Machine Learning in Natural Sciences: from Quantum Physics to Nanoscience and Structural Biology

Session

Molecular Structure Analysis

MSA

21 Sept 2022, 09:00

Seminarraum 1-3 (CFEL (Building 99))

45. AI Usage in Structural Biology

Dr Andrea Thorn (Universität Hamburg)

21/09/2022, 09:00

Invited Talk

10. Chiral Structure Classification by a Convolutional Neural Network

Peer Kasten (Institut für Angewandte Physik, Technische Universität Braunschweig)

21/09/2022, 09:45

Molecular Structure Analysis

Contributed Talk (15 min)

Scanning tunneling microscopy (STM) is an important tool to image surfaces at atomic scale, that allows to acquire significant amounts of data in comparably short time. Therefore, for example to examine large ensembles of molecules in STM images can be a difficult and time-consuming task. We present a method to recognize chirality within experimentally observed self-assembled molecular...

21. Learning Conductance: Gaussian Process Regression for Molecular Electronics

Michael Deffner (Universität Hamburg)

21/09/2022, 10:00

Molecular Structure Analysis

Contributed Talk (15 min)

Experimental studies of charge transport through single molecules often rely on break junction setups, where molecular junctions are repeatedly formed and broken while measuring the conductance, leading to a statistical distribution of conductance values.
Modeling this experimental situation and the resulting conductance histograms is challenging for theoretical methods, as computations need...

46. OpenFold: Democratizing access to modeling biomolecules and their interactions

Prof. Mohammed AlQuraishi (Columbia University)

21/09/2022, 11:00

Invited Talk

28. Quantum flows neural network for variational solutions of the Schrödinger equation

Andrey Yachmenev (Center for Free-Electron Laser Science, DESY)

21/09/2022, 11:45

Molecular Structure Analysis

Contributed Talk (30min)

The computational technology of highly expressive parametric neural-network-functions has allowed machine learning to make a major foray into disciplines of natural sciences. The neural network functions may be effectively “fitted” to a loss function, given in the form of a variational principle or virial theorem, to provide solutions to quantum mechanical problems. Recently, a few deep neural...