Toward the golden age of materials informatics: perspective and opportunities
K Takahashi, L Takahashi - The Journal of Physical Chemistry …, 2023 - ACS Publications
Materials informatics is reaching the transition point and is evolving from its early stages of
adoption and development and moving toward its golden age. Here, the transformation of …
adoption and development and moving toward its golden age. Here, the transformation of …
Probe microscopy is all you need
We pose that microscopy offers an ideal real-world experimental environment for the
development and deployment of active Bayesian and reinforcement learning methods …
development and deployment of active Bayesian and reinforcement learning methods …
Designing workflows for materials characterization
Experimental science is enabled by the combination of synthesis, imaging, and functional
characterization organized into evolving discovery loop. Synthesis of new material is …
characterization organized into evolving discovery loop. Synthesis of new material is …
Pair-Variational Autoencoders for Linking and Cross-Reconstruction of Characterization Data from Complementary Structural Characterization Techniques
S Lu, A Jayaraman - JACS Au, 2023 - ACS Publications
In materials research, structural characterization often requires multiple complementary
techniques to obtain a holistic morphological view of a synthesized material. Depending on …
techniques to obtain a holistic morphological view of a synthesized material. Depending on …
Unveiling the nanoscale architectures and dynamics of protein assembly with in situ atomic force microscopy
Proteins play a vital role in different biological processes by forming complexes through
precise folding with exclusive inter‐and intra‐molecular interactions. Understanding the …
precise folding with exclusive inter‐and intra‐molecular interactions. Understanding the …
Towards physics-informed explainable machine learning and causal models for materials research
A Ghosh - Computational Materials Science, 2024 - Elsevier
From emergent material descriptions to estimation of properties stemming from structures to
optimization of process parameters for achieving best performance–all key facets of …
optimization of process parameters for achieving best performance–all key facets of …
[HTML][HTML] Determining the density and spatial descriptors of atomic scale defects of 2H–WSe2 with ensemble deep learning
We have demonstrated atomic-scale defect characterization in scanning tunneling
microscopy images of single crystal tungsten diselenide using an ensemble of U-Net-like …
microscopy images of single crystal tungsten diselenide using an ensemble of U-Net-like …
Microscopy is all you need
We pose that microscopy offers an ideal real-world experimental environment for the
development and deployment of active Bayesian and reinforcement learning methods …
development and deployment of active Bayesian and reinforcement learning methods …
Multiscale structure-property discovery via active learning in scanning tunneling microscopy
Atomic arrangements and local sub-structures fundamentally influence emergent material
functionalities. The local structures are conventionally probed using spatially resolved …
functionalities. The local structures are conventionally probed using spatially resolved …
Modeling fission gas release at the mesoscale using multiscale DenseNet regression with attention mechanism and inception blocks
Mesoscale simulations of fission gas release (FGR) in nuclear fuel provide a powerful tool
for understanding how microstructure evolution impacts FGR, but they are computationally …
for understanding how microstructure evolution impacts FGR, but they are computationally …