Catalysis informatics is a distinct subfield that lies at the intersection of cheminformatics and materials informatics but with distinctive challenges arising from the dynamic, surface-sensitive, and multiscale nature of heterogeneous catalysis. The ideas behind catalysis informatics can be traced back decades, but the field is only recently emerging due to advances in data infrastructure, statistics, machine learning, and computational methods. In this work, we review the field from early works on expert systems and knowledge engines to more recent approaches utilizing machine-learning and uncertainty quantification. The data–information–knowledge hierarchy is introduced and used to classify various developments. The chemical master equation and microkinetic models are proposed as a quantitative representation of catalysis knowledge, which can be used to generate explanative and predictive hypotheses for the understanding and discovery of catalytic materials. We discuss future prospects for the field, including improved quantitative coupling of experiment/theory, advanced microkinetic models, and the development of open-source software tools. Ultimately, integration of existing chemical and physical models with emerging statistical and computational tools presents a promising route toward the automated design, discovery, and optimization of heterogeneous catalytic processes.

The thermal conductivity (TC) of isolated graphene with different concentrations of isotopes (C13) is studied with equilibrium molecular dynamics method at 300K. In the limit of pure C12 or C13 graphene, TC of graphene in zigzag and armchair directions are ~630 W/mK and ~1000W/mK, respectively. We find that the TC of graphene can be maximally reduced by ~80%, in both armchair and zigzag directions, when a random distribution of C12 and C13 is assumed at different doping concentrations. Therefore, our simulation results suggest an effective way to tune the TC of graphene without changing its atomic and electronic structure, thus yielding a promising application for nanoelectronics and thermoelectricity of graphene based nano-device.

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The Reactive empirical bond order (REBO) potential developed by Brenner et al. 1,2 for molecular dynamics (MD) simulations of hydrocarbons, and recently extended to include interactions with oxygen atoms by Ni et al. 3 is modified for graphene-oxide (GO). Based on DFT calculations, we optimized the REBO-CHO potential to improve its ability to calculate the binding energy of an oxygen atom to graphene and the equilibrium C-O bond distances. In this work, the approach towards the optimization is based on modifying the bond order term. The modified REBO-CHO potential is applied to investigate the properties of some GO samples.

Cross links between inner and outer walls of multiwalled carbon nanotubes are believed to increase nanotube modulus and therefore nanotube effectiveness for reinforcing composites. In order to investigate changes in the Young's modulus of individual double-walled nanotubes ͑DWNTs͒ as a function of cross-link density and type, molecular-dynamics simulations are employed to evaluate strain coupling and corresponding load transfer from outer to inner walls. Results show that interwall sp 3 bonds and interstitial carbon atoms can increase load transfer between DWNT walls and that interwall sp 3 bonds are most effective. However, the maximum size of the modulus increase is limited to about 25% for the investigated small-diameter, short DWNTs because the defects decrease the stiffnesses of the nanotube walls.