A survey of data-driven methods for materials modeling at nanoscale, mesoscale, and micro-to-continuum scales that identifies established capabilities, data quality issues, and obstacles to cross-scale integration.
The tensormol-0.1 model chemistry: a neural network augmented with long-range physics.Chemical science, 9(8):2261–2269, 2018
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Materials Informatics Across the Length Scales
A survey of data-driven methods for materials modeling at nanoscale, mesoscale, and micro-to-continuum scales that identifies established capabilities, data quality issues, and obstacles to cross-scale integration.