Autochemistry: A Research Paradigm Based on Artificial Intelligence and Big Data

Abstract

Artificial intelligence (AI) technologies affect every domain and process within industry. Many solutions with different maturity levels have been created or are in development. With this paper we collect the initiatives within the domain of chemical science, add missing items, and bring these resources together into a common process model, based on artificial neural networks (ANN). We define ten building blocks, analyze their role within the architecture, and evaluate their impact to the current system. Finally we discuss the changes and the transition that is experienced by the lab worker and the chemist. This paper introduces autochemistry as a meme describing a self-improving research approach. With this we begin development and discussion of an exciting new area of scientific principles, that is changing the anthropocentric fundament of chemistry research into a technocentric one.

Enamel Remineralization and Crystallization after Fluoride Iontophoresis

“…4A), the Ca K-edge XANES spectra slightly increased with more sharpening at the top after treatment when compared with that before treatment, indicating the more crystalline Ca species of the lesion volume obtained after CIP as the position and intensity of transitions in Ca K-edge XANES spectra are sensitive to the coordination number, geometry, bond length, and order at the intermediate range (Sindhupakorn and Kidkhunthod 2021). The dominant feature in spectra of Ca-HA is the transition associated with the first coordination sphere, with little or no features that depend on order beyond the first shell (Gressling 2020).…”