Informative Training Data for Efficient Property Prediction in Metal-Organic Frameworks by Active Learning.

In recent data-driven approaches to material discovery, scenarios where target quantities are expensive to compute and measure are often overlooked. In such cases, it becomes imperative to construct a training set that includes the most diverse, representative, and informative samples. Here, a novel regression tree-based active learning algorithm is employed for such a purpose.

Artificial Neural Network-Based Density Functional Approach for Adiabatic Energy Differences in Transition Metal Complexes.

During the past decades, approximate Kohn-Sham density functional theory schemes have garnered many successes in computational chemistry and physics, yet the performance in the prediction of spin state energetics is often unsatisfactory. By means of a machine learning approach, an enhanced exchange and correlation functional is developed to describe adiabatic energy differences in transition metal complexes. The functional is based on the computationally efficient revision of the regularized, strongly constrained, and appropriately normed functional and improved by an artificial neural network correction trained over a small data set of electronic densities, atomization energies, and/or spin state energetics.

Machine learning interatomic potentials for aluminium: application to solidification phenomena.

In studying solidification process by simulations on the atomic scale, the modeling of crystal nucleation or amorphization requires the construction of interatomic interactions that are able to reproduce the properties of both the solid and the liquid states. Taking into account rare nucleation events or structural relaxation under deep undercooling conditions requires much larger length scales and longer time scales than those achievable bymolecular dynamics (AIMD). This problem is addressed by means of classical molecular dynamics simulations using a well established high dimensional neural network potential trained on a set of configurations generated by AIMD relevant for solidification phenomena.

Unsupervised topological learning for identification of atomic structures.

We propose an unsupervised learning methodology with descriptors based on topological data analysis (TDA) concepts to describe the local structural properties of materials at the atomic scale. Based only on atomic positions and without a priori knowledge, our method allows for an autonomous identification of clusters of atomic structures through a Gaussian mixture model. We apply successfully this approach to the analysis of elemental Zr in the crystalline and liquid states as well as homogeneous nucleation events under deep undercooling conditions.

Unsupervised topological learning approach of crystal nucleation.

Nucleation phenomena commonly observed in our every day life are of fundamental, technological and societal importance in many areas, but some of their most intimate mechanisms remain however to be unravelled. Crystal nucleation, the early stages where the liquid-to-solid transition occurs upon undercooling, initiates at the atomic level on nanometre length and sub-picoseconds time scales and involves complex multidimensional mechanisms with local symmetry breaking that can hardly be observed experimentally in the very details. To reveal their structural features in simulations without a priori, an unsupervised learning approach founded on topological descriptors loaned from persistent homology concepts is proposed.

Excess entropy scaling law: A potential energy landscape view.

The relationship between excess entropy and diffusion is revisited by means of large-scale computer simulation combined to supervised learning approach to determine the excess entropy for the Lennard-Jones potential. Results reveal a strong correlation with the properties of the potential energy landscape (PEL). In particular the exponential law holding in the liquid is seen to be linked with the landscape-influenced regime of the PEL whereas the fluidlike power-law corresponds to the free diffusion regime.

The intimate relationship between structural relaxation and the energy landscape of monatomic liquid metals.

The characteristic property of a liquid, discriminating it from a solid, is its fluidity, which can be expressed by a velocity field. The reaction of the velocity field on forces is enshrined in the transport parameter viscosity. In contrast, a solid reacts to forces elastically through a displacement field, the particles are trapped in their potential minimum.

based interionic interactions in calcium aluminotitanate oxide melts: structure and diffusion.

Calcium aluminotitanate (CaO-AlO-TiO) ternary oxides are of fundamental interest in Materials as well as Earth and environmental science, and a key system for several industrial applications. As their properties at the atomic scale are scarcely known, interionic interactions for the melts are built from a bottom up strategy consisting in fitting first only AlO, CaO and TiOsingle oxide compounds separately with a unified description of the oxygen charge and O-O interaction term. For this purpose, a mean-square difference minimization of the partial pair-correlation functions with respect to thereference was performed.

Thermodynamics and structural properties of CaO: A molecular dynamics simulation study.

A detailed theoretical study of CaO in the solid and liquid states by means of combined classical and ab initio molecular dynamics simulations is presented. Evolution of the specific heat capacity at constant pressure as a function of temperature is studied, and the melting temperature and enthalpy of fusion are determined. It is shown that an empirical Born-Mayer-Huggins potential gives a good representation of pure CaO in the liquid and solid states as compared to available experimental data and density functional theory calculations.

Pressure-induced effects in the spectra of collective excitations in pure liquid metals.

Collective dynamics of metallic melts at high pressures is one of the open issues of condensed matter physics. By means of ab initio molecular dynamics simulations, we examine features of dispersions of collective excitations through transverse current spectral functions, as a function of pressure. Typical metallic melts, such as Li and Na monovalent metals as well as Al, Pb and In polyvalent metals are considered.

Molecular dynamics simulations of amorphous Ni-P alloy formation by rapid quenching and atomic deposition.

A combined experimental and simulation study is carried out to compare the properties of amorphous Ni P alloys obtained by electroless deposition and rapid melt-quenching. The onset of crystallization of experimental electroless deposited amorphous films is measured by differential scanning calorimetry experiments. Classical molecular dynamics simulations using Embedded Atom Model-based interactions are performed to obtain glassy Ni-P by melt-quenching the liquid with various quenching rates, as well as via low-energy chemical deposition to mimic experimental electroless deposition.

Structural and dynamic properties of aluminosilicate melts: a molecular dynamics study.

In the present work, the structural and dynamic properties of aluminosilicates (AlO) -(SiO) (AS) as a function of the AlO concentration x are studied by means of molecular dynamics simulations. Firstly, the parametrization of the Born-Mayer-Huggins type potential developed recently for the more general CaO-AlO-SiO ternary system is assessed. Comparison of local structural properties, such as the x-ray structure factor, partial pair-correlation functions, distributions of coordination numbers and bond angles, as well as the dynamics through the viscosity and self-diffusion coefficients to experimental data and other molecular dynamics simulations found in the literature, shows that this potential is transferable to AS melts for all compositions and is more reliable than other empirical potentials used so far.

Liquid Aluminum: atomic diffusion and viscosity from ab initio molecular dynamics.

We present a study of dynamic properties of liquid aluminum using density-functional theory within the local-density (LDA) and generalized gradient (GGA) approximations. We determine the temperature dependence of the self-diffusion coefficient as well the viscosity using direct methods. Comparisons with experimental data favor the LDA approximation to compute dynamic properties of liquid aluminum.

Molecular-dynamics study of liquid nickel above and below the melting point.

We have investigated the structural and dynamic properties of liquid nickel by means of large-scale molecular-dynamics simulations, using an effective-pair potential derived from the second-order pseudopotential perturbation theory. The model of interactions is assessed on the single-atom as well as collective dynamic properties. The short-range order in the stable and undercooled liquids is also examined.

Ab initio molecular dynamics simulations of local structure of supercooled Ni.

We report results of first-principles molecular dynamics simulations for stable and undercooled nickel liquids. The calculated structure factors as a function of temperature are discussed with respect to recent experimental measurements. In addition, structural analysis using bonding orientational order and three-dimensional pair analysis techniques have been performed in detail and the effect of undercooling on the microstructure has been analyzed.

Local order of liquid and supercooled zirconium by ab initio molecular dynamics.

It has been suggested that icosahedral short-range order (SRO) occurs in deeply undercooled melts of pure metallic elements. We report results of first-principles molecular dynamics simulations for stable and undercooled zirconium liquids. Our results emphasize the occurrence of a local order more complex than the icosahedral one.