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

9:00-9:30 am

Konstantinos Vogiatzis

Data-driven acceleration of quantum chemical   methods

9:30-10:00 am

Marivi

Fernández-Serra

Development of new and highly accurate density   functionals with machine learning

10:00-10:30 am

Fang Liu

Reducing uncertainty in quantum chemistry   discovery with machine learning

Break   & Discussions 10:30-11:00 am

11:00-11:30 am

Xin Xu

Computation-assisted structural assignment of   natural products: the SVM-M model based on the 13C NMR chemical shifts

11:30-12:00 pm

Guanhua Chen

Machine learning and accuracy of   density-functional theory

12:00-12:30 pm

Chao-Ping Hsu

Machine learned dynamics for charge transfer   coupling

Break   & Discussions 12:30-2:30 pm

2:30-3:00 pm

Sergei Manzhos

Insight with a black box method beyond automatic   relevance determination with the help of high-dimensional model representation

3:00-3:30 pm

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

4:30-5:00 pm

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

5:30-6:00 pm

Bingqing Cheng

Predicting material properties with the help of machine   learning

Sunday, November   14

9:00-9:30 am

Yingkai Zhang

Exploring chemical space with 3D geometry and   deep learning

9:30-10:00 am

Heather Kulik

Audacity of huge: machine learning for the   discovery of transition metal catalysts and materials

10:00-10:30 am

Ryosuke Akashi

Developing the DFT exchange-correlation   potentials using the neural network

Break   & Discussions 10:30-11:00 am

11:00-11:30 am

Manabu Sugimoto

Electronic-structure informatics for discovery of   functional molecules

11:30-12:00 pm

Jinlan Wang

Rapid discovery of functional materials via   machine learning

12:00-12:30 pm

Pavlo O. Dral

Quantum chemistry assisted by machine learning

Closing   12:30-1:00 pm