We here introduce the Aquamarine (AQM) dataset, an extensive quantum-mechanical (QM)
dataset that contains the structural and electronic information of 59,783 low-and high-energy …

Computer-driven molecular design combines the principles of chemistry, physics, and
artificial intelligence to identify chemical compounds with tailored properties. While quantum …

The repulsive part of the Buckingham potential, with parameters A and B, can be used to
model deformation energies and steric energies. Both are calculated using the interacting …

We introduce the MORE-Q dataset, a quantum-mechanical (QM) dataset encompassing the
structural and electronic data of non-covalent molecular sensors formed by combining 18 …

Machine learning (ML) has emerged as a powerful tool to navigate polymer structure–
property relationships. Despite recent progress, data sparsity is a major obstacle hindering …

Recent advances in machine-learning-based electronic coarse graining (ECG) methods
have demonstrated the potential to enable electronic predictions in soft materials at …

The influence of machine learning (ML) in chemistry is undeniable, and it is a powerful tool
to obtain chemical insights from large amounts of data. In particular, ML is a perfect tool for …

Abstract Machine learning has been proven to be an extremely valuable tool for simulations
with ab-initio accuracy at the computational cost between classical interatomic potentials …