AI in chemistry and beyond: Trends in the field (ChE-607)

The Machine Learning Seminars are organized by Prof. Clémence Corminboeuf, Prof. Berend Smit and Prof. Philipp Schwaller, the seminars usually take place on Tuesdays at 15:15. Unless indicated otherwise, the lecture takes place on Zoom.

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PAST SEMINARS

 

Machine learning in chemistry and beyond" (ChE-651) seminar by Prof. Kim Jelfs: "Remembering the lab in computational molecular material discovery"

01-10-202401-10-2024

With: Kim Jelfs completed her PhD in Computational Chemistry at University College London, working on the development and application of modelling to understand zeolite crystal growth and was awarded the Ramsay Medal for the best completing PhD student. She then went on research visits at the Universitat de Barcelona, the University of Liverpool, and finally Imperial as a research fellow, where she is now a Professor since 2022. Kim was awarded a 2018 Royal Society of Chemistry Harrison-Meldola Memorial Prize, a 2019 Philip Leverhulme Prize in Chemistry and was named the 2022 UK Blavatnik Awards Laureate in Chemistry. Kim holds an ERC Starting Grant and is an Associate Editor for Chemical Communications.        
Online: https://epfl.zoom.us/j/68447908297?pwd=OU5JUGJUSUhZc0ZNYjQ2WENvYlNRdz09
Category: Conferences – Seminars

Machine learning in chemistry and beyond" (ChE-651) seminar by Prof. Emma Schymanski: "Environmental Cheminformatics – Searching for Meaning Amongst Millions of Chemicals"

17-09-202417-09-2024

With: Prof. Emma Schymanski is chemist known for her work identifying unknown organic compounds, particularly pollutants. She graduated with a B.Sc. in Chemistry and a B.E. in Environmental Engineering from the University of Western Australia in 2003. She completed her PhD at the Helmholtz Centre for Enironmental Research in Leipzig, Germany in 2011, and a postdoc position at the Swiss Federal Institute of Aquatic Science and Technology. She is now head of the Environmental Cheminformatics Group as a Full Professor at the University of Luxembourg.
Online: https://epfl.zoom.us/j/68447908297?pwd=OU5JUGJUSUhZc0ZNYjQ2WENvYINRdz09
Category: Conferences – Seminars

3D de novo generation of organic molecules

25-06-202425-06-2024

With: Ian is a PhD Candidate in the joint Carnegie Mellon University – University of Pittsburgh Computational Biology PhD Program where he is advised by David Koes. His research is focused on developing deep generative models for applications in structure-based design.
Online: https://epfl.zoom.us/j/68447908297?pwd=OU5JUGJUSUhZc0ZNYjQ2WENvYlNRdz09
Category: Conferences – Seminars

Generative Models for Molecular Discovery

02-04-202402-04-2024

With: Dr. Bilodeau is currently an assistant professor in Chemical Engineering at the University of Virginia. She received her B.S. and M.S. from Northwestern University and her Ph.D. from Rensselaer Polytechnic Institute, both in Chemical and Biological Engineering. During her Ph.D., she received the Lawrence Livermore Advanced Simulations and Computation Graduate Fellowship, through which she carried out research at Lawrence Livermore National Laboratory. She completed a postdoc at MIT working with Klavs Jensen and Regina Barzilay. Her research explores the intersection between artificial intelligence and molecular simulations with the goal of designing new molecules and materials.
Online: https://epfl.zoom.us/j/68447908297?pwd=OU5JUGJUSUhZc0ZNYjQ2WENvYlNRdz09
Category: Conferences – Seminars

Automated Structure Elucidation using Transformer Models

05-03-202405-03-2024

With: Marvin is a PhD student jointly at IBM Research and the University of Zürich. His research focusses on multimodal language models applied to analytical chemistry. Before joining IBM he completed a Masters in Chemistry at Imperial College London.
Place and room: CH G1 495
Online: https://epfl.zoom.us/j/68447908297?pwd=OU5JUGJUSUhZc0ZNYjQ2WENvYlNRdz09
Category: Conferences – Seminars

ChatGPT for Reticular Chemists

12-12-202312-12-2023

With:   Dr. Zhiling Zheng is a Postdoctoral Associate at the Massachusetts Institute of Technology and a member of the Bakar Institute of Digital Materials for the Planet (BIDMaP). He completed his Bachelor’s degree in Chemistry and Chemical Biology at Cornell University in 2019, where he worked under the guidance of Prof. Kyle M. Lancaster. He then earned his Ph.D. in Chemistry from the University of California, Berkeley in 2023, supervised by Prof. Omar M. Yaghi. In the early phase of his Ph.D., Dr. Zheng was trained as an experimental chemist, focusing on the design and synthesis of Metal-Organic Frameworks (MOFs) for atmospheric water harvesting and CO2 capture. Later, his research scope expanded to the use of large language models (LLMs) and machine learning (ML) in accelerating the discovery of reticular materials and drug molecules. Dr. Zheng is recognized as a Merrill Presidential Scholar and has received the Kavli ENSI Graduate Student Fellowship for his contributions to AI and Chemistry.
Online: https://epfl.zoom.us/j/68447908297?pwd=OU5JUGJUSUhZc0ZNYjQ2WENvYlNRdz09
Category: Conferences – Seminars

Exploring Chemical Space with Machine Learning

04-12-202304-12-2023

With: Originally from Ukraine, Ganna (Anya) Gryn’ova received her BS and MSc in chemistry summa cum laude from Oles Honchar Dnipro National University. In 2014 she received a PhD in computational chemistry from Australian National University. Her doctoral thesis gathered a number of awards, including the IUPAC-Solvay International Award for Young Chemists for one of the five most outstanding PhD theses in the general area of the chemical sciences worldwide. Dr. Gryn’ova continued her research career at École Polytechnique Fédérale de Lausanne as a postdoctoral researcher working on in silico modeling of organic semiconductors. In 2016 she won the Marie Skłodowska-Curie Actions individual fellowship and focussed on the non-conventional architectures of single-molecule junctions. In 2019, Dr. Gryn’ova started her independent scientific career leading the junior research group “Computational Carbon Chemistry” (CCC) at the Heidelberg Institute for Theoretical Studies (HITS gGmbH) and Interdisciplinary Center for Scientific Computing (IWR) at Heidelberg University, Germany. The CCC group uses state-of-the-art computational chemistry and data science to explore and exploit diverse functional organic materials for applications in organocatalysis and environmental remediation. In 2021, Anya received the prestigious ERC Starting Grant for her project “PATTERNCHEM: Shape and Topology as Descriptors of Chemical and Physical Properties in Functional Organic Materials”; she is also a principal investigator in the Collaborative Research Centre SFB1249 “N-Heteropolycycles as Functional Materials” and the SIMPLAIX strategic research initiative on bridging scales from molecules to molecular materials by multiscale simulation and machine learning.
Place and room: BCH 2218
Online: https://epfl.zoom.us/j/68447908297?pwd=OU5JUGJUSUhZc0ZNYjQ2WENvYlNRdz09
Category: Conferences – Seminars

Accelerated Chemical Reaction Optimization using Multi-Task Learning

14-11-202314-11-2023

With: Kobi Felton is a chemical engineer interested in solving problems at the intersection of chemical engineering, chemistry and software. He holds a Bachelor of Science in Chemical Engineering from North Carolina State University and a MPhil Research and PhD in Chemical Engineering from the University of Cambridge, where he was a recipient of the Marshall Scholarship and the Cambridge-Marshall PhD Scholarship. His current projects include: Optimization of distillation control systems, Self-optimization of reactions using bayesian algorithms, Design of experiments for time-series datasets, and Designing descriptors for chemical reactions.
Online: https://epfl.zoom.us/j/68447908297?pwd=OU5JUGJUSUhZc0ZNYjQ2WENvYlNRdz09
Category: Conferences – Seminars

What can AI do for molecular simulation?

07-11-202307-11-2023

With: Frank Noé has a background in Electrical Engineering, Computer Science and Physics and did his PhD at University of Heidelberg in 2006. He became group leader at FU Berlin in 2007 and professor in 2013. Since 2022 he is Partner Research Manager in Microsoft Research AI4Science, also located in Berlin. Frank has received two European Research Commission (ERC) grants and the early career award in Theoretical Chemistry of the American Chemical Society (ACS). He is member of the Berlin-Brandenburg academy of sciences, a fellow in the European Laboratory for Learning and Intelligent Systems (ELLIS) and an ISI highly cited researcher. Frank’s research is highly interdisciplinary and focuses on developing Machine Learning methods to address fundamental questions in the natural Sciences.
Online: https://epfl.zoom.us/j/68447908297?pwd=OU5JUGJUSUhZc0ZNYjQ2WENvYlNRdz09
Category: Conferences – Seminars

Molecular set representation learning

24-10-202324-10-2023

With: Daniel received his BSc in computer science at the Bern University of Applied Sciences in 2013 and his MSc in Bioinformatics and Computational Biology at the University of Bern in 2016. In 2020 he received his PhD in Chemistry and Molecular Sciences for his thesis “Scalable Methods for the Exploration and Visualization of Large Chemical Spaces” from the University of Bern under the supervision of  Prof. Jean-Louis Reymond. His main research interest is efficient machine learning and data visualisation applied to natural sciences, focusing on the intersection of chemistry and biology. After a two-year stay as a permanent research staff member at  IBM Research in the Team of Teodoro Laino working on machine learning for biocatalysis, he started as a postdoctoral researcher in the group of  Prof. Pierre Vandergheynst at EPFL.
Place and room: MA A1 10
Category: Conferences – Seminars

Progress towards leveraging Machine Learning for Organic Synthesis

10-10-202310-10-2023

With: Jules Schleinitz. Jules is currently a postdoctoral scholar at CalTech in the group of Sarah E. Reisman and a current member of the NSF Center for Computer Assisted Synthesis. His research focuses on the development of computational and machine learning tools for organic synthesis planning through mechanistic understanding. Jules graduated in 2022 from the Ecole Normale Supérieure in Paris. His PhD intitled “Machine learning and Mechanistic Analysis” was supervised by Laurence Grimaud. Alongside with his research activities, Jules spent half of his PhD teaching chemistry at Ecole Normale Supérieure (Organic chemistry: lessons, electrochemistry: tutorials and experimental sessions, experimental projects for bachelor and master students.).
Online: https://epfl.zoom.us/j/68447908297?pwd=OU5JUGJUSUhZc0ZNYjQ2WENvYlNRdz09
Category: Conferences – Seminars

AI in chemistry and beyond: Learning Chemical Intuition from Humans in the Loop

16-05-202316-05-2023

With: Oh-hyeon studied computational neuroscience at EPFL with Prof. Herzog, where she researched the fundamental aspects of computer vision models. She then started her career at Novartis focusing on machine learning research for drug discovery. Soon, she will start a new challenge at a med-tech company (SynpleChem) for lab automation.  
Place and room: CH G1 495
Category: Conferences – Seminars

AI in chemistry and beyond: Machine learning for reactivity using expert descriptors and mechanistic information

09-05-202309-05-2023

With: Kjell Jorner is an Assistant Professor of Digital Chemistry at ETH Zurich since January 2023. His work focuses on accelerating chemical discovery with digital tools, with a special emphasis on reactivity and catalysis. His group does interdisciplinary research, drawing from the fields of computational chemistry, cheminformatics and machine learning. Before joining ETH Zurich, he was a postdoctoral researcher with Alán Aspuru-Guzik (2021-2022) and at AstraZenecaUK (2018-2020). Kjell has a PhD from Uppsala University (2018) on computational physical organic chemistry for the photochemistry of aromatic compounds.
Place and room: CH G1 495
Category: Conferences – Seminars

AI in chemistry and beyond: Molecular Generative Models: Diffusion for 3D Geometry Generation

02-05-202302-05-2023

With: Minkai Xu is a Ph.D. student in the Computer Science Department at Stanford University. Previously, he received his M.Sc degree from Mila and B.E. from Shanghai Jiaotong University. His research lies in probabilistic models, geometric representation learning, and ML for scientific discovery. He has published several influential papers on the above topics in top machine learning conferences (e.g., ICML, NeurIPS, ICLR, AAAI, and AAMAS) including the first diffusion models for molecular structure generation, which has been widely adopted in various drug and protein design problems. His research is generously supported by Sequoia Capital Stanford Graduate Fellowship.
Online: https://epfl.zoom.us/j/68447908297?pwd=OU5JUGJUSUhZc0ZNYjQ2WENvYlNRdz09
Category: Conferences – Seminars

"AI in chemistry and beyond: ML for modeling molecular interactions: DiffDock as example for docking prediction" seminar by Hannes Stärk

18-04-202318-04-2023

With: Hannes Stärk is a PhD student at MIT in the CS and AI Laboratory (CSAIL) co-advised by Tommi Jaakkola and Regina Barzilay.
Online: https://epfl.zoom.us/j/68447908297?pwd=OU5JUGJUSUhZc0ZNYjQ2WENvYlNRdz09#success
Category: Conferences – Seminars

Computational materials design with machine learning and atomistic simulations

04-04-202304-04-2023

With: Rafael Gomez-Bombarelli is the Jeffrey Cheah Career Development Professor in MIT’s Department of Materials Science and Engineering. His work aims to fuse machine learning and atomistic simulations for designing materials and their transformations. Through collaborations at MIT and beyond, Rafael’s group develops new practical materials such as catalysts, therapeutic peptides, organic electronics or electrolytes for batteries. Rafael joined MIT in 2018 after earning BS, MS, and PhD (2011) degrees in chemistry from Universidad de Salamanca (Spain) and carrying postdoctoral work at Heriot-Watt, Harvard and at Kyulux North America. His machine learning work has received faculty awards from the Dreyfus Foundation and Google. Rafael was a co-founder of Calculario, a Harvard spinout company, and served as Chief Learning Officer of ZebiAI, a drug discovery startup.
Online: https://epfl.zoom.us/j/68447908297?pwd=OU5JUGJUSUhZc0ZNYjQ2WENvYlNRdz09
Category: Conferences – Seminars

Advanced Machine Learning Methods to Accelerate Materials Discovery

21-03-202321-03-2023

With: Santiago Miret is an AI Researcher at Intel Labs where he focuses on applying AI for scientific problems with an emphasis on materials discovery and materials understanding. Through this effort, Santiago manages a wide range of academic collaborations focused on applying AI for scientific application. Among these collaboration, there have been notable engagements with the Matter Lab led by Alán Aspuru-Guzik at the University of Toronto and various AI laboratories at MILA in Montreal that have led to cross-institutional publications at various machine learning venues. Santiago was a primary organizer of the 1st AI for Accelerated Materials Discovery (AI4Mat) workshop at NeurIPS 2022, which brought together domain experts from various fields of materials science and AI to exchange research work and ideas in an interdisciplinary forum. Prior to working at Intel Labs, Santiago obtained his PhD in Materials Science and Engineering from the University of California, Berkeley.
Place and room: CH G1 495
Category: Conferences – Seminars

"AI in chemistry and beyond" seminar "AI4Science at Microsoft Research" by Rianne van den Berg

20-12-202220-12-2022

With: Rianne van den Berg is a Principal Researcher at MSR. 
Online: https://epfl.zoom.us/j/68447908297?pwd=OU5JUGJUSUhZc0ZNYjQ2WENvYlNRdz09
Category: Conferences – Seminars

"AI in chemistry and beyond: Highlights in the field" seminar "Deep learning for photochemical reaction discovery and targeted molecular design" by Julia Westermayr

06-12-202206-12-2022

With: Julia Westermayr is an Assistant Professor at the University of Leipzig.
Online: https://epfl.zoom.us/j/68447908297?pwd=OU5JUGJUSUhZc0ZNYjQ2WENvYlNRdz09
Category: Conferences – Seminars

"Machine learning in chemistry and beyond" (ChE-651) seminar by Geemi Wellawatte: "Model Agnostic Counterfactual Explanations for Molecular Property Predictions"

22-11-202222-11-2022

With: Geemi Wellawatte is a PhD student at the University of Rochester, NY, with Prof. Andrew White. She completed her BSc. in Computational Chemistry in 2017 at the University of Colombo Sri Lanka. Her research work focuses on developing computational models to solve chemistry problems, particularly in coarse-grained modeling, deep learning in chemistry, and explainable AI.
Online: https://epfl.zoom.us/j/68447908297?pwd=OU5JUGJUSUhZc0ZNYjQ2WENvYlNRdz09
Category: Conferences – Seminars

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