MDI provides a comprehensive set of computational and laboratory facilities for its faculty, students and researchers.
These unique laboratories serve as focal points for creating a shared and cooperative research environment for promoting interdisciplinary research among diverse research groups. The MDI facilities complement the comprehensive array of computational and experimental materials synthesis, processing and characterization facilities (e.g. Center for Computational Research and the Shared Instrumentation Laboratory) at UB.
The NSF Materials Data Engineering Laboratory (MaDE@UB Lab) is a joint initiative between MDI, the Center for Unified Biometrics and Sensors (CUBS) and the Center for Computational Research to develop machine intelligence driven tools to query and interpret text, graphs and images in the materials sciences.
The MaDE@UB Lab conducts a highly interdisciplinary research program harnessing expertise in machine learning, pattern recognition, materials informatics and modeling to create machines with the cognitive skills for linking both computational and experimentally derived materials science literature and data. With the capacity to explore massive amounts of literature and data, the MaDE@UB Lab provides a new trajectory of data-intensive materials science research impacting accelerated discovery of new materials.
The M2M Lab encompasses a range of newly installed advanced synthesis and processing capabilities including nano-synthesis, wet chemistry capabilities, soft materials processing, advanced CVD and MBE deposition systems and additive manufacturing tools for structural materials.
The M2M facility also houses the Materials Robot Sandbox, a development platform and workspace to advance the field of autonomous materials development. The Materials Robot Sandbox is co-located with the UB Sustainable Manufacturing and Advanced Robotic Technologies (SMART) Community of Excellence.
The Molecular Metrology Laboratory is a unique facility consisting of an array of advanced instrumentation that push the envelope in structural and chemical resolution for materials characterization; and integrates the use of advanced data science techniques for quantitative interpretation of data. These include: