Explore the vast range of titles available.

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.

Synthesis is a major challenge in the discovery of new inorganic materials. Currently, there is limited theoretical guidance for identifying optimal solid-state synthesis procedures. We introduce two selectivity metrics, primary and secondary competition, to assess the favorability of target/impurity phase formation in solid-state reactions. We used these metrics to analyze 3520 solid-state reactions in the literature, ranking existing approaches to popular target materials. Additionally, we implemented these metrics in a data-driven synthesis planning workflow and demonstrated its application in the synthesis of barium titanate (BaTiO3). Using an 18-element chemical reaction network with first-principles thermodynamic data from the Materials Project, we identified 82985 possible BaTiO3 synthesis reactions and selected 9 for experimental testing. Characterization of reaction pathways via synchrotron powder X-ray diffraction reveals that our selectivity metrics correlate with observed target/impurity formation. We discovered two efficient reactions using unconventional precursors (BaS/BaCl2 and Na2TiO3) that produce BaTiO3 faster and with fewer impurities than conventional methods, highlighting the importance of considering complex chemistries with additional elements during precursor selection. Our framework provides a foundation for predictive inorganic synthesis, facilitating the optimization of existing recipes and the discovery of new materials, including those not easily attainable with conventional precursors.

Non-crystalline materials exhibit unique properties that make them suitable for various applications in science and technology, ranging from optical and electronic devices and solid-state batteries to protective coatings. However, data-driven exploration and design of non-crystalline materials is hampered by the absence of a comprehensive database covering a broad chemical space. In this work, we present the largest computed non-crystalline structure database to date, generated from systematic and accurate ab initio molecular dynamics (AIMD) calculations. We also show how the database can be used in simple machine-learning models to connect properties to composition and structure, here specifically targeting ionic conductivity. These models predict the Li-ion diffusivity with speed and accuracy, offering a cost-effective alternative to expensive density functional theory (DFT) calculations. Furthermore, the process of computational quenching non-crystalline structures provides a unique sampling of out-of-equilibrium structures, energies, and force landscape, and we anticipate that the corresponding trajectories will inform future work in universal machine learning potentials, impacting design beyond that of non-crystalline materials. In addition, combining diffusion trajectories from our dataset with models that predict liquidus viscosity and melting temperature could be utilized to develop models for predicting glass-forming ability.

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.

Synthesis is a major challenge in the discovery of new inorganic materials. Currently, there is limited theoretical guidance for identifying optimal solid-state synthesis procedures. We introduce two selectivity metrics, primary and secondary competition, to assess the favorability of target/impurity phase formation in solid-state reactions. We used these metrics to analyze 3,520 solid-state reactions in the literature, ranking existing approaches to popular target materials. Additionally, we implemented these metrics in a data-driven synthesis planning workflow and demonstrated its application in the synthesis of barium titanate (BaTiO\\(_3\\)). Using an 18-element chemical reaction network with first-principles thermodynamic data from the Materials Project, we identified 82,985 possible BaTiO\\(_3\\) synthesis reactions and selected nine for experimental testing. Characterization of reaction pathways via synchrotron powder X-ray diffraction reveals that our selectivity metrics correlate with observed target/impurity formation. We discovered two efficient reactions using unconventional precursors (BaS/BaCl\\(_2\\) and Na\\(_2\\)TiO\\(_3\\)) that produce BaTiO\\(_3\\) faster and with fewer impurities than conventional methods, highlighting the importance of considering complex chemistries with additional elements during precursor selection. Our framework provides a foundation for predictive inorganic synthesis, facilitating the optimization of existing recipes and the discovery of new materials, including those not easily attainable with conventional precursors.

New computational tools for solid-state synthesis recipe design are needed in order to accelerate the experimental realization of novel functional materials proposed by high-throughput materials discovery workflows. This work contributes a cellular automaton simulation framework (ReactCA) for predicting the time-dependent evolution of intermediate and product phases during solid-state reactions as a function of precursor choice and amount, reaction atmosphere, and heating profile. The simulation captures rudimentary kinetic effects, the effects of reactant particle spatial distribution, particle melting and reaction atmosphere. It achieves conservation of mass using a stochastic, asynchronous evolution rule and estimates reaction rates using density functional theory data from the Materials Project [1] and machine learning estimators for the the melting point [2] and the vibrational entropy component of the Gibbs free energy [3]. The resulting simulation framework allows for the prediction of the likely outcome of a reaction recipe before any experiments are performed. We analyze five experimental solid-state recipes for BaTiO\\(_3\\), CaZrN\\(_2\\) and YMnO\\(_3\\) found in the literature to illustrate the performance of the model in capturing reaction pathways as a function of temperature, reaction selectivity and the effect of precursor choice. Our approach allows for straightforward comparison of predicted mass fractions of intermediates and products with experimental results. This simulation framework presents a step toward \\(\\textit{in silico}\\) synthesis recipe design and an easier way to optimize existing recipes, aid in the identification of intermediates and identify effective recipes for yet unrealized inorganic solids.

Amorphous materials exhibit unique properties that make them suitable for various applications in science and technology, ranging from optical and electronic devices and solid-state batteries to protective coatings. However, data-driven exploration and design of amorphous materials is hampered by the absence of a comprehensive database covering a broad chemical space. In this work, we present the largest computed amorphous materials database to date, generated from systematic and accurate \\textit{ab initio} molecular dynamics (AIMD) calculations. We also show how the database can be used in simple machine-learning models to connect properties to composition and structure, here specifically targeting ionic conductivity. These models predict the Li-ion diffusivity with speed and accuracy, offering a cost-effective alternative to expensive density functional theory (DFT) calculations. Furthermore, the process of computational quenching amorphous materials provides a unique sampling of out-of-equilibrium structures, energies, and force landscape, and we anticipate that the corresponding trajectories will inform future work in universal machine learning potentials, impacting design beyond that of non-crystalline materials.

The increasing toxicity of opioids in the unregulated drug market has led to escalating numbers of overdoses in Canada and worldwide; takehome naloxone (THN) is an evidence-based intervention that distributes kits containing naloxone to people in the community who may witness an overdose. The purpose of this guidance is to provide policy recommendations for territorial, provincial and federal THN programs, using evidence from scientific and grey literature and community evidence that reflects 11 years of THN distribution in Canada. The Naloxone Guidance Development Group — a multidisciplinary team including people with lived and living experience and expertise of drug use — used the Appraisal of Guidelines for Research & Evaluation (AGREE II) instrument to inform development of this guidance. We considered published evidence identified through systematic reviews of all literature types, along with community evidence and expertise, to generate recommendations between December 2021 and September 2022. We solicited feedback on preliminary recommendations through an External Review Committee and a public input process. The project was funded by the Canadian Institutes of Health Research through the Canadian Research Initiative in Substance Misuse. We used the Guideline International Network principles for managing competing interests. Existing evidence from the literature on THN was of low quality. We incorporated evidence from scientific and grey literature, and community expertise to develop our recommendations. These were in 3 areas: routes of naloxone administration, THN kit contents and overdose response. Take-home naloxone programs should offer the choice of both intramuscular and intranasal formulations of naloxone in THN kits. Recommended kit contents include naloxone, a naloxone delivery device, personal protective equipment, instructions and a carrying case. Trained community overdose responders should prioritize rescue breathing in the case of respiratory depression, and conventional cardiopulmonary resuscitation in the case of cardiac arrest, among other interventions. This guidance development project provides direction for THN programs in Canada in the context of limited published evidence, with recommendations developed in collaboration with diverse stakeholders.

Chlorhexidine skin antisepsis is frequently recommended for most surgical procedures; however, it is unclear if these recommendations should apply to surgery involving traumatic contaminated wounds where povidone-iodine has previously been preferred. We aimed to compare the effect of aqueous 10% povidone-iodine versus aqueous 4% chlorhexidine gluconate on the risk of surgical site infection in patients who required surgery for an open fracture. We conducted a multiple-period, cluster-randomised, crossover trial (Aqueous-PREP) at 14 hospitals in Canada, Spain, and the USA. Eligible patients were adults aged 18 years or older with an open extremity fracture treated with a surgical fixation implant. For inclusion, the open fracture required formal surgical debridement within 72 h of the injury. Participating sites were randomly assigned (1:1) to use either aqueous 10% povidone-iodine or aqueous 4% chlorhexidine gluconate immediately before surgical incision; sites then alternated between the study interventions every 2 months. Participants, health-care providers, and study personnel were aware of the treatment assignment due to the colour of the solutions. The outcome adjudicators and data analysts were masked to treatment allocation. The primary outcome was surgical site infection, guided by the 2017 US Centers for Disease Control and Prevention National Healthcare Safety Network reporting criteria, which included superficial incisional infection within 30 days or deep incisional or organ space infection within 90 days of surgery. The primary analyses followed the intention-to-treat principle and included all participants in the groups to which they were randomly assigned. This study is registered with ClinicalTrials.gov, NCT03385304. Between April 8, 2018, and June 8, 2021, 3619 patients were assessed for eligibility and 1683 were enrolled and randomly assigned to povidone-iodine (n=847) or chlorhexidine gluconate (n=836). The trial's adjudication committee determined that 45 participants were ineligible, leaving 1638 participants in the primary analysis, with 828 in the povidone-iodine group and 810 in the chlorhexidine gluconate group (mean age 44·9 years [SD 18·0]; 629 [38%] were female and 1009 [62%] were male). Among 1571 participants in whom the primary outcome was known, a surgical site infection occurred in 59 (7%) of 787 participants in the povidone-iodine group and 58 (7%) of 784 in the chlorhexidine gluconate group (odds ratio 1·11, 95% CI 0·74 to 1·65; p=0·61; risk difference 0·6%, 95% CI –1·4 to 3·4). For patients who require surgical fixation of an open fracture, either aqueous 10% povidone-iodine or aqueous 4% chlorhexidine gluconate can be selected for skin antisepsis on the basis of solution availability, patient contraindications, or product cost. These findings might also have implications for antisepsis of other traumatic wounds. US Department of Defense, Canadian Institutes of Health Research, McMaster University Surgical Associates, PSI Foundation.