Arun Mannodi-Kanakkithodi

Machine learning bandgaps of double perovskites.

The ability to make rapid and accurate predictions on bandgaps of double perovskites is of much practical interest for a range of applications. While quantum mechanical computations for high-fidelity bandgaps are enormously computation-time intensive and thus impractical in high throughput studies, informatics-based statistical learning approaches can be a promising alternative. Here we demonstrate a systematic feature-engineering approach and a robust learning framework for efficient and accurate predictions of electronic bandgaps of double perovskites. After evaluating a set of more than 1.2 million features, we identify lowest occupied Kohn-Sham levels and elemental electronegativities of the constituent atomic species as the most crucial and relevant predictors. The developed models are validated and tested using the best practices of data science and further analyzed to rationalize their prediction performance.

Machine learning strategy for accelerated design of polymer dielectrics

The ability to efficiently design new and advanced dielectric polymers is hampered by the lack of sufficient, reliable data on wide polymer chemical spaces, and the difficulty of generating such data given time and computational/experimental constraints. Here, we address the issue of accelerating polymer dielectrics design by extracting learning models from data generated by accurate state-of-the-art first principles computations for polymers occupying an important part of the chemical subspace. The polymers are ‘fingerprinted’ as simple, easily attainable numerical representations, which are mapped to the properties of interest using a machine learning algorithm to develop an on-demand property prediction model. Further, a genetic algorithm is utilised to optimise polymer constituent blocks in an evolutionary manner, thus directly leading to the design of polymers with given target properties. While this philosophy of learning to make instant predictions and design is demonstrated here for the example of polymer dielectrics, it is equally applicable to other classes of materials as well.

Accelerated materials property predictions and design using motif-based fingerprints

Data-driven approaches are particularly useful for computational materials discovery and design as they can be used for rapidly screening over a very large number of materials, thus suggesting lead candidates for further in-depth investigations. A central challenge of such approaches is to develop a numerical representation, often referred to as a fingerprint, of the materials. Inspired by recent developments in chem-informatics, we propose a class of hierarchical motif-based topological fingerprints for materials composed of elements such as C, O, H, N, F, etc., whose coordination preferences are well understood. We show that these fingerprints, when representing either molecules or crystals, may be effectively mapped onto a variety of properties using a similarity-based learning model and hence can be used to predict relevant properties of a material, given that its fingerprint can be defined. Two simple procedures are introduced to demonstrate that the learning model can be inverted to identify the desired fingerprints and then, to reconstruct molecules which possess a set of targeted properties.

Poly(dimethyltin glutarate) as a Prospective Material for High Dielectric Applications

Poly(dimethyltin glutarate) is presented as the first organometallic polymer, a high dielectric constant, and low dielectric loss material. Theoretical results correspond well in terms of the dielectric constant. More importantly, the dielectric constant can be tuned depending on the solvent a film of the polymer is cast from. The breakdown strength is increased through blending with a second organometallic polymer.

A polymer dataset for accelerated property prediction and design

Emerging computation- and data-driven approaches are particularly useful for rationally designing materials with targeted properties. Generally, these approaches rely on identifying structure-property relationships by learning from a dataset of sufficiently large number of relevant materials. The learned information can then be used to predict the properties of materials not already in the dataset, thus accelerating the materials design. Herein, we develop a dataset of 1,073 polymers and related materials and make it available at http://khazana.uconn.edu/. This dataset is uniformly prepared using first-principles calculations with structures obtained either from other sources or by using structure search methods. Because the immediate target of this work is to assist the design of high dielectric constant polymers, it is initially designed to include the optimized structures, atomization energies, band gaps, and dielectric constants. It will be progressively expanded by accumulating new materials and including additional properties calculated for the optimized structures provided.

Machine learning in materials informatics: recent applications and prospects

Propelled partly by the Materials Genome Initiative, and partly by the algorithmic developments and the resounding successes of data-driven efforts in other domains, informatics strategies are beginning to take shape within materials science. These approaches lead to surrogate machine learning models that enable rapid predictions based purely on past data rather than by direct experimentation or by computations/simulations in which fundamental equations are explicitly solved. Data-centric informatics methods are becoming useful to determine material properties that are hard to measure or compute using traditional methods—due to the cost, time or effort involved—but for which reliable data either already exists or can be generated for at least a subset of the critical cases. Predictions are typically interpolative, involving fingerprinting a material numerically first, and then following a mapping (established via a learning algorithm) between the fingerprint and the property of interest. Fingerprints, also referred to as “descriptors”, may be of many types and scales, as dictated by the application domain and needs. Predictions may also be extrapolative—extending into new materials spaces—provided prediction uncertainties are properly taken into account. This article attempts to provide an overview of some of the recent successful data-driven “materials informatics” strategies undertaken in the last decade, with particular emphasis on the fingerprint or descriptor choices. The review also identifies some challenges the community is facing and those that should be overcome in the near future.

Compounds based on Group 14 elements: building blocks for advanced insulator dielectrics design

Being in the group with the most diverse set of properties among all in the periodic table, the Group 14 elements (C, Si, Ge, Sn, and Pb) are particularly interesting candidates for structure–property investigation. Motivated by the need to create new insulators for energy storage and electronics applications, we study a few compounds based on Group 14 elements in this work, namely the dihydrides, dichlorides, and difluorides. Using density functional theory (DFT) calculations, we establish patterns in their properties, including favored coordination chemistry, stability, electronic structure, and dielectric behavior. While a coordination number (CN) of 4 is commonly associated with Group 14 elements, there is a significant deviation from it down the group, with CNs as high as 7 and 8 common in Pb. Further, there is an increase in the relative stability of the +2 oxidation state as opposed to +4 when we go from C to Pb, a direct consequence of which is the existence of the di-compounds of C and Si as polymers, whereas the compounds of Ge, Sn, and Pb are strictly 3D crystalline solids. The coordination chemistries are further linked with the band gaps and dielectric constants (divided into two components: the electronic part and the ionic part) of these compounds. We also see that the more stable difluorides and dichlorides have large band gaps and small electronic dielectric constants, and most of the Ge and Sn compounds have remarkably large ionic dielectric constants by virtue of having polar and more flexible bonds. The staggering variation in properties displayed by these parent compounds offers opportunities for designing derivative materials with a desired combination of properties.

Materials Data Infrastructure and Materials Informatics

Data-driven materials research requires two key supporting components: data infrastructure and informatics. In this chapter, we review the state of the art in materials data infrastructure, focusing in detail on four infrastructure projects spanning academia, government, and industry. We also discuss data standards as an enabling step on the path to community-scale materials data infrastructure. We then introduce materials informatics as a potent accelerator of materials development and highlight specific application areas, including polymer dielectrics and dielectric breakdown.

First-principles identification of novel double perovskites for water-splitting applications

Identification of new materials for photo-electrochemical conversion of water into hydrogen and oxygen using visible solar light is one of the grand challenges of our times. Toward this goal, here we employ a hierarchy of down-selection steps based on structural constraints, thermodynamic stability, constraints on bandgap and band-edge positions to identify potential candidates residing in a target double perovskite chemical space. The adopted screening strategy results in four new promising candidate materials, which were studied in greater detail using first-principles computations for their thermodynamic stability, electronic structure and octahedral structural distortions. Our theoretical investigation is expected to serve as a motivation for future experimental efforts targeted toward realizing these identified promising materials.

Critical role of morphology on the dielectric constant of semicrystalline polyolefins.

A particularly attractive method to predict the dielectric properties of materials is density functional theory (DFT). While this method is very popular, its large computational requirements allow practical treatments of unit cells with just a small number of atoms in an ordered array, i.e., in a crystalline morphology. By comparing DFT and Molecular Dynamics (MD) simulations on the same ordered arrays of functional polyolefins, we confirm that both methodologies yield identical estimates for the dipole moments and hence the ionic component of the dielectric storage modulus. Additionally, MD simulations of more realistic semi-crystalline morphologies yield estimates for this polar contribution that are in good agreement with the limited experiments in this field. However, these predictions are up to 10 times larger than those for pure crystalline simulations. Here, we show that the constraints provided by the surrounding chains significantly impede dipolar relaxations in the crystalline regions, whereas amorphous chains must sample all configurations to attain their fully isotropic spatial distributions. These results, which suggest that the amorphous phase is the dominant player in the context, argue strongly that the proper polymer morphology needs to be modeled to ensure accurate estimates of the ionic component of the dielectric constant.