Publications
2023
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Machine Learning the Electronic Structure of Matter across TemperaturesLenz Fiedler , Normand A. Modine , Kyle D. Miller , and Attila CangiPhysical Review B, Sep 2023We introduce machine learning (ML) models that predict the electronic structure of materials across a wide temperature range. Our models employ neural networks and are trained on density functional theory (DFT) data. Unlike most other ML models that use DFT data, our models directly predict the local density of states (LDOS) of the electronic structure. This provides several advantages, including access to multiple observables such as the electronic density and electronic total free energy. Moreover, our models account for both the electronic and ionic temperatures independently, making them ideal for applications like laser heating of matter. We validate the efficacy of our LDOS-based models on a metallic test system. They accurately capture energetic effects induced by variations in ionic and electronic temperatures over a broad temperature range, even when trained on a subset of these temperatures. These findings open up exciting opportunities for investigating the electronic structure of materials under both ambient and extreme conditions.
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Testing the Limits of the Global Instability IndexKyle D. Miller , and James M. RondinelliAPL Materials, Oct 2023The global instability index (GII) is a computationally inexpensive bond valence-based metric originally designed to evaluate the total bond strain in a crystal. Recently, the GII has gained popularity as a feature of data-driven models in materials research. Although prior studies have proven that GII is an effective predictor of structural distortions and decomposition energy when applied to small datasets, the wider use of GII as a global indicator of structural stability has yet to be evaluated. To that end, we compute GII for thousands of compounds in inorganic structure databases and partition compounds by chemical interactions underlying their stability to understand the GII values and their variations. Our results show that the GII captures relative chemical trends, such as electronegativity, even beyond the intended domain of strongly ionic compounds. However, we also find that GII magnitudes vary significantly with factors such as chemistry (cation–anion identities and bond character), geometry (connectivity), data source, and model bias, making GII suitable for comparisons within controlled datasets but unsuitable as an absolute, universal metric for structural feasibility.
2022
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Carrier-Induced Metal-Insulator Transition in Trirutile MgTa\(_2\)O\(_6\)Kyle D. Miller , and James M. RondinelliPhysical Review Materials, Jul 2022Materials exhibiting metal-insulator transitions (MITs) are proposed platforms for next-generation low-power electronics. Many of these materials exhibit strong coupling between the electronic and lattice degrees of freedom, which makes them ideal systems to examine the interplay between lattice dynamics, electronic structure, and magnetic order. The rutile structure, featuring edge-connected octahedral chains along its c axis, permits metal-metal interactions that can induce metal-insulator transitions. Although the MITs in rutile-structured compounds have been thoroughly studied, the derivative trirutile phase, which accommodates similar metal-metal interactions, has yet to be examined in the context of MITs. Here we use density functional theory calculations to investigate a carrier-driven MIT in trirutile MgTa\(_2\)O\(_6\), a \(d^0\) insulator with a suitable axial ratio. Our calculations suggest the existence of four distinct phases in MgTa\(_2\)O\(_6\) with increasing electron concentration: nonmagnetic insulator, ferromagnetic (FM) metal, FM half-metal, and nonmagnetic insulator. We explain how the resulting electronic phases arise from changes in atomic structure with increasing carrier density. Our results indicate that trirutile oxides may be a promising materials class for which to access and functionalize MITs.
2021
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Database, Features, and Machine Learning Model to Identify Thermally Driven Metal–Insulator Transition CompoundsAlexandru B. Georgescu , Peiwen Ren , Aubrey R. Toland , Shengtong Zhang , Kyle D. Miller , Daniel W. Apley , Elsa A. Olivetti , Nicholas Wagner , and James M. RondinelliChemistry of Materials, Jul 2021Metal–insulator transition (MIT) compounds are materials that may exhibit metallic or insulating behavior, depending on the physical conditions, and are of immense fundamental interest owing to their potential applications in emerging microelectronics. An important subset of MIT materials are those with a transition driven by temperature. The number of thermally driven MIT materials, however, is scarce, which makes delineating these compounds from those that are exclusively insulating or metallic challenging. Most research that addresses thermal MITs is limited by the domain knowledge of the scientists to a subset of MIT materials and is often focused on a limited subset of possible features. Here, using a combination of domain knowledge and natural language processing (NLP) searches, we have built a material database comprising thermally driven MITs as well as metals and insulators with similar chemical composition and stoichiometries to the MIT compounds. We featurized this data set using a wide variety of compositional, structural, and energetic descriptors, including two MIT relevant energy scales, the 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 performed supervised classification on this data set, constructing three electronic-state classifiers: metal vs nonmetal (M), insulator vs noninsulator (I), and MIT vs non-MIT (T). This classification allows us to identify new features separating MIT materials from non-MIT materials. These include the 2D feature space consisting of the average deviation of the covalent radius, the range of the Mendeleev number and Ewald energy. We discuss the relationship of these atomic features to the physical interactions underlying MITs in the rare-earth nickelate family. We then elaborate on other features (GII and Ewald energy) and examine how they affect the classification of binary vanadium and titanium oxides. Last, we implement an online and publicly accessible version of the classifiers, enabling quick probabilistic class predictions by uploading a crystallographic structure file. The broad accessibility of our database, newly identified features, and user-friendly classifier models will aid in accelerating the discovery of MIT materials.
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AB\(_2\)X\(_6\) Compounds and the Stabilization of Trirutile OxidesEmily C. Schueller , Yuzki M. Oey , Kyle D. Miller , Kira E. Wyckoff , Ruining Zhang , William Zhang , Stephen D. Wilson , James M. Rondinelli , and Ram SeshadriInorganic Chemistry, Jun 2021The properties of crystalline materials tend to be strongly correlated with their structures, and the prediction of crystal structure from only the composition is a coveted goal in the field of inorganic materials. However, even for the simplest compositions, such prediction relies on a complex network of interactions, including atomic or ionic radii, ionicity, electronegativity, position in the periodic table, and magnetism, to name only a few important parameters. We focus here on the AB\(_2\)X\(_6\) (AB\(_2\)O\(_6\) and AB\(_2\)F\(_6\)) composition space with the specific goal of finding new oxide compounds in the trirutile family, which is known for unusual one-dimensional (1D) antiferromagnetic behavior. Through machine learning methods, we develop an understanding of how geometric and bonding constraints determine the crystallization of compounds in the trirutile structure as opposed to other ternary structures in this space. In combination with density functional theory (DFT) calculations, we predict 16 previously unreported candidate trirutile oxides. We successfully prepare one of these and show it forms in the disordered rutile structure, under the preparation conditions adopted here.
2020
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Structural Signatures of the Insulator-to-Metal Transition in BaCo\(_{1-x}\)Ni\(_x\)S\(_2\)Emily C. Schueller , Kyle D. Miller , William Zhang , Julia L. Zuo , James M. Rondinelli , Stephen D. Wilson , and Ram SeshadriPhysical Review Materials, Oct 2020The solid solution BaCo\(_{1-x}\)Ni\(_x\)S\(_2\) exhibits an insulator-to-metal transition close to \(x=0.21\). Questions of whether this transition is coupled with structural changes remain open. Here we follow the structural evolution as a function of the Ni content \(x\) using synchrotron powder x-ray diffraction and pair distribution function analyses to reveal significant basal sulfide anion displacements occurring preferentially along the CoS\(_5\) pyramidal edges comprising the edge-connected bond network in BaCo\(_{1-x}\)Ni\(_x\)S\(_2\). These displacements decrease in magnitude as \(x\) increases and are nearly quenched in \(x=1\) BaNiS\(_2\). Density-functional-theory-based electronic structure calculations on \(x=0\) BaCoS\(_2\) suggest that these displacements arise as a dynamic first-order Jahn-Teller effect owing to partial occupancy of nominally degenerate Co\(^{2+}\) \(d_{xz}\) and \(d_{yz}\) orbitals, leading to local structural symmetry breaking in the \(xy\)-plane of the Co-rich phases. The Jahn-Teller instability is associated with the opening of a band gap that is further strengthened by electronic correlation. The Jahn-Teller effect is reduced upon increased electron filling as \(x\)→1, indicating that the local structure and band filling cooperatively result in the observed insulator-to-metal transition.
2018
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Optimization and Validation of Efficient Models for Predicting Polythiophene Self-AssemblyEvan D. Miller , Matthew L. Jones , Michael M. Henry , Paul Chery , Kyle Miller , and Eric JankowskiPolymers, Dec 2018We develop an optimized force-field for poly(3-hexylthiophene) (P3HT) and demonstrate its utility for predicting thermodynamic self-assembly. In particular, we consider short oligomer chains, model electrostatics and solvent implicitly, and coarsely model solvent evaporation. We quantify the performance of our model to determine what the optimal system sizes are for exploring self-assembly at combinations of state variables. We perform molecular dynamics simulations to predict the self-assembly of P3HT at ∼350 combinations of temperature and solvent quality. Our structural calculations predict that the highest degrees of order are obtained with good solvents just below the melting temperature. We find our model produces the most accurate structural predictions to date, as measured by agreement with grazing incident X-ray scattering experiments.
