Efficient Machine Learning Configuration Interaction for Bond Breaking Problems.

Machine learning-assisted configuration interaction (MLCI) has been shown earlier as a promising method in determining the electronic structure of the model and molecular Hamiltonians. In the MLCI approach to molecular Hamiltonians, it has been noticed that prediction is strongly dependent on the connectedness of the training and validation spaces. In this work, we have tested three different models with different output parameters (abs-MLCI, transformed-MLCI, and log-MLCI) to verify the robustness of training these models.

Safety and effectiveness of COVID-19 SPUTNIK V vaccine in dialysis patients.

Introduction: Given the vulnerability of chronic kidney disease individuals to SARS-CoV-2, nephrology societies have issued statements calling for prioritization of these patients for vaccination. It is not yet known whether COVID-19 vaccines grant the same high level of protection in patients with kidney disease compared to the non-dialysis population. The aims of this study were to evaluate the safety - measured by the adverse events potentially attributed to vaccines (ESAVI) - and the effectiveness - evaluated by the presence of antibodies - in dialysis patients immunized with the COVID-19 Sputnik V vaccine.

Comparative Stress Response of Black and Gold Howler Monkey (Alouatta caraya) in Urban and Rural Environments of Northern Argentina.

The expansion of human populations associated with urbanization results in extensive modification of natural habitats. While many species cannot persist in these highly modified environments, some species adopt new strategies, which contribute to their survival. Several primate species have persisted in altered habitats, including members of the genus Alouatta.

Configuration interaction trained by neural networks: Application to model polyaromatic hydrocarbons.

The main bottleneck of a stochastic or deterministic configuration interaction method is determining the relative weights or importance of each determinant or configuration, which requires large scale matrix diagonalization. Therefore, these methods can be improved significantly from a computational standpoint if the relative importance of each configuration in the ground and excited states of molecular/model systems can be learned using machine learning techniques such as artificial neural networks (ANNs). We have used neural networks to train the configuration interaction coefficients obtained from full configuration interaction and Monte Carlo configuration interaction methods and have tested different input descriptors and outputs to find the more efficient training techniques.

In the quest for a stable triplet state in small polyaromatic hydrocarbons: an tool for rational design and prediction.

Combining the roles of spin frustration and geometry of odd and even numbered rings in polyaromatic hydrocarbons (PAHs), we design small molecules that show exceedingly small singlet-triplet gaps and stable triplet ground states. Furthermore, a computationally efficient protocol with a model spin Hamiltonian is shown to be capable of qualitative agreement with respect to high level multireference calculations and therefore, can be used for fast molecular discovery and screening.