Crystal Structure Prediction Research Articles

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 .

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.

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

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.

Machine learning assisted crystal structure prediction made simple

Crystal structure prediction (CSP) plays a crucial role in condensed matter physics and materials science, with its importance evident not only in theoretical research but also in the discovery of new materials and the advancement of novel technologies. However, due to the diversity and complexity of crystal structures, trial-and-error experimental synthesis is time-consuming, labor-intensive, and insufficient to meet the increasing demand for new materials. In recent years, machine learning (ML) methods have significantly boosted CSP. In this review, we present a comprehensive review of the ML models applied in CSP. We first introduce the general steps for CSP and highlight the bottlenecks in conventional CSP methods. We further discuss the representation of crystal structures and illustrate how ML-assisted CSP works. In particular, we review the applications of graph neural networks (GNNs) and ML force fields in CSP, which have been demonstrated to significantly speed up structure search and optimization. In addition, we provide an overview of advanced generative models in CSP, including variational autoencoders (VAEs), generative adversarial networks (GANs), and diffusion models. Finally, we discuss the remaining challenges in ML-assisted CSP.

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.

Transferable machine learning interatomic potential for carbon hydrogen systems.

In this study, we developed a machine learning interatomic potential based on artificial neural networks (ANN) to model carbon-hydrogen (C-H) systems. The ANN potential was trained on a dataset of C-H clusters obtained through density functional theory (DFT) calculations. Through comprehensive evaluations against DFT results, including predictions of geometries and formation energies across 0D-3D systems comprising C and C-H, as well as modeling various chemical processes, the ANN potential demonstrated exceptional accuracy and transferability. Its capability to accurately predict lattice dynamics, crucial for stability assessment in crystal structure prediction, was also verified through phonon dispersion analysis. Notably, its accuracy and computational efficiency in calculating force constants facilitated the exploration of complex energy landscapes, leading to the discovery of a novel C polymorph. These results underscore the robustness and versatility of the ANN potential, highlighting its efficacy in advancing computational materials science by conducting precise atomistic simulations on a wide range of C-H materials.

Robust and efficient reranking in crystal structure prediction: a data driven method for real-life molecules

We present a novel machine learning crystal structure prediction reranking algorithm and demonstrate its robustness across pharmaceutical molecules.

Crystal structure prediction and property calculation of copper-oxygen compounds using innovative search software from first principles.

A Bayesian optimisation algorithm for deep learning crystal structure prediction software (CBD-GM) is used to predict the structures of Cu(I) and Cu(II) oxides of 2D and 3D materials. Two known 2D structures and two known 3D structures were anticipated, in addition to the prediction of 5 novel structures. All nine structures were optimised and analysed using density-functional theory (DFT). Firstly, DFT calculations using the PBE functional indicate that the structures should be thermodynamically and dynamically stable. Secondly, we calculated the elastic constants using the "stress-strain" method, and the predicted Young's modulus and Poisson's ratios of the materials suggest that they all should have excellent ductile mechanical properties. Calculations of the band structure of the materials performed using the Heyd-Scuseria-Ernzerhof (HSE) hybrid functional indicate that some of the materials should be semiconductors with useful bandgaps. The results therefore provide inspiration for the synthesis of new copper oxides for industrial applications.

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.