Due to the limitation of solvent models, quantum chemistry calculation of solution-phase
molecular properties often deviates from experimental measurements. Recently, Δ-machine …

Many molecular design tasks benefit from fast and accurate calculations of quantum-
mechanical (QM) properties. However, the computational cost of QM methods applied to …

Molecular interaction fields (MIFs), describing molecules in terms of their ability to interact
with any chemical entity, are one of the most established and versatile concepts in drug …

We investigate the impact of choosing regressors and molecular representations for the
construction of fast machine learning (ML) models of thirteen electronic ground-state …

Abstract Machine learning (ML) provides access to fast and accurate quantum chemistry
(QC) calculations for various properties of interest such as excitation energies. It is often the …

Atomic and molecular properties could be evaluated from the fundamental Schrodinger's
equation and therefore represent different modalities of the same quantum phenomena …

Machine learning (ML) models can potentially accelerate the discovery of tailored materials
by learning a function that maps chemical compounds into their respective target properties …

Abstract Machine learning (ML) is becoming a method of choice for modelling complex
chemical processes and materials. ML provides a surrogate model trained on a reference …

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

Non-bonding intermolecular interactions largely dominate the selective dissolution of trace
species into physical solvents and, therefore, are fundamentally important to solvent …