Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userMachine Learning the Metastable Phase Diagram of Materials
202
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
Phase diagrams are an invaluable tool for material synthesis and provide information on the phases of the material at any given thermodynamic condition. Conventional phase diagram generation involves experimentation to provide an initial estimate of thermodynamically accessible phases, followed by use of phenomenological models to interpolate between the available experimental data points and extrapolate to inaccessible regions. Such an approach, combined with first-principles calculations and data-mining techniques, has led to exhaustive thermodynamic databases albeit at distinct thermodynamic equilibria. In contrast, materials during their synthesis, operation, or processing, may not reach their thermodynamic equilibrium state but, instead, remain trapped in a local free energy minimum, that may exhibit desirable properties. Mapping these metastable phases and their thermodynamic behavior is highly desirable but currently lacking. Here, we introduce an automated workflow that integrates first principles physics and atomistic simulations with machine learning (ML), and high-performance computing to allow rapid exploration of the metastable phases of a given elemental composition. Using a representative material, carbon, with a vast number of metastable phases without parent in equilibrium, we demonstrate automatic mapping of hundreds of metastable states ranging from near equilibrium to those far-from-equilibrium. Moreover, we incorporate the free energy calculations into a neural-network-based learning of the equations of state that allows for construction of metastable phase diagrams. High temperature high pressure experiments using a diamond anvil cell on graphite sample coupled with high-resolution transmission electron microscopy are used to validate our metastable phase predictions. Our introduced approach is general and broadly applicable to single and multi-component systems.
rate research
Read More
Hyperuniform disordered photonic materials (HDPM) are spatially correlated dielectric structures with unconventional optical properties. They can be transparent to long-wavelength radiation while at the same time have isotropic band gaps in another frequency range. This phenomenon raises fundamental questions concerning photon transport through disordered media. While optical transparency is robust against recurrent multiple scattering, little is known about other transport regimes like diffusive multiple scattering or Anderson localization. Here we investigate band gaps, and we report Anderson localization in two-dimensional stealthy HDPM using numerical simulations of the density of states and optical transport statistics. To establish a unified view, we propose a transport phase diagram. Our results show that, depending only on the degree of correlation, a dielectric material can transition from localization behavior to a bandgap crossing an intermediate regime dominated by tunneling between weakly coupled states.
We propose a statistical mechanics for a general class of stationary and metastable equilibrium states. For this purpose, the Gibbs extremal conditions are slightly modified in order to be applied to a wide class of non-equilibrium states. As usual, it is assumed that the system maximizes the entropy functional $S$, subjected to the standard conditions; i.e., constant energy and normalization of the probability distribution. However, an extra conserved constraint function $F$ is also assumed to exist, which forces the system to remain in the metastable configuration. Further, after assuming additivity for two quasi-independent subsystems, and that the new constraint commutes with density matrix $rho$, it is argued that F should be an homogeneous function of the density matrix, at least for systems in which the spectrum is sufficiently dense to be considered as continuous. The explicit form of $F$ turns to be $F(p_{i})=p_{i}^{q}$, where $p_i$ are the eigenvalues of the density matrix and $q$ is a real number to be determined. This $q$ number appears as a kind of Tsallis parameter having the interpretation of the order of homogeneity of the constraint $F$. The procedure is applied to describe the results of the plasma experiment of Huang and Driscoll. The experimentally measured density is predicted with a similar precision as it is done with the use of the extremum of the enstrophy and Tsallis procedures. However, the present results define the density at all the radial positions. In particular, the smooth tail shown by the experimental distribution turns to be predicted by the procedure. In this way, the scheme avoids the non-analyticity of the density profile at large distances arising in both of the mentioned alternative procedures.
Understanding how the arrangement of atoms and their interactions determine material behavior has been the dominant paradigm in materials science. A complementary approach is studying the organizational structure of networks of materials, defined on the basis of interactions between materials themselves. In this work, we present the phase diagram of all known inorganic materials, an extremely-dense complex network of nearly $2.1 times 10^4$ stable inorganic materials (nodes) connected with $41 times 10^6$ tie-lines (edges) defining their two-phase equilibria, as computed via high-throughput density functional theory. We show that the degree distribution of this network follows a lognormal form, with each material connected to on average 18% of the other materials in the network via tie-lines. Analyzing the structure and topology of this network has potential to uncover new materials knowledge inaccessible from the traditional bottom-up (atoms to materials) approaches. As an example, we derive a data-driven metric for the reactivity of a material as characterized by its connectedness in the network, and quantitatively identify the noblest materials in nature.
Two-dimensional (2D) transition metal dichalcogenides (TMDCs) have been the subject of sustained research interest due to their extraordinary electronic and optical properties. They also exhibit a wide range of structural phases because of the different orientations that the atoms can have within a single layer, or due to the ways that different layers can stack. Here we report the first study of direct-visualization of structural transformations in atomically-thin layers under highly non-equilibrium thermodynamic conditions. We probe these transformations at the atomic scale using real-time, aberration corrected scanning transmission electron microscopy and observe strong dependence of the resulting structures and phases on both heating rate and temperature. A fast heating rate (25 C/sec) yields highly ordered crystalline hexagonal islands of sizes of less than 20 nm which are composed of a mixture of 2H and 3R phases. However, a slow heating rate (25 C/min) yields nanocrystalline and sub-stoichiometric amorphous regions. These differences are explained by different rates of sulfur evaporation and redeposition. The use of non-equilibrium heating rates to achieve highly crystalline and quantum-confined features from 2D atomic layers present a new route to synthesize atomically-thin, laterally confined nanostrucutres and opens new avenues for investigating fundamental electronic phenomena in confined dimensions.
On the basis of the first principles simulation, the structure, formation enthalpy, and mechanical properties (elastic constant, bulk, and shear modulus and hardness) of five Nb-doped Ni systems are systematically studied. The calculated equilibrium volume increases with the Nb concentration increasing. The computational elastic constants and formation enthalpy indicate that all Nb-doped Ni systems are mechanically and thermodynamically stable in our research. The hardness of these systems also be predicted after the bulk modulus and shear modulus have been accurately calculated. The results show that the hardness increases with the Nb concentration increasing when the Nb concentration below 4.9%, beyond which the hardness will decrease within the scope of our study.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
