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The use of statistical/machine learning (ML) approaches to materials science is experiencing explosive growth. Here, we review recent work focusing on the generation and application of libraries from both experiment and theoretical tools. The library data enables classical correlative ML and also opens the pathway for exploration of underlying causative physical behaviors. We highlight key advances facilitated by this approach and illustrate how modeling, macroscopic experiments, and imaging can be combined to accelerate the understanding and development of new materials systems. These developments point toward a data-driven future wherein knowledge can be aggregated and synthesized, accelerating the advancement of materials science.
We point out that the predictive power of tight binding potentials is not limited to obtaining fairly accurate total energy calculations and very satisfactory structural evolutions by molecular dynamics simulations. They also allow for a nice physical picture of the links between bonding and stability in different structures, which is particularly helpful in the case of binary suicides
In this paper we report a molecular dynamics simulation at constant pressure and constant temperature of the structural phase transition occurring in epitaxial FeSi2 from the fluorite phase (metallic and pseudomorphic) to orthorhombic one (semiconductor and bulk stable). The evolution of the electronic density of states is carefully monitored during the transformation and we can show that the Jahn-Teller coupling between the density of states at the Fermi level and the lattice deformation drives the metal-semiconductor transition.
In this paper we report the cohesion energy curves for different CoSi2 structures calculated by a semiempirical tight binding scheme. We set up a stability hierarchy among them and provide a kinetic model which could explain very recent and apparently contradictory Molecular Beam Epitaxy findings concerning the stability of a new pseudomorphic phase, i.e. the defected CsCl.
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