Using machine learning, the computational method can provide details of how materials work as catalysts, semiconductors, or battery components.

The molecules, known as acenes, could be useful as organic light-emitting diodes or solar cells, among other possible applications.

A new, data-driven approach could lead to better solutions for tricky optimization problems like global package routing or power grid operation.

More stable clocks could measure quantum phenomena, including the presence of dark matter.

MIT CSAIL researchers innovate with synthetic imagery to train AI, paving the way for more efficient and bias-reduced machine learning.

Two studies find “self-supervised” models, which learn about their environment from unlabeled data, can show activity patterns similar to those of the mammalian brain.

Complimentary approaches — “HighLight” and “Tailors and Swiftiles” — could boost the performance of demanding machine-learning tasks.

The SecureLoop search tool efficiently identifies secure designs for hardware that can boost the performance of complex AI tasks, while requiring less energy.

Researchers achieve a landmark in quantum squeezing.

MIT engineers develop a long, curved touch sensor that could enable a robot to grasp and manipulate objects in multiple ways.