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Plasma‐based biomedical applications rely on the reactive oxygen and nitrogen species generated in cold atmospheric plasmas, where complex chemical kinetic schemes occur. The optimization of plasma medicine is thus required for each specific biomedical purpose. In the view of pharmacology, it is to optimize the active pharmaceutical ingredients. This work is thus the first attempt of such a complex task utilizing the recent development of machine learning technologies. Herein, a general method of passive plasma chemical diagnostics and optimization in real time is proposed. Based on spontaneous emission spectroscopy, an artificial neural network provides the gas chemical compositions along with other information such as temperatures. The information further passes through the second neural network which outputs the adjustments of external control inputs including energy, gas injections, and extractions to optimize the plasma chemistry.
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Artificial Intelligence without Digital Computers: Programming Matter at a Molecular Scale
As the carriers and executors of algorithms, digital computers are limited by the density of semiconductors on chips, where the quantum uncertainty is significant at the nanometer scale. Based on the mathematical similarity between chemical pathway networks and artificial neural networks, a new construct to achieve artificial intelligence running on a matter is developed. A general theory is derived followed by an evaluation of its fitting capability and an example where a low‐temperature plasma is trained to play the board game Tic–Tac–Toe. The plasma can emit a spectrum representing its next move when we are feeding a gas combination carrying the board information. Finally, the fourth state of matter shows a significantly high winning rate against a random‐move player, reflecting its own strategies. This work reveals that any matter, with substantial chemical complexity, can process information based on particle collisions, like a programmable analog computer at the molecular level.
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- Award ID(s):
- 1747760
- PAR ID:
- 10381623
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Advanced Intelligent Systems
- Volume:
- 4
- Issue:
- 11
- ISSN:
- 2640-4567
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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