Crystal Structure Prediction Research Articles

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.

Systematic high-throughput exploration of quaternary M′2M″AlB4 phases

A desired prerequisite when performing a quantum mechanical calculation is to have an initial idea of the atomic positions within an approximate crystal structure. The atomic positions combined should result in a system located in, or close to, an energy minimum. Designing low-energy structures for large multi-component systems is, however, often a challenging task as the degrees of freedom are close to infinite. The low-energy basins of (M′xM″1-x)3AlB4 material systems are herein explored by combining cluster expansion and crystal structure prediction methodologies with density functional theory calculations. Low-energy structures are specifically found at the (M′1/3M″2/3)3AlB4 composition, and this for multiple crystal symmetries. A subsequent high-throughput phase stability search was performed considering the identified low-energy structures at the (M′1/3M″2/3)3AlB4 composition where M′ and M″ were alloyed with Sc, Y, Ti, Zr, Hf, V, Nb, Ta, Cr, Mo, W, Mn, Fe, and Co for both ordered and disordered structures. Nine quaternary phases were predicted stable with five of these favoring structures with M-sites in the form of a simulated solid solution.

Comparative study of crystal structure prediction approaches based on a graph network and an optimization algorithm

Comparative study of crystal structure prediction approaches based on a graph network and an optimization algorithm

Concurrent learning scheme for crystal structure prediction

Concurrent learning scheme for crystal structure prediction

Observation of Iron with Eight Coordination in Iron Trifluoride under High Pressure.

The chemistry of hypercoordination has been a subject of fundamental interest, especially for understanding structures that challenge conventional wisdom. The small ionic radii of Fe ions typically result in coordination numbers of 4 or 6 in stable Fe-bearing ionic compounds. While 8-coordinated Fe has been observed in highly compressed oxides, the pursuit of hypercoordinated Fe still faces significant challenges due to the complexity of synthesizing the anticipated compound with another suitable anion. Through first-principles simulation and advanced crystal structure prediction methods, we predict that an orthorhombic phase of FeF3 with exclusively 8-coordinated Fe is energetically stable above 18 GPa-a pressure more feasibly achieved compared to oxides. Inspired by this theoretical result, we conducted extensive experiments using a laser-heated diamond anvil cell technique to investigate the crystal structures of FeF3 at high-pressure conditions. We successfully synthesized the predicted orthorhombic phase of FeF3 at 46 GPa, as confirmed by in situ experimental X-ray diffraction data. This work establishes a new ionic compound featuring rare 8-coordinated Fe in a simple binary Fe-bearing system and paves the way for discovering Fe hypercoordination in similar systems.

Observation of Iron with Eight Coordination in Iron Trifluoride under High Pressure

AbstractThe chemistry of hypercoordination has been a subject of fundamental interest, especially for understanding structures that challenge conventional wisdom. The small ionic radii of Fe ions typically result in coordination numbers of 4 or 6 in stable Fe‐bearing ionic compounds. While 8‐coordinated Fe has been observed in highly compressed oxides, the pursuit of hypercoordinated Fe still faces significant challenges due to the complexity of synthesizing the anticipated compound with another suitable anion. Through first‐principles simulation and advanced crystal structure prediction methods, we predict that an orthorhombic phase of FeF3 with exclusively 8‐coordinated Fe is energetically stable above 18 GPa—a pressure more feasibly achieved compared to oxides. Inspired by this theoretical result, we conducted extensive experiments using a laser‐heated diamond anvil cell technique to investigate the crystal structures of FeF3 at high‐pressure conditions. We successfully synthesized the predicted orthorhombic phase of FeF3 at 46 GPa, as confirmed by in situ experimental X‐ray diffraction data. This work establishes a new ionic compound featuring rare 8‐coordinated Fe in a simple binary Fe‐bearing system and paves the way for discovering Fe hypercoordination in similar systems.

Crystal Polymorph Search in the NPT Ensemble via a Deposition/Sublimation Alchemical Path.

The formulation of active pharmaceutical ingredients involves discovering stable crystal packing arrangements or polymorphs, each of which has distinct pharmaceutically relevant properties. Traditional experimental screening techniques utilizing various conditions are commonly supplemented with in silico crystal structure prediction (CSP) to inform the crystallization process and mitigate risk. Predictions are often based on advanced classical force fields or quantum mechanical calculations that model the crystal potential energy landscape but do not fully incorporate temperature, pressure, or solution conditions during the search procedure. This study proposes an innovative alchemical path that utilizes an advanced polarizable atomic multipole force field to predict crystal structures based on direct sampling of the NPT ensemble. The use of alchemical (i.e., nonphysical) intermediates, a novel Monte Carlo barostat, and an orthogonal space tempering bias combine to enhance the sampling efficiency of the deposition/sublimation phase transition. The proposed algorithm was applied to 2-((4-(2-(3,4-dichlorophenyl)ethyl)phenyl)amino)benzoic acid (Cambridge Crystallography Database Centre ID: XAFPAY) as a case study to showcase the algorithm. Each experimentally determined polymorph with one molecule in the asymmetric unit was successfully reproduced via approximately 1000 short 1 ns simulations per space group where each simulation was initiated from random rigid body coordinates and unit cell parameters. Utilizing two threads of a recent Intel CPU (a Xeon Gold 6330 CPU at 2.00 GHz), 1 ns of sampling using the polarizable AMOEBA force field can be acquired in 4 h (equating to more than 300 ns/day using all 112 threads/56 cores of a dual CPU node) within the Force Field X software (https://ffx.biochem.uiowa.edu). These results demonstrate a step forward in the rigorous use of the NPT ensemble during the CSP search process and open the door to future algorithms that incorporate solution conditions using continuum solvation methods.

Advances in Carbamazepine Cocrystals: A Review

AbstractCarbamazepine (CBZ), a widely used antiepileptic drug, is known for its therapeutic efficacy but exhibits suboptimal physicochemical characteristics, including limited bioavailability, low solubility, and poor dissolution rates. In recent years, cocrystallization has emerged as a promising approach to improve these properties and enhance the overall performance of CBZ. This review paper presents a comprehensive exploration of the advances in CBZ cocrystals, focusing on their role in enhancing bioavailability, solubility/dissolution rates, morphology, and stability compared to raw CBZ. It delves into the in‐depth examination of computational techniques, such as molecular modelling and crystal structure prediction, that play a pivotal role in the rational design and prediction of CBZ cocrystals, thereby expediting the development process. Additionally, the morphological attributes of CBZ cocrystals are discussed, shedding light on how the unique crystal structures affect their physical properties. Stability is another critical aspect addressed in this review, encompassing thermal, physical, and chemical stability. The thermal behavior of CBZ cocrystals is analyzed through differential scanning calorimetry (DSC), and their crystal structures are characterized by techniques such as X‐ray diffraction (XRD) and Fourier‐transform infrared spectroscopy (FTIR). These analyses reveal the stability and structural changes of CBZ cocrystals under various conditions, providing valuable insights for formulation and storage. In summary, this comprehensive review paper amalgamates the latest advancements in CBZ cocrystals, demonstrating their capacity to significantly improve bioavailability, solubility, dissolution rates, and stability.

Crystal structure prediction using neural network potential and age-fitness Pareto genetic algorithm

While crystal structure prediction (CSP) remains a longstanding challenge, we introduce ParetoCSP, a novel algorithm for CSP, which combines a multi-objective genetic algorithm (GA) with a neural network inter-atomic potential model to find energetically optimal crystal structures given chemical compositions. We enhance the updated multi-objective GA (NSGA-III) by incorporating the genotypic age as an independent optimization criterion and employ the M3GNet universal inter-atomic potential to guide the GA search. Compared to GN-OA, a state-of-the-art neural potential-based CSP algorithm, ParetoCSP demonstrated significantly better predictive capabilities, outperforming by a factor of $$ 2.562 $$ across $$ 55 $$ diverse benchmark structures, as evaluated by seven performance metrics. Trajectory analysis of the traversed structures of all algorithms shows that ParetoCSP generated more valid structures than other algorithms, which helped guide the GA to search more effectively for the optimal structures. Our implementation code is available at https://github.com/sadmanomee/ParetoCSP .

Cocrystals of polynitrogen compounds as a basis for promising energetic materials: crystal structure prediction methods, their experimental verification, and evaluation of cocrystal properties

Cocrystals of polynitrogen compounds as a basis for promising energetic materials: crystal structure prediction methods, their experimental verification, and evaluation of cocrystal properties

Quasi-2D spin-Peierls transition through interstitial anionic electrons in K(NH3)2

Electron–phonon interactions and electron–electron correlations represent two crucial facets of condensed matter physics. For instance, in a half-filled spin-1/2 anti-ferromagnetic chain, the lattice dimerization induced by electron-nucleus interaction can be intensified by onsite Coulomb repulsion, resulting in a spin-Peierls state. Through first-principles calculations and crystal structure prediction methods, we have identified that under mild pressures, potassium and ammonia can form stable compounds: R3¯m K(NH3)2, Pm3¯m K(NH3)2, and Cm K2(NH3)3. Our predictions suggest that the R3¯m K(NH3)2 exhibits electride characteristics, marked by the formation of interstitial anionic electrons (IAEs) in the interlayer space. These IAEs are arranged in quasi-two-dimensional triangular arrays. With increasing pressure, the electronic van-Hove singularity shifts toward the Fermi level, resulting in an augmented density of states and the onset of both Peierls and magnetic instabilities. Analyzing these instabilities, we determine that the ground state of the R3¯m K(NH3)2 is the dimerized P21/m phase with zigzag-type anti-ferromagnetic IAEs. This state can be described by the triangular-lattice antiferromagnetic Heisenberg model with modulated magnetic interactions. Furthermore, we unveil the coexistence and positive interplay between magnetic and Peierls instability, constituting a scenario of spin-Peierls instability unprecedented in realistic 2D materials, particularly involving IAEs. This work provides valuable insights into the coupling of IAEs with the adjacent lattice and their spin correlations in quantum materials.

Machine-Learned Potentials by Active Learning from Organic Crystal Structure Prediction Landscapes.

A primary challenge in organic molecular crystal structure prediction (CSP) is accurately ranking the energies of potential structures. While high-level solid-state density functional theory (DFT) methods allow for mostly reliable discrimination of the low-energy structures, their high computational cost is problematic because of the need to evaluate tens to hundreds of thousands of trial crystal structures to fully explore typical crystal energy landscapes. Consequently, lower-cost but less accurate empirical force fields are often used, sometimes as the first stage of a hierarchical scheme involving multiple stages of increasingly accurate energy calculations. Machine-learned interatomic potentials (MLIPs), trained to reproduce the results of ab initio methods with computational costs close to those of force fields, can improve the efficiency of the CSP by reducing or eliminating the need for costly DFT calculations. Here, we investigate active learning methods for training MLIPs with CSP datasets. The combination of active learning with the well-developed sampling methods from CSP yields potentials in a highly automated workflow that are relevant over a wide range of the crystal packing space. To demonstrate these potentials, we illustrate efficiently reranking large, diverse crystal structure landscapes to near-DFT accuracy from force field-based CSP, improving the reliability of the final energy ranking. Furthermore, we demonstrate how these potentials can be extended to more accurately model structures far from lattice energy minima through additional on-the-fly training within Monte Carlo simulations.

Superconducting ternary hydrides in Ca-U-H under high pressure

The research on hydrogen-rich ternary compounds attract tremendous attention for it paves new route to room-temperature superconductivity at lower pressures. Here, we study the crystal structures, electronic structures, and superconducting properties of the ternary Ca-U-H system, combining crystal structure predictions with ab-initio calculations under high pressure. We found four dynamically stable structures with hydrogen clathrate cages: CaUH12-Cmmm, CaUH12-Fd-3m, Ca2UH18-P-3m1, and CaU3H32-Pm-3m. Among them, the Ca2UH18-P-3m1 and CaU3H32-Pm-3m are likely to be synthesized below 1 megabar. The f electrons in U atoms make dominant contribution to the electronic density of states around the Fermi energy. The electron-phonon interaction calculations reveal that phonon softening in the mid-frequency region can enhance the electron–phonon coupling significantly. The T c value of Ca2UH18-P-3m1 is estimated to be 57.5–65.8 K at 100 GPa. Our studies demonstrate that introducing actinides into alkaline-earth metal hydrides provides possibility in designing novel superconducting ternary hydrides.

Thermodynamic Stability Is a Poor Indicator of Cocrystallization in Models of Organic Molecules.

Cocrystallizing a given molecule with another can be useful for adjusting the physical properties of molecules in the solid state. However, most combinations of molecules do not readily cocrystallize but form either one-component crystals or amorphous solids. Computational methods of crystal structure prediction can, in principle, identify the thermodynamically stable cocrystal and thus predict if molecules will cocrystallize or not. However, the pronounced polymorphism and tendency of many organic molecules to form disordered solids suggest that kinetic factors can play an important role in cocrystallization. The question remains: if a binary system of molecules has a thermodynamically stable cocrystal, will it indeed cocrystallize? To address this question, we simulate the crystallization of more than 2600 distinct pairs of chiral model molecules of similar size in 2D and calculate accurate crystal energy landscapes for all of them. Our analysis shows that thermodynamic criteria alone are unreliable in the prediction of cocrystallization. While the vast majority of cocrystals that form in our simulations are thermodynamically favorable, most coformer systems that have a thermodynamically stable cocrystal do not cocrystallize. We furthermore show that cocrystallization rates increase 3-fold when coformers are used that do not form well-ordered single-component crystals. Our results suggest that kinetic factors of cocrystallization are decisive in many cases.

Organic crystal structure prediction via coupled generative adversarial networks and graph convolutional networks

Organic crystal structures exert a profound impact on the physicochemical properties and biological effects of organic compounds. Quantum mechanics (QM)-based crystal structure predictions (CSPs) have somewhat alleviated the dilemma that experimental crystal structure investigations struggle to conduct complete polymorphism studies, but the high computing cost poses a challenge to its widespread application. The present study aims to construct DeepCSP, a feasible pure machine learning framework for minute-scale rapid organic CSP. Initially, based on 177,746 data entries from the Cambridge Crystal Structure Database, a generative adversarial network was built to conditionally generate trial crystal structures under selected feature constraints for the given molecule. Simultaneously, a graph convolutional attention network was used to predict the density of stable crystal structures for the input molecule. Subsequently, the distances between the predicted density and the definition-based calculated density would be considered to be the crystal structure screening and ranking basis, and finally, the density-based crystal structure ranking would be output. Two such distinct algorithms, performing the generation and ranking functionalities, respectively, collectively constitute the DeepCSP, which has demonstrated compelling performance in marketed drug validations, achieving an accuracy rate exceeding 80% and a hit rate surpassing 85%. Inspiringly, the computing speed of the pure machine learning methodology demonstrates the potential of artificial intelligence in advancing CSP research.

Graph theory and graph neural network assisted high-throughput crystal structure prediction and screening for energy conversion and storage

Prediction of crystal structures with desirable material properties is a grand challenge in materials research. We deployed graph theory assisted structure searcher and combined with universal machine learning potentials to accelerate the process.

Growth mechanism prediction for nanoparticles via structure matching polymerization.

Exploring structural and component evolution remains a challenging scientific problem for nanoscience. We propose a novel approach called principle of minimization of structure matching polymerization (SMP) change to rapidly explore the global minimum structure on the potential energy surface (PES). The new method can map low-dimensional stable structures to high-dimensional local minima, and this will make it possible for us to study the growth mechanisms of nanoparticles. Some new lowest-energy structures were found by SMP methods for sulfuric acid (SA)-dimethylamine (DMA) systems relative to previous studies. Additionally, we found that the growth process of boron clusters is mainly that the small-size boron clusters are continuously added to large-size boron clusters by structure matching for Bn (n = 2-36) systems, Bm + Bk → Bn, where m + k = n and 1 ≤ k ≤ 3. The SMP approach can greatly improve the search efficiency of other unbiased global optimization algorithms, such as basin-hopping (BH) and genetic algorithm (GA), with an enhancement of up to 19- and 7-fold relative to traditional BH and GA algorithms for searching the global minima of Bn (n = 14-22) systems. The SMP approach is general and flexible and can be applied to different kinds of problems, such as material structure design, crystal structure prediction, and new drug generation.