Access the Presentation Material and Rercordings Here!
Date: 2 April 2021
Speaker: Stelios Karozis
The exponential growth of computational power offers
significant opportunities for more detailed studies of physical systems.
The simulation of the physical world could be categorized based on the
spatial and time resolution under study, as (a) macroscopic, (b)
mesoscopic and (c) microscopic.
The latter consist of molecular simulation studies, whereas the physical world is described by the average behavior of molecular interactions. As a result, the phase space is explored by deterministic or stochastic algorithms and thermodynamics properties are calculated by using statistical mechanics tools. In addition, the big computational power permits the production of “Big Data” and as a result, the necessity to process and analyze them, is emerged.
Machine Learning algorithms are data analytics tools used in many fields and in the case of Natural Sciences, such as molecular simulations and material design. The process of studying a system is by far extended. No equation or model that describes the system exists, and the goal of the study is to deduce (“learn”) the model from the data.
The current presentation will try to appeal in both computer scientist and non computer scientist, aka physicists, engineers, chemists etc and make an introduction in high level about:
(i) molecular simulations
(ii) machine learning
(iii) and the use of ML in a materials design case study
Dr. Stelios Karozis is a postdoctoral researcher at N.C.S.R Demokritos. His Ph.D was in Molecular Simulations from the Chemistry Department of University of Crete and his research is focused on application of machine learning methods to deterministic/probabilistic simulations.