Since their introduction about 25 years ago, machine learning (ML) potentials have become
an important tool in the field of atomistic simulations. After the initial decade, in which neural …

Machine learning models are poised to make a transformative impact on chemical sciences
by dramatically accelerating computational algorithms and amplifying insights available from …

The choice of simulation methods in computational materials science is driven by a
fundamental trade-off: bridging large time-and length-scales with highly accurate …

Electronically excited states of molecules are at the heart of photochemistry, photophysics,
as well as photobiology and also play a role in material science. Their theoretical description …

As the quantum chemistry (QC) community embraces machine learning (ML), the number of
new methods and applications based on the combination of QC and ML is surging. In this …

The development of accurate and transferable machine learning (ML) potentials for
predicting molecular energetics is a challenging task. The process of data generation to train …

Deep learning is revolutionizing many areas of science and technology, especially image,
text, and speech recognition. In this paper, we demonstrate how a deep neural network (NN) …

Machine learning (ML) methods have become powerful, predictive tools in a wide range of
applications, such as facial recognition and autonomous vehicles. In the sciences …

Modern simulation techniques have reached a level of maturity which allows a wide range of
problems in chemistry and materials science to be addressed. Unfortunately, the application …

This paper presents TorchANI, a PyTorch-based program for training/inference of ANI
(ANAKIN-ME) deep learning models to obtain potential energy surfaces and other physical …