a novel birefringence-enhanced fundamental module with sp orbital hybridization.

Inorganic planar π-conjugated groups are advantageous to generate large birefringence in optical functional materials, and many excellent materials contain CO3, BO3 or B3O6, such as CaCO3, α/β-BaB2O4 (α/β-BBO), and KBe2BO3F2 (KBBF). In view of their microscopic structures, the common characteristics are the planar structures, which are regarded as birefringence-enhanced fundamental modules (FMs). Nowadays, exploring novel birefringence-enhanced FMs is becoming a burning issue. Herein, we investigated the birefringence-enhanced FMs in B–N systems and found that the BN2 linear unit could produce great birefringence. Through the investigation based on the Inorganic Crystal Structure Database, some compounds with the BN2 linear group were screened out with the formulas A3BN2 (A = Li, Na), A3BN3 (A = Mg, Ca), and Ba3(BN2)2. Particularly, Ca3(BN2)N exhibits a great birefringence of about 0.411 at 1064 nm, which is 3.5, 2.5 and 2.0 times those of the most commercially used birefringent crystals α-BaB2O4 (Δn = 0.116 at 1064 nm), CaCO3 (Δn = 0.164 at 1064 nm) and YVO4 (Δn = 0.208 at 1064 nm), respectively. To find the origins of the optical properties of compounds with the BN2 linear group, the first-principles, REDA and polarizability anisotropy analysis methods were used. Owing to the structural arrangement and the polarization anisotropy of the BN2 linear group, it can influence the birefringence significantly. This work will provide a general way for exploring birefringence-enhanced FMs in B–N compounds.

Accelerated Discovery and Design of Ultralow Lattice Thermal Conductivity Materials Using Chemical Bonding Principles

AbstractSemiconductors with very low lattice thermal conductivities are highly desired for applications relevant to thermal energy conversion and management, such as thermoelectrics and thermal barrier coatings. Although the crystal structure and chemical bonding are known to play vital roles in shaping heat transfer behavior, material design approaches of lowering lattice thermal conductivity using chemical bonding principles are uncommon. In this work, an effective strategy of weakening interatomic interactions and therefore suppressing lattice thermal conductivity based on chemical bonding principles is presented and a high‐efficiency approach of discovering low κL materials by screening the local coordination environments of crystalline compounds is developed. The resulting first‐principles calculations uncover 30 hitherto unexplored compounds with (ultra)low lattice thermal conductivities from 13 prototype crystal structures contained in the Inorganic Crystal Structure Database. Furthermore, an approach of rationally designing high‐performance thermoelectrics is demonstrated by additionally incorporating cations with stereochemically active lone‐pair electrons. These results not only provide atomic‐level insights into the physical origin of the low lattice thermal conductivity in a large family of copper/silver‐based compounds but also offer an efficient approach to discover and design materials with targeted thermal transport properties.

Automated prediction of lattice parameters from X-ray powder diffraction patterns.

A key step in the analysis of powder X-ray diffraction (PXRD) data is the accurate determination of unit-cell lattice parameters. This step often requires significant human intervention and is a bottleneck that hinders efforts towards automated analysis. This work develops a series of one-dimensional convolutional neural networks (1D-CNNs) trained to provide lattice parameter estimates for each crystal system. A mean absolute percentage error of approximately 10% is achieved for each crystal system, which corresponds to a 100- to 1000-fold reduction in lattice parameter search space volume. The models learn from nearly one million crystal structures contained within the Inorganic Crystal Structure Database and the Cambridge Structural Database and, due to the nature of these two complimentary databases, the models generalize well across chemistries. A key component of this work is a systematic analysis of the effect of different realistic experimental non-idealities on model performance. It is found that the addition of impurity phases, baseline noise and peak broadening present the greatest challenges to learning, while zero-offset error and random intensity modulations have little effect. However, appropriate data modification schemes can be used to bolster model performance and yield reasonable predictions, even for data which simulate realistic experimental non-idealities. In order to obtain accurate results, a new approach is introduced which uses the initial machine learning estimates with existing iterative whole-pattern refinement schemes to tackle automated unit-cell solution.

NH4+-based frameworks as a platform for designing electrodes and solid electrolytes for Na-ion batteries: A screening approach

The development of new high-performance materials can boost the implementation of sodium-ion batteries. In this work, we report on a data-driven two-step algorithm for searching novel electrodes and solid electrolytes with superior estimated Na+ transport properties based on the crystal chemistry concept of flexibility and adaptability of polyhedral host structures to different mobile ions during the ion exchange reaction at low temperatures. At the first stage, polyanion frameworks derived from compositionally suitable NH4+-containing inorganic compounds are screened to study the dimensionality of Na+ diffusion pathways and migration barriers via the Bond Valence Energy Landscapes (BVEL) method. At the second stage, the downselected frameworks are optimized and examined using Density Functional Theory (DFT), which was adapted for the first time for the isomorphous ion exchange to model and validate the expected fast Na+ ionic diffusion. Such a screening of Inorganic Crystal Structure Database (2020) yields almost 70 compounds with anticipated high Na+ ionic conductivity to serve as potential electrodes or solid electrolytes. As a result, new promising KTiOPO4-type NaFeSO4F electrode material and langbeinite-type NaZr2(PO4)3 electrolyte that can be obtained from the corresponding NH4FeSO4F and NH4Zr2(PO4)3 parental frameworks, respectively, reveal low migration barriers in three crystallographic directions as per Nudged Elastic Band calculations.

Lattice Thermal Conductivity: An Accelerated Discovery Guided by Machine Learning.

In the present work, we used machine learning (ML) techniques to build a crystal-based model that can predict the lattice thermal conductivity (LTC) of crystalline materials. To achieve this, first, LTCs of 119 compounds at various temperatures (100-1000 K) were obtained based on density functional theory (DFT) and phonon calculations, and then, these data were employed in the next learning process to build a predictive model using various ML algorithms. The ML results showed that the model built based on the random forest (RF) algorithm with an R2 score of 0.957 was the most accurate compared with the models built using other algorithms. Additionally, the accuracy of this model was validated using new cases of four compounds, which was not seen for the model before, where a good matching between calculated and predicted LTCs of the new compounds was found. To find candidates with ultralow LTCs (<1 W m-1 K-1) at room temperature, the model was used to screen compounds (32116) in the Inorganic Crystal Structure Database. From the screened compounds, Cs2SnI6 and SrS were selected to validate the ML prediction using the counterpart theoretical calculations (DFT and phonon), and it was found that the outcome behaviors by both methods (ML prediction and DFT/phonon calculations) are fairly consistent. Considering the type of employed feature, the prime novelty in this work is that the built model can credibly predict the LTC-temperature behaviors of new compounds that are constructed based on prototype structures and chemical compositions, without the use of any DFT-relaxed structure parameters. Accordingly, using the periodic table, prototype structures, and the RF-based model, the LTC-temperature behavior of a huge number of compounds can be predicated.

Materials informatics platform with three dimensional structures, workflow and thermoelectric applications

Since the proposal of the “Materials Genome Initiative”, several material databases have emerged and advanced many materials fields. In this work, we present the Materials Informatics Platform with Three-Dimensional Structures (MIP-3d). More than 80,000 structural entries, mainly from the inorganic crystal structural database, are included in MIP-3d. Density functional theory calculations are carried out for over 30,000 entries in the database, which contain the relaxed crystal structures, density of states, and band structures. The calculation of the equations of state and sound velocities is performed for over 12,000 entries. Notably, for entries with band gap values larger than 0.3 eV, the band degeneracies for the valence band maxima and the conduction band minima are analysed. The electrical transport properties for approximately 4,400 entries are also calculated and presented in MIP-3d under the constant electron-phonon coupling approximation. The calculations of the band degeneracies and electrical transport properties make MIP-3d a database specifically designed for thermoelectric applications.

The theoretical evaluation of new promising solid ion conductors for zinc-ion batteries

Recently, we have screened the Inorganic Crystal Structure Database (ICSD, version 2020/1) to find new Zn2+-ion conductors. After a stepwise selection procedure, we have identified five most promising Zn-ion conductors ZnFe2O4, ZnV2O4, ZnCr2O4, ZnP2O6 and Zn3S2O9. In this work we describe the ion conductivity in the selected structures in detail by using of a set of methods: analysis of free space in crystals (geometrical-topological analysis, based on the Voronoi partitioning approach), calculation of the Zn2+-ion migration energy barrier within the bond valence site energy method, and calculation of the Zn2+-ion energy barriers within the density functional theory approach.

Crystal structure of silver carbonate iodide Ag10(CO3)3I4.

The title silver carbonate iodide, Ag10(CO3)3I4, deca-silver(I) tris-(carbonate) tetra-iodide, was recently reported as a precursor of the new superionic conductor Ag17(CO3)3I11. Ag10(CO3)3I4, was prepared by heating a stoichiometric powder mixture of AgI and Ag2CO3 at 430 K. A single-crystal suitable for X-ray diffraction analysis was obtained by slow cooling of a melt with an AgI-rich composition down from 453 K. Ag10(CO3)3I4 exhibits a layered crystal structure packed along [10], in which Ag atoms are inter-calated between the layers of hexa-gonally close-packed I atoms, and CO3 groups. Up to now, Cs3Pb2(CO3)3I is the only other compound containing carbonate groups and iodide ions registered in the Inorganic Crystal Structure Database.

Combination of recommender system and single-particle diagnosis for accelerated discovery of novel nitrides.

Discovery of new compounds from wide chemical space is attractive for materials researchers. However, theoretical prediction and validation experiments have not been systematically integrated. Here, we demonstrate that a new combined approach is powerful in significantly accelerating the discovery rate of new compounds, which should be useful for exploration of a wide chemical space in general. A recommender system for chemically relevant composition is constructed by machine learning of Inorganic Crystal Structure Database using chemical compositional descriptors. Synthesis and identification experiments are made at the chemical compositions with high recommendation scores by the single-particle diagnosis method. Two new compounds, La4Si3AlN9 and La26Si41N80O, and two new variants (isomorphic substitutions) of known compounds, La7Si6N15 and La4Si5N10O, are successfully discovered. Finally, density functional theory calculations are conducted for La4Si3AlN9 to confirm the energetic and dynamical stability and to reveal its atomic arrangement.

Bulk O2 formation and Mg displacement explain O-redox in Na0.67Mn0.72Mg0.28O2

Summary O-redox in compounds with Li on the transition-metal layers (TML) has recently been attributed to the formation of molecular O2 on charge, trapped in the lattice. Here, we show that a similar process occurs for P2-Na0.67[Mn0.72Mg0.28]O2, which contains Mg2+ on the TML. The molecular O2 is identified by high-resolution RIXS and quantified by magnetometry, showing that it equates to the charge passed. This O2 is trapped in voids that are formed by Mg2+ out-of-plane displacement and Mn4+ in-plane disordering and is then reduced on discharge associated with a large voltage hysteresis. In contrast to compounds containing Li+ in the TML, in which the honeycomb ordering and the high-voltage plateau are irreversibly lost after the first cycle, in P2-Na0.67[Mn0.72Mg0.28]O2, the plateau reappears partially on the second charge due to the partial reversibility of Mn in-plane and Mg out-of-plane migration and the local reformation of the honeycomb ordering.

Enlisting Potential Cathode Materials for Rechargeable Ca Batteries

The development of rechargeable batteries based on a Ca metal anode demands the identification of suitable cathode materials. This work investigates the potential application of a variety of compounds, which are selected from the inorganic crystal structural database, considering 3d-transition metal oxysulfides, pyrophosphates, silicates, nitrides, and phosphates with a maximum of four different chemical elements in their composition. Cathode performances of CaFeSO, CaCoSO, CaNiN, Ca3MnN3, Ca2Fe(Si2O7), CaM(P2O7) (M = V, Cr, Mn, Fe, and Co), CaV2(P2O7)2, Ca(VO)2(PO4)2, and α-VOPO4 are evaluated through the calculation of operation voltages, volume changes associated with the redox reaction, and mobility of Ca2+ ions. Some materials exhibit attractive specific capacities and intercalation voltages combined with energy barriers for Ca migration around 1 eV (CaFeSO, Ca2FeSi2O7, and CaV2(P2O7)2). Based on the Density Functional Theory results, αI-VOPO4 is identified as a potential Ca cathode with a maximum theoretical specific capacity of 312 mAh/g, an average intercalation voltage of 2.8 V, and calculated energy barriers for Ca migration below 0.65 eV.

CRYSPNet: Crystal structure predictions via neural networks

Structure is the most basic and important property of crystalline solids; it determines directly or indirectly most materials characteristics. However, predicting crystal structure of solids remains a formidable and not fully solved problem. Standard theoretical tools for this task are computationally expensive and at times inaccurate. Here we present an alternative approach utilizing machine learning for crystal structure prediction. We developed a tool called Crystal Structure Prediction Network (CRYSPNet) that can predict the Bravais lattice, space group, and lattice parameters of an inorganic material based only on its chemical composition. CRYSPNet consists of a series of neural network models, using as inputs predictors aggregating the properties of the elements constituting the compound. It was trained and validated on more than 100,000 entries from the Inorganic Crystal Structure Database. The tool demonstrates robust predictive capability and outperforms alternative strategies by a large margin. Made available to the public (at https://github.com/AuroraLHT/cryspnet), it can be used both as an independent prediction engine or as a method to generate candidate structures for further computational and/or experimental validation.

Electrochemical Performance of Nickel Silicide Electrodes for Lithium-Ion Batteries in an Ionic-Liquid Electrolyte

Developing lithium-ion batteries (LIBs) with a high energy density, long cycle, and high safety is essential for establishing a sustainable society because LIBs are used as electric vehicle batteries and stationary batteries for utilizing renewables. Silicon (Si) has great potential as an anode active material for next-generation LIBs due to its high theoretical capacity (3580 mA h g- 1 for Li15Si4). However, Si electrodes show poor cycle stability, which is mainly caused by a large volume change in Si during the lithiation (charge) and delithiation (discharge) reactions. Additionally, Si has other disadvantages, such as high electrical resistivity and a low Li+ diffusion coefficient. We have previously investigated the lithiation and delithiation properties of pure binary silicide (M ySiz, M : transition metal) electrodes. Some electrodes maintained a high reversible capacity in an ionic-liquid electrolyte, whereas they showed a poor cycling performance in a conventional organic-liquid electrolyte. In contrast, the effect of difference in crystal phase of silicides composed of the same elements on the lithiation and delithiation properties has not yet been clarified. Herein, we synthesized various pure nickel silicide (Ni-Si) powders. and investigated their lithiation and delithiation properties in an ionic-liquid electrolyte. Ni-Si powders (NiSi2, NiSi, Ni2Si, and Ni3Si) were synthesized by a mechanical alloying (MA) method. The obtained powders were characterized by X-ray diffraction (XRD) and Raman spectroscopy. XRD patterns were identified compared with patterns in the Inorganic Crystal Structure Database (ICSD). Each silicide electrode was prepared by a gas deposition (GD) method, which does not require a binder or conductive agent. We assembled 2032-type coin cells, which consisted of the silicide electrode as the working electrode, Li metal foil as the counter electrode and a glass fiber filter as the separator. 1 mol dm- 3 (M) lithium bis(fluorosulfonyl)amide (LiFSA) in N-methyl-N-propylpyrrolidinium bis(fluorosulfonyl)amide (Py13-FSA) was used as an ionic-liquid electrolyte. A galvanostatic charge-discharge test was carried out in the potential range between 0.005 and 2.000 V vs. Li+/Li at 303 K. The current density was set at 50 mA g- 1. To discuss the charge density of each element in Li x Ni y Si z , a first-principle calculation based on density functional theory (DFT) was performed using the projector augmented wave (PAW) method as implemented in the plane wave code of the Vienna Ab initio Simulation Package (VASP). A generalized gradient approximation (GGA) was used as the term exchange correlation with a kinetic cutoff of 350 eV. Brillouin zone sampling was performed with an 8×8×8 k point mesh within a Gamma point centered mesh scheme. We synthesized various Ni-Si powders by a MA method. Comparing the XRD pattern of the prepared samples and the corresponding ICSD pattern, all peaks were assigned to pure Ni-Si phase. Additionally, there were no peaks arising from the raw materials (Ni and Si). In Raman spectra, peaks assigned to crystalline Si (c-Si) and amorphous Si (a-Si) appear at approximately 520 and 490 cm- 1, respectively. Each silicide also gives no Raman peaks of c-Si and/or a-Si which indicates that neither c-Si nor a-Si is included in the synthesized powders. Therefore, the MA treatment successfully produced a pure Ni-Si phase. Fig.1 shows the dependence of the gravimetric discharge capacity of various nickel silicide electrodes on the cycle number in 1 M LiFSA/Py13-FSA.The NiSi2 electrode exhibited the highest initial capacity of approximately 800 mA h g- 1, and the Ni3Si electrode showed the second highest capacity. The results demonstrated that there was no correlation between initial capacity and silicon content in Ni-Si. To reveal the difference in the initial capacity, we determined the charge density of each element in Li x Ni y Si z based on computational chemistry. Li and Si have positive charges in all silicides, whereas Ni has a negative charge in Li x Ni y Si z . These results indicate that Li has a high affinity with Ni in Li x Ni y Si z . In addition, it is suggested that the higher the affinity of Ni with Li is, the higher the initial capacity is. Furthermore, we investigated the distance between Li and close atoms (Ni or Si) in Li x Ni y Si z . The initial capacity increased with an increase in the distance. Therefore, both the affinity of Ni with Li and the distance between Li and neighboring atom should influence on the initial capacity. Figure 1

A highly efficient and informative method to identify ion transport networks in fast ion conductors

High-throughput analysis of the ion transport pathways is critical for screening fast ion conductors. Currently, empirical methods, such as the geometric analysis and bond valence site energy (BVSE) methods, are respectively used for the task. Geometric analysis method can only extract geometric and topological pathway properties without considering the interatomic interactions, while the BVSE method alone does not yield a geometric classification of the sites and interstices forming the pathway. Herein, we propose a highly efficient and informative method to identify interstices and connecting segments constructing an ion transport network by combining topological pathway network and BVSE landscape, which enables to obtain both the geometry and energy profiles of nonequivalent ion transport pathways between adjacent lattice sites. These pathways can be further used as the input for first-principles nudged elastic band calculations with automatically generated chains of images. By performing high-throughput screening of 48,321 Li-, Na-, Mg- and Al-containing ionic compounds from the Inorganic Crystal Structure Database based on the filter combining geometric analysis and BVSE methods, we obtain 1,270 compounds with connected ionic migration pathways of suitable sizes and low migration energy barriers, which include both previously reported fast ion conductors, and new promising materials to be explored further.

High-throughput screening of two-dimensional materials and prediction of photocatalytic performances

<p indent=0mm>In recent years, a variety of excellent two-dimensional (2D) materials have been successfully prepared by mechanical isolation, liquid-phase exfoliation and molecular beam epitaxial growth, which greatly promotes the exploration of the special properties of 2D materials. Traditional “trail and error methods” are facing challenges such as low efficiency, high cost and lack of guidance, so it is extremely difficult to explore novel 2D materials. In contrast, first-principles calculations can not only compute and simulate a large number of new 2D materials to predict their structure and physical properties, but also design their possible synthesis paths and methods by referring to the predicted properties. With the continuous improvement of supercomputers and the establishment of a large number of material databases, potential 2D structures can be screened out by using different algorithms, and targeted design of 2D materials can be achieved by combining first-principles calculations. The first-principles calculations can simulate the structure of 2D materials, predict their stability, and rationally design the synthesis route according to the simulation results. At present, many 2D material structures have been predicted by theoretical calculations and many of them have been synthesized by experiments. For example, the graphene analogue silicene has attracted much attention. The results of first-principles calculations show that itexhibitsa buckled honeycomb structure. Based on the calculation of phonon spectrum and molecular dynamics simulation, the structure is stable below<sc>1000 K.</sc> The calculation results show that Si can be well adsorbed on the Ag(110) surface and silicene was successfully obtained on the Ag(111) surface several years later. Compared with the traditional “trial and error method”, although the first-principles calculations can accelerate the discovery of 2D materials, it still does not get rid of the constraints of “intuition” and causes a waste of resources. First-principles calculations heavily rely on scientific intutition, which would lead to the waste of calcualtion resources. The emergence of high-throughput computing can reduce the waste of computing resources. It provides an effective method for the discovery and screening of novel 2D materials. Through various algorithms, novel 2D materials are excavated from theconstructed databases, such as inorganic crystal structure database, crystal open database, Materials Project database, etc. High-throughput screening combined with first-principles calculations can realize the targeted design of materials. For example, through high-throughput calculations, potential 2D materials are screened from materials databases. After that, the stability, band gap, carrier mobility, light absorption and the position of band edge for the screened 2D materials can be predicted by first-principles calculations. Therefore, the design of 2D photocatalytic materials with guidance and the evaluation of photocatalytic performance greatly promote the development of 2D photocatalytic materials. Based on high-throughput screening and first-principles calculations, predicting the structural and physical properties of 2D materials exhibits advantages that traditional experiments can not achieve. These advantages have greatly promoted the discovery and research of 2D materials. We introduced the methods of theoretical calculation and summarized the research results of structure design and performance prediction of 2D materials. Finally, based on these research advances, the future development of this field is prospected.