Building Materials Genome from Ground-State Configuration to Engineering Advance

Abstract

Individual phases are commonly considered as the building blocks of materials. However, the accurate theoretical prediction of properties of individual phases remains elusive. The top-down approach by decoding genomic building blocks of individual phases from experimental observations is non-unique. The density functional theory (DFT), as the state-of-the-art solution of quantum mechanics, prescribes the existence of a ground-state configuration at zero K for a given system. It is self-evident that the ground-state configuration alone is insufficient to describe a phase at finite temperatures as symmetry-breaking non-ground-state configurations are excited statistically at temperatures above zero K. Our multi-scale entropy approach (recently terms as Zentropy theory) postulates that the entropy of a phase is composed of the sum of the entropy of each configuration weighted by its probability plus the configurational entropy among all configurations. Consequently, the partition function of each configuration in statistical mechanics needs to be evaluated by its free energy rather than total energy. The combination of the ground- and non-ground-state configurations represents the building blocks of materials and can quantitively predict free energy of individual phases with the free energies of ground- and non-ground-state configurations predicted from DFT, plus all properties derived from free energy of individual phases.

References in corpus (19)

• Computational complexity and fundamental limitations to fermionic quantum Monte Carlo simulations
• Quantum Monte Carlo methods for nuclear physics
• The Effect of Lattice Vibrations on Substitutional Alloy Thermodynamics
• Ab initio study of ferroelectric domain walls in PbTiO3
• Continuum variational and diffusion quantum Monte Carlo calculations
• Domain walls of ferroelectric BaTiO3 within the Ginzburg-Landau-Devonshire phenomenological model
• A critical examination of robustness and generalizability of machine learning prediction of materials properties
• Interpretations of ground-state symmetry breaking and strong correlation in wavefunction and density functional theories
• Theory of Cross Phenomena and Their Coefficients Beyond Onsager Theorem
• Monte Carlo Studies of Quantum Critical Metals
• ESPEI for efficient thermodynamic database development, modification, and uncertainty quantification: application to Cu-Mg
• Thermodynamics and its Prediction and CALPHAD Modeling: Review, State of the Art, and Perspectives
• Zentropy Theory for Positive and Negative Thermal Expansions
• Quantified Uncertainty in Thermodynamic Modeling for Materials Design
• Thermodynamic Fluctuations in Magnetic Phase Transition: Invar as a Prototype
• Extensible Structure-Informed Prediction of Formation Energy with Improved Accuracy and Usability employing Neural Networks
• Genomic Materials Design: CALculation of PHAse Dynamics
• Parameter-free prediction of phase transition in PbTiO3 through combination of quantum mechanics and statistical mechanics
• Sensitivity estimation for calculated phase equilibria