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.

A general-purpose material property data extraction pipeline from large polymer corpora using natural language processing.

The ever-increasing number of materials science articles makes it hard to infer chemistry-structure-property relations from literature. We used natural language processing methods to automatically extract material property data from the abstracts of polymer literature. As a component of our pipeline, we trained MaterialsBERT, a language model, using 2.4 million materials science abstracts, which outperforms other baseline models in three out of five named entity recognition datasets. Using this pipeline, we obtained ~300,000 material property records from ~130,000 abstracts in 60 hours. The extracted data was analyzed for a diverse range of applications such as fuel cells, supercapacitors, and polymer solar cells to recover non-trivial insights. The data extracted through our pipeline is made available at polymerscholar.org which can be used to locate material property data recorded in abstracts. This work demonstrates the feasibility of an automatic pipeline that starts from published literature and ends with extracted material property information.

Polymer informatics with multi-task learning.

Modern data-driven tools are transforming application-specific polymer development cycles. Surrogate models that can be trained to predict properties of polymers are becoming commonplace. Nevertheless, these models do not utilize the full breadth of the knowledge available in datasets, which are oftentimes sparse; inherent correlations between different property datasets are disregarded. Here, we demonstrate the potency of multi-task learning approaches that exploit such inherent correlations effectively. Data pertaining to 36 different properties of over 13,000 polymers are supplied to deep-learning multi-task architectures. Compared to conventional single-task learning models, the multi-task approach is accurate, efficient, scalable, and amenable to transfer learning as more data on the same or different properties become available. Moreover, these models are interpretable. Chemical rules, that explain how certain features control trends in property values, emerge from the present work, paving the way for the rational design of application specific polymers meeting desired property or performance objectives.

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.

Accelerated Data-Driven Accurate Positioning of the Band Edges of MXenes.

Functionalized MXene has emerged a promising class of two-dimensional materials having more than tens of thousands of compounds, whose uses may range from electronics to energy applications. Other than the band gap, these properties rely on the accurate position of the band edges. Hence, to synthesize MXenes for various applications, a prior knowledge of the accurate position of their band edges at an absolute scale is essential; computing these with conventional methods would take years for all the MXenes. Here, we develop a machine learning model for positioning the band edges with GW level of accuracy having a minimum root-mean-squared error of 0.12 eV. An intuitive model is proposed based on the combination of Perdew-Burke-Ernzerhof band edge and vacuum potential having a correlation of 0.93 with GW band edges. These models can be utilized to identify MXenes for a desired application in an accelerated manner.

Molecular sensing using armchair graphene nanoribbon.

In molecular electronics, the conductance strongly depends on the frontier energy levels and spatial orientations of molecules. Utilizing these features, we investigate the electron transport characteristics of conjugated molecules attached on an armchair graphene nanoribbon. The resulting sharp reduction in the transmission which represents molecular fingerprints and the change of the transmission depending on the molecular orientation, are examined in accordance with a unified picture of the Fano-Anderson model. These characteristics, being unique for each molecule, would be applicable to molecular recognition and configurational analysis.

Two dimensional molecular electronics spectroscopy for molecular fingerprinting, DNA sequencing, and cancerous DNA recognition.

Laser-driven molecular spectroscopy of low spatial resolution is widely used, while electronic current-driven molecular spectroscopy of atomic scale resolution has been limited because currents provide only minimal information. However, electron transmission of a graphene nanoribbon on which a molecule is adsorbed shows molecular fingerprints of Fano resonances, i.e., characteristic features of frontier orbitals and conformations of physisorbed molecules. Utilizing these resonance profiles, here we demonstrate two-dimensional molecular electronics spectroscopy (2D MES). The differential conductance with respect to bias and gate voltages not only distinguishes different types of nucleobases for DNA sequencing but also recognizes methylated nucleobases which could be related to cancerous cell growth. This 2D MES could open an exciting field to recognize single molecule signatures at atomic resolution. The advantages of the 2D MES over the one-dimensional (1D) current analysis can be comparable to those of 2D NMR over 1D NMR analysis.