Machine learning for morphable materials New platform can program the transformation of 2D stretchable surfaces into specific 3D shapes Date: January 12, 2022 Source: Harvard John A. Paulson ...

A neural field network can create a continuous 3D model from a limited number of 2D images, and it does it without being trained on other samples.

Machine learning transforms the landscape of 2D materials design, particularly in accelerating discovery, optimization, and screening processes. This review has delved into the historical and ongoing ...

These simulations unveiled a range of intriguing phenomena, including two-dimensional (2D) ice-to-water melting, novel ice behavior, water splitting, and proton dynamics in nano ice. The research team ...

Researchers at NVIDIA have come up with a clever machine learning technique for taking 2D images and fleshing them out into 3D models.

The first 2D mica resistive random access memory (RRAM) device has been demonstrated, which exhibit unique non-Markov chain characteristic. The migration of inner potassium ions in mica under ...

A new algorithm developed at Imperial College London can convert 2D images of composite materials into 3D structures. The machine learning algorithm could help materials scientists and manufacturers ...

Researchers from the McKelvey School of Engineering at Washington University in St. Louis have developed a machine learning algorithm that can create a continuous 3D model of cells from a partial set ...