This book includes within its scope: computational models in physics and physical chemistry; computer programs in physics and physical chemistry; computational models and programs associated with the design, control, and analysis of experiments; numerical methods and algorithms; algebraic computation; impact of advanced computer architecture and special purpose computers on computing in the physical sciences; software topics, including programming environments, languages, data bases, expert systems, and graphics packages related to physical sciences; and, analysis of computer systems performance.

Aims to reinforce the interface between physical sciences, theoretical computer science, and discrete mathematics. This book assembles theoretical physicists and specialists of theoretical informatics and discrete mathematics in order to learn about developments in cryptography, algorithmics, and more.

The objectives of the conference are to develop greater understanding of physics research and its applications to promote new industries; to innovate knowledge about recent breakthroughs in physics, both the fundamental and technological aspects; to implement of international cooperation in new trends in physics research and to improve the performance of the physics research facilities in Egypt. This proceedings highlights the latest results in the fields of astrophysics, atomic, molecular, condensed matter, laser, nuclear and particle physics. The peer refereed papers collected in this volume were written by international experts in these laser fields.

Designing molecules and materials with desired properties is an important prerequisite for advancing technology in our modern societies. This requires both the ability to calculate accurate microscopic properties, such as energies, forces and electrostatic multipoles of specific configurations, as well as efficient sampling of potential energy surfaces to obtain corresponding macroscopic properties. Tools that can provide this are accurate first-principles calculations rooted in quantum mechanics, and statistical mechanics, respectively. Unfortunately, they come at a high computational cost that prohibits calculations for large systems and long time-scales, thus presenting a severe bottleneck both for searching the vast chemical compound space and the stupendously many dynamical configurations that a molecule can assume. To overcome this challenge, recently there have been increased efforts to accelerate quantum simulations with machine learning (ML). This emerging interdisciplinary community encompasses chemists, material scientists, physicists, mathematicians and computer scientists, joining forces to contribute to the exciting hot topic of progressing machine learning and AI for molecules and materials. The book that has emerged from a series of workshops provides a snapshot of this rapidly developing field. It contains tutorial material explaining the relevant foundations needed in chemistry, physics as well as machine learning to give an easy starting point for interested readers. In addition, a number of research papers defining the current state-of-the-art are included. The book has five parts (Fundamentals, Incorporating Prior Knowledge, Deep Learning of Atomistic Representations, Atomistic Simulations and Discovery and Design), each prefaced by editorial commentary that puts the respective parts into a broader scientific context.

Those who operate in physics education frequently ask research operators for suggestions, reference models, updated content and answers for their professional work. So far, the sector has not achieved significant advances specifically in terms of both content updates and methodology approaches. In the special issue, titled New Trends in Physics Education Research, the authors, in addition to presenting some new topics in physics education, take into account the greater relevance that in recent years the Evidence Based Education has taken place. In this framework, the main points of issue include: 1) Dealing with new trends in teaching and learning processes in physics; highlighting new mathematics content for physics courses; 3) giving evidence of the key role played by laboratory activities in physics training courses; and 4) stressing the importance of interdisciplinary approaches as well as scientific culture, communication and dissemination. Physics teaching involves several fields and different disciplines (such as mathematics, philosophy, laboratory activities, etc.) where the same arguments are often explained without clarifying that often there is a close correlation between disciplines. In particular, an integrated theoretical and experimental approach can improve the knowledge of some subjects of physics and mathematics; furthermore, it is also useful to employ a joint approach with laboratory activities, and by doing so enriching topics of meaning. In such cases, mathematics provides the adapt tools for physics and also is able to drive physical intuition; on the other hand, physics and its laboratory activities provide simple access to mathematical topics of complex comprehension. The issue is addressed to academics and schoolteachers as well as researchers in the field of physics education.

A core principle of physics is knowledge gained from data. Thus, deep learning has instantly entered physics and may become a new paradigm in basic and applied research.This textbook addresses physics students and physicists who want to understand what deep learning actually means, and what is the potential for their own scientific projects. Being familiar with linear algebra and parameter optimization is sufficient to jump-start deep learning. Adopting a pragmatic approach, basic and advanced applications in physics research are described. Also offered are simple hands-on exercises for implementing deep networks for which python code and training data can be downloaded.