The 6thSMLQC seminar will be given by Johannes Margraf on May 4, 2023 (22:00 Beijing | 16:00 Paris | 10:00 New York).
Title
Physical Description of Long-Range Interactions in Atomistic Machine Learning Models
Abstract
The dominating paradigm of state-of-the-art machine learning (ML) interatomic potentials is the use of local representations of atomic environments. While this locality has many computational and practical advantages, it ultimately also limits the achievable accuracy of a potentials, since information beyond the cutoff radius is not taken into account. Indeed, long-range interactions can be substantial in bulk systems, most prominently due to the Coulomb interaction, which decays slowly (~ 1/r) with the interatomic distance. These electrostatic interactions are often screened in practice, so that local potentials can still effectively describe polar solids and liquids with surprising accuracy. Unfortunately, this cannot always be relied upon. The inclusion of long-range interactions in ML potentials has therefore been an active field of study in recent years with many different approaches. In this seminar, I will focus on approaches that tackle the problem of long-range electrostatics by describing charge distributions via partial charges within the ML model itself. In particular, our recently reported Kernel Charge Equilibration (kQEq) method uses sparse Gaussian Processes to learn atomic electronegativities as a function of the chemical environment. This allows predicting partial charges with a charge equilibration model, including full long-range interactions and non-local charge transfer. Applications of kQEq in predicting molecular dipole moments and developing long-ranged interatomic potentials will be discussed.
Introduction to the speaker
Johannes T. Margraf studied chemistry at the University of Erlangen, where he also obtained his Ph.D. Subsequently he joined the Quantum Theory Project at the University of Florida as a PostDoc, funded by a Feodor-Lynen fellowship. This was followed by another postdoctoral fellowship at the Technical University of Munich. Since 2021, he is a group leader at the Theory Department of the Fritz-Haber-Institute in Berlin. His group focuses on using and developing machine-learning and electronic structure methods to study chemical reactions and discover new functional materials.
Quantum Dissipative Dynamics with Machine Learning
Abstract
In this talk, I will present our work on Machine Learning based Quantum Dissipative Dynamics methods such as AIQD[1] and OSTL[2]. I will also talk about our recently released MLQD package (GitHub) and QDDSET-1 (GitHub) database. The talk will be followed by a tutorial demonstrating the applicability of our methods on our MLatom@XACS cloud computing platform.
