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Program of the International CECAM Workshop

Thinking outside the box - beyond machine learning for quantum chemistry

Conference site: House of Science, Downtown

Monday, October 7^th 2019 (Radisson Blu Hotel Bremen)
18:00	–	21:00	Registration
Tuesday, October 8^th 2019 (House of Science Bremen, Downtown)
08:00	–	08:50	Registration
08:50	–	09:00	Opening and welcome, Thomas Frauenheim
Session:			Machine learning for Complex Quantum Systems
			*Chair: Sheng Meng*
09:00	–	09:40	Anatole von Lilienfeld, University of Basel, Switzerland Quantum machine learning
09:40	–	10:20	Tristan Bereau, Max Planck Institute for Polymer Research, Mainz, Germany Modeling intermolecular interactions with physics and ML
10:20	–	10:50	Coffee Break
10:50	–	11:30	Karsten Reuter, Munich University of Technology, Germany Knowledge-based approaches in catalysis and energy modelling
11:30	–	12:10	Hiromi Nakai, Waseda University, Shinjuku, Japan Semi-local machine-learned kinetic energy density functional with third-order gradients of electron density
12:10	–	12:15	Group Photo
12:15	–	13:50	Lunch Break (Restaurant Q1) and Coffee
13:50	–	14:30	Guanhua Chen, University of Hong Kong, China Deep learnt exchange-correlation potential
Session:			Machine Learning for Structure Prediction
			*Chair: Thomas A. Niehaus*
14:30	–	15:10	Bjørk Hammer, Aarhus University, Denmark Speeding up atomistic structure search with machine learning
15:10	–	15:50	Rickard Armiento, Lynköping University, Sweden Machine Learning for materials stability
15:50	–	16:20	Coffee Break
16:20	–	17:00	Stefano Leoni, University of Cardiff, UK ML in multiple timescale molecular dynamics simulations
17:00	–	17:40	Jacek Jakowski, Oak Ridge National Laboratory, Tennessee, USA Directed transformations of nanomaterials and beam-matter interactions

19:00	–	21:30	Welcome Reception (Bremen Town Hall)
Wednesday, October 9^th2019 (House of Science Bremen, Downtown)
Session:			Machine Learning for DFTB repulsive interactions (I)
			*Chair: Malte Schüler*
08:30	–	09:10	Maxime Van den Bossche, Sorbonne University, Paris, France Accelerating global optimization searches with an adaptive DFTB parametrization scheme
09:10	–	09:50	David J. Yaron, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA A DFTB layer for deep learning of electronic hamiltonians
09:50	–	10:30	Nir Goldman, Lawrence Livermore National Laboratory, California, USA Combining ML approaches with DFTB for simulations of reactive materials
10:30	–	11:00	Coffee Break
11:00	–	11:40	Benjamin Hourahine, University of Strathclyde, Glasgow, UK Learning around the DFTB model
11:40	-	12:20	Stefan Grimme, University of Bonn, Germany New tight-binding quantum chemistry methods
12:20	–	14:00	Lunch Break (Restaurant Q1) and Coffee
Session:			Machine Learning and MD
			*Chair: Adam McSloy*
14:00	–	14:40	Kipton Barros, Los Alamos National Laboratory, New Mexico, USA Advances in machine learned potentials for molecular dynamics simulation
14:40	–	15:20	Weitao Yang, Duke University Durham, North Carolina, USA Machine learning in simulations and force fields with quantum mechanics/molecular mechanics and in DFT
15:20	–	16:00	Stephan Irle, Oak Ridge National Laboratory, Tennessee, USA Neural network corrected DFTB/MD simulations of long-timescale self-assembly and transport processes
16:00	–	16:30	Coffee Break
16:30	–	17:10	Roland Mitric, University of Wuerzburg, Germany Simulation of light-induced nonadiabatic dynamics in molecular aggregates
17:10	–	17:50	Franco P. Bonafé, Max Planck Institute for the Structure and Dynamics of Matter, Hamburg, Germany Simulations of impulsive vibrational spectra using Ehrenfest real-time TDDFTB
19:00	–	22:30	Conference Dinner (Restaurant Juergenshof)
Thursday, October 10^th 2019 (House of Science Bremen, Downtown)
Session:			Machine Learning for Quantum Chemistry & Electronic Structure
			*Chair: Alessandro Pecchia*
08:30	–	09:10	Benjamin T. Nebgen, Los Alamos National Laboratory, New Mexico, USA Hückel theory resurrected: dynamic parameterization of effective hamiltonians using deep learning
09:10	–	10:50	Volker W. Blum, Duke University, Durham, North Carolina, USA The ELSI infrastructure
09:50	–	10:30	Ursula Röthlisberger, Swiss Federal Institute of Technology, Lausanne, Switzerland Computational Chemistry Meets Artificial Intelligence
10:30	–	11:00	Coffee Break
11:00	–	11:40	Julian Gebhardt, Fraunhofer Institute for Mechanics of Materials, Freiburg, Germany Big data approach for next level hybrid perovskite solar cells
11:40	–	12:20	Alexandre Tkatchenko, University of Luxembourg, Luxembourg Towards exact molecular dynamics simulations with quantum chemistry and machine learning
12:20	–	14:00	Lunch Break (Restaurant Q1) and Coffee
Session:			Machine Learning for DFTB repulsive interactions (II)
			*Chair: Cristopher Camacho*
14:00	–	14:40	Chiyung Yam, Beijing Computational Science Research Center, China Theoretical investigation of current-induced light emission in scanning tunneling microscopy molecular junctions
14:40	-	15:20	Qiang Cui, Boston University, Massachusetts, USA Improvement of DFTB model for condensed phase simulations
15:20	-	16:00	Jolla Kullgren, University of Uppsala, Sweden Physically constrained splines – a step towards transferable repulsive potentials for SCC-DFTB
17:20			Poster Mounting
17:30	–	21:00	Poster Session, Catering Buffet (House of Science)
Friday, October 11^th 2019 (House of Science Bremen, Downtown)
Session:			ML for electronic and spectroscopic properties
			*Chair: Balint Aradi*
08:30	–	09:10	Patrick Rinke, Aalto University, Helsinki, Finland ARTIST: artificial intelligence for spectroscopy
09:10	–	09:50	Olexandr Isayev, University of North Carolina, Chapel Hill, USA Accurate and transferable multitask prediction of chemical properties with an atoms-in-molecule neural network
09:50	–	10:30	Anders M. N. Niklasson, Los Alamos National Laboratory, New Mexico, USA Graph-based linear scaling electronic structure theory
10:30	–	11:00	Coffee Break
11:00	–	11:40	Sergei Tretiak, Los Alamos National Laboratory, New Mexico, USA Multiple cloning and polaritons in excited state non-adiabatic molecular dynamics
11:40	–	12:20	Gotthard Seifert, Technical University of Dresden, Germany Bridging scales in materials simulations - quantum versus classical simulations
12:20	–	12:25	*Closing words: Thomas Frauenheim*
12:25			Departure

Program of the International CECAM Workshop Thinking outside the box - beyond machine learning for quantum chemistry

Conference site: House of Science, Downtown

Program of the International CECAM Workshop

Thinking outside the box - beyond machine learning for quantum chemistry