Unsupervised Classification of Single-Molecule Data with Autoencoders and Transfer Learning

Datasets from single-molecule experiments often reflect a large variety of molecular behaviour. The exploration of such datasets can be challenging, especially if knowledge about the data is limited and a priori assumptions about expected data characteristics are to be avoided. Indeed, searching for pre-defined signal characteristics is sometimes useful, but it can also lead to information loss and the introduction of expectation bias. Here, we demonstrate how Transfer Learning-enhanced dimensionality reduction can be employed to identify and quantify hidden features in single-molecule charge transport data, in an unsupervised manner. Taking advantage of open-access neural networks trained on millions of seemingly unrelated image data, our results also show how Deep Learning methodologies can readily be employed, even if the amount of problem-specific, 'own' data is limited.Comment: 23 pages in total, incl. supporting information; 8 figure

Towards Structural Reconstruction from X-Ray Spectra

We report a statistical analysis of Ge K-edge X-ray emission spectra simulated for amorphous GeO$_2$ at elevated pressures. We find that employing machine learning approaches we can reliably predict the statistical moments of the K$\beta''$ and K$\beta_2$ peaks in the spectrum from the Coulomb matrix descriptor with a training set of $\sim 10^4$ samples. Spectral-significance-guided dimensionality reduction techniques allow us to construct an approximate inverse mapping from spectral moments to pseudo-Coulomb matrices. When applying this to the moments of the ensemble-mean spectrum, we obtain distances from the active site that match closely to those of the ensemble mean and which moreover reproduce the pressure-induced coordination change in amorphous GeO$_2$. With this approach utilizing emulator-based component analysis, we are able to filter out the artificially complete structural information available from simulated snapshots, and quantitatively analyse structural changes that can be inferred from the changes in the K$\beta$ emission spectrum alone

Detailed analysis of single molecular junctions for novel computing architectures

Molecular electronics, as a concept to embed molecular compounds into electrical circuits, can be traced back to 1950s. Since that time, a lot of investigations were done, including design and development of molecular-based analogs of electronic compounds such as diodes, transistors etc. A long-term vision of the molecular-scale electronics is the development of unconventional computing scheme based on the properties of individual molecules. Turing-style computers is expected to reach the limit, since further scaling down the computing units has a fundamental constrains in the dimensions. In addition, there are still a lot of computational problems which require exponential amount of resources and computing power. Thereby, the development and implementation of new, unconventional computing paradigms is required. One of the most advanced concepts of unconventional computing is brain-inspired approach. Indeed, human brain has unique computing performance with extremely low power consumption. The hypothetical modern computer to simulate human brain behavior requires gigawatts of power, while the brain itself consumes around 20 W. Therefore even at the beginning of computing era in 1950s von Neumann was looking at the brain for the future developments. All the approaches to mimic brain behavior in the conventional devices get the name of neuromorphic engineering. There are two main approaches in this field: first, to simulate the neuron and its synaptic behavior with possible scaling to the network level, and second, to achieve computing from the network of identical objects. This thesis covers a wide range of experimental investigations in the field of molecular electronics from the level of individual molecular junctions to hybrid devices combining self-assembled molecular networks and graphene

Neural networks in interpretation of electronic core-level spectra

We explore the applicability of artificial intelligence for molecular structure - core-level spectrum interpretation. We focus on the electronic Hamiltonian using the H$_2$O molecule in the classical-nuclei approximation as our test system. For a systematic view we studied both predicting structures from spectra and, vice versa, spectra from structures, using polynomial approaches and neural networks. We find predicting spectra easier than predicting structures, where a tighter grid of the spectrum improves prediction. However, the accuracy of the structure prediction worsens when moving outwards from the center of mass of the training set in the structural parameter space

Emulator-based decomposition for structural sensitivity of core-level spectra

We explore the sensitivity of several core-level spectroscopic methods to the underlying atomistic structure by using the water molecule as our test system. We first define a metric that measures the magnitude of spectral change as a function of the structure, which allows for identifying structural regions with high spectral sensitivity. We then apply machine-learning-emulator-based decomposition of the structural parameter space for maximal explained spectral variance, first on overall spectral profile and then on chosen integrated regions of interest therein. The presented method recovers more spectral variance than partial least-squares fitting and the observed behaviour is well in line with the aforementioned metric for spectral sensitivity. The analysis method is able to independently identify spectroscopically dominant degrees of freedom, and to quantify their effect and significance.</p

Machine learning in interpretation of electronic core-level spectra

Electronic transitions involving core-level orbitals offer a localized, atomic-site and element specific peek window into statistical systems such as molecular liquids. Although formally understood, the complex relation between structure and spectrum -- and the effect of statistical averaging of highly differing spectra of individual structures -- render the analysis of an ensemble-averaged core-level spectrum complicated. We explore the applicability of machine learning for molecular structure -- core-level spectrum interpretation. We focus on the electronic Hamiltonian using the \ce{H2O} molecule in the classical-nuclei approximation as our test system. For a systematic view we studied both predicting structures from spectra and, vice versa, spectra from structures, using polynomial approaches and neural networks. We find predicting spectra easier than predicting structures, where a tighter grid (even unphysical) of the spectrum improves prediction, possibly inviting for over-interpretation of the model. The accuracy of the structure prediction worsens when moving outwards from the center of mass of the training set in the structural parameter space, which can not be overcome by model selection based on generalizability.</p