Machine learning for electrocatalyst and photocatalyst design and discovery
Electrocatalysts and photocatalysts are key to a sustainable future, generating clean fuels,
reducing the impact of global warming, and providing solutions to environmental pollution …
reducing the impact of global warming, and providing solutions to environmental pollution …
Machine learning for a sustainable energy future
Transitioning from fossil fuels to renewable energy sources is a critical global challenge; it
demands advances—at the materials, devices and systems levels—for the efficient …
demands advances—at the materials, devices and systems levels—for the efficient …
Artificial intelligence applied to battery research: hype or reality?
This is a critical review of artificial intelligence/machine learning (AI/ML) methods applied to
battery research. It aims at providing a comprehensive, authoritative, and critical, yet easily …
battery research. It aims at providing a comprehensive, authoritative, and critical, yet easily …
Atom probe tomography
Atom probe tomography (APT) provides three-dimensional compositional map** with sub-
nanometre resolution. The sensitivity of APT is in the range of parts per million for all …
nanometre resolution. The sensitivity of APT is in the range of parts per million for all …
Deep learning analysis on microscopic imaging in materials science
Microscopic imaging providing the real-space information of matter, plays an important role
for understanding the correlations between structure and properties in the field of materials …
for understanding the correlations between structure and properties in the field of materials …
Data-driven-aided strategies in battery lifecycle management: prediction, monitoring, and optimization
Predicting, monitoring, and optimizing the performance and health of a battery system
entails a variety of complex variables as well as unpredictability in given conditions. Data …
entails a variety of complex variables as well as unpredictability in given conditions. Data …
Machine learning in electron microscopy for advanced nanocharacterization: current developments, available tools and future outlook
In the last few years, electron microscopy has experienced a new methodological paradigm
aimed to fix the bottlenecks and overcome the challenges of its analytical workflow. Machine …
aimed to fix the bottlenecks and overcome the challenges of its analytical workflow. Machine …
Deep learning in electron microscopy
JM Ede - Machine Learning: Science and Technology, 2021 - iopscience.iop.org
Deep learning is transforming most areas of science and technology, including electron
microscopy. This review paper offers a practical perspective aimed at developers with …
microscopy. This review paper offers a practical perspective aimed at developers with …
Symmetry prediction and knowledge discovery from X-ray diffraction patterns using an interpretable machine learning approach
Determination of crystal system and space group in the initial stages of crystal structure
analysis forms a bottleneck in material science workflow that often requires manual tuning …
analysis forms a bottleneck in material science workflow that often requires manual tuning …
Machine learning for analysis of experimental scattering and spectroscopy data in materials chemistry
The rapid growth of materials chemistry data, driven by advancements in large-scale
radiation facilities as well as laboratory instruments, has outpaced conventional data …
radiation facilities as well as laboratory instruments, has outpaced conventional data …