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Third Seminar on Machine Learning in Quantum Che ... (No replies)
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SMLQC seminar by Pascal Friederich, March 2, 2023
The third SMLQC seminar will be given by Pascal Friederich on March 2, 2023 (15:00 Paris | 22:00 Beijing | 09:00 New York).
Title
Machine Learning for Simulation, Understanding, and Design of Molecules and Materials
Abstract
Machine learning can accelerate the design of new molecules and materials in multiple ways, e.g. by learning from large amounts of (simulated or experimental) data to predict molecular or materials properties faster, or by interfacing machine learning algorithms for autonomous decision-making directly with automated high-throughput experiments. In this talk, I focus on our research activities on graph neural networks for materials property prediction [1,2] and understanding of structure property relations [3], as well as on the use of graph neural networks for accelerated atomistic simulations [4,5]. Application areas range from superconductors, metal-organic frameworks and organic semiconductors to photochemical reactions. In the subsequent hands-on tutorial, we will introduce KGCNN, our in-house developed graph neural network library based on Keras/TensorFlow [6].
References
Biography
After his B.S. and M.Sc. in physics and a Ph.D. in physics on multiscale modeling of organic semiconductors, Pascal Friederich received a Marie-Sklodowska-Curie Postdoctoral Fellowship at Harvard University and the University of Toronto where he worked on machine learning methods for chemistry. In 2020, Pascal Friederich joined the Informatics Department of the Karlsruhe Institute of Technology as a tenure-track professor, leading the AI for Materials Science (AiMat, https://aimat.science) research group. The AiMat research group focuses on developing and applying machine learning methods for property prediction, simulation, understanding, and design of molecules and materials, as well as on interfacing machine learning methods with automated materials experiments. In 2022, Pascal Friederich received the Heinz-Maier-Leibnitz Prize from the German Research Foundation.
Seminars on Machine Learning in Quantum Chemistry and Quantum Computing for Quantum Chemistry (SMLQC) 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.
Format
Format is flexible with roughly biweekly sessions online usually divided into two sections:
– Scientific section
focused on recent developments which can be, e.g., highlighting recently published papers.
Talks are limited to 20 min so that people can attend them during their “coffee break”. The question part will be up to 5 min if scientific section is followed by a tutorial session where the speaker can elaborate more on the questions. Otherwise, if no tutorial session is given, then question part will be open until the speaker or participants give up.
– Tutorial/hands-on section
Dedicated to providing instructions on program use and practical understanding of the underlying ML theory. The materials used in the talks (e.g., Jupyter notebooks, Python scripts, etc.) could be made available for the participants via GitHub, Google Colab, etc. or the SMLQC website under the allowance of the speaker. No strict time limitations but we recommend up to 1 hour.
We particularly encourage and give platform to young researchers (e.g., postdocs, PhDs, etc.) to give seminars.
All our sessions will also have break out rooms in Zoom where participants can have further exchange or fix some specific problems during hands-on session.
If speakers agree, the seminars will be recorded and uploaded on SMLQC YouTube channel.
Speakers
Speakers are invited by the organizing committee and can be selected according to the suggestions of SMLQC participants, e.g., via voting in our Slack workspace. Self-nominations are also encouraged!
Organizers
Current organizers in alphabetic order: