Understanding Defect-Mediated Ion Migration in Semiconductors using Atomistic Simulations and Machine Learning.

Ion migration in semiconductor devices is facilitated by the presence of point defects and has a major influence on electronic and optical properties. It is important to understand and identify ways to mitigate photoinduced and electrically induced defect-mediated ion migration in semiconductors. In this Perspective, we discuss the fundamental mechanisms of defect-mediated ion migration and diffusion as understood through atomistic simulations.

Deterministic Synthesis of a Two-Dimensional MAPbI Nanosheet and Twisted Structure with Moiré Superlattice.

The synthesis of extremely thin 2D halide perovskites and the exploration of their interlayer interactions have garnered significant attention in current research. A recent advancement we have made involves the development of a successful technique for generating ultrathin MAPbI nanosheets with controlled thickness and an exposed intrinsic surface. This innovative method relies on utilizing the Ruddlesden-Popper (RP) phase perovskite (BAMAPbI) as a template.

Screening of novel halide perovskites for photocatalytic water splitting using multi-fidelity machine learning.

Photocatalytic water splitting is an efficient and sustainable technology to produce high-purity hydrogen gas for clean energy using solar energy. Despite the tremendous success of halide perovskites as absorbers in solar cells, their utility for water splitting applications has not been systematically explored. A band gap greater than 1.

Discovering novel halide perovskite alloys using multi-fidelity machine learning and genetic algorithm.

Expanding the pool of stable halide perovskites with attractive optoelectronic properties is crucial to addressing current limitations in their performance as photovoltaic (PV) absorbers. In this article, we demonstrate how a high-throughput density functional theory (DFT) dataset of halide perovskite alloys can be used to train accurate surrogate models for property prediction and subsequently perform inverse design using genetic algorithm (GA). Our dataset consists of decomposition energies, bandgaps, and photovoltaic efficiencies of nearly 800 pure and mixed composition ABX3 compounds from both the GGA-PBE and HSE06 functionals, and are combined with ∼100 experimental data points collected from the literature.

Two-Dimensional Halide Pb-Perovskite-Double Perovskite Epitaxial Heterostructures.

Epitaxial heterostructures of two-dimensional (2D) halide perovskites offer a new platform for studying intriguing structural, optical, and electronic properties. However, difficulties with the stability of Pb- and Sn-based heterostructures have repeatedly slowed the progress. Recently, Pb-free halide double perovskites are gaining a lot of attention due to their superior stability and greater chemical diversity, but they have not been successfully incorporated into epitaxial heterostructures for further investigation.

A framework for materials informatics education through workshops.

Abstract: The burgeoning field of materials informatics necessitates a focus on educating the next generation of materials scientists in the concepts of data science, artificial intelligence (AI), and machine learning (ML). In addition to incorporating these topics in undergraduate and graduate curricula, regular hands-on workshops present the most effective medium to initiate researchers to informatics and have them start applying the best AI/ML tools to their own research. With the help of the Materials Research Society (MRS), members of the MRS AI Staging Committee, and a dedicated team of instructors, we successfully conducted workshops covering the essential concepts of AI/ML as applied to materials data, at both the Spring and Fall Meetings in 2022, with plans to make this a regular feature in future meetings.

Photocatalytic Ketyl Radical Initiated C -C /C Coupling on ZnIn S.

Visible-light-driven C-C bond formation utilizing ketyl radical (C ) species has attracted increasing attention recently, as it provides a direct route for the synthesis of complex molecules. However, the most-developed homogeneous photocatalytic systems for the generation and utilization of ketyl radicals usually entail noble metal-based (e. g.

Universal machine learning framework for defect predictions in zinc blende semiconductors.

We develop a framework powered by machine learning (ML) and high-throughput density functional theory (DFT) computations for the prediction and screening of functional impurities in groups IV, III-V, and II-VI zinc blende semiconductors. Elements spanning the length and breadth of the periodic table are considered as impurity atoms at the cation, anion, or interstitial sites in supercells of 34 candidate semiconductors, leading to a chemical space of approximately 12,000 points, 10% of which are used to generate a DFT dataset of charge dependent defect formation energies. Descriptors based on tabulated elemental properties, defect coordination environment, and relevant semiconductor properties are used to train ML regression models for the DFT computed neutral state formation energies and charge transition levels of impurities.

Machine learning for impurity charge-state transition levels in semiconductors from elemental properties using multi-fidelity datasets.

Quantifying charge-state transition energy levels of impurities in semiconductors is critical to understanding and engineering their optoelectronic properties for applications ranging from solar photovoltaics to infrared lasers. While these transition levels can be measured and calculated accurately, such efforts are time-consuming and more rapid prediction methods would be beneficial. Here, we significantly reduce the time typically required to predict impurity transition levels using multi-fidelity datasets and a machine learning approach employing features based on elemental properties and impurity positions.

Defective Ultrathin ZnIn S for Photoreductive Deuteration of Carbonyls Using D O as the Deuterium Source.

Deuterium (D) labeling is of great value in organic synthesis, pharmaceutical industry, and materials science. However, the state-of-the-art deuteration methods generally require noble metal catalysts, expensive deuterium sources, or harsh reaction conditions. Herein, noble metal-free and ultrathin ZnIn S (ZIS) is reported as an effective photocatalyst for visible light-driven reductive deuteration of carbonyls to produce deuterated alcohols using heavy water (D O) as the sole deuterium source.

Spectroscopic Comparison of Thermal Transport at Organic-Inorganic and Organic-Hybrid Interfaces Using CsPbBr and FAPbBr (FA = Formamidinium) Perovskite Nanocrystals.

Thermal transport across interfaces depends on the matching of vibrational structure at the interface. This work examines the transfer of thermal excitation from an organic ligand coating to either all-inorganic cesium lead tribromide (CsPbBr) nanocrystals or hybrid organic-inorganic formamidinium lead tribromide (FAPbBr) nanocrystals using selective infrared optical excitation. These two semiconductors are directly compared because they (or similar semiconductors) are currently envisioned as strong candidates in many optoelectronic technologies and they differ due to the presence of an organic or inorganic cation, which introduces substantial differences in the phonon density of states in otherwise quite similar semiconductors.

Charge Transfer Dynamics of Phase-Segregated Halide Perovskites: CHNHPbCl and CHNHPbI or (CHNH)(CHNH) Pb I Mixtures.

Lead halide perovskites present a versatile class of solution-processable semiconductors with highly tunable bandgaps that span ultraviolet, visible, and near-infrared portions of the spectrum. We explore phase-separated chloride and iodide lead perovskite mixtures as candidate materials for intermediate band applications in future photovoltaics. X-ray diffraction and scanning electron microscopy reveal that deposition of precursor solutions across the MAPbCl/MAPbI composition space affords quasi-epitaxial cocrystallized films, in which the two perovskites do not alloy but instead remain phase-segregated.

Infrared-pump electronic-probe of methylammonium lead iodide reveals electronically decoupled organic and inorganic sublattices.

Organic-inorganic hybrid perovskites such as methylammonium lead iodide (CHNHPbI) are game-changing semiconductors for solar cells and light-emitting devices owing to their defect tolerance and exceptionally long carrier lifetimes and diffusion lengths. Determining whether the dynamically disordered organic cations with large dipole moment benefit the optoelectronic properties of CHNHPbI has been an outstanding challenge. Herein, via transient absorption measurements employing an infrared pump pulse tuned to a methylammonium vibration, we observe slow, nanosecond-long thermal dissipation from the selectively excited organic mode to the inorganic sublattice.

Heterogeneous mesoporous manganese/cobalt oxide catalysts for selective oxidation of 5-hydroxymethylfurfural to 2,5-diformylfuran.

We report a heterogeneous catalytic protocol for the oxidation of 5-hydroxymethylfurfural (HMF) to 2,5-diformylfuran (DFF) using a mesoporous manganese doped cobalt oxide material. The absence of precious metals and additives, use of air as the sole oxidant, and easy isolation of products, along with proper catalyst reusability, make our catalytic protocol attractive for the selective oxidation of HMF to DFF.

Optimization of Organotin Polymers for Dielectric Applications.

Recently, there has been a growing interest in developing wide band gap dielectric materials as the next generation insulators for capacitors, photovoltaic devices, and transistors. Organotin polyesters have shown promise as high dielectric constant, low loss, and high band gap materials. Guided by first-principles calculations from density functional theory (DFT), in line with the emerging codesign concept, the polymer poly(dimethyltin 3,3-dimethylglutarate), p(DMTDMG), was identified as a promising candidate for dielectric applications.

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.

Rational Co-Design of Polymer Dielectrics for Energy Storage.

Although traditional materials discovery has historically benefited from intuition-driven experimental approaches and serendipity, computational strategies have risen in prominence and proven to be a powerful complement to experiments in the modern materials research environment. It is illustrated here how one may harness a rational co-design approach-involving synergies between high-throughput computational screening and experimental synthesis and testing-with the example of polymer dielectrics design for electrostatic energy storage applications. Recent co-design efforts that can potentially enable going beyond present-day "standard" polymer dielectrics (such as biaxially oriented polypropylene) are highlighted.

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.

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.

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.