For grid-scale energy storage and national energy resilience, the U.S. needs better batteries. Lawrence Livermore National Laboratory (LLNL) scientists are tackling that challenge in many ways, but one approach is making a significant impact: physics-informed machine learning.

Machine learning is a subfield of artificial intelligence, which explores how to computationally simulate (or surpass) humanlike intelligence. While some AI techniques (such as expert systems) use other approaches, machine learning drives most of the field ...

The Martin-Hopkins equation to assess low-density lipoprotein (LDL) cholesterol levels in blood samples has been used by laboratories in the U.S. and other countries to guide efforts to lower cardiovascular disease risk.

A large study found that a LightGBM machine learning model accurately predicted survival and early death risk in patients with breast cancer bone metastasis.

Machine learning is rapidly reshaping how we model molecules, and a growing body of work suggests that neural networks are not merely statistical

Sometimes, the best response for a predictive ML system is to pause, acknowledge that it does not have enough information and escalate the case to a clinician.

The Martin-Hopkins equation to assess low-density lipoprotein (LDL) cholesterol levels in blood samples has been used by laboratories in the U.S. and other countries to guide efforts to lower cardiovascular disease risk.

Physics-informed machine learning connects atomic structure with ion transport and electrolyte stability, accelerating better sodium- and lithium-ion batteries.

Southwestern Adventist University is expanding its academic offerings with a new Machine Learning Certificate Program designed to equip students with skills in one of the fastest-growing areas of