Accelerated Discovery of Single?Atom Catalysts for Nitrogen Fixation via Machine Learning

Toward Sustainable Nitrogen Fixation: Elucidating the Mechanism of Nitrogen Reduction by Molecular Catalysts

Machine-learning models for combinatorial catalyst discovery

A variety of machine learning algorithms, including hierarchical clustering, decision trees, k-nearest neighbours, support vector machines and bagging, were applied to construct models to predict the molecular weight of the polymers produced by a set of 96 homogeneous catalysts. The goal of the study was to develop models that could be used to screen large virtual libraries of catalysts in orde...

Human Discovery and Machine Learning

Submission to IJCINI This paper studies machine learning paradigms from the point of view of human cognition. Indeed, conceptions in both mahine learning and human learning evolved from a passive to an active conception of learning. Our objective is to provide an interaction protocol suited to both humans and machines, to enable assisting human discoveries by learning machines. We identify the ...

Machine Learning Strategy for Accelerated Design of Polymer Dielectrics.

The ability to efficiently design new and advanced dielectric polymers is hampered by the lack of sufficient, reliable data on wide polymer chemical spaces, and the difficulty of generating such data given time and computational/experimental constraints. Here, we address the issue of accelerating polymer dielectrics design by extracting learning models from data generated by accurate state-of-t...

Accelerated learning on the connection machine

The complexity of most machine learning techniques can be improved by transforming iterative components into their parallel equivalent. Although this parallelization has been considered in theory, few implementations have been performed on existing parallel machines. The parallel architecture of the Connection Machine provides a platform for the implementation and evaluation of parallel learnin...