Workshop: Data Driven and Computational Modeling of Materials Across Scales; Los Angeles, California; 10-12 May 2023

University Of California-Los Angeles, Los Angeles CA

Investigators

Abstract

This award provides registration and travel support for 20 early career U.S. researchers to attend the Workshop on Data Driven and Computational Modeling of Materials Across Scales, which will be held 10-12 May 2023 in Los Angeles, California. The objective of this workshop is to bring together experts from a wide variety of technical backgrounds to present and deliberate on the topics of multiscale modeling of materials, computational mechanics, and data science/machine learning methods. The workshop will feature invited talks, poster presentations, lightning talks, panel discussions, and networking opportunities for researchers. The award will broaden the participation from graduate students, postdoctoral fellows, and junior faculty participants, particularly women and underrepresented minority researchers. In the selection process, consideration will be given to the inclusion of members of underrepresented groups, diversity of institutions that the participants represent, and the diversity of disciplines. A detailed workshop report, abstracts of all presentations, and video recordings of invited talks will be widely disseminated beyond the workshop. This workshop will engage researchers in the exchange of scientific ideas on current and future research directions in multiscale modeling of materials, computational mechanics, and data science/machine learning methods. The success of computational approaches to materials modeling hinges on having access to accurate, reliable and efficient simulation techniques across length and time scales. Most materials phenomena are multiscale in nature and properties of common engineering materials are often dictated by the properties of defects that interact at length scales much smaller than everyday macroscopic objects. As such, predictive computational tools suited to the length and time scales relevant to the problem physics, and techniques which span across scales are required. Data analytics and machine learning tools have provided many new and exciting avenues in computational materials research and computational mechanics. The best strategies for using these powerful new tools in multiscale materials modeling constitute an important and active area of ongoing research. The invited talks, poster presentations and panel discussions will inform the participants of the latest technical developments in the field and also provide the opportunity for experts to deliberate openly on challenges related to the aforementioned topics. These discussions are expected to eventually lead to new computational and data driven techniques for materials modeling, and to also drive the development of novel materials via bottom-up design strategies. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.