Crystal Structure Prediction Research Articles

Crystal Structure Prediction Using Generative Adversarial Network with Data-Driven Latent Space Fusion Strategy.

Crystal structure prediction (CSP) is an important field of material design. Herein, we propose a novel generative adversarial network model, guided by a data-driven approach and incorporating the real physical structure of crystals, to address the complexity of high-dimensional data and improve prediction accuracy in materials science. The model, termed GAN-DDLSF, introduces a novel sampling method called data-driven latent space fusion (DDLSF), which aims to optimize the latent space of generative adversarial networks (GANs) by combining the statistical properties of real data with a standard Gaussian distribution, effectively mitigating the "mode collapse" problem prevalent in GANs. Our approach introduces a more refined generation mechanism specifically for binary crystal structures such as gallium nitride (GaN). By optimizing for the specific crystallographic features of GaN while maintaining structural rationality, we achieve higher precision and efficiency in predicting and designing structures for this particular material system. The model generates 9321 GaN binary crystal structures, with 16.59% reaching a stable state and 24.21% found to be metastable. These results can significantly enhance the accuracy of crystal structure predictions and provide valuable insights into the potential of the GAN-DDLSF approach for the discovery and design of binary, ternary, and multinary materials, offering new perspectives and methods for materials science research and applications.

Thin film synthesis, structural analysis, and magnetic properties of novel ternary transition metal nitride MnCoN2

Recent high-throughput computational searches have predicted many novel ternary nitride compounds providing new opportunities for materials discovery in underexplored phase spaces. Nevertheless, there are hardly any predictions and/or syntheses that incorporate only transition metals into new ternary nitrides. Here, we report on the synthesis, structure, and properties of MnCoN2, a new ternary nitride material comprising only transition metals and N. We find that crystalline MnCoN2 can be stabilized over its competing binaries, and over a tendency of this system to become amorphous, by controlling growth temperature within a narrow window slightly above ambient condition. We find that single-phase MnCoN2 thin films form in a cation-disordered rocksalt crystal structure. X-ray photoelectron spectroscopy analysis suggests that MnCoN2 is sensitive to oxygen through various oxides and hydroxides binding to cobalt on the surface. X-ray absorption spectroscopy is used to verify that Mn3+ and Co3+ cations exist in an octahedrally coordinated environment, which is distinct from a combination of CoN and MnN binaries and in agreement with the rocksalt-based crystal structure prediction. Magnetic measurements suggest that MnCoN2 has a canted antiferromagnetic ground state below 10 K. We extract a Weiss temperature of θ=−49.7K, highlighting the antiferromagnetic correlations in MnCoN2. Published by the American Physical Society 2024

Prediction of crystal structures and superconductivity of actinium borides at high pressures

Abstract To investigate potential compounds that may emerge in actinium borides at high-pressure conditions. In this work, we carried out systematic crystal structure search procedure on the Ac-B compounds from 0 to 300 GPa conditions based on evolutionary algorithms and density functional theory (DFT). We discovered a stable phase AcB6 at 0 GPa with common structure of hexaboride and three phases AcB, AcB8 and Ac2B11 at higher pressures in the Ac-B system. Our results show that Ac2B11 exhibits a Vickers hardness of approximately 31 GPa and a superconducting transition temperature Tc of 14.4 K at 0 GPa. The structure of Ac2B11 resembles a middle phase evolving from the hexaboride AcB6 towards a class of pentaboride structures, characterized by the formation of a B22 cage structure resulting from further boron enrichment based on the B20 cage, or derived from the partial dimerization of the B24 cage found in hexaborides. We believe that this configuration may hold significant potential as a parent matrix for the design of interesting ultra-hard materials with relatively high superconducting transition temperatures.

High-Throughput Screening, Crystal Structure Prediction, and Carrier Mobility Calculations of Organic Molecular Semiconductors as Hole Transport Layer Materials in Perovskite Solar Cells

High-Throughput Screening, Crystal Structure Prediction, and Carrier Mobility Calculations of Organic Molecular Semiconductors as Hole Transport Layer Materials in Perovskite Solar Cells

Assessment of the exchange-hole dipole moment dispersion correction for the energy ranking stage of the seventh crystal structure prediction blind test.

The seventh blind test of crystal structure prediction (CSP) methods substantially increased the level of complexity of the target compounds relative to the previous tests organized by the Cambridge Crystallographic Data Centre. In this work, the performance of density-functional methods is assessed using numerical atomic orbitals and the exchange-hole dipole moment dispersion correction (XDM) for the energy-ranking phase of the seventh blind test. Overall, excellent performance was seen for the two rigid molecules (XXVII, XXVIII) and for the organic salt (XXXIII). However, for the agrochemical (XXXI) and pharmaceutical (XXXII) targets, the experimental polymorphs were ranked fairly high in energy amongst the provided candidate structures and inclusion of thermal free-energy corrections from the lattice vibrations was found to be essential for compound XXXI. Based on these results, it is proposed that the importance of vibrational free-energy corrections increases with the number of rotatable bonds.

Phase Prediction via Crystal Structure Similarity in the Periodic Number Representation.

The periodic number (PN) representation of the chemical systems, introduced by Dmitri Mendeleev, uncovers the fundamental principle of chemical similarity in a straightforward way. In this framework, the rows correspond to the principal quantum numbers of the elements' electronic configurations when considered isolated atoms. This systematic arrangement allows for a deeper understanding of the relationships and patterns among the elements. In this study, we propose a novel strategy for structure type (prototype) prediction by utilizing the PN concept to identify possible modifications and phase stability of unexplored chemical systems. Our PN-based crystal structure prediction (PNcsp) program, which evaluates similarity through PN neighboring in the phase map, provides the most probable prototypes for unknown and unreported modifications of given phases in binary and higher order chemical systems. We applied PNcsp to 59 distinct chemical systems whose equimolar phases are indicated in the respective phase diagrams but lack accurate experimental structure determination. Our methodology identified 93 prototypes for these 59 equiatomic phases, of which 47 exhibit mechanical and dynamic stability. Notably, this approach discovered 19 entirely novel, fully stable polymorphic phases, thereby expanding the known landscape of potential materials. Furthermore, we demonstrated that this method is also effective for nonequimolar and higher order systems.

Contrasting conformational behaviors of molecules XXXI and XXXII in the seventh blind test of crystal structure prediction.

Accurate modeling of conformational energies is key to the crystal structure prediction of conformational polymorphs. Focusing on molecules XXXI and XXXII from the seventh blind test of crystal structure prediction, this study employs various electronic structure methods up to the level of domain-local pair natural orbital coupled cluster singles and doubles with perturbative triples [DLPNO-CCSD(T1)] to benchmark the conformational energies and to assess their impact on the crystal energy landscapes. Molecule XXXI proves to be a relatively straightforward case, with the conformational energies from generalized gradient approximation (GGA) functional B86bPBE-XDM changing only modestly when using more advanced density functionals such as PBE0-D4, ωB97M-V, and revDSD-PBEP86-D4, dispersion-corrected second-order Møller-Plesset perturbation theory (SCS-MP2D), or DLPNO-CCSD(T1). In contrast, the conformational energies of molecule XXXII prove difficult to determine reliably, and variations in the computed conformational energies appreciably impact the crystal energy landscape. Even high-level methods such as revDSD-PBEP86-D4 and SCS-MP2D exhibit significant disagreements with the DLPNO-CCSD(T1) benchmarks for molecule XXXII, highlighting the difficulty of predicting conformational energies for complex, drug-like molecules. The best-converged predicted crystal energy landscape obtained here for molecule XXXII disagrees significantly with what has been inferred about the solid-form landscape experimentally. The identified limitations of the calculations are probably insufficient to account for the discrepancies between theory and experiment on molecule XXXII, and further investigation of the experimental solid-form landscape would be valuable. Finally, assessment of several semi-empirical methods finds r2SCAN-3c to be the most promising, with conformational energy accuracy intermediate between the GGA and hybrid functionals and a low computational cost.

Pressure-induced anomaly of transport properties and structural transition in layered ferromagnetic metal TlCo2S2

In this work, the structural and transport properties of ferromagnetic metal TlCo2S2 with ThCr2Si2-type crystal structure are systematically studied using high-pressure synchrotron x-ray powder diffraction, electrical transport measurements and first-principles calculations. All the resistances of TlCo2S2 between 1.4 GPa and 5.2 GPa exhibit an upturn at TS and an obvious hysteresis is observed below TS when measured in the cooling and heating processes. As the pressure increases, TS gradually increases and the resistances display a metallic behavior without any anomaly in the whole temperature region above 5.2 GPa, indicating that a possible structural phase transition occurs at TS and is beyond the room temperature above 5.2 GPa. High-pressure x-ray diffraction measurements combined with crystal structure prediction indicate that at room temperature TlCo2S2 undergoes a structural phase transition from a tetragonal phase (I4/mmm) to an orthorhombic structure (Bmab) at Pc ≈6.8 GPa, which is consistent with the results of the resistances under high pressure. Our results indicate that a first-order structural phase transition of TlCo2S2 is induced by high pressure and then results in the anomaly of the electrical transport properties.

Quantitatively Deciphering the Local Structure and Luminescence Spectroscopy of Pr3+-Doped Yttrium Lithium Fluoride.

Praseodymium (Pr3+)-doped LiYF4 nanophosphors have garnered significant interest for their potential applications in lasers, phosphors, and quantum memories. However, there remains a lack of comprehensive research on the local coordination environment and luminescence spectroscopy of Pr3+:LiYF4 nanocrystals. This study presents the first investigation of the ground-state structure of Pr3+:LiYF4 crystals by employing the crystal structure prediction method, and a [PrF8]5- ligand complex with S4 local symmetry is determined. The complete energy levels of the Pr3+ ions in LiYF4 nanocrystals are unveiled by using our newly developed well-established parametrization matrix diagonalization method. A novel set of free-ion and crystal-field parameters is derived through a good simulation with 45 experimental energy levels. Many of the emissions of Pr3+-doped LiYF4 are successfully reproduced based on Judd-Ofelt theory, and these transitions are comparable to the experimental ones. Moreover, two new prominent emission bands with their peaks at 675 and 849 nm originating from 1I6 → 3F4 and 1I6 → 1G4 transitions, respectively, are predicted by us for the first time. This study could provide a feasible method to search for practical laser transition channels of solid-state lasers based on Pr3+: LiYF4 nanophosphors.

Prediction of crystal structure, phase group, and stability of 2D materials through data science coupled with DFT

Two-dimensional materials offer unique mechanical strength, electrical conductivity, and tunable electronic properties, driving advancements in electronics, energy storage, and catalysis. The crystal structure of the materials is fundamental to several properties. In this study, we employ solely the elemental features to construct a random forest classification model achieving an 82.56% accuracy in predicting the crystal structure of the compounds. Additionally, one vs rest binary classifiers are developed for each crystal structure within our 2D materials dataset, with accuracies ranging from 86% to 99%, enhancing their practical utility. Features based on stoichiometric norms have been found to play a major role in the prediction because of the fact that certain crystal structures are associated with more balanced compositions, leading to lower norm values, while others with imbalances have higher norm values. It has also been observed that material compositions with an overall higher amount of p valence electrons tend to show monoclinic, orthorhombic and triclinic crystal structures. Moreover, in compounds with a lower total electronegativity of the constituent elements, the occurrence of trigonal and tetragonal systems has been found to be more probable. In a similar fashion, hexagonal crystal systems have been more often found in compounds where the sum of Van Der Waals radius of the constituent atoms is lower. To validate our model, Crystal structure AnaLYsis by Particle Swarm Optimization (CALYPSO) method, and Density Functional Theory (DFT) are utilized to predict the crystal structure, phase group, and stability of a 2D material.

The Changing Phases of Bromopentafluorobenzene.

As part of a much larger study on non-covalent interactions in binary adducts, we have explored the solid-state structures of bromopentafluorobenzene (C6F5Br) using differential scanning calorimetry (DSC), variable-temperature powder X-ray diffraction (VT-PXRD), and single-crystal X-ray diffraction (SXD). DSC data initially indicated a single solid-state phase below the freezing point, but revealed additional weak transitions upon heating. The crystal structures of three solid-state phases have been solved. The SXD data showed that phases I and IV are centrosymmetric, whilst phase II is polar. However, the structure of phase III remains elusive due to the changing phase behaviour of C6F5Br that is determined as much as by kinetics as thermodynamics. The results underline the need for multiple analytical techniques to study non-covalent interactions and offer valuable data for refining computational models in crystal structure prediction and machine learning. A comparison with the iodinated counterpart is also made.

Identification of the high-pressure phases of α-SnWO4 combining x-ray diffraction and crystal structure prediction

We have characterized the high-pressure behavior of -SnWO4. The compound has been studied up to 30 GPa using a diamond-anvil cell and synchrotron powder X-ray diffraction. We report evidence of two structural phase transitions in the pressure range covered in our study, and we propose a crystal structure for the two high-pressure phases. The first one, observed around 12.9 GPa, has been obtained combining indexation using DICVOL and density-functional theory calculations. The second high-pressure phase, observed around 17.5 GPa, has been determined by using the CALYPSO code, the prediction of which was supported by a Le Bail fit to the experimental X-ray diffraction patterns. The proposed structural sequence involves two successive collapses of the unit-cell volume and an increase in the coordination number of Sn and W atoms. The room-temperature equations of state, the principal axes of compression and their compressibility, the elastic constants, and the elastic moduli are reported for -SnWO4 and for the two high-pressure phases.

Emergence of superconductivity and superionicity in the electride Li[formula omitted]La under extreme conditions

Superconducting electrides, a unique type of conventional superconductors, have recently attracted considerable attention. Inspired by the discovery of high critical temperature (Tc) in lanthanum hydrides and the feasibility of forming electrides in lithium-based compounds, we have conducted a systematic investigation the phase diagram and superconductivity of Li-La alloys under high pressure. Using the crystal structure prediction method and first-principles calculations, we uncovered three pressure-stabilized compounds with stoichiometries LiLa3, LiLa2 and Li6La. All three compounds exhibit metallic behavior with electrons transferred from Li to La atoms. Notably, Li6La emerges as a prototypical superconducting electride with Tc of 11.6 K at 350 GPa, which increases to 16.3 K as pressure decreases to 200 GPa. Electron–phonon analysis reveal that the enhancement of superconductivity mainly originates from the coupling between the increased interstitial quasiatoms (ISQs) and low-frequencies phonons. Additionally, akin to lanthanum hydrides, Li6La is predicted to enter a superionic phase characterized by high lithium diffusion coefficients under high pressure and temperature. Our findings indicate the coexistence of superconductivity and superionicity in the electride Li6La, which may prompt further experimental investigations.

Accurate Crystal Structure Prediction of New 2D Hybrid Organic-Inorganic Perovskites.

Low-dimensional hybrid organic-inorganic perovskites (HOIPs) are promising electronically active materials for light absorption and emission. The design space of HOIPs is extremely large, as a variety of organic cations can be combined with different inorganic frameworks. This not only allows for tunable electronic and mechanical properties but also necessitates the development of new tools for in silico high throughput analysis of candidate materials. In this work, we present an accurate, efficient, and widely applicable machine learning interatomic potential (MLIP) trained on 86 diverse experimentally reported HOIP materials. This MLIP was tested on 73 experimentally reported perovskite compositions and achieves a high accuracy, relative to density functional theory (DFT). We also introduce a novel random structure search algorithm designed for the crystal structure prediction of 2D HOIPs. The combination of MLIP and the structure search algorithm reliably recovers the crystal structure of 14 known 2D perovskites by specifying only the organic molecule and inorganic cation/halide. Performing this crystal structure search with ab initio methods would be computationally prohibitive but is relatively inexpensive with the MLIP. Finally, the developed procedure is used to predict the structure of a totally new HOIP with cation (cis-1,3-cyclohexanediamine). Subsequently, the new compound was synthesized and characterized, which matches the predicted structure, confirming the accuracy of our method. This capability will enable the efficient and accurate screening of thousands of combinations of organic cations and inorganic layers for further investigation.

Random forest prediction of crystal structure from electron diffraction patterns incorporating multiple scattering

<b>Random forest prediction of crystal structure from electron diffraction patterns incorporating multiple scattering</b>

Impact of heteroatoms and chemical functionalisation on crystal structure and carrier mobility of organic semiconductors

The substitution of heteroatoms and the functionalisation of molecules are established strategies in chemical synthesis. They target the precise tuning of the electronic properties of hydrocarbon molecules to improve their performance in various applications and increase their versatility. Modifications to the molecular structure often lead to simultaneous changes in the morphology such as different crystal structures. These changes can have a stronger and unpredictable impact on the targeted property. The complex relationships between substitution/functionalization in chemical synthesis and the resulting modifications of properties in thin films or crystals are difficult to predict and remain elusive. Here we address these effects for charge carrier transport in organic crystals by combining simulations of carrier mobilities with crystal structure prediction based on density functional theory and density functional tight binding theory. This enables the prediction of carrier mobilities based solely on the molecular structure and allows for the investigation of chemical modifications prior to synthesis and characterisation. Studying nine specific molecules with tetracene and rubrene as reference compounds along with their combined modifications of the molecular cores and additional functionalisations, we unveil systematic trends for the carrier mobilities of their polymorphs. The positive effect of phenyl groups that is responsible for the marked differences between tetracene and rubrene can be transferred to other small molecules such as NDT and NBT leading to a mobility increase by large factors of about five.

Stable crystal structure prediction using machine learning-based formation energy and empirical potential function

Crystal structure prediction aims to predict stable and easily experimentally synthesized materials, which accelerates the discovery of new materials. It is worth noting that the stability of materials is the basis for ensuring high performance and reliable application of materials. Among which, the thermodynamic and molecular dynamics stability is especially important. Therefore, this paper proposes a method to predict stable crystal structures using formation energy and Lennard-Jones potential as evaluation indicators. Specifically, we use graph neural network models to predict the formation energy of crystals, and employ empirical formulas to calculate the Lennard-Jones potential. Then, we apply Bayesian optimization algorithms to search for crystal structures with low formation energy and Lennard-Jones potential approaching zero, in order to ensure the thermodynamic stability and dynamics stability of materials. In addition, considering the impact of the bonding situation between atoms in the crystal on the structural stability, this article uses contact map to analyze the atomic bonding situation of each crystal to screen out more stable materials. Finally, the experimental results show that the method we proposed can not only reduce the time for crystal structure prediction, but also ensure the stability of crystal materials.

Computational design of energy‐related materials: From first‐principles calculations to machine learning

AbstractEnergy‐related materials are crucial for advancing energy technologies, improving efficiency, reducing environmental impacts, and supporting sustainable development. Designing and discovering these materials through computational techniques necessitates a comprehensive understanding of the material space, which is defined by the constituent atoms, composition, and structure. Depending on the search space involved in the investigation, the computational materials design can be categorized into four primary approaches: atomic substitution in fixed prototype structures, crystal structure prediction (CSP), variable‐composition CSP, and inverse design across the entire materials space. This review provides an overview of these paradigms, detailing the concepts, strategies, and applications pertinent to energy‐related materials. The progression from first‐principles calculations to machine learning techniques is emphasized, with the aim of enhancing understanding and elucidating new advancements in computationally design of energy‐related materials.This article is categorized under: Structure and Mechanism &gt; Computational Materials Science Data Science &gt; Artificial Intelligence/Machine Learning Electronic Structure Theory &gt; Density Functional Theory

Prediction of novel layered indium halide superconductors

Abstract We design two new layered indium halide compounds LaOInF2 and LaOInCl2 by means of first-principles calculations and evolutionary crystal structure prediction. We find both compounds crystallize in a tetragonal structure with P4/nmm space group and have indirect band gaps of 2.58 eV and 3.21 eV, respectively. By substituting O with F, both of them become metallic and superconducting at low temperature. The F-doping leads to strong electron–phonon coupling in the low-energy acoustic phonon modes which is mainly responsible for the induced superconductivity. The total electron–phonon coupling strength are 1.86 and 1.48, while the superconducting transition temperature (T c) are about 7.2 K and 6.5 K with 10% and 5% F doping for LaOInF2 and LaOInCl2, respectively.

Powder-diffraction-based structural comparison for crystal structure prediction without prior indexing

The objective of crystal structure prediction (CSP) is to predict computationally the thermodynamically stable crystal structure of a compound from its stoichiometry or its molecular diagram. Crystal similarity indices measure the degree of similarity between two crystal structures and are essential in CSP because they are used to identify duplicates. Powder-based indices, which are based on comparing X-ray diffraction patterns, allow the use of experimental X-ray powder diffraction data to inform the CSP search. Powder-assisted CSP presents two unique difficulties: (i) the experimental and computational structures are not entirely comparable because the former is subject to thermal expansion from lattice vibrations, and (ii) experimental patterns present features (noise, background contribution, varying peak shapes etc.) that are not easily predictable computationally. This work presents a powder-based similarity index (GPWDF) based on a modification of the index introduced by de Gelder, Wehrens &amp; Hageman [J. Comput. Chem. (2001), 22, 273–289] using cross-correlation functions that can be calculated analytically. Based on GPWDF, a variable-cell similarity index (VC-GPWDF) is also proposed that assigns a high similarity score to structures that differ only by a lattice deformation and which takes advantage of the analytical derivatives of GPWDF with respect to the lattice parameters. VC-GPWDF can be used to identify similarity between two computational structures generated using different methods, between a computational and an experimental structure, and between two experimental structures measured under different conditions (e.g. different temperature and pressure). VC-GPWDF can also be used to compare crystal structures with experimental patterns in combination with an automatic pre-processing step. The proposed similarity indices are simple, efficient and fully automatic. They do not require indexing of the experimental pattern or a guess of the space group, they account for deformations caused by varying experimental conditions, they give meaningful results even when the experimental pattern is of very poor quality, and their computational cost does not increase with the flexibility of the molecular motif.