Search

High-level ab initio chemical calculations, such as second-order Moller-Plesset perturbation (MP2), are highly accurate but time-consuming, making it inefficient to apply to macromolecular systems. Here, we propose a newly efficient approach based on the neural network and fragment method to predict the Gibbs free energy, structural characteristics, and thus phase transition of solid crystal structures. The proposed approach has the same prediction accuracy as the MP2 calculation but is hundreds of times faster than the MP2. The predicted structures and phase transitions of two selected ice phases (IX and XV) under extreme conditions are in excellent agreement with the MP2 calculations and experimental results but with an extremely low computational cost. It not only predicts the high-pressure structures and phase diagrams of solid systems accurately and efficiently but also solves the problem of extreme calculation cost during a high-precision theoretical study on high-pressure molecular crystals with potentially essential applications.

Metal-organic frameworks (MOFs) have emerged as highly versatile materials with applications in gas storage and separation, solar light energy harvesting and photocatalysis. The design of new MOFs, however, has been hampered by the lack of computational methods for ab initio MOF structure prediction, which could be used to inspire and direct experimental synthesis. Here, we report the first ab initio method for the prediction of MOF structures and test it against a diverse set of known MOFs that were chosen for their differences in topology, metal coordination geometry, and ligand binding sites. In all cases, our calculations produced structures that match experiment using only the target composition and ligand molecular structure, proving the versatility of our procedure. The herein presented methodology utilizes the point group symmetry of ligands to enable, for the first time, prediction of MOF structures from first principles, without having to resort to empirical guidelines based on rigid connectivity of nodes and linkers, or to previously determined crystal structures and topologies of known MOFs. This advance provides the first tool to change MOF design from an empirically based process that is based on chemistʼs intuition rooted in literature- or database-established knowledge of node-and-linker connectivity to a more general and theory-driven materials development. This ab initio MOF structure prediction approach, which is here validated on a range of known MOF classes, provides a unique opportunity to explore the phase landscape of MOFs computationally and enables MOF research and development even in case of limited access to laboratory resources, as for example in case of a global pandemic.

Here we present an ab initio prediction of an order-disorder transition (ODT) from a $I\overline{4}2d$-type to ${\mathrm{Th}}_{3}{\mathrm{P}}_{4}$-type phase in the cation sublattices of ${\mathrm{Mg}}_{2}\mathrm{Ge}{\mathrm{O}}_{4}$, a post-post-perovskite phase. This uncommon type of prediction is achieved by carrying out a high-throughput sampling of atomic configurations in a 56-atom supercell followed by a Boltzmann ensemble statistics calculation. ${\mathrm{Mg}}_{2}\mathrm{Ge}{\mathrm{O}}_{4}$ is a low-pressure analog of $I\overline{4}2d$-type ${\mathrm{Mg}}_{2}\mathrm{Si}{\mathrm{O}}_{4}$, a predicted major planet-forming phase of super-Earths' mantles. Therefore, a similar ODT is anticipated in $I\overline{4}2d$-type ${\mathrm{Mg}}_{2}\mathrm{Si}{\mathrm{O}}_{4}$ as well, which should impact the internal structure and dynamics of these planets. The prediction of this ${\mathrm{Th}}_{3}{\mathrm{P}}_{4}$-type phase in ${\mathrm{Mg}}_{2}\mathrm{Ge}{\mathrm{O}}_{4}$ further enhances the relationship between the crystal structures of Earth/planet-forming silicates and oxides at extreme pressures and those of rare-earth sesquisulfides at low pressures.

The relation between thermodynamically stable and electronic structure preparation is one of the fundamental questions in physics, geophysics and chemistry. Since the discovery of the novel structure, this has remained as one of the main questions regarding the very foundation of elemental metals. Needless to say this has also bearings on extreme conditions physics, where again the relation between structure and performance is of direct interest. Crystal structures have been mainly at ambient conditions, i.e. at room temperature and ambient pressure. Nevertheless it was realized early that there is also a fundamental relation between volume and structure, and that this dependence could be most fruitfully studied by means of high pressure experimental techniques. From a theoretical point of view this is an ideal type of experiment, since only the volume is changed, which is a very clean variation of the external conditions. Therefore, at least in principle, the theoretical approach remains the same irrespective of the high pressure loading of the experimental sample. Theoretical modeling is needed to explain the measured data on the pressure volume relationships in crystal structures. Among those physical properties manifested itself under high pressure, superconductivity has emerged as a prominent property affected by pressure. Several candidate structure of materials are explored by ab initio random structure searching (AIRSS). This has been carried out in combination with density functional theory (DFT). The remarkable solution of AIRSS is possible to expect a superconductivity under high pressure. This chapter provide a systematically review of the structural prediction and superconductivity in elemental metals, i.e. lithium, strontium, scandium, arsenic.

The crystal structure of procarbazine molecule was predicted using first principles of quantum mechanics. The gas phase optimization was carried out by density functional theory and the resulted geometric coordinates were utilized for the search of hypothetical packings which reveals the possible stable conformers under a repulsion alone potential field with minimum cell volume. The thermodynamically favor structure was resulted from the lattice energy minimization of these hypothetical structures from the using the repulsion-dispersion potential field. The stability of global minimum structure was confirmed from the hydrogen bond interactions and second derivative properties.

Background Accurate prediction of protein tertiary structures is highly desired as the knowledge of protein structures provides invaluable insights into protein functions. We have designed two approaches to protein structure prediction, including a template-based modeling approach (called ProALIGN) and an ab initio prediction approach (called ProFOLD). Briefly speaking, ProALIGN aligns a target protein with templates through exploiting the patterns of context-specific alignment motifs and then builds the final structure with reference to the homologous templates. In contrast, ProFOLD uses an end-to-end neural network to estimate inter-residue distances of target proteins and builds structures that satisfy these distance constraints. These two approaches emphasize different characteristics of target proteins: ProALIGN exploits structure information of homologous templates of target proteins while ProFOLD exploits the co-evolutionary information carried by homologous protein sequences. Recent progress has shown that the combination of template-based modeling and ab initio approaches is promising. Results In the study, we present FALCON2, a web server that integrates ProALIGN and ProFOLD to provide high-quality protein structure prediction service. For a target protein, FALCON2 executes ProALIGN and ProFOLD simultaneously to predict possible structures and selects the most likely one as the final prediction result. We evaluated FALCON2 on widely-used benchmarks, including 104 CASP13 (the 13th Critical Assessment of protein Structure Prediction) targets and 91 CASP14 targets. In-depth examination suggests that when high-quality templates are available, ProALIGN is superior to ProFOLD and in other cases, ProFOLD shows better performance. By integrating these two approaches with different emphasis, FALCON2 server outperforms the two individual approaches and also achieves state-of-the-art performance compared with existing approaches. Conclusions By integrating template-based modeling and ab initio approaches, FALCON2 provides an easy-to-use and high-quality protein structure prediction service for the community and we expect it to enable insights into a deep understanding of protein functions.

Organometallic molecules based on [Dy(CpR)2]+ cations have emerged as clear front-runners in the search for high-temperature single-molecule magnets. However, despite a growing family of structurally-similar molecules, these molecules show significant variations in their magnetic properties, demonstrating the importance of understanding magneto-structural relationships towards developing more efficient design strategies. Here we refine our ab initio spin dynamics methodology and show that it is capable of quantitative prediction of relative relaxation rates in the Orbach region. Applying it to all reported [Dy(CpR)2]+ cations allows us to tease out differences in their relaxation dynamics, highlighting that the main discriminant is the magnitude of the crystal field splitting. We subsequently employ the method to predict relaxation rates for a series of hypothetical organometallic sandwich compounds, revealing an upper limit to the effective barrier to magnetic relaxation of around 2200 K, which has been reached. However, we show that further improvements to single-molecule magnets can be made by moving vibrational modes off-resonance with electronic excitations.

International audience; Here, we report a theoretical investigation devoted to the ab initio determination of the redox potentials E(D n+ /D n+1) of a dopant D in a given host lattice. The knowledge of these potentials is of capital importance to anticipate its attainable oxidation states (versus the synthesis conditions). Hereafter the host lattice has been selected to be the well-known rutile TiO2 compound due to its interest for many applications, the simplicity of its crystal structure and the large number of already collected data. Dopants are 3d transition metals (i.e., V, Mn, Fe, Ni and Cu) substituting titanium atoms. First-principles methods combined to the SCAN functional were used to determine the electronic properties of doped materials considering the supercell approach. The stability of point defects (intrinsic and extrinsic) at different charge states are discussed based on the estimation of their defect formation enthalpies, and the associated charge transition levels are calculated and positioned in the gap of the un-doped material.

The cluster multipole (CMP) expansion for magnetic structures provides a scheme to systematically generate candidate magnetic structures specifically including noncollinear magnetic configurations adapted to the crystal symmetry of a given material. A comparison with the experimental data collected on MAGNDATA shows that the most stable magnetic configurations in nature are linear combinations of only few CMPs. Furthermore, a high-throughput calculation for all candidate magnetic structures is performed in the framework of spin-density functional theory (SDFT). We benchmark the predictive power of CMP+SDFT with $2935$ calculations, which show that (i) the CMP expansion administers an exhaustive list of candidate magnetic structures, (ii) CMP+SDFT can narrow down the possible magnetic configurations to a handful of computed configurations, and (iii) SDFT reproduces the experimental magnetic configurations with an accuracy of $\pm0.5\,\mu_\text{B}$. For a subset the impact of on-site Coulomb repulsion $U$ is investigated by means of $1545$ CMP+SDFT+U calculations revealing no further improvement on the predictive power., Comment: 16 pages, 7 figures

[Resumen]: Uno de los problemas que ha sido considerado como uno de los más grandes desafíos durante los últimos 50 años es el problema de predicción computacional de la estructura terciaria de las proteínas. El entorno Rosetta es un paquete software que se usa para la predicción de la estructura de las proteínas. El objetivo principal del proyecto es definir un protocolo alternativo al que usa Rosetta, con el fin de obtener conformaciones optimizadas con diversidad estructural, de modo que se incremente la probabilidad de encontrar estructuras cercanas a la estructura nativa real. Para ello se experimentará con diferentes medidas de diferencia estructural y con diferentes umbrales de distancia. Los experimentos se realizarán sobre un conjunto amplio de proteínas y se analizará el impacto que tiene el protocolo alternativo sobre los resultados. [Abstract]: One of the problems that has been considered one of the greatest challenges during the last 50 years is the problem of the computational prediction of the tertiary structure of proteins. The Rosetta environment is a software package used for the prediction of the structure of proteins. The main objective of the project is to define an alternative protocol to the one used by Rosetta, in order to obtain optimized conformations with structural diversity, so that the probability of finding structures close to the real native structure is increased. To do this, we will experiment with different measures of structural difference and with different distance thresholds. The experiments will be carried out on an ample set of proteins and the impact of the alternative protocol on the results will be analyzed. Traballo fin de grao (UDC.FIC). Enxeñaría Informática. Curso 2020/2021

Proteins are vital molecules that play many important roles in the human body; they contribute to tissue growth and maintenance, catalysis of organic reactions, communication between cells, tissues and organs and help improve immune health. Therefore one of the most important and frequently studied issues in biological and medical research is understanding the function of proteins. A thorough understanding of a protein’s function and activity requires determining its structures. In this paper, we propose an Automatic Protein Production Framework, which aims to completely determine the different structures in order to construct three-dimensional physical proteins and provide all information that will contribute to the study of the functions and activities of the proteins. The proposed framework is based on computational methods by combining three bioinformatics methods (i.e. comparative modeling, fold recognition, and ab initio prediction). We also present a software application that uses our framework and an experiment to illustrate our proposed Automatic Protein Production Framework, using the model application.

The problem of the practical implementation of quantum computers is an important scientific and technological task at the present time. In this work, using first-principles calculations, a quantum qubit behavior based on a doped phosphorus atom in a Si lattice was theoretically investigated. The local magnetic field

The accurate prediction of aqueous pKa values for tautomerizable compounds is a formidable task, even for the most established in silico tools. Empirical approaches often fall short due to a lack of pre-existing knowledge of dominant tautomeric forms. In a rigorous first-principles approach, calculations for low-energy tautomers must be performed in protonated and deprotonated forms, often both in gas and solvent phases, thus representing a significant computational task. Here we report an alternative approach, predicting pKa values for herbicide/therapeutic derivatives of 1,3-cyclohexanedione and 1,3-cyclopentanedione to within just 0.24 units. A model, using a single ab initio bond length from one protonation state, is as accurate as other more complex regression approaches using more input features, and outperforms the program Marvin. Our approach can be used for other tautomerizable species, to predict trends across congeneric series and to correct experimental pKa values. Ab initio prediction of aqueous pKa values is complicated by the presence of tautomerisable moieties. Here a model based on a small number of easily-calculated bond lengths is shown to accurately predict the pKas of 1,3-dicarbonyls including industrially significant herbicides.

Prediction of protein structure is one of the key research areas in bioinformatics. These identified structures can be used in the development of effective drug, nutrient, etc., design for living organism. This chapter provides an overview of different computational methods for protein structure prediction like comparative modelling, threading and ab initio methods. Finally, a case study on homology modelling of superoxide dismutase is discussed.

A hybrid of high level and low level quantum mechanics (QM) methods has been employed to predict intrinsic and apparent energy barriers for the direct proton exchange mechanism of methane, ethane, propane, n-butane, and i-butane on Brønsted sites of H-MFI. The specific hybrid MP2:PBE+D2 + ΔCC implementation used is known to yield the so-called "chemical accuracy" (±4 kJ/mol). Whereas the apparent enthalpy barriers decrease with increasing C number from 104 to 63 kJ/mol, in line with the decreasing heat of adsorption, the intrinsic enthalpy barriers are constant within 124-127 kJ/mol at 500 K. For methane, ethane, propane, and n-butane, we find the expected agreement of apparent barriers with activation energies from batch recirculation reactor experiments. The activation energies derived from NMR experiments (103-113 kJ/mol) are similarly constant as the predicted intrinsic barriers but systematically lower. For i-butane the predicted intrinsic and apparent barriers for the direct proton exchange step are the same as for n-butane with deviations of 2-5 kJ/mol, while the experiments yield values that are 50-60 kJ/mol lower, far outside the estimated range of combined experimental and computational uncertainty (±14 kJ/mol). A change to the indirect proton exchange mechanism, in which a hydride ion is transferred between the alkane and a tert-butyl carbenium ion can be excluded, because we confirm previous findings that the barrier for dehydrogenation that would create a tert-butyl cation from i-butane is much too high, 188 and 132 kJ/mol for the intrinsic and apparent enthalpy barriers, respectively, at 500 K. The possible role of extraframework- and framework-bound alumina species is discussed.

Despite vibrational properties being critical for the ab initio prediction of the finite temperature stability and transport properties of solids, their inclusion in ab initio materials repositories has been hindered by expensive computational requirements. Here we tackle the challenge, by showing that a good estimation of force constants and vibrational properties can be quickly achieved from the knowledge of atomic equilibrium positions using machine learning. A random-forest algorithm trained on only 121 metastable structures of KZnF$_3$ reaches a maximum absolute error of 0.17 eV/$\textrm\AA^2$ for the interatomic force constants, and it is much less expensive than training the complete force field for such compound. The predicted force constants are then used to estimate phonon spectral features, heat capacities, vibrational entropies, and vibrational free energies, which compare well with the ab initio ones. The approach can be used for the rapid estimation of stability at finite temperatures., Comment: 18 pages, 4 figures

The electronic structure and elastic properties of the ternary layered carbide V3GeC2 were investigated by the first-principle plane-wave pseudopotential total energy calculation method based on density functional theory. It is found that the computed P63/mmc lattice constants and internal coordinates are a = 2.9636?, c = 17.2256? and zV2 = 0.1325, zC = 0.5712, respectively. The predictable cohesive energy of V3GeC2 reflects that it could be a stable Mn+1AXn phase like Ti3GeC2 and V2GeC, while the band structure shows that the V3GeC2 has anisotropic electrical conductivity, with a high density of states at the Fermi energy. The V3GeC2 exhibits potential anisotropic elastic properties, as well as self-lubricating and ductile behaviour, related to the V-Ge bonds being relatively weaker than the V-C bonds.

The threshold displacement energies (Ed) of C and Zr atoms in ZrC have been determined using ab initio molecular dynamics simulation. The values of Ed have been predicted along the three main high-symmetry crystallographic directions [001], [011], and [111], and the averaged Ed values are 16 eV and 37 eV for the C and Si sublattices, respectively. We further explore the dependence of Ed on small deviations from these high-symmetry directions and on the presence of C vacancies, which are often encountered in ZrCx alloys. The trends in values are explained in terms of the structural and chemical properties of ZrC. The predicted Ed values provide relevant parameters for future modeling of radiation damage in ZrC.

A model for predicting the anticipated second harmonic generation (SHG) activity of crystalline active pharmaceutical ingredients (APIs) was developed based on combining time-dependent Hartree–Fock calculations of the molecular building block with analytical theory for the predicted intensity from the lattice. Predicted trends agreed well overall with experimental measurements for 18 representative APIs. Inspection of the SHG images suggests that outliers from the predicted trends likely arose from the presence of trace SHG-active metastable crystal forms not considered in the predictions. The success of this relatively simple computational approach suggests a route for reliably predicting the anticipated SHG activities of API target molecules, which in turn can serve to indicate their potential compatibility with analysis by SHG.

We combine first-principles atomistic thermodynamics with a Wulff–Kaichew construction to determine the equilibrium shape of Ag particles supported on α-Al 2 O 3 (0 0 0 1) under gas-phase conditions representative for ethylene epoxidation. The formation of three-dimensional truncated octahedra is predicted for both the Al-terminated (AlO 3 Al-R) and the fully hydroxylated O-terminated (H 3 O 3 Al-R) alumina surface, albeit with largely different contact angles. Overall, this is fully consistent with the common description of hemispherical Ag microcrystals on this support, but at variance with the cubic Ag nanoparticles reported recently by Christopher and Linic (2008). This result suggests the latter particles to be stabilized kinetically. We establish a framework from which strategies to thermodynamically stabilize cubic particles can be explored. The Wulff–Kaichew construction is proposed to provide a straightforward criterion that may be used as a suitable descriptor in the computational screening of surface additives that would stabilize the arguably highly selective Ag(1 0 0) facets.

Predicting 3D structure of proteins from the amino acid sequences is one of the most important unsolved problems in computational biology and biophysics. This review article attempts to introduce the most recent effort and progress on this problem. After a brief introduction of the background and basic concepts involved in protein structure prediction, we went through the specific steps that have been taken by most typical structural modeling approaches, including fold recognition, model initialization, conformational search, model selection, and atomic-level structure refinement. Several representative structure prediction methods were introduced in detail, including those from both template-based modeling and ab initio folding approaches. Finally, we overview the results shown in the community-wide Critical Assessment of protein Structure Prediction (CASP) experiments that have been developed for benchmarking the state of the art of the field.

We describe several notable aspects of our structure predictions using Rosetta in CASP12 in the free modeling (FM) and refinement (TR) categories. First, we had previously generated (and published) models for most large protein families lacking experimentally determined structures using Rosetta guided by co-evolution based contact predictions, and for several targets these models proved better starting points for comparative modeling than any known crystal structure-our model database thus starts to fulfill one of the goals of the original protein structure initiative. Second, while our "human" group simply submitted ROBETTA models for most targets, for six targets expert intervention improved predictions considerably; the largest improvement was for T0886 where we correctly parsed two discontinuous domains guided by predicted contact maps to accurately identify a structural homolog of the same fold. Third, Rosetta all atom refinement followed by MD simulations led to consistent but small improvements when starting models were close to the native structure, and larger but less consistent improvements when starting models were further away.