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...
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...
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...
