{"id":21789,"date":"2018-02-14T13:59:27","date_gmt":"2018-02-14T12:59:27","guid":{"rendered":"http:\/\/www.h-its.org\/?p=21789"},"modified":"2019-09-24T11:40:19","modified_gmt":"2019-09-24T09:40:19","slug":"machine-learning2018","status":"publish","type":"post","link":"https:\/\/www.h-its.org\/de\/2018\/02\/14\/machine-learning2018\/","title":{"rendered":"SS 2018: Machine learning for molecular world"},"content":{"rendered":"\n

by Frauke Gr\u00e4ter and Rebecca Wade<\/p>\n\n\n\n

Over the last decade machine learning revolutionized computer vision and language processing.
\nThis progress was fueled by the development of new methods as well as the availability of ever powerful hardware.
\nNow a new wave of research adapts these advances to increase our understanding of the molecular world.
\nIn this seminar we will explore the recent literature on these efforts ranging from protein dynamics and drug design to materials science.
\nThe seminar is targeted towards advanced Bachelor, Master and interested PhD students. As the seminar’s topics cover a broad range, we are happy to welcome students from all scientific backgrounds with a strong interest in interdisciplinary work, preferably with some background in machine learning, molecular simulations, or quantum chemistry.<\/p>\n\n\n\n

Time:<\/strong> Tue, 3-4.30pm,
Place:<\/strong> Mathematikon, INF 205, SR11<\/p>\n\n\n\n

In our Vorbesprechung on Tue, April 17, 2018, 3-4.30pm, we will distribute topics and dates. The first ~2 seminars will be used to introduce some basics of machine learning and relevant molecular simulation techniques.<\/p>\n\n\n\n

Registration:<\/h2>\n\n\n\n

As we only can accept a limited number of participants, please send us an email to frauke.graeter@h-its.org<\/a> before the course starts, if you want to make sure to be accepted.<\/p>\n\n\n\n

Credit points:<\/h2>\n\n\n\n
– master physics: 6 CP<\/div>\n\n\n\n

– master molecular biotechnology and other masters in biology: 4 CP<\/p>\n\n\n\n

Recommended reading:<\/h2>\n\n\n\n

Andrew R. Leach, \u201cMolecular Modeling: Principles and Applications\u201d
\nGareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani, „An Introduction to Statistical Learning“ http:\/\/www-bcf.usc.edu\/~gareth\/ISL\/
\nIan Goodfellow and Yoshua Bengio and Aaron Courville, \u201cDeep Learning Book\u201d http:\/\/www.deeplearningbook.org\/<\/p>\n\n\n\n

Preliminary list of references:<\/h2>\n\n\n\n

Machine Learning Force Field Parameters from Ab Initio Data.
\nLi Y, Li H, Pickard FC 4th, Narayanan B, Sen FG, Chan MKY, Sankaranarayanan SKRS, Brooks BR, Roux B.
\nJ Chem Theory Comput. 2017 Sep 12;13(9):4492-4503. doi: 10.1021\/acs.jctc.7b00521.<\/p>\n\n\n\n

Neural network based prediction of conformational free energies – a new route towards coarse-grained simulation models
\nT. Lemke and C. Peter,
\nJ. Chem. Theory Comput., Just Accepted Manuscript, 2017.<\/p>\n\n\n\n

VAMPnets: Deep learning of molecular kinetics.
\nNat. Comm., 9 . p. 5.
\nMardt, A. and Pasquali, L. and Wu, H. and No\u00e9, F. 2018<\/p>\n\n\n\n

The face of crystals: insightful classification using deep learning
\nA Ziletti, D Kumar, M Scheffler, LM Ghiringhelli
\narXiv preprint arXiv:1709.02298 2017<\/p>\n\n\n\n

Machine learning of accurate energy-conserving molecular force fields
\nS Chmiela, A Tkatchenko, HE Sauceda, I Poltavsky, KT Sch\u00fctt, KR M\u00fcller
\nScience advances 3 (5), e1603015 2017<\/p>\n\n\n\n

Quantum-chemical insights from deep tensor neural networks
\nKT Sch\u00fctt, F Arbabzadah, S Chmiela, KR M\u00fcller, A Tkatchenko
\nNature communications 8, 13890 2017<\/p>\n\n\n\n

Machine Learning Energies of 2 Million Elpasolite (A B C 2 D 6) Crystals
\nFA Faber, A Lindmaa, OA Von Lilienfeld, R Armiento
\nPhysical review letters 117 (13), 135502, 2016<\/p>\n\n\n\n

Big data meets quantum chemistry approximations: the \u0394-machine learning approach
\nR Ramakrishnan, PO Dral, M Rupp, OA von Lilienfeld
\nJournal of chemical theory and computation 11 (5), 2087-2096, 2015<\/p>\n\n\n\n

Molecular Dynamics Simulations with Quantum Mechanics\/Molecular Mechanics and Adaptive Neural Networks
\nLin Shen and Weitao Yang
\nJ. Chem. Theory Comput., Article ASAP, Feb. 13, 2018<\/p>\n","protected":false},"excerpt":{"rendered":"

by Frauke Gr\u00e4ter and Rebecca Wade Over the last decade machine learning revolutionized computer vision and language processing. 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