Advancing nanomaterials design using novel machine learning methods

Abstract

The rise of machine learning (ML) has revolutionized the usage of data. Researchers continue to develop new ways to use ML and find new targets to apply ML on. One of these areas of application is found in nanoscience. Nanoscience is a constantly expanding field with applications in almost every part of life, such as medicine, materials design, and consumer products. The experimental research of nanoscience is expensive, augmented by computational research. Computational research is, however, also resource-intensive and time-consuming due to the complexity of the simulation models. Machine learning promises to alleviate that strain. This work and the articles presented focus on a family of distance-based machine learning algorithms, Minimal Learning Machine (MLM), and Extreme Minimal Learning Machine (EMLM), in the context of computational nanoscience. Specifically in the context of monolayer protected nanoclusters (MPC). The distance-based ML methods are studied as surrogates in feature selection and knowledge discovery. A set of benchmark, generated, and molecular dynamics-based datasets were used in the included articles. The performance of MLM was studied by using it as a surrogate, comparing it to other methods, and inspecting the effect of a solver on its function. EMLM was used as the ML model in feature selection and knowledge discovery. A set of scaling-focused benchmark datasets were developed based on the simulation data of Au₃₈(SCH₃)₂₄ MPC and a set of synthetic benchmark & development datasets were created to test the performance of a feature selection algorithm. A Mean Absolute Sensitivity (MAS) utilizing distance-based feature selection algorithm, Distance-based one-shot wrapper, was developed and then extended to Feature Importance Detector. An umbrella review was made to contextualize the one-shot wrapper to feature selection literature. The results prove the viability of distance-based ML methods in the context of computational nanoscience. Keywords: Machine Learning, Distance–Based Regression, Nanoscience, MLM, EMLM, Hybrid Nanoparticles, Feature Selection, Knowledge discovery