Machine learning (ML) using data sets of atomic and molecular force fields (FFs) has made
significant progress and provided benefits in the fields of chemistry and material science …

The study of molecular recognition patterns is crucial for understanding the interactions
between inorganic (nano) particles and biomolecules. In this review we focus on hydroxyls …

Protein lysine methyltransferases (PKMTs) transfer up to three methyl groups to the side
chains of lysine residues in proteins and fulfill important regulatory functions by controlling …

The use of nanoparticles (NPs) in biomedicine has made a gradual transition from proof-of-
concept to clinical applications, with several NP types meeting regulatory approval or …

Bio-derived polyethylene furanoate (PEF) has recently gained attention as a sustainable
alternative to polyethylene terephthalate (PET), amidst environmental concerns over fossil …

Machine-learned potentials (MLPs) trained on ab initio data combine the computational
efficiency of classical interatomic potentials with the accuracy and generality of the first …

Amino acids make up a promising family of molecules capable of direct air capture (DAC) of
CO2 from the atmosphere. Under alkaline conditions, CO2 reacts with the anionic form of an …

To identify effective wetting additives for extinguishing wood fires, this study employed both
simulation and experimental approaches to investigate the wetting and adsorption …

Solid-state electrolytes, particularly polymer/ceramic composite electrolytes, are emerging
as promising candidates for lithium-ion batteries due to their high ionic conductivity and …

The focus of this review is on the physical and magnetic properties that are related to the
efficiency of monometallic magnetic nanoparticles used in biomedical applications, such as …