We develop a new computing paradigm, which we refer to as data-driven computing,
according to which calculations are carried out directly from experimental material data and …

The slow pace of new/improved materials development and deployment has been identified
as the main bottleneck in the innovation cycles of most emerging technologies. Much of the …

The Coupled Cluster (CC) method is used to compute the electronic correlation energy in
atoms and molecules and often leads to highly accurate results. However, due to its single …

A novel data science workflow is developed and demonstrated to extract process-structure
linkages (ie, reduced-order model) for microstructure evolution problems when the final …

The goal of the Materials Genome Initiative is to substantially reduce the time and cost of
materials design and deployment. Achieving this goal requires taking advantage of the …

摘要依赖于科学直觉与试错的传统材料研究方法日益成为社会发展与技术进步的瓶颈.
革新材料研发方法, 加速材料从研究到应用的进程成为世界各国共同的需求 …

Materials research and development today still depends primarily on scientific intuitions,
experiences, as well as trial-anderror experiments. This process is time-consuming and …

Abstract Data-Driven Computing is a new field of computational analysis which uses
provided data to directly produce predictive outcomes. Recent works in this developing field …

Polymer nanocomposites (PNC) are complex material systems in which the prevailing
length scales, ie, the particle size, radii of gyration of the polymer and the interparticle …

This dissertation utilizes traditional machine learning methods such as partial least squares
and support vector machines to create quantitative structure-activity (or structure-property) …