ArcaNN: automated enhanced sampling generation of training sets for chemically reactive machine learning interatomic potentials.

The emergence of artificial intelligence is profoundly impacting computational chemistry, particularly through machine-learning interatomic potentials (MLIPs). Unlike traditional potential energy surface representations, MLIPs overcome the conventional computational scaling limitations by offering an effective combination of accuracy and efficiency for calculating atomic energies and forces to be used in molecular simulations. These MLIPs have significantly enhanced molecular simulations across various applications, including large-scale simulations of materials, interfaces, chemical reactions, and beyond.

Dissecting the hydrogen bond network of water: Charge transfer and nuclear quantum effects.

The molecular structure of water is dynamic, with intermolecular hydrogen (H) bond interactions being modified by both electronic charge transfer and nuclear quantum effects (NQEs). Electronic charge transfer and NQEs potentially change under acidic or basic conditions, but such details have not been measured. In this work, we developed correlated vibrational spectroscopy, a symmetry-based method that separates interacting from noninteracting molecules in self- and cross-correlation spectra, giving access to previously inaccessible information.

Neural Network-Based Sum-Frequency Generation Spectra of Pure and Acidified Water Interfaces with Air.

The affinity of hydronium ions (HO) for the air-water interface is a crucial question in environmental chemistry. While sum-frequency generation (SFG) spectroscopy has been instrumental in indicating the preference of HO for the interface, key questions persist regarding the molecular origin of the SFG spectral changes in acidified water. Here we combine nanosecond long neural network (NN) reactive simulations of pure and acidified water slabs with NN predictions of molecular dipoles and polarizabilities to calculate SFG spectra of long reactive trajectories including proton transfer events.

How the Acidity of Water Droplets and Films Is Controlled by the Air-Water Interface.

Acidity is a key determinant of chemical reactivity in atmospheric aqueous aerosols and water microdroplets used for catalysis. However, many fundamental questions about these systems have remained elusive, including how their acidity differs from that of bulk solutions, the degree of heterogeneity between their core and surface, and how the acid-base properties are affected by their size. Here, we perform hybrid density functional theory (DFT)-quality neural network-based molecular simulations with explicit nuclear quantum effects and combine them with an analytic model to describe the pH and self-ion concentrations of droplets and films for sizes ranging from nm to μm.

Acids at the Edge: Why Nitric and Formic Acid Dissociations at Air-Water Interfaces Depend on Depth and on Interface Specific Area.

Whether the air-water interface decreases or increases the acidity of simple organic and inorganic acids compared to the bulk is critically important in a broad range of environmental and biochemical processes. However, a consensus has not yet been achieved on this key question. Here we use machine learning-based reactive molecular dynamics simulations to study the dissociation of paradigmatic nitric and formic acids at the air-water interface.

Structural Damage of Jailed Guidewire During the Treatment of Coronary Bifurcation Lesions: A Microscopic Randomized Trial.

Objectives: The study sought to compare the safety (resistance to damage) and efficacy (ability to cross the side branch) of polymer-coated and non-polymer-coated guidewires in the jailed wire technique used during the percutaneous treatment of bifurcation lesions.

Background: The jailed wire technique is a useful strategy in the treatment of bifurcation lesions by provisional stenting. However, these wires can be damaged or even be broken during their removal.

[Medical reflections on a case of pulmonary thromboembolism in pregnancy].

Maternal mortality is still high in the world. Pulmonary thromboembolism as a cause of death is the second in developed countries. The frequency of reported events in Latin-American is 0.

In silico studies using Radial Distribution Function approach for predicting affinity of 1 alpha,25-dihydroxyvitamin D(3) analogues for Vitamin D receptor.

The Radial Distribution Function (RDF) approach has been applied to the study of the chick intestinal VDR affinity of 49 Vitamin D analogues. A model able to describe more than 77.5% of the variance in the experimental activity was developed with the use of the mentioned approach.