ProgramMaster Logo
Conference Tools for 2020 TMS Annual Meeting & Exhibition
Login
Register as a New User
Help
Submit An Abstract
Propose A Symposium
Presenter/Author Tools
Organizer/Editor Tools
About this Abstract
Meeting 2020 TMS Annual Meeting & Exhibition
Symposium Expanding the Boundaries of Materials Science: Unconventional Collaborations
Presentation Title Accelerating Materials Design Through Community, Open Data and Collaboration
Author(s) Matthew Horton, Kristin Persson
On-Site Speaker (Planned) Matthew Horton
Abstract Scope The Materials Project is the worlds foremost database of inorganic materials, including over a hundred thousand crystal structures and millions of computed properties including thermodynamic information, electronic structure, elastic, dielectric and piezoelectric tensors, phonon band structures, magnetic properties, and more. This computed data and the code used to generate it is shared openly and free of charge for users worldwide, along with many 'apps' to explore the data for different applications. This has allowed a rich multi-faceted collaboration within both academia and industry to accelerate the design of new functional materials. This talk will discuss our latest advances, including ways in which MP has benefited from the larger community and in particular from experimental data, and how it has in turn helped drive experimental inquiry. We will explore the outlook for future collaboration between scientists using both compuational and experimental methods, including a discussion of our new user contribution platform.
Proceedings Inclusion? Planned: Supplemental Proceedings volume

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

Accelerating Materials Design Through Community, Open Data and Collaboration
Additive Manufacturing for Novel Thermal Devices
Convergence: Supporting Multidisciplinary Research at the National Science Foundation
Creating the Next-Generation Materials Genome Initiative Workforce
Innovation in Materials Research Collaborations: DOE Basic Energy Sciences
Integrating Experiment, Data, and Computations to Accelerate the Design of Materials
Machine Learning for Materials Design and Discovery
Mechanical Properties of Molecular Crystals--Connecting with Chemistry
Regularization of Materials Failure Data for Damage Mechanism Categorization by Machine Learning

Questions about ProgramMaster? Contact programming@programmaster.org