Native Chemical Automata and the Thermodynamic Interpretation of Their Experimental Accept/Reject Responses
Marta Duenas-Diez, Juan Perez-Mercader

TL;DR
This paper demonstrates physical realizations of finite automata, push-down automata, and Turing machines using inorganic chemistry, and introduces thermodynamic metrics to evaluate their computational results and energetic costs.
Contribution
It provides the first inorganic chemistry implementations of fundamental automata and develops thermodynamic measures to analyze their computational and energetic properties.
Findings
Successful laboratory realizations of FA, PDA, and TM automata in inorganic chemistry.
Thermodynamic metrics based on enthalpy and Gibbs free energy to assess computation.
Quantification of energetic costs associated with automata computations.
Abstract
The theory of computation is based on abstract computing automata which can be classified into a three-class hierarchy: Finite Automata (FA), Push-down Automata (PDA) and the Turing Machines (TM). Each class corresponds to grammar/language classes. The function of the automata consists on recognizing words in a language generated by some grammar and expressed with letters from an alphabet. Such automata are, in principle, abstract entities and with suitable combinations of them we can represent any computation, no matter how complex. Their physical implementations are possible in any information carrying and recognition contexts and media, such as electrons in semiconductors, certain biomolecules in biology or even non-biological molecules. Here we describe and build non-biochemistry (inorganic chemistry) examples of a FA, PDA and TM computations carried out by specific laboratory…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMolecular Junctions and Nanostructures · Machine Learning in Materials Science · Surface Chemistry and Catalysis
