Posted：2021-11-09 Visits：
The International Symposium on Machine Learning in Quantum Chemistry (SMLQC) will be held in Nov. 12–14, 2021. It will gather the theoretical and computational chemists, who use machine learning to accelerate and improve quantum chemical simulations. The topics of the conference include, but not be limited to, the development of new quantum chemical techniques improved by machine learning, development of new machine learning methods for describing potential energy surfaces and running molecular dynamics, and application of machine learning for description of various physicochemical processes.
The symposium is organized and supported by the State Key Laboratory of Physical Chemistry of Solid Surfaces (PCOSS), Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry (FTCC), and College of Chemistry and Chemical Engineering at Xiamen University, China
For more details, please visit the website: http://mlatom.com/smlqc-2021/
Preliminary Program
Friday, November 12
Opening Ceremony 8:30-9:00 am
9:00-9:30 am
Olexandr Isayev
Accelerating design of organic materials with machine learning and AI
9:30-10:00 am
Bin Jiang
Physically inspired neural network models: symmetry and completeness
10:00-10:30 am
Oleg Prezhdo
Machine learning nonadiabatic molecular dynamics
Break & Discussions 10:30-11:00 am
11:00-11:30 am
Linfeng Zhang
Machine learning assisted molecular modeling in the cloud-native era
11:30-12:00 pm
Jing Ma
Applications of ML models to predict electronic structure properties
12:00-12:30 pm
Zhipan Liu
Machine learning for catalysis: atomic simulation and activity prediction
Break & Discussions 12:30-2:30 pm
2:30-3:00 pm
Zhenggang Lan
Nonadiabatic dynamics and machine learning
3:00-3:30 pm
Michele Ceriotti
Machine learning for chemistry, beyond potentials
Break & Discussions 3:30-4:00 pm
4:00-4:30 pm
Markus Reiher
Reflections on the synergy of machine learning and first-principles modeling
4:30-5:00 pm
Johannes Kästner
Gaussian-moment neural networks provide transferable and uniformly accurate interatomic potentials
Break & Discussions 5:00-5:30 pm
5:30-6:00 pm
Alexandre Tkatchenko
On electrons and machine learning force fields
6:00-6:30 pm
Ove Christiansen
Adaptive methods and gaussian processes for molecular potential energy surfaces and accurate anharmonic energies
Break & Discussions 6:30-8:00 pm
Poster Session I 8:00-11:00 pm
Saturday, November 13
Poster Session II 6:30-8:30 am
Break & Discussions 8:30-9:00 am
Konstantinos Vogiatzis
Data-driven acceleration of quantum chemical methods
Marivi
Fernández-Serra
Development of new and highly accurate density functionals with machine learning
Fang Liu
Reducing uncertainty in quantum chemistry discovery with machine learning
Xin Xu
Computation-assisted structural assignment of natural products: the SVM-M model based on the 13C NMR chemical shifts
Guanhua Chen
Machine learning and accuracy of density-functional theory
Chao-Ping Hsu
Machine learned dynamics for charge transfer coupling
Sergei Manzhos
Insight with a black box method beyond automatic relevance determination with the help of high-dimensional model representation
Jun Jiang
Machine learning in molecular spectroscopy study
3:30-4:00pm
Roland Lindh
Machine learning supported molecular geometry optimizations: the restricted variance optimization procedure
Break & Discussions 4:00-4:30pm
Mario Barbatti
Nonadiabatic dynamics in the long timescale: the next challenge in computational photochemistry
5:00-5:30 pm
Nongnuch Artrith
Modelling of complex energy materials with machine learning
Bingqing Cheng
Predicting material properties with the help of machine learning
Sunday, November 14
Yingkai Zhang
Exploring chemical space with 3D geometry and deep learning
Heather Kulik
Audacity of huge: machine learning for the discovery of transition metal catalysts and materials
Ryosuke Akashi
Developing the DFT exchange-correlation potentials using the neural network
Manabu Sugimoto
Electronic-structure informatics for discovery of functional molecules
Jinlan Wang
Rapid discovery of functional materials via machine learning
Pavlo O. Dral
Quantum chemistry assisted by machine learning
Closing 12:30-1:00 pm
