Many techniques in computational materials science require scientists to identify the right set of parameters that capture the physics of the specific material they are studying. Calculating these ...

Theoretical physicists use machine-learning algorithms to speed up difficult calculations and eliminate untenable theories—but could they transform what it means to make discoveries? Theoretical ...

A research team of mathematicians and computer scientists has used machine learning to reveal new mathematical structure within the theory of finite groups. By training neural networks to recognise ...

Deep learning models have shown great potential in predicting and engineering functional enzymes and proteins. Does this prowess extend to other fields of biology as well? Contrary to expectations, a ...

The creative new approach could lead to more energy-efficient machine-learning hardware. On a table in his lab at the University of Pennsylvania, physicist Sam Dillavou has connected an array of ...