Recent decades have seen a rise in the use of physics methods to study different societal
phenomena. This development has been due to physicists venturing outside of their …

Quantum matter, the research field studying phases of matter whose properties are
intrinsically quantum mechanical, draws from areas as diverse as hard condensed matter …

A core challenge for both physics and artificial intelligence (AI) is symbolic regression:
finding a symbolic expression that matches data from an unknown function. Although this …

A major challenge of AI+ Science lies in their inherent incompatibility: today's AI is primarily
based on connectionism, while science depends on symbolism. To bridge the two worlds …

Despite the success of neural networks at solving concrete physics problems, their use as a
general-purpose tool for scientific discovery is still in its infancy. Here, we approach this …

Symbolic regression (SR) is the study of algorithms that automate the search for analytic
expressions that fit data. While recent advances in deep learning have generated renewed …

We present an improved method for symbolic regression that seeks to fit data to formulas
that are Pareto-optimal, in the sense of having the best accuracy for a given complexity. It …

We present an approach for using machine learning to automatically discover the governing
equations and unknown properties (in this case, masses) of real physical systems from …

Deep learning is transforming most areas of science and technology, including electron
microscopy. This review paper offers a practical perspective aimed at developers with …

Fundamental mathematical constants such as e and π are ubiquitous in diverse fields of
science, from abstract mathematics and geometry to physics, biology and chemistry …