Database, Features, and Machine Learning Model to Identify Thermally Driven Metal-Insulator Transition Compounds

Abstract

Metal-insulator transition (MIT) compounds are materials that may exhibit insulating or metallic behavior, depending on the physical conditions, and are of immense fundamental interest owing to their potential applications in emerging microelectronics. There is a dearth of thermally-driven MIT materials, however, which makes delineating these compounds from those that are exclusively insulating or metallic challenging. Here we report a material database comprising temperature-controlled MITs (and metals and insulators with similar chemical composition and stoichiometries to the MIT compounds) from high quality experimental literature, built through a combination of materials-domain knowledge and natural language processing. We featurize the dataset using compositional, structural, and energetic descriptors, including two MIT relevant energy scales, an estimated Hubbard interaction and the charge transfer energy, as well as the structure-bond-stress metric referred to as the global-instability index (GII). We then perform supervised classification, constructing three electronic-state classifiers: metal vs non-metal (M), insulator vs non-insulator (I), and MIT vs non-MIT (T). We identify two important descriptors that separate metals, insulators, and MIT materials in a 2D feature space: the average deviation of the covalent radius and the range of the Mendeleev number. We further elaborate on other important features (GII and Ewald energy), and examine how they affect classification of binary vanadium and titanium oxides. We discuss the relationship of these atomic features to the physical interactions underlying MITs in the rare-earth nickelate family. Last, we implement an online version of the classifiers, enabling quick probabilistic class predictions by uploading a crystallographic structure file.

References in corpus (25)

• Scikit-learn: Machine Learning in Python
• XGBoost: A Scalable Tree Boosting System
• UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
• New Tolerance Factor to Predict the Stability of Perovskite Oxides and Halides
• A General-Purpose Machine Learning Framework for Predicting Properties of Inorganic Materials
• Frequency-dependent local interactions and low-energy effective models from electronic structure calculations
• Charge disproportionation without charge transfer in the rare-earth nickelates as a possible mechanism for the metal-insulator transition
• Origin of band gaps in 3d perovskite oxides
• Structurally Triggered Metal-Insulator Transition in Rare-Earth Nickelates
• Structural instability of the rutile compounds and its relevance to the metal-insulator transition of VO2
• Machine learning of quantum phase transitions
• Interplay of octahedral rotations and breathing distortions in charge ordering perovskite oxides
• Searching for Materials with High Refractive Index and Wide Band Gap: A First-Principles High-Throughput Study
• Machine Learning Many-Body Localization: Search for the Elusive Nonergodic Metal
• Length-scales of interfacial coupling between metal-insulator phases in oxides
• Extrapolating quantum observables with machine learning: Inferring multiple phase transitions from properties of a single phase
• Mechanism and Control Parameters of the Coupled Structural and Metal-Insulator Transition in Nickelates
• Lattice Energetics and Correlation-Driven Metal-Insulator Transitions: The Case of Ca${}_2$RuO${}_4$
• A Unique Crystal Structure of Ca${}_2$RuO${}_4$ in the Current Stabilized Semi-Metallic State
• Disentangling lattice and electronic contributions to the metal-insulator transition from bulk vs. layer confined RNiO${}_3$
• Featureless adaptive optimization accelerates functional electronic materials design
• Magnetic phase diagram of $S{r}_{3}F{e}_2{O}_{7-x}$
• Control of hidden ground-state order in NdNiO${}_3$ superlattices
• Effect of the electron-lattice coupling on the charge and magnetic order in rare-earth nickelates
• Boson Slave Solver (BoSS) v1.1

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• Bridging Fidelities to Predict Nanoindentation Tip Radii Using Interpretable Deep Learning Models
• Learning the Crystal Structure Genome for Property Classification
• Emerging Microelectronic Materials by Design: Navigating Combinatorial Design Space with Scarce and Dispersed Data
• Energy Landscape analysis of metal-insulator transitions: theory and application to Ca${}_2$RuO${}_4$, $R$NiO${}_3$ and their heterostructures
• Gaussian Processes for Finite Size Extrapolation of Many-Body Simulations