Virtual Materials Intelligence for Design and Discovery of Advanced Electrocatalysts.

Similar to advancements gained from big data in genomics, security, internet of things, and e-commerce, the materials workflow could be made more efficient and prolific through advances in streamlining data sources, autonomous materials synthesis, rapid characterization, big data analytics, and self-learning algorithms. In electrochemical materials science, data sets are large, unstructured/heterogeneous, and difficult to process and analyze from a single data channel or platform. Computer-aided materials design together with advances in data mining, machine learning, and predictive analytics are expected to provide inexpensive and accelerated pathways towards tailor-made functionally optimized energy materials.

Nanophase segregation and water dynamics in hydrated Nafion: molecular modeling and experimental validation.

Reported results of coarse-grained molecular dynamics simulations rationalize the effect of water on the phase-segregated morphology of Nafion ionomers. We analyzed density maps and radial distribution functions and correlated them with domain structures, distributions of protogenic side chains, and water transport properties. The mesoscopic structures exhibit spongelike morphologies.