Crystal Structure Prediction Research Articles

XtalOpt version 13: Multi-objective evolutionary search for novel functional materials

Version 13 of XtalOpt, an evolutionary algorithm for crystal structure prediction, is now available for download from the CPC program library or the XtalOpt website, https://xtalopt.github.io. In the new version of the XtalOpt code, a general platform for multi-objective global optimization is implemented. This functionality is designed to facilitate the search for (meta)stable phases of functional materials through minimization of the enthalpy of a crystalline system coupled with the simultaneous optimization of any desired properties that are specified by the user. The code is also able to perform a constrained search by filtering the parent pool of structures based on a user-specified feature, while optimizing multiple objectives. Here, we present the implementation and various technical details, and we provide a brief overview of additional improvements that have been introduced in the new version of XtalOpt. Program summaryProgram Title: XtalOptCPC Library link to program files:https://doi.org/10.17632/jt5pvnnm39.4Developer's repository link:https://github.com/xtalopt/XtalOptLicensing provisions: BSD 3-clauseProgramming language: C++.Journal reference of previous version: Comput. Phys. Commun. 237 (2019) 274–275.Does the new version supersede the previous version?: Yes.Reasons for the new version: Implementation of a multi-objective evolutionary search within the XtalOpt program package.Summary of revisions: Implemented a general user-friendly multi-objective search capability, made various improvements to user interface and functionalities, performed bug fixes.Nature of problem: The XtalOpt algorithm is designed to search for (meta)stable crystal structures, optionally with specific functionalities – a grand challenge in computational materials science, chemistry and physics.Solution method: A generalized scalar fitness function, where a set of user-specified objectives contribute to the fitness value for candidate structures, is implemented within XtalOpt. This generalized fitness biases the search towards the discovery of (meta)stable phases with structural motifs that are key for the desired characteristics. As a result, the evolutionary search explores regions of the energy landscape of higher relevance in terms of target properties.

Thermodynamic stability of Li-pyrocarbonate at atmospheric and high pressures

For the first time, the formation of compound Li2C2O5 was demonstrated based on the first-principles calculations and crystal structure prediction algorithms. This compound is formed at a pressure of 4.5 GPa and a temperature of 0 K as a result of interaction Li2CO3 with CO2 and stable in the structure with triclinic P1¯ symmetry. The thermodynamic stability field of Li2C2O5-P1¯ is limited by a pressure of 27 GPa at 0 K and 40 GPa at high temperatures, above which it decomposes into Li2CO3+CO2. The structure of Li2C2O5-P1¯ is characterized by isolated [C2O5]2− pyrogroups, which consist of two oxygen-sharing [CO3]2− triangles. The structure of Li-pyrocarbonate is isostructural to Mg-pyroborate.

Force Field X: A computational microscope to study genetic variation and organic crystals using theory and experiment.

Force Field X (FFX) is an open-source software package for atomic resolution modeling of genetic variants and organic crystals that leverages advanced potential energy functions and experimental data. FFX currently consists of nine modular packages with novel algorithms that include global optimization via a many-body expansion, acid-base chemistry using polarizable constant-pH molecular dynamics, estimation of free energy differences, generalized Kirkwood implicit solvent models, and many more. Applications of FFX focus on the use and development of a crystal structure prediction pipeline, biomolecular structure refinement against experimental datasets, and estimation of the thermodynamic effects of genetic variants on both proteins and nucleic acids. The use of Parallel Java and OpenMM combines to offer shared memory, message passing, and graphics processing unit parallelization for high performance simulations. Overall, the FFX platform serves as a computational microscope to study systems ranging from organic crystals to solvated biomolecular systems.

Machine learned interatomic potentials for ternary carbides trained on the AFLOW database

Large-density functional theory (DFT) databases are a treasure trove of energies, forces, and stresses that can be used to train machine-learned interatomic potentials for atomistic modeling. Herein, we employ structural relaxations from the AFLOW database to train moment tensor potentials (MTPs) for four carbide systems: CHfTa, CHfZr, CMoW, and CTaTi. The resulting MTPs are used to relax ~6300 random symmetric structures, and are subsequently improved via active learning to generate robust potentials (RP) that can relax a wide variety of structures, and accurate potentials (AP) designed for the relaxation of low-energy systems. This protocol is shown to yield convex hulls that are indistinguishable from those predicted by AFLOW for the CHfTa, CHfZr, and CTaTi systems, and in the case of the CMoW system to predict thermodynamically stable structures that are not found within AFLOW, highlighting the potential of the employed protocol within crystal structure prediction. Relaxation of over three hundred (Mo1−xWx)C stoichiometry crystals first with the RP then with the AP yields formation enthalpies that are in excellent agreement with those obtained via DFT.

Crystal structure prediction of Li4x Mg2(1−x)P2O7 by first-principles calculations

We conduct first-principles calculations to investigate the phase stability of the pseudo-binary Li4x Mg2(1−x)P2O7 system, a promising solid electrolyte material. Our approach involves exploring stable structures through crystal structure prediction simulations and the generation of structures by cation substitution. We then discuss the phase stability of four P2O7 frameworks. For the composition of Li2MgP2O7, we find that the structure with the same P2O7 framework as Li2.2Zn0.8P2O7 appears as a metastable phase. Our results agree well with the experimental results and provide insights into the material design within the Li–Mg–P–O quaternary system for next-generation battery technology.

Unveiling unconventional CH4–Xe compounds and their thermodynamic properties at extreme conditions

Inert gases (e.g., He and Xe) can exhibit chemical activity at high pressure, reacting with other substances to form compounds of unexpected chemical stoichiometry. This work combines first-principles calculations and crystal structure predictions to propose four unexpected stable compounds of CH4Xe3, (CH4)2Xe, (CH4)3Xe, and (CH4)3Xe2 at pressure ranges from 2 to 100 GPa. All structures are composed of isolated Xe atoms and CH4 molecules except for (CH4)3Xe2, which comprises a polymerization product, C3H8, and hydrogen molecules. Ab initio molecular dynamics simulations indicate that pressure plays a very important role in the different temperature driving state transitions of CH4–Xe compounds. At lower pressures, the compounds follow the state transition of solid–plastic–fluid phases with increasing temperature, while at higher pressures, the stronger Xe–C interaction induces the emergence of a superionic state for CH4Xe3 and (CH4)3Xe2 as temperature increases. These results not only expand the family of CH4–Xe compounds, they also contribute to models of the structures and evolution of planetary interiors.

Chemical Bonding and Crystal Structure Schemes in Atomic/Molecular Layer Deposited Fe-Terephthalate Thin Films.

Advanced deposition routes are vital for the growth of functional metal-organic thin films. The gas-phase atomic/molecular layer deposition (ALD/MLD) technique provides solvent-free and uniform nanoscale thin films with unprecedented thickness control and allows straightforward device integration. Most excitingly, the ALD/MLD technique can enable the in situ growth of novel crystalline metal-organic materials. An exquisite example is iron-terephthalate (Fe-BDC), which is one of the most appealing metal-organic framework (MOF) type materials and thus widely studied in bulk form owing to its attractive potential in photocatalysis, biomedicine, and beyond. Resolving the chemistry and structural features of new thin film materials requires an extended selection of characterization and modeling techniques. Here we demonstrate how the unique features of the ALD/MLD grown in situ crystalline Fe-BDC thin films, different from the bulk Fe-BDC MOFs, can be resolved through techniques such as synchrotron grazing-incidence X-ray diffraction (GIXRD), Mössbauer spectroscopy, and resonant inelastic X-ray scattering (RIXS) and crystal structure predictions. The investigations of the Fe-BDC thin films, containing both trivalent and divalent iron, converge toward a novel crystalline Fe(III)-BDC monoclinic phase with space group C2/c and an amorphous Fe(II)-BDC phase. Finally, we demonstrate the excellent thermal stability of our Fe-BDC thin films.

Synthesizable manganese nitride with giant magnetocrystalline anisotropy energy

Using a crystal structure prediction program (CALYPSO), we found many experimentally synthesizable low-energy structures with perfect or nearly perfect easy-axis magnetocrystalline anisotropy energy (MAE) in manganese nitride, including MnN, Mn2N, Mn3N2, Mn5N2, and Mn4N, respectively, which are the stoichiometries more frequently studied by experimental researchers. MnN ( I4―2d ) shows giant MAE with values of E 001 = 1006, E 010 = 0, and E 100 = 920 μeV/atom (same hereafter in the abstract for the MAE unit). Mn3N2 (P42/mmc) shows perfect easy-axis MAE, whose E 010 = E 100 = 12, whereas Ibam has a nearly perfect easy-axis MAE with values of E 001 = 324 and E 010 = 345. Mn2N has four almost totally perfect easy-axis MAE structures, including P4/mmm (E 001 = 249, E 100 = 250), Pccm (E 001 = E 100 = 62), P4/nmm (E 001 = 58, E 100 = 60), and Imma (E 001 = 108, E 100 = 109). Mn4N has one perfect structure, I4/mmm (E 001 = E 100 = 169), and two nearly perfect structures, including Fmmm (E 001 = 126, E 010 = 121) as well as I4/mmm (E 010 = 127, E 100 = 133). Too many valuable structures deserve to be further studied by both theoretical and experimental scientists regarding their other magnetic properties except for the present MAE. The present study might attract close attention to several of these compounds.

A voting-based ensemble classifier to predict phases and crystal structures of high entropy alloys through thermodynamic, electronic, and configurational parameters

This study aims to predict the various phases present in high entropy alloys (HEAs) and consequently classify their crystal structure employing multiple machine learning (ML) algorithms utilizing five thermodynamic, electronic and configurational parameters which are considered to be essential for the formation of HEA phases. The properties of a high entropy alloy can eventually be traced through accurate phase and crystal structure prediction, which is essential for selecting the ideal elements for designs. Twelve distinct ML algorithms were executed to predict the phases of HEAs, adopting an experimental database of 322 different HEAs, involving 33 amorphous (AM), 31 intermetallics (IM), and 258 solid solutions (SS) phases. Among the twelve ML models, Cat Boost Classifier displayed the optimum accuracy of 98.06 % for phase predictions. Further, crystal structure classification of the SS phase (body-centered cubic- BCC, face-centered cubic- FCC, and mixed body-centered and face-centered cubic- BCC+FCC) has endeavoured for better microstructure evolution using a different database containing of 194 additional HEAs data with 61 FCC, 76 BCC, and 57 BCC+FCC crystal structures and in comparison to the other models tested, the Gradient Boosting Classifier evolved with the highest accuracy of 86.90 %. An ensemble classifier was also introduced to improve the performance of the ML models, resulting in an accuracy increase to 98.70 % and 86.95 % for phase and crystal structure predictions, respectively. Additionally, the influence of parameters on model accuracy was determined independently.

Electron-phonon driven superconductivity in the novel A15 phase of the Ti–Te system under pressure

We investigate the high-pressure phase diagram of Ti–Te system using first-principles calculations combined with evolutionary crystal structure prediction. Given the isostructural behavior of Se and Te at higher pressures, the same stoichiometric ratios as in the Ti–Se system were found in the Ti–Te system at high pressures. A novel A15 stable phase Ti3Te is predicted under pressure. Following this, its electronic, mechanical, vibrational, dynamical and superconducting properties at 0 GPa have been systematically scrutinized. The Ti-d orbitals occupy mainly at the Fermi level and Ti3Te exhibits intrinsic metallic property. Meanwhile, the electron-phonon coupling constant λ is 1.03, and the calculated superconducting transition temperature Tc (μ∗ = 0.10–0.15) is 14.2-11 K. Furthermore, we have studied the A15 structural candidates that can be formed by Ti and sulphur group elements under pressure. The mechanism of pressure-enhanced superconductivity present in these structures is also elucidated from a microscopic perspective.

Suppression of Disorder in Benzamide and Thiobenzamide Crystals by Fluorine Substitution.

Disorder is a common feature of molecular crystals that complicates determination of structures and can potentially affect electric and mechanical properties. Suppression of disorder is observed in otherwise severely disordered benzamide and thiobenzamide crystals by substituting hydrogen with fluorine in the ortho-position of the phenyl ring. Fluorine occupancies of 20-30% are sufficient to suppress disorder without changing the packing motif. Crystal structure prediction calculations reveal a much denser lattice energy landscape for benzamide compared to 2-fluorobenzamide, suggesting that fluorine substitution makes disorder less likely.

Enhancement of superconducting transition temperature and exotic stoichiometries in the Lu−S system under high pressure

Binary metal sulfides are a potential family of materials for exploring high Tc superconductors under high pressure. In this work, we study the crystal structures, electronic structures, and superconducting properties of the Lu-S system in the pressure range from 0 to 200 GPa, combining crystal structure predictions with calculations. We predict 15 unique structures, encompassing seven unidentified stoichiometries. Within the S-rich structures, the formation of S atom cages is beneficial for superconductivity, with the superconducting transition temperature 25.86 and 25.30 K for LuS6−C2/m at 70 GPa and LuS6−R−3m at 90 GPa, respectively. With the Lu/(Lu+S) ratio increases, the Lu-d electrons participate more in the electronic properties at the Fermi energy, resulting in the coexistence of superconductivity and topological nontriviality of LuS2−Cmca, as well as the superconductivity of predicted Lu-rich compounds. Our calculation is helpful for understanding the exotic properties in the transition metal sulfides system under high pressure, providing the possibility of designing alternative superconductors for future experimental and theoretical works. Published by the American Physical Society 2024

Linear photogalvanic effect in two-dimensional PSi photodetector by vacancy- and substitution-doping

Using particle swarm optimization methodology for crystal structure prediction and first-principles density functional theory, a graphene-like honeycombed PSi monolayer has been found by our group with its thermodynamical, dynamical and mechanical stability having been listed. To extend the application of the PSi monolayer, the armchair and zigzag photodetector devices based on the photogalvanic effect have been built. When the linearly polarized light illuminates on the armchair or zigzag photodetector, due to the C1 symmetry of PSi monolayer the PSi photodetector can directly produce high photocurrents. In addition, the produced photocurrents show the relations cos(2θ) and sin(2θ) on the polarization angle θ for the armchair and zigzag photodetector, respectively. Moreover, the value of photon energy can effectively influence the distribution of generated photocurrents, namely the relations on the angle θ. Especially, the vacancy- and substitution-doping in the PSi photodetector can enhance the generated photocurrents. These results demonstrated great potential applications of the PSi monolayer on the low energy-consumption optoelectronics devices.

Flavone Cocrystals: A Comprehensive Approach Integrating Experimental and Virtual Methods.

The dapsone/flavone cocrystal system served as a benchmark for both experimental and virtual screening methods. Expanding beyond this, two additional active pharmaceutical ingredients (APIs), sulfanilamide and sulfaguanidine, structurally related to dapsone were chosen to investigate the impact of substituents on cocrystal formation. The experimental screening involved mechanochemical methods, slurry experiments, hot-melt extrusion, and the contact preparation method. The virtual screening focused on crystal structure prediction (CSP), molecular complementarity, hydrogen-bond propensity, and molecular electrostatic potentials. The CSP studies not only indicated that each of the three APIs should form cocrystals with flavone but also reproduced the known single- and multicomponent phases. Experimentally, dapsone/flavone cocrystals ACC, BCC, CCC, and DCC were reproduced, CCC was identified as a nonstoichiometric hydrate, and a fifth cocrystal (ECC), a t-butanol solvate, was discovered. The cocrystal polymorphs ACC and BCC are enantiotripically related, and DCC, exhibiting a different stoichiometric ratio, is enthalpically stabilized over the other cocrystals. For the sulfaguanidine/flavone system, two novel, enantiotripically related cocrystals were identified. The crystal structures of two cocrystals and a flavone polymorph were solved from powder X-ray diffraction data, and the stability of all cocrystals was assessed through differential scanning calorimetry and lattice energy calculations. Despite computational indications, a diverse array of cocrystallization techniques did not result in a sulfanilamide/flavone cocrystal. The driving force behind dapsone's tendency to cocrystallize with flavone can be attributed to the overall strength of flavone interactions in the cocrystals. For sulfaguanidine, the potential to form strong API···API and API···coformer interactions in the cocrystal is a contributing factor. Furthermore, flavone was found to be trimorphic.

Two polymorphs of a new AIEgen from transition-metal-free cross-coupling reactions: A combined experimental and crystal structure prediction study

We synthesized a new double aromatic pyrrole [2,5-bis(2,5-dimethoxyphenyl)-1H-pyrrole, abbreviated as BDMP] without using transition metals. This compound exhibits a conjugated skeleton, ideal for aggregation-induced emission luminogens (AIEgens). Two methods, Lewis acid catalysis and photoreaction, were used for synthesis. Different methods produce different crystal structures with the same molecular skeleton. The crystals show different AIE properties. The photocatalytically formed crystal (BDMP-B) is highly luminescent with a 10.4 % fluorescence yield. In contrast, the Lewis acid-catalyzed crystal (BDMP-A) has a lower fluorescence yield of 3.0 %. These differences in emission are due to varying crystal packings, confirmed by quantum calculations.To understand these differences, Crystal Structure Prediction (CSP) studies were conducted. Our goal was to find if more stable crystal packings exist. Several energy ranking approaches were used to identify the most feasible polymorphic forms. These approaches include static lattice energy, lattice binding energy, enthalpy, cohesive enthalpy, Gibbs Free Energy, cohesive Gibbs Free Energy, and high-accurate electronic binding energy from large molecule-cluster crystal packings. Our findings suggest that the most stable crystal forms can be predicted accurately using appropriate theoretical methods.This research improves our understanding of AIEgens. It shows the value of combining experimental work with both cost-effective and highly accurate theoretical methods for crystal structure analysis and prediction.

Assessing the feasibility of near-ambient conditions superconductivity in the Lu-N-H system

The report of near-ambient superconductivity in nitrogen-doped lutetium hydrides (Lu-N-H) has generated a great interest. However, conflicting results raised doubts regarding superconductivity. Here, we combine high-throughput crystal structure predictions with a fast predictor of superconducting critical temperature (Tc) based on electron localization function to shed light on the properties of Lu-N-H at 1 GPa. None of the predicted structures supports high-temperature superconductivity and the inclusion of nitrogen in the crystal structure predictions leads to more insulating structures than metallic ones in quantity. Despite the lack of near-ambient superconductivity, we consider alternative metastable templates and study their Tc and dynamical stability including quantum anharmonic effects. Lu4H11N exhibits a Tc of 100 K at only 20 GPa, a large increase compared to 30 K of its parent LuH3. Interestingly, it has a similar X-ray pattern to the experimental one. The LaH10-like LuH10 and CaH6-like LuH6 become high-temperature superconductors at 175 GPa and 100 GPa, with Tc of 286 K and 246 K, respectively. Our findings suggest that high-temperature superconductivity is not possible in stable phases at near-ambient pressure. However, at a slightly enhanced pressure of 20 GPa, high-Tc superconductivity emerges in Lu-H-N, and metastable room-temperature superconducting templates persist at high pressures.

Improving the performance of machine learning model predicting phase and crystal structure of high entropy alloys by the synthetic minority oversampling technique

It is critical to select the suitable integration of elements that has an influence on phase formation when selecting the proper High Entropy Alloys (HEAs) with desired qualities. Machine Learning (ML) models were used to predict the phase and crystal structure of HEAs in this study. Trained on extensive experimental datasets, the set of input characteristics includes Atomic size difference, Mixing Enthalpy, Mixing Entropy, Valence Electron concentration, Electronegativity difference, Ω and Average Melting Temperature. The phase is divided into three categories: Solid solution (S), Dual phase (D), and Intermetallic (Im). Crystal structure classifications include BCC, FCC, and FCC+BCC. For phase and crystal structure prediction, five ML algorithms were used: Decision Tree (DT), K Nearest Neighbour (KNN), Random Forest (RF), Gradient Boosting (GB) and eXtreme Gradient Boosting (XG Boosting), The classification of the HEAs phase has been predicted with great accuracy using an XG Boosting (80 %) and Random Forest exhibits the highest accuracy (94 %) for crystal structure prediction when model is trained on an experimental dataset. Even though the ML model predicts the classes with a better degree of accuracy (>80 %), the F1 value of each class varies more dramatically. This issue has arisen as a result of an imbalance in the dataset's class distribution. To boost performance, this work uses an ML model in conjunction with the Synthetic Minority Oversampling Technique (SMOTE). According to this approach, XG boosting identified the crystal structure with 93 % accuracy and predicted the phase of HEAs with 84 % accuracy. Additionally, it has been noted that each class's F1 score improved and the difference between them was reduced after using the SMOTE technique. It is evident that the SMOTE approach has improved the ML model's ability to differentiate between the various classes.

Linking solid-state phenomena via energy differences in `archetype crystal structures'

Categorization underlies understanding. Conceptualizing solid-state structures of organic molecules with `archetype crystal structures' bridges established categories of disorder, polymorphism and solid solutions and is herein extended to special position and high-Z′ structures. The concept was developed in the context of disorder modelling [Dittrich, B. (2021). IUCrJ, 8, 305–318] and relies on adding quantum chemical energy differences between disorder components to other criteria as an explanation as to why disorder – and disappearing disorder – occurs in an average structure. Part of the concept is that disorder, as probed by diffraction, affects entire molecules, rather than just the parts of a molecule with differing conformations, and the finding that an R·T energy difference between disorder archetypes is usually not exceeded. An illustrative example combining disorder and special positions is the crystal structure of oestradiol hemihydrate analysed here, where its space-group/subgroup relationship is required to explain its disorder of hydrogen-bonded hydrogen atoms. In addition, we show how high-Z′ structures can also be analysed energetically and understood via archetypes: high-Z′ structures occur when an energy gain from combining different rather than overall alike conformations in a crystal significantly exceeds R·T, and this finding is discussed in the context of earlier explanations in the literature. Twinning is not related to archetype structures since it involves macroscopic domains of the same crystal structure. Archetype crystal structures are distinguished from crystal structure prediction trial structures in that an experimental reference structure is required for them. Categorization into archetype structures also has practical relevance, leading to a new practice of disorder modelling in experimental least-squares refinement alluded to in the above-mentioned publication.

Predicted High-Energy Density MN8 Containing Anionic 18-Crown-6 Ring-Based Polynitrogen Monolayers Acting as Cryptand.

In this study, we investigate the potential of the 18-crown-6-like two-dimensional (2D)-N8 structure to accommodate electrons from metals without compromising its covalent nitrogen network. Employing the crystal structure prediction enhanced by evolutionary algorithm and density functional theory methodology, we successfully predicted the existence of 16 layered M@2D-N8 complexes from a total of 39 MN8 systems investigated at 100 GPa (M = s-block Na-Cs, Be-Ba and d-block Ag, Au, Cd, Hg, Hf, W, and Y). Among those, there are 13 quenchable M@2D-N8 compounds that are dynamically stable at 1 atm. Orbital interactions and bonding analysis show that 2D-N8 presents a flat localized π* band that can accommodate one or two electrons without breaking the 2D covalent nitrogen network. Depending on the metal-to-polynitrogen charge transfer (formally, 1-4 electrons), these N-rich phases are semiconducting or metallic under ambient conditions. Ab initio molecular dynamics simulations show that K(I)@2D-N8 and Ca(II)@2D-N8 are thermally stable up to 600 K, while the Hf(IV)@2D-N8 compound is thermally not viable at 400 K because of the weakening of the N═N bonds due to a strong four-electron reduction. These metal 18-crown-6 ring-based polynitrogen compounds, as expected due to their high nitrogen content (eight nitrogen atoms per metal), could potentially serve as new high-energy density materials.

Methods for research on structures and functions of traditional Chinese medicine proteins

Traditional Chinese medicine proteins(TCMPs) not only have nutritional values and biological activities but also serve as key enzymes in the synthesis of pharmacodynamic components in traditional Chinese medicines. They play a role in the synthesis of pharmacodynamic components by regulating biosynthesis and selective synthesis pathways and controlling drug quality and stability. The recent years have witnessed great progress in the research on the structures and functions of proteins using various methods and technologies. However, the research on the structures and functions of TCMPs lags behind. Therefore, it is urgent to study the structures and functions of TCMPs using modern means to promote the discovery of innovative drugs based on TCMPs and clarify the synthesis pathways of pharmacodynamic components. This study introduces the latest techniques for studying protein structures and functions, including spectroscopy, mass spectrometry, nuclear magnetic resonance, X-ray crystal diffraction, microscopy, and structure prediction. Furthermore, this paper introduces the methods for protein functional studies, including liquid chromatography-mass spectrometry, co-immunoprecipitation, yeast two-hybrid, and pull-down assay. By systematically reviewing these techniques and methods, this paper provides technical references for the structural identification and functional studies of TCMPs, with the aim of promoting the in-depth exploration of the structures and functions of TCMPs.