In the literature, machine learning (ML) and artificial intelligence (AI) applications tend to
start with examples that are irrelevant to process engineers (eg classification of images …

The field of computational chemistry has seen a significant increase in the integration of
machine learning concepts and algorithms. In this Perspective, we surveyed 179 open …

Although the tailored metal active sites and porous architectures of MOFs hold great promise
for engineering challenges ranging from gas separations to catalysis, a lack of …

Uncertainty quantification (UQ) is an important component of molecular property prediction,
particularly for drug discovery applications where model predictions direct experimental …

The design rules for materials are clear for applications with a single objective. For most
applications, however, there are often multiple, sometimes competing objectives where …

We present a novel simulation-based hybrid emulator approach that maximally derives
cosmological and Halo Occupation Distribution (HOD) information from non-linear galaxy …

We present the BACCO project, a simulation framework specially designed to provide highly-
accurate predictions for the distribution of mass, galaxies, and gas as a function of …

We present a machine learning (ML) method for predicting electronic structure correlation
energies using Hartree–Fock input. The total correlation energy is expressed in terms of …

With the demand for renewable energy and efficient devices rapidly increasing, a need
arises to find and optimize novel (nano) materials. With sheer limitless possibilities for …

We address the degree to which machine learning (ML) can be used to accurately and
transferably predict post-Hartree-Fock correlation energies. Refined strategies for feature …