While AI has proven itself to be extremely capable in discerning optimal chemical structures, synthetic feasibility remains a challenge.
Results for "machine learning"
Finding new “needles” in a materials haystack using machine learning
A new machine learning algorithm called “Active Learning” could help identify the best materials for any desired application.
Machine learning methods provide new insights into organic-inorganic interfaces
Simulations at Graz University of Technology refute earlier theories on long-range charge transfer between organic and inorganic materials.
Machine learning provides new design possibility for 3D-printable robots
An automatic design approach with a new 3D-printing method is established to fabricate soft composites that can change to predetermined shapes and generate controllable robotic motions under a magnetic field.
Machine learning scopes out previously “invisible” microplastics
A new approach combines 3D coherent imaging with machine learning to detect microscale microplastics in filtered water samples.
Machine learning shapes microwaves for a computer’s eye
Hardware and software tweak microwave patterns to discover the most efficient way to identify objects.
Improving Image Recognition to Accelerate Machine Learning
By combining two types of memory arrays, researchers can accelerate image recognition for more efficient machine learning.
Predicting Rechargeable Battery Performance with Machine Learning
A new machine learning algorithm that can predict battery performance and failure beyond the expert level.
Machine Learning for Better Materials
Machine learning enables the manufacturing of highly compressible nano-scale devices.
Intersecting Machine Learning and Cybersecurity
Machine learning technology has become mainstream in a large number of domains, and cybersecurity applications of machine learning techniques are plenty.
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