129 structures-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"CEA-Saclay" positions at NIST
Sort by
Refine Your Search
-
for AM, and for identifying suitable approaches for integrating these surrogate models into an AI/UQ framework. This capability is expected to provide a detailed understanding of the structure/property
-
the structural changes of materials during indentation deformation. Recently, this methodology has been employed in in-situ studies on the phase transformation of crystalline and amorphous silicon thin films and
-
models. The current application domain is the relationship between the macroscopic deformation behavior of structural and mechanical materials and the corresponding microstructural deformation, internal
-
on determination of structural, thermophysical, and transport properties of systems and materials. We also aim to enable materials design from the function-structure relationship standpoint, as applied to metal
-
are a fascinating subfamily of porous crystalline materials. The unique structural flexibility related to pore opening/closing makes them promising candidates for many gas storage and separation
-
guiding materials measurement experiments to acclerate learning the synthesis-process-structure-property relationship. Machine learning methods include, but are not limited to, Bayesian inference
-
routinely consist of films only Angstroms in thickness and forming spatially isolated patterns with some lateral dimensions in the low nanometers. Example systems include core-shell structures used in
-
environment. Field and laboratory measurements of fire and fire protection engineering consider a wide range of fundamental and applied research issues associated with wildland-urban interface fires, structure
-
introduces artifacts in the measurement of structures and functions of the molecules. Furthermore, specific molecular labeling sometimes is not practical in many clinical imaging and diagnostics. This research
-
the synthesis-process-structure-property relationship for quantum solid state materials. References: Liang, et al., 2025. Real-time experiment-theory closed-loop interaction for autonomous materials science