Catalysts are everywhere. They help accelerate and enable essential industrial processes towards success by being selective towards certain products and producing higher yields of said products at an agile manner. Nowadays, the relevance of catalysts is not only in industrial production, but also in the development of novel structures which are able to provide reaction pathways of relevant processes, such as the production of green hydrogen, or conversion of $CO_2$ into relevant products. For this, computational simulations are used as the first step in screening potential candidates that are able to provide higher yields or selectivity in heterogeneous catalysis reactions. However, these simulations are mainly done through the use of DFT, which requires a high computational cost and convergence time. Machine Learned Interatomic Potentials (MLIPs) have risen as complements for DFT simulations via training and learning from DFT energies and forces data to provide a platform for molecular dynamics simulations used to study the movement and behavior of atoms and molecules over time. In this research project, an active learning loop is engineered with the purpose of automating the workflow of training, using, and fine-tuning a MLIP (in this case, MACE) for its further use in catalysis energetics calculations.

We demonstrate the ability of computer vision methods and unsupervised machine learning models to analyze atom-scale images acquired by scanning tunneling microscopy (STM). The imaged system of interest consists of Fe and Ti atoms adsorbed on bilayer MgO islands grown on an Ag substrate, a system studied for its quantum magnetic properties. The purpose of this analysis is to detect and classify each atom site, which includes Ti atoms, Fe atoms, defects on the surface of the sample, and unknown contaminants. Our method identifies Fe and Ti atoms from the rest in an effort to aid laboratory researchers during single-atom probing tasks. Each acquired STM image is processed with computer vision libraries to locate each atom site and to build a dataset of atom crops that highlight their features. Atom site classification and characterization tasks are performed using Gaussian mixture models (GMM), principal component analyses (PCA), and density-based clustering to classify each atom site by species and quantify structural differences and similarities between them. Data and code are openly available at https://github.com/AudreySabri/unsupervised-ml-for-microscopy.git

Transparent Natural Fiber Composite Materials may pose a more environmentally friendly alternative regarding energy efficiency and low CO2 emissions in contrast to traditional composites, exhibiting transparency for future smart building or photovoltaic applications. The present study involved a research effort for the development of a lightweight flax fiber-reinforced composite material, which will exemplify high optical transparency. A detailed focus is conducted, on minimizing light absorption phenomena with delignification methods of flax fibers, eliminating light scattering via a Refractive Index Matching procedure between the reinforcement and the polymer matrix and via fiber modifications which tackle several issues such as enhancing the imperfect interphase between the hydrophilic reinforcement and the hydrophobic matrix, or providing finer polymer impregnation of the fibers. Acetylation, nitric acid, or TEMPO-oxidation treatments that modify the fiber’s nature and morphology are implemented to tackle these obstacles. Transparent Flax Fiber Reinforced Composites of 35-45 % fiber volume fraction were fabricated with wet-lamination and hot press forming techniques, indicating a maximum total light transmittance of approximately 70% (at low thickness) and 56-58% (at high thickness), depending on the fiber modification implemented. Regarding mechanical properties, a maximum specific flexural strength of 153 MPa/(g/(cm3)) and a specific flexural stiffness of 11.95 GPa/(g/(cm3)) was achieved for these materials. The transparent FFCMs showed comparable values of total light transmission correlated to reference materials like GFRPs or other natural fiber-reinforced composite materials, with similar volume fractions, signifying the success of achieving high optical transparency.