Overview of Computational Methods and Machine Learning: Panel Discussion
Online Presentations | 14 Jun 2019 | Contributor(s): Brett Matthew Savoie, Pradeep Kumar Gurunathan, Peilin Liao, Xiulin Ruan, Guang Lin
The individual Panel Talks which accompanies this discussion can be found here.Why do we need experiments?Are your methods “descriptive” or “predictive”?Do you work with any other theory/simulation groups?On the 5 year timescale: is machine-learning hype or a real...
Overview of Computational Methods and Machine Learning: Panel Talks
The Panel Discussion which follows these individual presentations can be found here.Individucal Presentations:Theory and Machine Learning in the Chemical Sciences, Brett Matthew Savoie;Divide and Conquer with QM/MM Methods, Pradeep Kumar Gurunathan;Computational Chemistry/Materials, Peilin...
Applying Machine Learning to Computational Chemistry: Can We Predict Molecular Properties Faster without Compromising Accuracy?
Presentation Materials | 14 Aug 2017 | Contributor(s): Hanjing Xu, Pradeep Kumar Gurunathan
Non-covalent interactions are crucial in analyzing protein folding and structure, function of DNA and RNA, structures of molecular crystals and aggregates, and many other processes in the fields of biology and chemistry. However, it is time and resource consuming to calculate such interactions...
The Effective Fragment Potential Method Calculation Tool
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Tools | 07 Jun 2017 | Contributor(s): Lyudmila V. Slipchenko, Pradeep Kumar Gurunathan, Hanjing Xu
LIBEFP facilitates extension of unique electronic structure methodologies designed for accurate simulations in the gas phase to condensed phases via QM/EFP. This tool provides a easy-to-use GUI for input generation and output visualization.
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