A Machine-Learning-Assisted Crystalline Structure Prediction Framework To Accelerate Materials Discovery.

Modern crystal structure prediction methods based on structure generation algorithms and first-principles calculations play important roles in the design of new materials. However, the cost of these methods is very expensive because their success mostly relies on the efficient sampling of structures and the accurate evaluation of energies for those sampled structures. Herein, we develop a Machine-learning-Assisted CRYStalline Materials sAmpling sysTem (MAXMAT) aiming to accelerate the prediction of new crystal structures. For a given chemical composition, MAXMAT can generate efficient crystal structures with the help of a Python package for crystal structure generation (PyXtal) and can quickly evaluate the energies of these generated structures using a well-developed machine learning interaction potential model (M3GNET). We have used MAXMAT to perform crystal structure searches for three different chemical systems (TiO2, MgAl2O4, and BaBOF3) to test its accuracy and efficiency. Furthermore, we apply MAXMAT to predict new nonlinear optical materials, suggesting several thermodynamically synthesizable structures with high performance in LiZnGaS3 and CaBOF3 systems.

Structure-Prediction-Oriented Synthesis of Thiophosphates as Promising Infrared Nonlinear Optical Materials.

Oriented synthesis of functional materials is a focus of attention in material science. As one of the most important function materials, infrared nonlinear optical materials with large second harmonic generation effects and broad optical band gap are in urgent need. In this work, directed by the theoretical structure prediction, the first series of non-centrosymmetric (NCS) alkali-alkaline earth metal [PS4]-based thiophosphates LiCaPS4 (Ama2), NaCaPS4 (P21), KCaPS4 (Pna21), RbCaPS4 (Pna21), CsCaPS4 (Pna21) were successfully synthesized. Comprehensive characterizations reveal that ACaPS4 could be regarded as promising IR NLO materials, exhibiting wide band gap (3.77-3.86 eV), moderate birefringence (0.027-0.064 at 1064 nm), high laser-induced damage threshold (LIDT, ~10×AGS), and suitable phase-matching second harmonic generation responses (0.4-0.6×AGS). Structure-properties analyses illustrate that the Ca-S bonds show non-ignorable covalent feature, and [PS4] together with [CaSn] units play dominant roles to determine the band gap and SHG response. This work indicates that Li-, Na- and K- analogs may be promising infrared nonlinear optical material candidates, and this is the first successful case of "prediction to synthesis" involving infrared (IR) nonlinear optical (NLO) crystals in the thiophosphate system and may provide a new avenue to the design and oriented synthesis of high-performance function materials in the future.

Target-Driven Design of Deep-UV Nonlinear Optical Materials via Interpretable Machine Learning.

The development of a data-driven science paradigm is greatly revolutionizing the process of materials discovery. Particularly, exploring novel nonlinear optical (NLO) materials with the birefringent phase-matching ability to deep-ultraviolet (UV) region is of vital significance for the field of laser technologies. Herein, a target-driven materials design framework combining high-throughput calculations (HTC), crystal structure prediction, and interpretable machine learning (ML) is proposed to accelerate the discovery of deep-UV NLO materials. Using a dataset generated from HTC, an ML regression model for predicting birefringence is developed for the first time, which exhibits a possibility of achieving fast and accurate prediction. Essentially, crystal structures are adopted as the only known input of this model to establish a close structure-property relationship mapping birefringence. Utilizing the ML-predicted birefringence which can affect the shortest phase-matching wavelength, a full list of potential chemical compositions based on an efficient screening strategy is identified. Further, eight structures with good stability are discovered to show potential applications in the deep-UV region, owing to their promising NLO-related properties. This study provides a new insight into the discovery of NLO materials and this design framework can identify desired materials with high performances in the broad chemical space at a low computational cost.

Unbiased Screening of Novel Infrared Nonlinear Optical Materials with High Thermal Conductivity: Long-neglected Nitrides and Popular Chalcogenides.

Traditional infrared (IR) nonlinear optical (NLO) materials such as AgGaS2 are crucial to key devices for solid-state lasers, however, low laser damage thresholds intrinsically hinder their practical application. Here, a robust strategy is proposed for unbiased high-throughput screening of more than 140 000 materials to explore novel IR NLO materials with high thermal conductivity and wide band gap which are crucial to intrinsic laser damage threshold. Via our strategy, 106 compounds with desired band gaps, NLO coefficients and thermal conductivity are screened out, including 8 nitrides, 68 chalcogenides, in which Sr2 SnS4 is synthesized to verify the reliability of our process. Remarkably, thermal conductivity of nitrides is much higher than that of chalcogenides, e.g., 5×AgGaS2 (5.13 W/m K) for ZrZnN2 , indicating that nitrides could be a long-neglected system for IR NLO materials. This strategy provides a powerful tool for searching NLO compounds with high thermal conductivity.

PNO: a promising deep-UV nonlinear optical material with the largest second harmonic generation effect.

In this work, the polar tetrahedron [PN2O2] was revealed as a new deep-ultraviolet (deep-UV) nonlinear optically active unit. Accordingly, a thermodynamically stable compound (PNO) consisting of the polar [PN2O2] units was predicted and suggested as a promising candidate for deep-UV nonlinear optical (NLO) materials. Compared with other deep-UV materials known to date, PNO possesses the strongest second harmonic generation (SHG) coefficient (about 6 times that of KH2PO4 (KDP)). Moreover, its three-dimensional connectivity endows it with good mechanical and thermal properties. Therefore, PNO should be a new option for non-π-conjugated deep-UV NLO materials.

From Phosphate Fluoride to Fluorophosphate: Design of Novel Ultraviolet/Deep-Ultraviolet Nonlinear Optical Materials for BePO3F with Optical Property Enhancement.

Fluorine-containing compounds have stimulated the exploration of ultraviolet/deep-ultraviolet nonlinear optical (NLO) materials. Alkali/alkaline-earth metal phosphates are one of the important potential systems as NLO materials, while the common small birefringence limits the phase-matching (PM) ability in the ultraviolet/deep-ultraviolet region. Herein, by applying a "fluorination synergy-induced enhancement of optical property" strategy, novel structures of phosphate fluoride/fluorophosphate in BePO3F with good thermodynamic/dynamic stability and promising NLO-related properties are discovered via performing crystal structure prediction combined with first-principles calculations. BePO3F-I-VI exhibit relatively large birefringence of 0.025, 0.048, 0.049, 0.049, 0.059, and 0.063 at 1064 nm, respectively. Simultaneously, BePO3F-I (Pc) is a new thermodynamically stable phosphate fluoride which possesses a wide band gap (Eg = 8.03 eV), large second-harmonic generation (SHG) coefficient (d11 = 0.67 pm/V, 1.7 × KDP), and the shortest PM wavelength of 292 nm. Other five thermodynamically metastable noncentrosymmetric (NCS) BePO3F structures (II-VI) belong to fluorophosphates and exhibit deep-ultraviolet PM wavelengths of 187, 183, 186, 188, and 196 nm. It reveals that the aligned nonbonding O 2p orbitals of [BeO2F2] and [PO4] units lead to a large SHG coefficient in the phosphate fluoride BePO3F-I. For fluorophosphates (BePO3F-II-VI), the synergy of [BeO3] planar units and [PO3F] units induces relatively large birefringence. Our research results provide an idea for exploring novel high-performance NLO materials.

Design of Infrared Nonlinear Optical Compounds with Diamond-like Structures and Balanced Optical Performance.

Infrared (IR) nonlinear optical (NLO) crystals are the major materials to widen the output range of solid-state lasers to mid-infrared regions, but they are still inadequate for application due to the difficulties in balancing the large band gaps and strong NLO response. The diamond-like structure is a potential structural template to explore IR NLO materials. Herein, a computational workflow is proposed for exploring compounds with diamond-like structures, a series of LiMgGaSe3 structures were predicted successfully through this workflow, and LiMgGaSe3-I-III exhibited good optical performances in a large band gap (2.75-2.92 eV), strong SHG response (1.2-1.3 × AGS), and suitable birefringence (0.0470-0.0783 at 1064 nm). The in-depth mechanism explorations strongly demonstrate that the synergistic effect of alkaline earth metal tetrahedral [MgSe4] and [GaSe4] units is the main origin of large SHG response. The foregoing results suggest that our workflow can accelerate the discovery of new mid-IR NLO materials with diamond-like structures.

The Combination of Structure Prediction and Experiment for the Exploration of Alkali-Earth Metal-Contained Chalcopyrite-Like IR Nonlinear Optical Material.

Design and fabrication of new infrared (IR) nonlinear optical (NLO) materials with balanced properties are urgently needed since commercial chalcopyrite-like (CL) NLO crystals are suffering from their intrinsic drawbacks. Herein, the first defect-CL (DCL) alkali-earth metal (AEM) selenide IR NLO material, DCL-MgGa2 Se4 , has been rationally designed and fabricated by a structure prediction and experiment combined strategy. The introduction of AEM tetrahedral unit MgSe4 effectively widens the band gap of DCL compounds. The title compound exhibits a wide band gap of 2.96 eV, resulting in a high laser induced damage threshold (LIDT) of ≈3.0 × AgGaS2 (AGS). Furthermore, the compound shows a suitable second harmonic generation (SHG) response (≈0.9 × AGS) with a type-I phase-matching (PM) behavior and a wide IR transparent range. The results indicate that DCL-MgGa2 Se4 is a promising mid-to-far IR NLO material and give some insights into the design of new CL compound with outstanding IR NLO properties based on the AEM tetrahedra and the structure predication and experiment combined strategy.

Changing light promotes isoflavone biosynthesis in soybean pods and enhances their resistance to mildew infection.

Mildew severely reduces soybean yield and quality, and pods are the first line of defence against pathogens. Maize-soybean intercropping (MSI) reduces mildew incidence on soybean pods; however, the mechanism remains unclear. Changing light (CL) from maize shading is the most important environmental feature in MSI. We hypothesized that CL affects isoflavone accumulation in soybean pods, affecting their disease resistance. In the present study, shading treatments were applied to soybean plants during different developmental stages according to various CL environments under MSI. Chlorophyll fluorescence imaging (CFI) and classical evaluation methods confirmed that CL, especially vegetative stage shading (VS), enhanced pod resistance to mildew. Further metabolomic analyses and exogenous jasmonic acid (JA) and biosynthesis inhibitor experiments revealed the important relationship between JA and isoflavone biosynthesis, which had a synergistic effect on the enhanced resistance of CL-treated pods to mildew. VS promoted the biosynthesis and accumulation of constitutive isoflavones upstream of the isoflavone pathway, such as aglycones and glycosides, in soybean pods. When mildew infects pods, endogenous JA signalling stimulated the biosynthesis of downstream inducible malonyl isoflavone (MIF) and glyceollin to improve pod resistance.

Prediction of Fluorooxoborates with Colossal Second Harmonic Generation (SHG) Coefficients and Extremely Wide Band Gaps: Towards Modulating Properties by Tuning the BO3 /BO3 F Ratio in Layers.

Fluorooxoborates have inspired investigations of deep-ultraviolet (DUV) nonlinear optical (NLO) materials that can meet the multiple criteria. Herein, five stable structures with the composition of BaB2 O3 F2 (I-V) are discovered using the ab initio evolutionary algorithm. Among them, BaB2 O3 F2 -I has been synthesized experimentally and confirms the reliability of the method. All of the predicted structures possess extremely wide band gaps (8.1-9.0 eV). Moreover, four new structures exhibit giant second harmonic generation (SHG) coefficients (>3×KDP, d36 =0.39 pm V-1 ). A novel type of the [BOF] layer with BO3 :BO3 F ratio of [1:1] is found in BaB2 O3 F2 -II and BaB2 O3 F2 -III. While BaB2 O3 F2 -IV and BaB2 O3 F2 -V are solely composed of the BO3 F group and have colossal SHG coefficients (ca. 4×KDP). It gives the direct evidence that the BO3 F group could generate strong SHG effect. Most importantly, the influences of BO3 :BO3 F ratio and their number density on band gap, birefringence and SHG effects are investigated.

New Tungsten Borides, Their Stability and Outstanding Mechanical Properties.

We predict new tungsten borides, some of which are promising hard materials that are expected to be stable in a wide range of conditions, according to the computed composition-temperature phase diagram. New boron-rich compound WB5 is predicted to be superhard, with a Vickers hardness of 45 GPa, to possess high fracture toughness of ∼4 MPa·m0.5, and to be thermodynamically stable in a wide range of temperatures at ambient pressure. Temperature dependences of the mechanical properties of the boron-richest WB3 and WB5 phases were studied using quasiharmonic and anharmonic approximations. Our results suggest that WB5 remains a high-performance material even at very high temperatures.

Computer-aided design of metal chalcohalide semiconductors: from chemical composition to crystal structure.

The standard paradigm in computational materials science is INPUT: Structure; OUTPUT: Properties, which has yielded many successes but is ill-suited for exploring large areas of chemical and configurational hyperspace. We report a high-throughput screening procedure that uses compositional descriptors to search for new photoactive semiconducting compounds. We show how feeding high-ranking element combinations to structure prediction algorithms can constitute a pragmatic computer-aided materials design approach. Techniques based on structural analogy (data mining of known lattice types) and global searches (direct optimisation using evolutionary algorithms) are combined for translating between chemical composition and crystal structure. The properties of four novel chalcohalides (Sn5S4Cl2, Sn4SF6, Cd5S4Cl2 and Cd4SF6) are predicted, of which two are calculated to have bandgaps in the visible range of the electromagnetic spectrum.

First-principles study of thermoelectric properties of Mg2Si-Mg2Pb semiconductor materials.

Mg2XIV (XIV = Si, Ge, Sn) compounds are semiconductors and their solid solutions are believed to be promising mid-temperature thermoelectric materials. By contrast, Mg2Pb is a metal and few studies have been conducted to investigate the thermoelectric properties of Mg2Si-Mg2Pb solid solutions. Here, we present a theoretical study exploring whether Mg2Pb-Mg2Si solid solutions can be used as thermoelectric materials or not. We firstly constructed several Mg2Si1-x Pb x (0 ≤ x ≤ 1) structures and calculated their electronic structures. It is suggested that Mg2Si1-x Pb x are potential thermoelectric semiconductors in the range of 0 ≤ x ≤ 0.25. We then explicitly computed the electron relaxation time and both the electronic and lattice thermal conductivities of Mg2Si1-x Pb x (0 ≤ x ≤ 0.25) and studied the effect of Pb concentration on the Seebeck coefficient, electrical conductivity, thermal conductivity, and thermoelectric figure of merit (ZT). At low Pb concentration (x = 1/16), the ZT of the Mg2Si1-x Pb x solid solutions (up to 0.67 at 900 K) reaches a maximum and is much higher than that of Mg2Si.

Effects of carbon vacancies on the structures, mechanical properties, and chemical bonding of zirconium carbides: a first-principles study.

Interstitial carbides are able to maintain structural stability even with a high concentration of carbon vacancies. This feature provides them with tunable properties through the design of carbon vacancies, and thus making it important to reveal how carbon vacancies affect their properties. In the present study, using first-principles, we have calculated the properties of a number of stable and metastable zirconium carbides ZrC1-x (x = 0 and 1/n, n = 2-8) which were predicted by the evolutionary algorithm USPEX. Effects of carbon vacancies on the structures, mechanical properties, and chemical bonding of these zirconium carbides were systematically investigated. The distribution of carbon vacancies has significant influence on mechanical properties, especially Pugh's ratio. Nonadjacent carbon vacancies enhance Pugh's ratio, while grouped carbon vacancies decrease Pugh's ratio. This is explained by the changes in strength of Zr-C and Zr-Zr bonding around differently distributed carbon vacancies. We further explored the mechanical properties of zirconium carbides with impurities (N and O) by inserting N and O atoms into the sites of carbon vacancies. The enhanced mechanical properties of zirconium carbides were found.

Rational design of inorganic dielectric materials with expected permittivity.

Techniques for rapid design of dielectric materials with appropriate permittivity for many important technological applications are urgently needed. It is found that functional structure blocks (FSBs) are helpful in rational design of inorganic dielectrics with expected permittivity. To achieve this, coordination polyhedra are parameterized as FSBs and a simple empirical model to evaluate permittivity based on these FSB parameters is proposed. Using this model, a wide range of examples including ferroelectric, high/low permittivity materials are discussed, resulting in several candidate materials for experimental follow-up.

Evolutionary search for new high-k dielectric materials: methodology and applications to hafnia-based oxides.

High-k dielectric materials are important as gate oxides in microelectronics and as potential dielectrics for capacitors. In order to enable computational discovery of novel high-k dielectric materials, we propose a fitness model (energy storage density) that includes the dielectric constant, bandgap, and intrinsic breakdown field. This model, used as a fitness function in conjunction with first-principles calculations and the global optimization evolutionary algorithm USPEX, efficiently leads to practically important results. We found a number of high-fitness structures of SiO2 and HfO2, some of which correspond to known phases and some of which are new. The results allow us to propose characteristics (genes) common to high-fitness structures--these are the coordination polyhedra and their degree of distortion. Our variable-composition searches in the HfO2-SiO2 system uncovered several high-fitness states. This hybrid algorithm opens up a new avenue for discovering novel high-k dielectrics with both fixed and variable compositions, and will speed up the process of materials discovery.