The recent successes of the Materials Genome Initiative have opened up new opportunities for data-centric informatics approaches in several subfields of materials research, including in polymer science and engineering. Polymers, being inexpensive and possessing a broad range of tunable properties, are widespread in many technological applications. The vast chemical and morphological complexity of polymers though gives rise to challenges in the rational discovery of new materials for specific applications. The nascent field of polymer informatics seeks to provide tools and pathways for accelerated property prediction (and materials design) via surrogate machine learning models built on reliable past data. We have carefully accumulated a data set of organic polymers whose properties were obtained either computationally (bandgap, dielectric constant, refractive index, and atomization energy) or experimentally (glass transition temperature, solubility parameter, and density). A fingerprinting scheme that captures atomistic to morphological structural features was developed to numerically represent the polymers. Machine learning models were then trained by mapping the fingerprints (or features) to properties. Once developed, these models can rapidly predict properties of new polymers (within the same chemical class as the parent data set) and can also provide uncertainties underlying the predictions. Since different properties depend on different length-scale features, the prediction models were built on an optimized set of features for each individual property. Furthermore, these models are incorporated in a user-friendly online platform named Polymer Genome (). Systematic and progressive expansion of both chemical and property spaces are planned to extend the applicability of Polymer Genome to a wide range of technological domains.

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A strategic approach toward functionalization can change properties: effect of the “oxidizer of oxygen” on hexagonal boron nitride.

Determining a suitable noble-metal-free catalyst for hydrogen evolution reaction (HER) by photoelectrocatalytic (PEC) water splitting is an enduring challenge. Here, the molecular origin of number of layers and stacking sequence-dependent PEC HER performance of MoS 2 /graphene (MoS 2 /GR) van der Waals (vdW) vertical heterostructures is studied. Density functional theory (DFT) based calculations show that the presence of MoS 2 induces p-type doping in GR, which facilitates hydrogen adsorption in the GR side compared to the MoS 2 side with ΔG H closer to 0 eV in the MoS 2 /GR bilayer vertical stacks. The activity maximizes in graphene with monolayer MoS 2 and reduces further for bilayer and multilayers of MoS 2 . The PEC HER performance is studied in various electrodes, namely, single-layer graphene, single-and few-layered MoS 2 , and their two different types of vertical heterojunctions having different stacking sequences. The graphene on top of MoS 2 sequence showed the highest photoresponse with large reaction current density and lowest charge-transfer resistance toward HER, in aggrement with the DFT calculations. These findings establish the role of stacking sequence in the electrochemistry of atomic layers, leading to the design of new electrocatalysts by combinatorial stacking of a minimal number of layers.

We investigate the effect of nitrogen and boron doping on Li diffusion through defected graphene using first principles based density functional theory. While a high energy barrier rules out the possibility of Li- diffusion through the pristine graphene, the barrier reduces with the incorporation of defects. Among the most common defects in pristine graphene, Li diffusion through the divacancy encounters the lowest energy barrier of 1.34 eV. The effect of nitrogen and boron doping on the Li diffusion through doped defected-graphene sheets has been studied. N-doping in graphene with a monovacancy reduces the energy barrier significantly. The barrier reduces with the increasing number of N atoms. On the other hand, for N doped graphene with a divacancy, Li binds in the plane of the sheet, with an enhanced binding energy. The B doping in graphene with a monovacancy leads to the enhancement of the barrier. However, in the case of B-doped graphene with a divacancy, the barrier reduces to 1.54 eV, which could lead to good kinetics. The barriers do not change significantly with B concentration. Therefore, divacancy, B and N doped defected graphene has emerged as a better alternative to pristine graphene as an anode material for Li ion battery.