Automated Chemistry Combines Chemical Robotics and AI to Accelerate Pace for Advancing Solar Energy Technologies

Automated Chemistry Combines Chemical Robotics and AI to Accelerate Pace for Advancing Solar Energy Technologies


Researchers at ORNL and the College of Tennessee developed an automatic workflow that mixes chemical robotics and machine studying to hurry the seek for secure perovskites. Credit score: Jaimee Janiga/ORNL, U.S. Dept of Vitality

Researchers on the Division of Vitality’s Oak Ridge Nationwide Laboratory and the College of Tennessee are automating the seek for new supplies to advance photo voltaic vitality applied sciences.

A novel workflow revealed in ACS Vitality Letters combines robotics and machine studying to review metallic halide perovskites, or MHPs — skinny, light-weight, versatile supplies with excellent properties for harnessing mild that can be utilized to make photo voltaic cells, energy-efficient lighting and sensors.

“Our method speeds exploration of perovskite supplies, making it exponentially sooner to synthesize and characterize many materials compositions directly and determine areas of curiosity,” mentioned ORNL’s Sergei Kalinin.

The research, a part of an ORNL-UT Science Alliance collaboration, goals to determine probably the most secure MHP supplies for machine integration.

“Automated experimentation might help us carve an environment friendly path ahead in exploring what’s an immense pool of potential materials compositions,” mentioned UT’s Mahshid Ahmadi.

Though MHPs are engaging for his or her excessive effectivity and low fabrication prices, their sensitivity to the setting limits operational use. Actual-world examples are likely to degrade too rapidly in ambient circumstances, reminiscent of mild, humidity or warmth, to be sensible.

The big potential for perovskites presents an inherent impediment for supplies discovery. Scientists face an enormous design area of their efforts to develop extra sturdy fashions. Greater than a thousand MHPs have been predicted, and every of those might be chemically modified to generate a close to limitless library of attainable compositions.

“It’s troublesome to beat this problem with standard strategies of synthesizing and characterizing samples one after the other,” mentioned Ahmadi. “Our method permits us to display as much as 96 samples at a time to speed up supplies discovery and optimization.”

The group chosen 4 mannequin MHP programs — yielding 380 compositions complete — to exhibit the brand new workflow for solution-processable supplies, compositions that start as moist mixtures however dry to strong types.

The synthesis step employed a programmable pipetting robotic designed to work with customary 96-well microplates. The machine saves time over manually meting out many alternative compositions; and it minimizes error in replicating a tedious course of that must be carried out in precisely the identical ambient circumstances, a variable that’s troublesome to regulate over prolonged durations.

Subsequent, researchers uncovered samples to air and measured their photoluminescent properties utilizing a regular optical plate reader.

“It’s a easy measurement however is the de facto customary for characterizing stability in MHPs,” mentioned Kalinin. “The bottom line is that standard approaches could be labor intensive, whereas we had been in a position to measure the photoluminescent properties of 96 samples in about 5 minutes.”

Repeating the method over a number of hours captured advanced section diagrams during which wavelengths of sunshine differ throughout compositions and evolve over time.

The group developed a machine-learning algorithm to research the information and residential in on areas with excessive stability.

“Machine studying allows us to get extra info out of sparse knowledge by predicting properties between measured factors,” mentioned ORNL’s Maxim Ziatdinov, who led growth of the algorithm. “The outcomes information supplies characterization by displaying us the place to look subsequent.”

Whereas the research focuses on supplies discovery to determine probably the most secure compositions, the workflow may be used to optimize materials properties for particular optoelectronic purposes.

The automated course of might be utilized to any solution-processable materials for time and value financial savings over conventional synthesis strategies.

Reference: “Chemical Robotics Enabled Exploration of Stability in Multicomponent Lead Halide Perovskites through Machine Studying” by Kate Higgins, Sai Mani Valleti, Maxim Ziatdinov, Sergei V. Kalinin and Mahshid Ahmadi, 15 October 2020, ACS Vitality Letters.
DOI: 10.1021/acsenergylett.0c01749

The analysis was supported by the Science Alliance, a Tennessee Heart of Excellence, and the Heart for Nanophase Supplies Sciences, a DOE Workplace of Science Consumer Facility.





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