Informatics-Driven Design of Superhard B-C-O Compounds.

Materials containing B, C, and O, due to the advantages of forming strong covalent bonds, may lead to materials that are superhard, i.e., those with a Vicker's hardness larger than 40 GPa. However, the exploration of this vast chemical, compositional, and configurational space is nontrivial. Here, we leverage a combination of machine learning (ML) and first-principles calculations to enable and accelerate such a targeted search. The ML models first screen for potentially superhard B-C-O compositions from a large hypothetical B-C-O candidate space. Atomic-level structure search using density functional theory (DFT) within those identified compositions, followed by further detailed analyses, unravels on four potentially superhard B-C-O phases exhibiting thermodynamic, mechanical, and dynamic stability.

Low-temperature 3D printing of transparent silica glass microstructures.

Transparent silica glass is one of the most essential materials used in society and industry, owing to its exceptional optical, thermal, and chemical properties. However, glass is extremely difficult to shape, especially into complex and miniaturized structures. Recent advances in three-dimensional (3D) printing have allowed for the creation of glass structures, but these methods involve time-consuming and high-temperature processes. Here, we report a photochemistry-based strategy for making glass structures of micrometer size under mild conditions. Our technique uses a photocurable polydimethylsiloxane resin that is 3D printed into complex structures and converted to silica glass via deep ultraviolet (DUV) irradiation in an ozone environment. The unique DUV-ozone conversion process for silica microstructures is low temperature (~220°C) and fast (<5 hours). The printed silica glass is highly transparent with smooth surface, comparable to commercial fused silica glass. This work enables the creation of arbitrary structures in silica glass through photochemistry and opens opportunities in unexplored territories for glass processing techniques.

Polymer Structure Predictor (PSP): A Python Toolkit for Predicting Atomic-Level Structural Models for a Range of Polymer Geometries.

Three-dimensional atomic-level models of polymers are the starting points for physics-based simulation studies. A capability to generate reasonable initial structural models is highly desired for this purpose. We have developed a python toolkit, namely, polymer structure predictor (psp), to generate a hierarchy of polymer models, ranging from oligomers to infinite chains to crystals to amorphous models, using a simplified molecular-input line-entry system (SMILES) string of the polymer repeat unit as the primary input. This toolkit allows users to tune several parameters to manage the quality and scale of models and computational cost. The output structures and accompanying force field (GAFF2/OPLS-AA) parameter files can be used for downstream ab initio and molecular dynamics simulations. The psp package includes a Colab notebook where users can go through several examples, building their own models, visualizing them, and downloading them for later use. The psp toolkit, being a first of its kind, will facilitate automation in polymer property prediction and design.

Dielectric Polymers Tolerant to Electric Field and Temperature Extremes: Integration of Phenomenology, Informatics, and Experimental Validation.

Flexible polymer dielectrics tolerant to electric field and temperature extremes are urgently needed for a spectrum of electrical and electronic applications. Given the complexity of the dielectric breakdown mechanism and the vast chemical space of polymers, the discovery of suitable candidates is nontrivial. We have laid the foundation for a systematic search of the polymer chemical space, which starts with "gold-standard" experimental measurements and data on the temperature-dependent breakdown strength (Ebd) for a benchmark set of commercial dielectric polymer films. Phenomenological guidelines are derived from this data set on easily accessible properties (or "proxies") that are correlated with Ebd. Screening criteria based on these proxy properties (e.g., band gap, charge injection barrier, and cohesive energy density) and other necessary characteristics (e.g., a high glass transition temperature to maintain the thermal stability and a high dielectric constant for high energy density) were then setup. These criteria, along with machine learning models of these properties, were used to screen polymers candidates from a candidate list of more than 13 000 previously synthesized polymers, followed by experimental validation of some of the screened candidates. These efforts have led to the creation of a consistent and high-quality data set of temperature-dependent Ebd, and the identification of screening criteria, chemical design rules, and a list of optimal polymer candidates for high-temperature and high-energy-density capacitor applications, thus demonstrating the power of an integrated and informatics-based philosophy for rational materials design.

An Informatics Approach for Designing Conducting Polymers.

Doping conjugated polymers, which are potential candidates for the next generation of organic electronics, is an effective strategy for manipulating their electrical conductivity. However, selecting a suitable polymer-dopant combination is exceptionally challenging because of the vastness of the chemical, configurational, and morphological spaces one needs to search. In this work, high-performance surrogate models, trained on available experimentally measured data, are developed to predict the p-type electrical conductivity and are used to screen a large candidate hypothetical data set of more than 800 000 polymer-dopant combinations. Promising candidates are identified for synthesis and device fabrication. Additionally, new design guidelines are extracted that verify and extend knowledge on important molecular fragments that correlate to high conductivity. Conductivity prediction models are also deployed at www.polymergenome.org for broader open-access community use.

Unraveling Correlations between Molecular Properties and Device Parameters of Organic Solar Cells Using Machine Learning.

Understanding the relationships between molecular properties and device parameters is highly desired not only to improve the overall performance of an organic solar cell but also to fulfill the requirements of a device for a particular application such as solar-to-fuel energy conversion (high open-circuit voltage (VOC)) or solar window applications (high short circuit current (JSC)). In this work, a series of machine learning models are built for three important device characteristics (VOC, JSC, and fill factor) using 13 crucial molecular properties as descriptors, resulting in an impressive predictive performance (r = 0.7). These models may play a vital role in designing promising organic materials for a specific photovoltaic application with high VOC/JSC. The importance of descriptors for each device parameter is unraveled, which may assist in tuning them and improve understanding of the energy conversion process.

Structure and optoelectronic properties of helical pyridine-furan, pyridine-pyrrole and pyridine-thiophene oligomers.

Density functional theory based calculations have been carried out to systematically investigate the structural and optoelectronic properties of pyridine-furan, pyridine-pyrrole and pyridine-thiophene oligomers. Comparison of results obtained at B3LYP/6-31G(d) and B3LYP-D3/6-31G(d) levels of theories reveals that the inclusion of dispersion correction with the B3LYP functional has a major impact on ground state structures and stabilities of the most stable conformers, which are helical for our studied systems. Calculation of stabilization energies, gained due to non-bonding interaction between adjacent helical turns, shows that stabilities of helical oligomers increase with an increase in the chain length. Ground state dipole moment values of these helical oligomers fluctuate between a certain range and these values depend on the number of repeating units (n) and the number of repeating units needed to complete one helical turn (u) of a helix. To obtain vertical excitation energies, oscillator strengths and absorption spectra of each oligomer, time dependent density functional theory single point calculations were carried out at the B3LYP-D3/6-31G(d) level using optimized geometries obtained at the same level. The absorption spectrum of a helical oligomer is composed of multiple electronic transitions having significant oscillator strengths and the transition with the largest oscillator strength is blue shifted with an increase in the size of the oligomer. Furthermore, for the most important electronic transition (S0→ Sm) of oligomers with n > u, m increases with increasing n. For these helices, excitations involving molecular orbitals other than frontier molecular orbitals significantly contribute to major electronic transitions.

Computational investigation of charge injection and transport properties of a series of thiophene-pyrrole based oligo-azomethines.

The present study explores the structural, charge carrier injection and transport properties of a series of thiophene-pyrrole based oligo-azomethines using density functional theory (DFT) methods. Our findings show that the presence of a bulky substituent adversely affects these properties. However, the electronic effect of substituents may be utilized to tune these properties by substitutions at suitable positions. Values of frontier orbitals, ionization energies, and electron affinities are calculated for each compound to predict the ease of charge injection from metal electrodes to these azomethines and the stabilities of their ionic forms. In addition to having large injection barriers, lack of stability of the anions may hinder the electron injection. However, most of the compounds have excellent hole injection capability. Computation of reorganization energies and electronic couplings followed by charge transfer rates and mobilities show large carrier mobilities for some of the studied compounds. Considering both the injection capability and carrier mobilities, it is found that a thiophene-pyrrole azomethine without any substituent and substituted azomethines with a methyl, methoxy or amine group at the 3 position of the pyrrole ring may act as efficient materials for the hole transport layer.