AIR-Chem: Authentic Intelligent Robotics for Chemistry

Abstract

The new era with prosperous artificial intelligence (AI) and robotics technology is reshaping the materials discovery process in a more radical fashion. Here we present authentic intelligent robotics for chemistry (AIR-Chem), integrated with technological innovations in the AI and robotics fields, functionalized with modules including gradient descent-based optimization frameworks, multiple external field modulations, a real-time computer vision (CV) system, and automated guided vehicle (AGV) parts. AIR-Chem is portable and remotely controllable by cloud computing. AIR-Chem can learn the parametric procedures for given targets and carry on laboratory operations in standalone mode, with high reproducibility, precision, and availability for knowledge regeneration. Moreover, an improved nucleation theory of size focusing on inorganic perovskite quantum dots (IPQDs) is theoretically proposed and experimentally testified to by AIR-Chem. This work aims to boost the process of an unmanned chemistry laboratory from the synthesis of chemical materials to the analysis of physical chemical properties, and it provides a vivid demonstration for future chemistry reshaped by AI and robotics technology.

Toward “On‐Demand” Materials Synthesis and Scientific Discovery through Intelligent Robots

“…Previous work reported that the additional injection during the growth could significantly improve the size distribution . Here we apply MAOS by variant injection time interval τ and the times of the injection operation η, and we keep the total injection amount η* c η constant for different η value .…”

“…The CV analyzer based on OpenCV library was applied to extract red, green and blue (RGB) information from video streams captured by the in‐built camera, and then mapped it into the average particle size 〈 D RGB 〉 or 〈 D 〉 From a well‐benchmarked machine learning scheme. Details of the analyzing algorithms were described in our previous work . The spectrum analyzer is used to extract the demand optical information from spectrum files generated by spectrometer software.…”

“…Today, motived by the breakthrough of the AI and intelligent robots, it is right time emerging to revisit and transfer these day's advanced technology to the fundamental research areas . The preciseness of computer language and robot operation benefits a lot on solving the reproducibility problem, which has been proved both in molecular synthesis, 2D materials assembling, and nanocrystal growth . Recently, the robotics technology starts to apply in an automatic chemical laboratory vigorously widely .…”

Automated Liquid-Level Monitoring and Control using Computer Vision

“…), and perform in situ analysis. [6][7][8][9] When combined with smart machinery and a clear set of instructions, computer vision can also be used to drive control decisions.…”

Chemical Robotics Enabled Exploration of Stability and Photoluminescent Behavior in Multicomponent Hybrid Perovskites via Machine Learning

“…The approach of iterating between automated experimentation and machine learning-based experiment planning has resulted in early successes in addressing high-dimensional problems in experimental physics 21 , chemistry 22 , life-sciences 23 , and recently starting to be implemented in the materials sciences [24][25][26][27] . To get insight into the variability of the optical band gap and photoluminescent properties across the compositional series, we will adopt the approach based on the multivariate statistical analysis.…”