Biological diversity of the coastal zone of the Crimean peninsula: problems, preservation and restoration pathways

The results of complex hydrochemical, hydrobiological and ichthyological investigations by IBSS, NAS of Ukraine, realized in 6 regions of the coastal zone of the Crimean peninsula in the Black Sea and the Sea of Azov are given. The main negative factors causing changes in structural and functional characteristics of hydrobiocenoses in the regions studied are analyzed and “hot ecological spots” are isolated. Variants of different methods of management of the coastal ecosystems, including construction of artificial reefs and usage of biological filters for water cleaning, protection and recreation of biological diversity are taken into consideration

Roadmap on Machine learning in electronic structure

AbstractIn recent years, we have been witnessing a paradigm shift in computational materials science. In fact, traditional methods, mostly developed in the second half of the XXth century, are being complemented, extended, and sometimes even completely replaced by faster, simpler, and often more accurate approaches. The new approaches, that we collectively label by machine learning, have their origins in the fields of informatics and artificial intelligence, but are making rapid inroads in all other branches of science. With this in mind, this Roadmap article, consisting of multiple contributions from experts across the field, discusses the use of machine learning in materials science, and share perspectives on current and future challenges in problems as diverse as the prediction of materials properties, the construction of force-fields, the development of exchange correlation functionals for density-functional theory, the solution of the many-body problem, and more. In spite of the already numerous and exciting success stories, we are just at the beginning of a long path that will reshape materials science for the many challenges of the XXIth century

Roadmap on Machine learning in electronic structure

In recent years, we have been witnessing a paradigm shift in computational materials science. In fact, traditional methods, mostly developed in the second half of the XXth century, are being complemented, extended, and sometimes even completely replaced by faster, simpler, and often more accurate approaches. The new approaches, that we collectively label by machine learning, have their origins in the fields of informatics and artificial intelligence, but are making rapid inroads in all other branches of science. With this in mind, this Roadmap article, consisting of multiple contributions from experts across the field, discusses the use of machine learning in materials science, and share perspectives on current and future challenges in problems as diverse as the prediction of materials properties, the construction of force-fields, the development of exchange correlation functionals for density-functional theory, the solution of the many-body problem, and more. In spite of the already numerous and exciting success stories, we are just at the beginning of a long path that will reshape materials science for the many challenges of the XXIth century.</p

Potential energy surfaces fitted by artificial neural networks

Molecular mechanics is the tool of choice for the modeling of systems that are so large or complex that it is impractical or impossible to model them by ab initio methods. For this reason (here is a need for accurate potentials that are able to quickly reproduce ab initio quality results at the fraction of the cost. The interactions within force fields are represented by a number Of functions. Some interactions are well understood and can be represented by simple mathematical functions while others are not SO Well understood and their functional form is represented in a simplistic manner or not even known. In the last 20),cars there have been the first examples of a new design ethic, where novel and contemporary methods using I, machine learning, in particular, artificial neural networks, have been used to find the nature of the Underlying Functions of a force field. Here we appraise what has been achieved over this time and what requires further improvements, while offering some insight and guidance for the development Of future force fields