Abstract
The materials discovery process can be significantly expedited and simplified if we can learn effectively from available knowledge and data. In the present contribution, we show that efficient and accurate prediction of a diverse set of properties of material systems is possible by employing machine (or statistical) learning methods trained on quantum mechanical computations in combination with the notions of chemical similarity. Using a family of one-dimensional chain systems, we present a general formalism that allows us to discover decision rules that establish a mapping between easily accessible attributes of a system and its properties. It is shown that fingerprints based on either chemo-structural (compositional and configurational information) or the electronic charge density distribution can be used to make ultra-fast, yet accurate, property predictions. Harnessing such learning paradigms extends recent efforts to systematically explore and mine vast chemical spaces, and can significantly accelerate the discovery of new application-specific materials.
Highlights
The materials discovery process can be significantly expedited and simplified if we can learn effectively from available knowledge and data
We show that efficient and accurate prediction of a diverse set of properties of material systems is possible by employing machine learning methods trained on quantum mechanical computations in combination with the notions of chemical similarity
These are systems that contain 2 or more contiguous SnF2 units, but with an overall CH2 mole fraction of at least 25%. Such organotin systems may be appropriate for applications requiring high-dielectric constant polymers. Such diagrams can aid in the extraction of knowledge from data eventually leading to Hume-Rothery-like semi-empirical rules that dictate materials behavior
Summary
The materials discovery process can be significantly expedited and simplified if we can learn effectively from available knowledge and data. It is shown that fingerprints based on either chemo-structural (compositional and configurational information) or the electronic charge density distribution can be used to make ultra-fast, yet accurate, property predictions Harnessing such learning paradigms extends recent efforts to systematically explore and mine vast chemical spaces, and can significantly accelerate the discovery of new application-specific materials. The present contribution, aimed at materials property predictions, falls under a radically different paradigm[1,2], namely, machine (or statistical) learning—a topic central to network theory[3], cognitive game theory[4,5], pattern recognition[6,7,8], artificial intelligence[9,10], and event forecasting[11] We show that such learning methods may be used to establish a mapping between a suitable representation of a material (i.e., its ‘fingerprint’ or its ‘profile’) and any or all of its properties using known historic, or intentionally generated, data.
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