Machine learning has brought transformative advancements to computational chemistry, enabling breakthroughs that were previously limited by the high computational demands of traditional chemical simulations. One of the most promising applications of machine learning in this field is the development of machine learning force fields (MLFFs), which are revolutionizing molecular dynamics techniques used to study the behavior of atoms and molecules.  

With applications ranging from materials science to chemistry and biology, MLFFs are enabling high accuracy calculations with lower computational costs, unlocking applications that had previously stayed out of reach for classical computational methods.