SMLQC seminar by Johannes Margraf, May 4, 2023

The 6th SMLQC 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.

How to join

Join Zoom Meeting
https://zoom.us/j/86004422973?pwd=WjNKQlEydmdFL3hJbUx4NjByYjVJZz09

Meeting ID: 860 0442 2973

Passcode: 703098

Recordings of the 3rd SMLQC seminar are now available, Speaker Pascal Friederich

The 3rd seminar was given by Pascal Friederich on ML for Simulation, Understanding, and Design of Molecules and Materials. The 1st part of the seminar was a Lecture (https://youtu.be/WWxdZjXeK7w) followed by the 2nd part with hands-on Tutorial (https://youtu.be/8c8KB0V1bC4). Recordings are also embedded below.

To get updated

Lecture
Tutorial

Recordings of the 2nd SMLQC seminar are now available, Speaker Max Pinheiro Jr

The 2nd seminar was given by Max Pinheiro Jr about his work on nonadiabatic molecular dynamics with machine learning. The 1st part of the seminar was a Lecture (https://youtu.be/9jRxeMzpkLg) followed by the 2nd part with hands-on Tutorial (https://youtu.be/yMDUKhzipj0). Recordings are also embedded below.

To get updated

Lecture
Tutorial

Recordings of the 1st SMLQC seminar are now available, Speaker Arif Ullah

The 2nd seminar was given by Arif Ullah about his work on Quantum Dissipative Dynamics with Machine Learning. The 1st part of the seminar was a Lecture (https://youtu.be/Nx0mSPUaof8) followed by the 2nd part with hands-on Tutorial (https://youtu.be/AuWGiK53P6Y). Recordings are also embedded below.

To get updated

Lecture
Tutorial

SMLQC seminar by Arif Ullah, February 2, 2023

The very first SMLQC seminar will be given by Arif Ullah on Feb. 2, 2023 (15:00 Paris | 22:00 Beijing | 09:00 New York).

Zoom link

Title

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.

  1. Arif Ullah*, Pavlo O. Dral*, Predicting the future of excitation energy transfer in light-harvesting complex with artificial intelligence-based quantum dynamicsNat. Commun. 202213, 1930. DOI: 10.1038/s41467-022-29621-w. (blog post)
  2. Arif Ullah*, Pavlo O. Dral*, One-shot trajectory learning of open quantum systems dynamics, J. Phys. Chem. Lett. 202213, 6037–6041. DOI: https://doi.org/10.1021/acs.jpclett.2c01242 | (blog post

Seminars on Machine Learning in  Quantum Chemistry and Quantum Computing for Quantum Chemistry (SMLQC)

We are pleased to announce the launch of Seminars on Machine Learning in  Quantum Chemistry and Quantum Computing for Quantum Chemistry (SMLQC) that are flexible online lectures and tutorials for highlighting recent developments and providing hands-on experience on the title topics as well as networking opportunities. SMLQC sessions are organized to bridge Symposia on Machine Learning and Quantum Chemistry, the first one held in 2021 in Xiamen, China (online) and the second one to be held in 2023 in Uppsala, Sweden (hybrid). The real-time interaction is enabled via a dedicated Slack workspace which already has many researchers active in the title fields.

Continue reading