Deep-HP is a scalable extension of the Tinker-HP multi-GPU molecular dynamics (MD)
package enabling the use of Pytorch/TensorFlow Deep Neural Network (DNN) models …

Geometric deep learning models, which incorporate the relevant molecular symmetries
within the neural network architecture, have considerably improved the accuracy and data …

Data-driven techniques are increasingly used to replace electronic-structure calculations of
matter. In this context, a relevant question is whether machine learning (ML) should be …

Computational spectroscopy has emerged as a critical tool for researchers looking to
achieve both qualitative and quantitative interpretations of experimental spectra. Over the …

The performance of a machine learning (ML) algorithm for chemistry is highly contingent
upon the architect's choice of input representation. This work introduces the partial density of …

Chemical (molecular, quantum) machine learning relies on representing molecules in
unique and informative ways. Here, we present the matrix of orthogonalized atomic orbital …

Synthetic musks (SMs), as an indispensable odor additive, are widely used in various
personal care products. However, due to their physico-chemical properties, SMs were …

Recently, we introduced a class of molecular representations for kernel-based regression
methods─ the spectrum of approximated Hamiltonian matrices (SPAHM)─ that takes …

In recent years, there has been a surge of interest in predicting computed activation barriers,
to enable the acceleration of the automated exploration of reaction networks. Consequently …

We review the utility and application of artificial intelligence (AI) and machine learning (ML)
at various process scales in this work, from molecules and reactions to materials to …