10 datasets found

<p>The quality, consistency, and information content of training data is often what determines the practical value of machine-learning models for atomistic simulations....

<p>The past few years have seen the development of ``universal'' machine-learning interatomic potentials (uMLIPs) capable of approximating the ground-state potential...

<p>Long-range interactions and electric response are essential for accurate modeling of condensed-phase systems, but capturing them efficiently remains a challenge for...

<p>Long-range interactions and electric response are essential for accurate modeling of condensed-phase systems, but capturing them efficiently remains a challenge for...

<p>The past few years have seen the development of ``universal'' machine-learning interatomic potentials (uMLIPs) capable of approximating the ground-state potential...

<p>The development of machine-learning models for atomic-scale simulations has benefitted tremendously from the large databases of materials and molecular properties...

<p>Machine-learning interatomic potentials (MLIPs) have greatly extended the reach of atomic-scale simulations, offering the accuracy of first-principles calculations at a...

<p>Machine-learning interatomic potentials (MLIPs) have greatly extended the reach of atomic-scale simulations, offering the accuracy of first-principles calculations at a...

Interatomic potentials are often necessary to describe complex realistic systems that would be too costly to study from first-principles. Commonly, interatomic potentials are...

<p>The development of machine-learning models for atomic-scale simulations has benefitted tremendously from the large databases of materials and molecular properties...