SMLQC stands for webSite and web-reSources for Machine Learning in Quantum Chemistry. SMLQC is a platform for the theoretical and computational chemists, who use machine learning to accelerate and improve quantum chemical simulations. The topics covered here 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. SMLQC hosts biannual international Symposia on Machine Learning in Quantum Chemistry (the first one, SMLQC-2021, in Xiamen, the second one, SMLQC-2023, in Uppsala) and biweekly talks and tutorials.
To contact us, write an email to smlqc@mlatom.com.
Contributors of the SMLQC content:
- Pavlo O. Dral, Xiamen University
Technical assistance:
- Shuang Zhang, Xiamen University