We study the one-dimensional contact process in its quantum version using a recently proposed real-space renormalization technique for stochastic many-particle systems. Exploiting the duality and other properties of the model, we can apply the method for cells with up to 37 sites. After suitable extrapolation, we obtain exponent estimates that are comparable in accuracy with the best known in the literature.

With the current expanded technical capabilities to perform mass spectrometry-based biomedical proteomics experiments, an improved focus on the design of experiments is crucial. As it is clear that...

Coarse-grained models have emerged as valuable tools to simulate long DNA molecules while maintaining computational efficiency. These models aim at preserving interactions among coarse-grained variables in a manner that mirrors the underlying atomistic description. We explore here a method for testing coarse-grained vs all-atom models using stiffness matrices in Fourier space (q-stiffnesses), which are particularly suited to probe DNA elasticity at different length scales. We focus on a class of coarse-grained rigid base DNA models known as cgDNA and its most recent version, cgDNA+. Our analysis shows that while cgDNA+ closely follows the q-stiffnesses of the all-atom model, the original cgDNA shows some deviations for twist and bending variables, which are rather strong in the q → 0 (long length scale) limit. The consequence is that while both cgDNA and cgDNA+ give a suitable description of local elastic behavior, the former misses some effects that manifest themselves at longer length scales. In particular, cgDNA performs poorly on twist stiffness, with a value much lower than expected for long DNA molecules. Conversely, the all-atom and cgDNA+ twist are strongly length scale dependent: DNA is torsionally soft at a few base pair distances but becomes more rigid at distances of a few dozen base pairs. Our analysis shows that the bending persistence length in all-atom and cgDNA+ is somewhat overestimated.

Organic solvent nanofiltration (OSN) is a membrane separation method that has gained much interest due to its promising ability to offer an energy-lean alternative for traditional thermal separation methods. Industrial acceptance, however, is held back by the slow process of membrane screening based on trial and error for each solute-solvent couple to be separated. Such time-consuming screening is necessary due to the absence of predictive models, caused by a lack of fundamental understanding of the complex separation mechanism complicated by the wide variety of solute and solvent properties, and the importance of all mutual solute-solvent-membrane affinities and competing interactions. Recently, data-driven approaches have gained a lot of attention due to their unprecedented predictive power, significantly outperforming traditional mechanistic models. In this review, we give an overview of both mechanistic models and the recent advances in data-driven modeling. In addition to other reviews, we want to emphasize the coherence of all mechanistic models and discuss their relevance in an increasingly data-driven field. We reflect on the use of data in the field of OSN and its compliance with the FAIR principles, and we give an overview of the state of the art of data-driven models in OSN. The review can serve as inspiration for any further modeling activities, both mechanistic and data-driven, in the field.