Symposium Schedule (Friday, November 12)

Opening 8:30-9:00am
Time
Speaker
Title
9:00-9:30am
Olexandr Isayev
Carnegie Mellon University
Accelerating design of organic materials with
machine learning and AI
9:30-10:00am
Bin Jiang

University of Science and Technology of China

Physically Inspired Neural Network Models:
Symmetry and Completeness
10:00-10:30am
Oleg Prezhdo

University of Southern California

Machine Learning Nonadiabatic Molecular
Dynamics
Break & Discussions 10:30-11:00am
11:00-11:30am
Linfeng Zhang
Machine learning assisted molecular modeling
in the cloud-native era
11:30-12:00pm

Tong Zhu

New York University Shanghai

Automated Construction of Neural Network
Potential Energy Surface: The Enhanced Self-
Organizing Incremental Neural Network Deep
Potential Method
12:00-12:30pm
Zhipan Liu

Fudan University

Machine Learning for Catalysis: Atomic
Simulation and Activity Prediction
Break & Discussions 12:30-2:30pm
2:30-3:00pm
Zhenggang Lan

South China Normal University

Nonadiabatic Dynamics and Machine Learning
3:00-3:30pm
Michele Ceriotti

École Polytechnique Fédérale de Lausanne

Machine learning for chemistry, beyond
Potentials
Break & Discussions 3:30-4:00pm
4:00-4:30pm
Markus Reiher
ETH Zurich
Reflections on the synergy of machine learning
and first-principles modeling
4:30-5:00pm
Johannes Kästner

University of Stuttgart

Gaussian-Moment Neural Networks Provide
Transferable and Uniformly Accurate
Interatomic Potentials
Break & Discussions 5:00-5:30pm
5:30-6:00pm
Alexandre Tkatchenko

University of Luxembourg

On Electrons and Machine Learning Force
Fields
6:00-6:30pm
Ove Christiansen

Aarhus Universitet

Adaptive methods and gaussian processes for
molecular potential energy surfaces and
accurate anharmonic energies
Break & Discussions 6:30-8:00pm
Poster Session 1 8:00-11:00pm
Symposium Schedule (Saturday, November 13)
Poster Session 2 6:30-8:30am
Break & Discussions 8:30-9:00am
Time
Speaker
Title
9:00-9:30am
Konstantinos Vogiatzis

University of Tennessee

Data-driven Acceleration of Quantum Chemical
Methods
9:30-10:00am
Marivi Fernández-Serra

Stony Brook University

Development of new and highly accurate
density functionals with machine learning
10:00-10:30am
Fang Liu

Emory University

Reducing Uncertainty in Quantum Chemistry
Discovery with Machine Learning
Break & Discussions 10:30-11:00am
11:00-11:30am

Xin Xu

Fudan University

Computation-Assisted Structural Assignment of
Natural Products: The SVM-M Model Based on
the 13C NMR Chemical Shifts
11:30-12:00pm
Guanhua Chen

The University of Hong Kong

Machine Learning and Accuracy of Density-
Functional Theory
12:00-12:30pm
Chao-Ping Hsu

Academia Sinica

Machine learned dynamics for charge transfer
Coupling
Break & Discussions 12:30-2:30pm
2:30-3:00pm
Sergei Manzhos

 Tokyo Institute of Technology

Insight with a black box method beyond
automatic relevance determination with the help
of high-dimensional model representation
3:00-3:30pm
Jun Jiang

University of Science and Technology of China

Machine Learning in Molecular Spectroscopy
Study
3:30-4:00pm
Roland Lindh

Uppsala University

Machine Learning Supported Molecular
Geometry Optimizations: the Restricted
Variance Optimization Procedure
Break & Discussions 4:00-4:30pm
4:30-5:00pm
Mario Barbatti 
Aix-Marseille University
Nonadiabatic dynamics in the long timescale:
the next challenge in computational
photochemistry
5:00-5:30pm
Nongnuch Artrith
Utrecht University
Modelling of Complex Energy Materials with
Machine Learning
5:30-6:00pm
Bingqing Cheng
University of Cambridge
Predicting material properties with the help of
machine learning
Symposium Schedule (Sunday, November 14)
Time
Speaker
Title
9:00-9:30am
Yingkai Zhang
New York University
Exploring Chemical Space with 3D Geometry
and Deep Learning
9:30-10:00am
Heather Kulik
Massachusetts Institute of Technology
Audacity of huge: machine learning for the
discovery of transition metal catalysts and
materials
10:00-10:30am
Ryosuke Akashi
The University of Tokyo
Developing the DFT exchange-correlation
potentials using the neural network
Break & Discussions 10:30-11:00am
11:00-11:30am
Manabu Sugimoto
Kumamoto University
Electronic-Structure Informatics for Discovery
of Functional Molecules
11:30-12:00pm
Jinlan Wang
Southeast University
Rapid discovery of functional materials via
machine learning
12:00-12:30pm
Pavlo O. Dral
Xiamen University
Quantum Chemistry Assisted by Machine
Learning
Closing 12:30-1:00pm