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量子化学机器学习国际研讨会

发布日期:2021年11月09日   浏览次数:

量子化学机器学习国际研讨会(The International Symposium on Machine Learning in Quantum Chemistry, SMLQC-2021)将于20211112日至14日在厦门大学召开。本次会议已从全球邀请众多理论和计算化学家,他们长期致力于通过机器学习加速量子化学模拟等研究工作。会议的主题包括但不限于:发展基于机器学习的量子化学技术,发展新的可用于势能面和分子动力学的机器学习新方法,以及机器学习在各种物理化学过程中的应用。

本次会议由固体表面物理化学国家重点实验室(厦门大学)、福建省理论与计算化学重点实验室和厦门大学化学化工学院共同举办。会议将采用线上与线下相结合的方式,线下同步转播会场设在卢嘉锡楼202会议室。

会议网站: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

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