Crystal Structure Prediction Research Articles

Large-Scale Parameter Estimation for Crystal Structure Prediction. Part 1: Dataset, Methodology, and Implementation.

Crystal structure prediction (CSP) seeks to identify all thermodynamically accessible solid forms of a given compound and, crucially, to establish the relative thermodynamic stability between different polymorphs. The conventional hierarchical CSP workflow suggests that no single energy model can fulfill the needs of all stages in the workflow, and energy models across a spectrum of fidelities and computational costs are required. Hybrid ab initio/empirical force-field (HAIEFF) models have demonstrated a good balance of these two factors, but the force-field component presents a major bottleneck for model accuracy. Existing parameter estimation tools for fitting this empirical component are inefficient and have severe limitations on the manageable problem size. This, combined with a lack of reliable reference data for parameter fitting, has resulted in development in the force-field component of HAIEFF models having mostly stagnated. In this work, we address these barriers to progress. First, we introduce a curated database of 755 organic crystal structures, obtained using high quality, solid-state DFT-D calculations, which provide a complete set of geometry and energy data. Comparisons to various theoretical and experimental data sources indicate that this database provides suitable diversity for parameter fitting. In tandem, we also put forward a new parameter estimation algorithm implemented as the CrystalEstimator program. Our tests demonstrate that CrystalEstimator is capable of efficiently handling large-scale parameter estimation problems, simultaneously fitting as many as 62 model parameters based on data from 445 structures. This problem size far exceeds any previously reported works related to CSP force-field parametrization. These developments form a strong foundation for all future work involving parameter estimation of transferable or tailor-made force-fields for HAIEFF models. This ultimately opens the way for significant improvements in the accuracy achieved by the HAIEFF models.

Deep learning generative model for crystal structure prediction

Deep learning generative model for crystal structure prediction

Discovery of PdHx compounds using genetic algorithms and first-principles calculations

Abstract In the present work, the variable-composition crystal structure prediction algorithm USPEX was combined with first-principles calculations to systematically explore stable PdH x compounds in the pressure range from 0 to 100 GPa. The predicted structures are PdH, Pd4H5, Pd2H, and PdH4, of which PdH has previously been synthesized. The electronic structures show that these compounds can be metallic, with Pd states dominating at the Fermi level. However, the hydrogen atoms do not play a main role in the electronic properties; their vibrations do contribute to the electron–phonon coupling. The predicted Pd4H5 has a superconducting transition temperature of ∼13 K, which is higher than that of PdH (∼8 K in the experiment). Additionally, PdH, Pd4H5, Pd2H, and PdH4 can be classified as phonon-mediated superconductors, falling into the low-coupling regime. Our results demonstrate that as the hydrogen concentration is increased in PdH x compounds, the superconducting transition temperature rises due to hydrogen vibrations, which is consistent with the experiment. Hence, the present study paves the way for discovering new PdH x compounds with promising superconducting transition temperatures.

Rapid prediction of molecular crystal structures using simple topological and physical descriptors.

Organic molecular crystals constitute a class of materials of critical importance in numerous industries. Despite the ubiquity of these systems, our ability to predict molecular crystal structures starting only from a two-dimensional diagram of the constituent compound(s) remains a significant challenge. Most structure-prediction protocols require a customized interatomic interaction model on which the quality of the results can depend sensitively. To overcome this problem, we introduce a new topological approach to molecular crystal structure prediction. The approach posits that in a stable structure, molecules are oriented such that principal axes and normal ring plane vectors are aligned with specific crystallographic directions and that heavy atoms occupy positions that correspond to minima of a set of geometric order parameters. By minimizing an objective function that encodes these orientations and atomic positions, and filtering based on the vdW free volume and intermolecular close contact distributions derived from the Cambridge Structural Database, stable structures and polymorphs for a given crystal can be predicted entirely mathematically without reliance on an interaction model.

Structure Prediction and Mechanical Properties of Tantalum Carbide (TaC) on ab initio Level

AbstractTantalum carbide (TaC) is an extremely hard, brittle, refractory ceramic material with excellent physical properties, which makes it a desirable material in e. g. aerospace industries. In order to explore the range of feasible modifications of TaC, we have executed a crystal structure prediction study of the TaC chemical system using a multi‐methodological approach, via enthalpy landscape explorations of pristine TaC at different pressures, supplemented by data mining searches in the ICSD database. Local structure relaxations have been accomplished by using Density Functional Theory (DFT). The global minimum is found to correspond to the equilibrium rock salt (NaCl) type modification. Additionally, eight new phases of tantalum carbide are predicted to be feasible: the WC‐type, the NiAs‐type, the 5‐5‐type, the ZnS‐type, the RingTaC‐type, the CsCl‐type, the OrthoTaC‐type, and the TetraTaC‐type. Furthermore, the elastic and mechanical properties of the predicted TaC modifications were explored on the DFT level of computation. The promising values of some of the mechanical properties of the proposed tantalum carbide modifications suggest that various scientific, industrial, and technological applications of TaC should be possible.

Crystal structure prediction of lithium-beryllium alloys under pressure with distinctive electronic and dynamic behaviors

Crystal structure prediction of lithium-beryllium alloys under pressure with distinctive electronic and dynamic behaviors

Discovery of the Most Stable Form of an Adenosine Receptor Antagonist through Virtual Polymorph Screening and Targeted Crystallization.

An innovative approach was developed to identify the optimal crystalline form, usually the thermodynamically most stable form. This method involves using virtual polymorph screening and targeted crystallization based on in silico solid-state modeling. By utilizing advanced crystal structure prediction (CSP) technology, the virtual polymorph screening method helps confirm whether the most stable crystalline form has been identified in actual crystallization experiments. If the predicted most stable form is not observed in experiments, predictions based on the method of COnductor like Screening MOdel for Real Solvents (COSMO-RS) are used to highlight solvent systems that can increase the likelihood of experimentally obtaining the desired form through a targeted crystallization process. In this work, such an approach has enabled the rapid discovery of the most stable polymorphic form and the development of a crystallization process of an adenosine receptor antagonist using minimal amounts of the sample within a shortened timeframe. Additionally, it provides a scientific rationale for ensuring the selection of the most stable form in the early stages of drug discovery, thereby reducing risks in future pharmaceutical development.

Generative artificial intelligence for discovering new materials

Crystal structure prediction has long fascinated scientists. There has been intense investigation over the last century ranging from simplistic rules to data-driven predictions and, most recently, generative artificial intelligence tools developed by academics and now deployed at scale by private companies like DeepMind. The author describes the timeline of crystal structure prediction and how machine learning has supplemented and, in some cases, replaced traditional approaches. The video article compares generative models including variational autoencoders, generative adversarial networks, and diffusion models and describes new efforts to condition these models to achieve inverse design of new crystal structures. Specific examples of xtal2png and CrysTens representations were given.

Two-dimensional Sc2O3 monolayer with tunable ultrawide bandgap for solar-blind ultraviolet photodetector

A novel 2D scandia (Sc2O3) monolayer was identified using an evolutionary algorithm-based crystal structure prediction method. The Sc2O3 monolayer exhibits a P3‾m1 symmetry and excellent energetic, thermal, dynamical, and mechanical stability, as well as good mechanical flexibility. The monolayer possesses an ultrawide indirect band gap of 6.246 eV. The monolayer is transparent in the visible light zone, while the large exciton effect leads to significant absorption in the solar-blind and vacuum ultraviolet regions. The band gap of this monolayer can be reduced monotonically by external biaxial tensile strain, resulting in the absorption spectrum covering the entire solar blind spectral region when the load reaches 5.0 %. Additionally, the monolayer has an ultra-high in-plane dielectric constant of approximately 50. The superior stability, flexibility, and strain-tunable electronic and optical properties, as well as the ultra-high dielectric constant, suggest its potential application in a solar-blind photodetector in harsh environments.

V doped Orth-Ga2O3: Half-metallic ferromagnetism, large magnetic anisotropy energy and high Curie temperature

Half-metallic ferromagnets have attracted intense interest worldwide and are considered to be an important component of spintronics devices. In this paper, an unpredicted Ga2O3 phase (named as Orth-Ga2O3) with good mechanical, dynamic, and thermodynamic stability is proposed by employing the adaptive genetic algorithm (AGA) crystal structure prediction approach. Surprisingly, V-doped Orth-Ga2O3 (V@Orth-Ga2O3) exhibits a strong ferromagnetic (FM) half-metallicity with a magnetic moment of 2 μB per formula. A detailed analysis of the density of states shows that the FM half-metallicity originates from the double exchange interaction between V-3d and O-2p orbitals. Besides, V@Orth-Ga2O3 has a big magnetic anisotropy energy (MAE) of 0.36 meV, mainly due to the (dxy, dx2-y2) orbitals of the V atom. According to Monte Carlo simulations, the Curie temperature TC of V@Orth-Ga2O3 is 225 K. The effect of carrier concentration and strain on the ferromagnetic stability of V@Orth-Ga2O3 system is also investigated. These unique magnetic properties make V@Orth-Ga2O3 an extremely promising material in spintronic nanodevices.

Emergence of superconductivity and superionicity in the electride [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.

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.