Stereo-electronic factors influencing the stability of hydroperoxyalkyl radicals: transferability of chemical trends across hydrocarbons and methods.

The hydroperoxyalkyl radicals (˙QOOH) are known to play a significant role in combustion and tropospheric processes, yet their direct spectroscopic detection remains challenging. In this study, we investigate molecular stereo-electronic effects influencing the kinetic and thermodynamic stability of a ˙QOOH along its formation path from the precursor, alkylperoxyl radical (ROO˙), and the depletion path resulting in the formation of cyclic ether + ˙OH. We focus on reactive intermediates encountered in the oxidation of acyclic hydrocarbon radicals: ethyl, isopropyl, isobutyl, -butyl, neopentyl, and their alicyclic counterparts: cyclohexyl, cyclohexenyl, and cyclohexadienyl.

Effect of Normal Saline versus PlasmaLyte on Coagulation and Metabolic Status in Patients Undergoing Neurosurgical Procedures.

 The choice of intraoperative fluid in neurosurgical patients is important as we need to maintain adequate cerebral perfusion and oxygenation and also avoid cerebral edema. Normal saline (NS) is commonly used in neurosurgeries, but it leads to hyperchloremic metabolic acidosis, which may result in coagulopathy. Balanced crystalloid with physiochemical composition akin to that of plasma has favorable effects on metabolic profile and may avoid the problems associated with NS.

Capturing the potential energy landscape of large size molecular clusters from atomic interactions up to a 4-body system using deep learning.

Exploring the structure and properties of molecular clusters with accuracy using the methods is a resource intensive task due to the increasing cost of the methods and the number of distinct conformers as the size increases. The energy landscape of methanol clusters has been previously explored using computationally efficient empirical models to collect a database of structurally distinct minima, followed by re-optimization using methods. In this work, we propose a new method that utilizes the database of stable conformers and borrow the fragmentation concept of many-body-expansion (MBE) methods in methods to train a deep-learning machine learning (ML) model using SchNet.

Structural evolution in gold nanoparticles using artificial neural network based interatomic potentials.

Relativistic effects of gold make its behavior different from other metals. Unlike silver and copper, gold does not require symmetrical structures as the stable entities. We present the evolution of gold from a cluster to a nanoparticle by considering a majority of stable structural possibilities.

A transferable artificial neural network model for atomic forces in nanoparticles.

We have designed a new method to fit the energy and atomic forces using a single artificial neural network (SANN) for any number of chemical species present in a molecular system. The traditional approach for fitting the potential energy surface for a multicomponent system using artificial neural network (ANN) is to consider number of networks for number of chemical species in the system. This shoots the computational cost and makes it difficult to apply to a system containing more number of species.

Correlation of structure with UV-visible spectra by varying SH composition in Au-SH nanoclusters.

In the present work, we model artificial neural network (ANN) potentials for Au (SH) nanoclusters in the range of = 10 to = 38. The accuracy of ANN potentials is tested by comparing the global minimum (GM) structures of Au (SH) nanoclusters, at saturated amount of SH, with the earlier reported structures. The GM structures are reported for the first time for nanoclusters with compositions lower than the saturated SH composition.

c-T phase diagram and Landau free energies of (AgAu) nanoalloy via neural-network molecular dynamic simulations.

For understanding the structure, dynamics, and thermal stability of (AgAu) nanoalloys, knowledge of the composition-temperature (c-T) phase diagram is essential due to the explicit dependence of properties on composition and temperature. Experimentally, generating the phase diagrams is very challenging, and therefore theoretical insight is necessary. We use an artificial neural network potential for (AgAu) nanoalloys.

Spherical harmonics based descriptor for neural network potentials: Structure and dynamics of Au nanocluster.

We propose a highly efficient method for fitting the potential energy surface of a nanocluster using a spherical harmonics based descriptor integrated with an artificial neural network. Our method achieves the accuracy of quantum mechanics and speed of empirical potentials. For large sized gold clusters (Au), the computational time for accurate calculation of energy and forces is about 1.

Neural network potentials for dynamics and thermodynamics of gold nanoparticles.

For understanding the dynamical and thermodynamical properties of metal nanoparticles, one has to go beyond static and structural predictions of a nanoparticle. Accurate description of dynamical properties may be computationally intensive depending on the size of nanoparticle. Herein, we demonstrate the use of atomistic neural network potentials, obtained by fitting quantum mechanical data, for extensive molecular dynamics simulations of gold nanoparticles.