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The response of a composite material is the result of a complex interplay between the prevailing mechanics and the heterogenous structure at disparate spatial and temporal scales. Understanding and capturing the multiscale phenomena is critical for materials modeling and can be pursued both by physical simulation-based modeling as well as data-driven machine learning-based modeling. In this work,...
Formation energy is one of the most important properties of a compound that is directly related to its stability. More negative the formation energy, the more stable the compound is likely to be. Here we describe the development and deployment of predictive models for formation energy, given the chemical composition of the material. The data-driven models described here are built using nearly 100,000...
Incorporating accurate prognostic information into clinical decision making could advance evidence-based, person-centered healthcare by more effectively targeting healthcare services to those patients most likely to benefit. Here we describe the deployment of predictive models for five year life expectancy of patients, built on electronic health records (EHR) of nearly 7,500 patients aged 50 and above,...
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