The symposium will focus on recent advances in algorithm development of novel atomistic simulation methodologies, both at the level of electronic structure calculations and of empirical-potential-based simulations, and on their applications. The symposium will be centered on methods that aim at addressing size and time-scale limitations of conventional techniques, two problems that often severely limit the scope of atomistic simulations in materials science. As a first-principle method, density functional theory (DFT) has become an invaluable tool for materials modeling. However, with conventional implementation of Kohn-Sham DFT, one is usually limited to systems containing at most several hundred atoms. In recent years, tremendous progress towards relaxing the time and lengthscale limitations has been made in the DFT community. This symposium will address these new exciting advances in DFT such as orbital-free DFT, time-reversible ab-initio molecular dynamics, quasi-continuum DFT, hybrid quantum/classical modeling and machine learning approaches. At the other end of the spectrum, Molecular Dynamics (MD) algorithms based on empirical or semi-empirical potentials allow for greatly extended simulation sizes and times. However, these traditional algorithms are not suitable to study long time phenomena, such as defect diffusion, as they become communication bound. In systems where the dynamics is activated, advanced simulation techniques, such as accelerated molecular dynamics and kinetic Monte Carlo methods, can be leveraged to extend the simulation times up to experimentally relevant scales. These methods often provide invaluable insight into the microstructural evolution of materials. The symposium will focus on recent advances in the development of these accelerated techniques, such as adaptive KMC methods, and on the new physics that can be learned as the timescale horizon is pushed further. Atomistic to continuum approaches and their recent coupling with accelerated MD models, and the phase field crystal method are promising methodologies under development with the potential of extending the time and size scales of the atomistic systems under consideration. Another active field of research is the development of accurate and efficient interatomic potentials based on Machine Learning approaches, which combined with accelerated MD, KMC or Quasi-continuum methods provide powerful tools to study materials behavior.

kinetics machine learning modeling