Simulations based on solving the Kohn-Sham (KS) equation of density functional theory (DFT) have become a vital component of modern materials and chemical sciences research and development portfolios. Despite its versatility, routine DFT calculations are usually limited to a few hundred atoms due to the computational bottleneck posed by the KS equation. Here we introduce a machine-learning-based scheme to efficiently assimilate the function of the KS equation, and by-pass it to directly, rapidly, and accurately predict the electronic structure of a material or a molecule, given just its atomic configuration. A new rotationally invariant representation is utilized to map the atomic environment around a grid-point to the electron density and local density of states at that grid-point. This mapping is learned using a neural network trained on previously generated reference DFT results at millions of grid-points. The proposed paradigm allows for the high-fidelity emulation of KS DFT, but orders of magnitude faster than the direct solution. Moreover, the machine learning prediction scheme is strictly linear-scaling with system size.

structured zeolites, with the main focus on the synthesis strategies that are available, with examples given from the literature. Available approaches are reviewed for the preparation of micro-mesoporous structured zeolites, micro-macroporous structured zeolites and micro-meso-macroporous structured zeolites. Furthermore, the enhanced mass transport properties of hierarchically structured zeolites, featuring additional larger pores in addition to the crystalline micropores, have also been described. The significant improvement in catalytic properties in a range of important reactions resulting from enhanced mass transport properties have also been discussed through several representative cases. It is the intent of this work to stimulate intuition into the optimal design of related hierarchically organized zeolites with desired characteristics.

operate concurrently under high electric fields and elevated temperatures approaching or surpassing 150 °C. [2,4,8,[11][12][13] However, to date, the search for polymer dielectrics that provide appreciable energy densities at temperatures well above 100 °C has led to only marginal success. High temperature operation under high electric field is challenging for polymer dielectrics. For example, biaxially oriented polypropylene (BOPP), the state-of-the-art commercially available dielectric polymer used for energy storage, has a remarkable breakdown strength of ≈700 MV m −1 and ultralow loss, but can only operate continuously at temperatures up to 85 °C and for a short duration with significant derating at 105 °C. [14,15] Many heat resistant polymers have been designed and studied for high-temperature applications, but they are incapable of operating at an electric field similar to BOPP. [11,16,17] This is because their conjugated aromatic backbones that are able to withstand high temperature, built at the cost of largely reduced bandgaps, lead to high electrical conductivities and poor energy densities especially at elevated temperatures. Recent efforts for enhanced energy storage performance at high temperature via nanocomposites or coating modifications of polymer films, although encouraging, are prohibitively challenging for industrial-scale production due to requirements of (either) materials cost and (or) laborious multi-step synthesis and processing. [2,12,13,18,19] As a result, BOPP is still used today with cumbersome active cooling. The availability of flexible polymer dielectrics, capable of stable operation under ultrahigh electric field and elevated temperature is the limiting factor for high power density electrification and electronics.Due to hot carrier excitation, injection, and transport, assisted under thermal and electric extremes, polymers exhibit a nonlinear increase in electrical conduction, [20][21][22] leading to the reduction of the discharged energy density, largely increased energy loss and ultimately dielectric breakdown failure [23] . While the complexity of these processes makes the study of engineering conduction mechanism under critical electric fields far from fully understood, past studies revealed the dominant role of the bandgap in determining electrical conduction and intrinsic breakdown strength of the polymer dielectrics. [20,[24][25][26][27] However, careful evaluation of common high-temperature polymers reveals, unfortunately, an inverse Flexible dielectrics operable under simultaneous electric and thermal extremes are critical to advanced electronics for ultrahigh densities and/or harsh conditions. However, conventional high-performance polymer dielectrics generally have conjugated aromatic backbones, leading to limited bandgaps and hence high conduction loss and poor energy densities, especially at elevated temperatures. A polyoxafluoronorbornene is reported, which has a key design feature in that it is a polyolefin consisting of repeating units of fairly rigid fused bicycl...

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Emerging machine learning (ML)-based approaches provide powerful and novel tools to study a variety of physical and chemical problems. In this contribution, we outline a universal strategy to create ML-based atomistic force fields, which can be used to perform high-fidelity molecular dynamics simulations. This scheme involves (1) preparing a big reference dataset of atomic environments and forces with sufficiently low noise, e.g., using density functional theory or higher-level methods, (2) utilizing a generalizable class of structural fingerprints for representing atomic environments, (3) optimally selecting diverse and non-redundant training datasets from the reference data, and (4) proposing various learning approaches to predict atomic forces directly (and rapidly) from atomic configurations. From the atomistic forces, accurate potential energies can then be obtained by appropriate integration along a reaction coordinate or along a molecular dynamics trajectory. Based on this strategy, we have created model ML force fields for six elemental bulk solids, including Al, Cu, Ti, W, Si, and C, and show that all of them can reach chemical accuracy. The proposed procedure is general and universal, in that it can potentially be used to generate ML force fields for any material using the same unified workflow with little human intervention. Moreover, the force fields can be systematically improved by adding new training data progressively to represent atomic environments not encountered previously.

Conjugated microporous polymers having thiophene building blocks (SCMPs), which originated from ethynylbenzene monomers with 2,3,5-tribromothiophene, were designedly synthesized through Pd(0)/CuI catalyzed Sonogashira-Hagihara cross-coupling polymerization. The morphologies, structure and physicochemical properties of the as-synthesized products were characterized through scanning electron microscope (SEM), thermogravimeter analysis (TGA), (13)C CP/MAS solid state NMR and Fourier transform infrared spectroscope (FTIR) spectra. Nitrogen sorption-desorption analysis shows that the as-synthesized SCMPs possesses a high specific surface area of 855 m(2) g(-1). Because of their abundant porosity, π-conjugated network structure, as well as electron-rich thiophene building units, the SCMPs show better adsorption ability for iodine and a high uptake value of 222 wt % was obtained, which can compete with those nanoporous materials such as silver-containing zeolite, metal-organic frameworks (MOFs) and conjugated microporous polymers (CMPs), etc. Our study might provide a new possibility for the design and synthesis of functional CMPs containing electron-rich building units for effective capture and reversible storage of volatile iodine to address environmental issues.

Polymer Genome is a web-based machine-learning capability to perform near-instantaneous predictions of a variety of polymer properties. The prediction models are trained on (and interpolate between) an underlying database of polymers and their properties obtained from first principles computations and experimental measurements. In this contribution, we first provide an overview of some of the critical technical aspects of Polymer Genome, including polymer data curation, representation, learning algorithms, and prediction model usage. Then, we provide a series of pedagogical examples to demonstrate how Polymer Genome can be used to predict dozens of polymer properties, appropriate for a range of applications. This contribution is closed with a discussion on the remaining challenges and possible future directions.

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