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Improving sample and feature selection with principal covariates regression

Michele Ceriotti, Edgar Albert Engel, Benjamin Aaron Helfrecht, Sergei Kliavinek

Selecting the most relevant features and samples out of a large set of candidates is a task that occurs very often in the context of automated data analysis, where it improves the computational performance and often the transferability of a model. Here we focus on two popular subselection schemes applied to this end: CUR decomposition, derived from a low-rank approximation of the feature matrix, and farthest point sampling (FPS), which relies on the iterative identification of the most diverse samples and discriminating features. We modify these unsupervised approaches, incorporating a supervised component following the same spirit as the principal covariates (PCov) regression method. We show how this results in selections that perform better in supervised tasks, demonstrating with models of increasing complexity, from ridge regression to kernel ridge regression and finally feed-forward neural networks. We also present adjustments to minimise the impact of any subselection when performing unsupervised tasks. We demonstrate the significant improvements associated with PCov-CUR and PCov-FPS selections for applications to chemistry and materials science, typically reducing by a factor of two the number of features and samples required to achieve a given level of regression accuracy.

IOP PUBLISHING LTD2021

Structure-Property Relationships in Complex Materials by Combining Supervised and Unsupervised Machine Learning

Benjamin Aaron Helfrecht

The work presented in this thesis combines supervised and unsupervised machine learning to examine structure-property relationships in databases of materials. While either supervised learning or unsupervised learning alone can be a powerful tool for assessing materials and their properties, the focus here is to demonstrate the utility of combining both supervised and unsupervised learning to gain actionable insight about complex materials, whether through a unified approach or in sequential workflows. To this end, the application of combined supervised-unsupervised learning schemes will be presented for two examples, each focusing on a different class of materials. The first is an analysis of hydrogen bonding and backbone dihedral angle motifs in protein crystal structures from the Protein Data Bank, and demonstrates that data-driven definitions of structural motifs obtained through unsupervised learning can be more detailed and precise than conventional heuristics and can also be validated through supervised learning. We found that the motifs identified using a Gaussian mixture model largely agreed with more "traditional" definitions, but proved to be more precise for edge cases. Furthermore, we found that outside the more well-defined secondary structure motifs such as helices and sheets, several conventional secondary structure definitions did not coincide with the observed data-driven structural motifs, suggesting that the heuristic definitions corresponding to less-ordered secondary structure motifs do not strongly reflect the distribution of structural patterns in protein crystals in the Protein Data Bank. At the same time, there also exist clear, though as-yet unnamed motifs in the configuration space of proteins. The second example centers around the exploration of structure-property relationships in all-silica zeolites, ultimately aiming to address the challenge of finding new zeolite frameworks that might be experimentally synthesizable. We begin by constructing a map of atom-centered environments in a database of hypothetical zeolite frameworks based on principal component analysis, where we validate our choice of "cardinal directions" by demonstrating that they correlate with the predicted atomic contributions to the molar volume and energy of the frameworks while emphasizing the diversity of the structural space. We extend this exploration of the structural space to a supervised classification exercise to distinguish hypothetical zeolite frameworks from those that have been experimentally synthesized, where frameworks that share several structural characteristics with synthesized frameworks are likely to be misclassified, and therefore may serve as promising synthesis candidates. To further filter the synthesis candidates based on their thermodynamic stability, we apply a convex hull construction based on a measure of classification prediction strength and the lattice energies of the zeolite frameworks. Through this combined supervised-unsupervised learning workflow we are able to propose a collection of hypothetical zeolites as likely candidates for experimental synthesis. These two examples show that by combining supervised and unsupervised learning, it is possible to gain deeper insight into the structure-property relationships in a wide array of materials than through either set of methods in isolation.

EPFL2021

Opportunities and challenges of 2D materials in back-end-of-line interconnect scaling

Benjamin Aaron Helfrecht, Alejandro Strachan

As the challenges in continued scaling of the integrated circuit technology escalate every generation, there is an urgent need to find viable solutions for both the front-end-of-line (transistors) and the back-end-of-line (interconnects). For the interconnect technology, it is crucial to replace the conventional barrier and liner with much thinner alternatives so that the current driving capability of the interconnects can be maintained or even improved. Due to the inherent atomically thin body thicknesses, 2D materials have recently been proposed and explored as Cu diffusion barrier alternatives. In this Perspective article, a variety of 2D materials that have been studied, ranging from graphene, h-BN, MoS2, WSe2 to TaS2, will be reviewed. Their potentials will be evaluated based on several criteria, including fundamental material properties as well as the feasibility for technology integration. Using TaS2 as an example, we demonstrate a large set of promising properties and point out that there remain challenges in the integration aspects with a few possible solutions waiting for validation. Applications of 2D materials for other functions in Cu interconnects and for different metal types will also be introduced, including electromigration, cobalt interconnects, and radio-frequency transmission lines.

AMER INST PHYSICS2020