Generalized convex hull construction for materials discovery

Abstract

High-throughput computational materials searches generate large databases of locally-stable structures. Conventionally, the needle-in-a-haystack search for the few experimentally-synthesizable compounds is performed using a convex hull construction, which identifies structures stabilized by manipulation of a particular thermodynamic constraint (for example pressure or composition) chosen based on prior experimental evidence or intuition. To address the biased nature of this procedure we introduce a generalized convex hull framework. Convex hulls are constructed on data-driven principal coordinates, which represent the full structural diversity of the database. Their coupling to experimentally-realizable constraints hints at the conditions that are most likely to stabilize a given configuration. The probabilistic nature of our framework also addresses the uncertainty stemming from the use of approximate models during database construction, and eliminates redundant structures. The remaining small set of candidates that have a high probability of being synthesizable provide a much needed starting point for the determination of viable synthetic pathways.

arXiv commentAccepted Manuscript

References in corpus (15)

• Quantum ESPRESSO: a modular and open-source software project for quantum simulations of materials
• Conventional superconductivity at 203 K at high pressures
• Comparing molecules and solids across structural and alchemical space
• Machine Learning Unifies the Modelling of Materials and Molecules
• High-pressure phases of silane
• Machine-learning based interatomic potential for amorphous carbon
• Hydrogen sulphide at high pressure: a strongly-anharmonic phonon-mediated superconductor
• Simulating rare events in equilibrium or non-equilibrium stochastic systems
• Crystal structure prediction using the Minima Hopping method
• Zero-Temperature Structures of Atomic Metallic Hydrogen
• Density functional theory study of phase IV of solid hydrogen
• Constrained evolutionary algorithm for structure prediction of molecular crystals: methodology and applications
• Dissociation of high-pressure solid molecular hydrogen: Quantum Monte Carlo and anharmonic vibrational study
• Quantum Monte Carlo study of the phase diagram of solid molecular hydrogen at extreme pressures
• Hexagonal structure of phase III of solid hydrogen

Cited by in corpus (11)

• Machine learning and the physical sciences
• Physics-inspired structural representations for molecules and materials
• Unsupervised machine learning in atomistic simulations, between predictions and understanding
• Ternary Nitride Materials: Fundamentals and Emerging Device Applications
• Quantum-mechanical exploration of the phase diagram of water
• Exploring the robust extrapolation of high-dimensional machine learning potentials
• Predicting the phase diagram of titanium dioxide with random search and pattern recognition
• Modeling the Ga/As binary system across temperaturesand compositions from first principles
• Ranking the Synthesizability of Hypothetical Zeolites with the Sorting Hat
• Inorganic Crystal Structure Prototype Database based on Unsupervised Learning of Local Atomic Environments
• Accelerating the theoretical study of Li-polysulphide adsorption on single-atom catalysts via machine learning approaches