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To close the gap between the rates of computational screening and experimental realization of novel materials , we introduce the A-Lab, an autonomous laboratory for the solid-state synthesis of inorganic powders. This platform uses computations, historical data from the literature, machine learning (ML) and active learning to plan and interpret the outcomes of experiments performed using robotics. Over 17 days of continuous operation, the A-Lab realized 41 novel compounds from a set of 58 targets including a variety of oxides and phosphates that were identified using large-scale ab initio phase-stability data from the Materials Project and Google DeepMind. Synthesis recipes were proposed by natural-language models trained on the literature and optimized using an active-learning approach grounded in thermodynamics. Analysis of the failed syntheses provides direct and actionable suggestions to improve current techniques for materials screening and synthesis design. The high success rate demonstrates the effectiveness of artificial-intelligence-driven platforms for autonomous materials discovery and motivates further integration of computations, historical knowledge and robotics.

Treated wood timbers employed in ground contact are often installed with a cement collar to firmly fix the structural wood post in place. Few prior studies have determined the effect of concrete on decay efficacy on treated wood, however. Treated wood nominal 4 × 4 posts were installed at four locations, with the upper ground-contact portion of each post encased in concrete, and the samples removed at various times for pH measurements. The wood alkalinity quickly increased at all four sites for the portion of the treated wood in concrete contact compared to the wood in ground contact without concrete. In laboratory decay tests employing three decay fungi, untreated wood which was first exposed or unexposed to concrete had no consistent difference in decay susceptibility. For wood treated with three different commercial copper/organic systems, cement exposure had no effect on wood treated with an amine copper azole system, while treatment with amine copper quat showed a statistically significant fungal efficacy enhancement for cement-exposed samples with both copper-tolerant fungi. Conversely, with a micronized copper azole preservative, cement exposure resulted in reduced fungal efficacy compared to treated samples which were not cement-exposed for all three decay fungi.

Lack of dimensional stability and susceptibility to the action of xylophagous organisms make wood a challenging material to work with. E-Caprolactam is a water-soluble cyclic amide chemical with low mammalian toxicity, that can be used as a bulking agent to improve the dimensional stability of wood and offers protection against subterranean termites and several wood degradant fungi. E-Caprolactam delivers a similar level of dimensional stability as that provided by PEG-1000, a chemical extensively studied in the past decades and a standard dimensional stabilizer agent for wood. Regarding wood protection, E-Caprolactam exhibits efficacy against wood decay fungi and termites at very low levels compared to PEG-1000. If water leaching can be inhibited for this product, E-Caprolactam can be considered as a strong candidate for the wood decking industry. In addition to the evaluation of E-Caprolactam for wood dimensional stabilization and preservation this dissertation covers the development of an apparatus developed to automate wood sorption and desorption studies, and their influence in the wood dimensional behavior. Product design techniques and prototyping studies were conducted, and calibration procedures were made. This apparatus is still in development, but it shows potential for increasing the accuracy of shrinking and swelling measurements and efficiency for dimensional stabilization studies.

To close the gap between the rates of computational screening and experimental realization of novel materials 1 , 2 , we introduce the A-Lab, an autonomous laboratory for the solid-state synthesis of inorganic powders. This platform uses computations, historical data from the literature, machine learning (ML) and active learning to plan and interpret the outcomes of experiments performed using robotics. Over 17 days of continuous operation, the A-Lab realized 36 compounds from a set of 57 targets including a variety of oxides and phosphates that were identified using large-scale ab initio phase-stability data from the Materials Project and Google DeepMind. Synthesis recipes were proposed by natural-language models trained on the literature and optimized using an active-learning approach grounded in thermodynamics. Analysis of the failed syntheses provides direct and actionable suggestions to improve current techniques for materials screening and synthesis design. The high success rate demonstrates the effectiveness of artificial-intelligence-driven platforms for autonomous materials synthesis and motivates further integration of computations, historical knowledge and robotics. An autonomous laboratory, the A-Lab, is presented that combines computations, literature data, machine learning and active learning, which discovered and synthesized 41 novel compounds from a set of 58 targets after 17 days of operation.

The recent advent of autonomous laboratories, coupled with algorithms for high-throughput screening and active learning, promises to accelerate materials discovery and innovation. As these autonomous systems grow in complexity, the demand for robust and efficient workflow management software becomes increasingly critical. In this paper, we introduce AlabOS, a general-purpose software framework for orchestrating experiments and managing resources, with an emphasis on automated laboratories for materials synthesis and characterization. We demonstrate the implementation of AlabOS in a prototype autonomous materials laboratory. AlabOS features a reconfigurable experiment workflow model, enabling the simultaneous execution of varied workflows composed of modular tasks. Therefore, AlabOS is well-suited to handle the rapidly changing experimental protocols defining the progress of self-driving laboratory development for materials research.

Contributions should be sent care of Richard Millbank, Guinness Publishing, 33 London Road, Enfield, Middlesex EN2 6DJ.