Autonomous and dynamic precursor selection for solid-state materials synthesis

Fast-Charging Solid-State Li Batteries: Materials, Strategies, and Prospects

The ability to rapidly charge batteries is crucial for widespread electrification across a number of key sectors, including transportation, grid storage, and portable electronics. Nevertheless, conventional Li-ion batteries with organic liquid electrolytes face significant technical challenges in achieving rapid charging rates without sacrificing electrochemical efficiency and safety. Solid-state batteries (SSBs) offer intrinsic stability and safety over their liquid counterparts, which can potentially bring exciting opportunities for fast charging applications. Yet realizing fast-charging SSBs remains challenging due to several fundamental obstacles, including slow Li+ transport within solid electrolytes, sluggish kinetics with the electrodes, poor electrode/electrolyte interfacial contact, as well as the growth of Li dendrites. This article examines fast-charging SSB challenges through a comprehensive review of materials and strategies for solid electrolytes (ceramics, polymers, and composites), electrodes, and their composites. In particular, methods to enhance ion transport through crystal structure engineering, compositional control, and microstructure optimization are analyzed. The review also addresses interface/interphase chemistry and Li+ transport mechanisms, providing insights to guide material design and interface optimization for next-generation fast-charging SSBs.

Synthesis and catalytic application of nanostructured metal oxides and phosphates

The design and development of new high-performance catalysts is one of the most important and challenging issues to achieve sustainable chemical and energy production. This Feature Article describes the synthesis of nanostructured metal oxides and phosphates mainly based on earth-abundant metals and their thermocatalytic application to selective oxidation and acid-base reactions. A simple and versatile methodology for the control of nanostructures based on crystalline complex oxides and phosphates with diverse structures and compositions is proposed as another approach to catalyst design. Herein, two unique and verstile methods for the synthesis of metal oxide and phosphate nanostructures are introduced; an amino acid-aided method for metal oxides and phosphates and a precursor crystallization method for porous manganese oxides. Nanomaterials based on perovskite oxides, manganese oxides, and metal phosphates can function as effective heterogeneous catalysts for selective aerobic oxidation, biomass conversion, direct methane conversion, one-pot synthesis, acid-base reactions, and water electrolysis. Furthermore, the structure-activity relationship is clarified based on experimental and computational approaches, and the influence of oxygen vacancy formation, concerted activation of molecules, and the redox/acid-base properties of the outermost surface are discussed. The proposed methodology for nanostructure control would be useful not only for the design and understanding of the complexity of metal oxide catalysts, but also for the development of innovative catalysts.

A critical reflection on attempts to machine-learn materials synthesis insights from text-mined literature recipes

Synthesis of predicted materials is the key and final step needed to realize a vision of computationally accelerated materials discovery. Because so many materials have been previously synthesized, one would anticipate that text-mining synthesis recipes from the literature would yield a valuable dataset to train machine-learning models that can predict synthesis recipes for new materials. Between 2016 and 2019, the corresponding author (Wenhao Sun) participated in efforts to text-mine 31 782 solid-state synthesis recipes and 35 675 solution-based synthesis recipes from the literature. Here, we characterize these datasets and show that they do not satisfy the "4 Vs" of data-science-that is: volume, variety, veracity and velocity. For this reason, we believe that machine-learned regression or classification models built from these datasets will have limited utility in guiding the predictive synthesis of novel materials. On the other hand, these large datasets provided an opportunity to identify anomalous synthesis recipes-which in fact did inspire new hypotheses on how materials form, which we later validated by experiment. Our case study here urges a re-evaluation on how to extract the most value from large historical materials-science datasets.

Self-Driving Laboratories for Chemistry and Materials Science

Self-driving laboratories (SDLs) promise an accelerated application of the scientific method. Through the automation of experimental workflows, along with autonomous experimental planning, SDLs hold the potential to greatly accelerate research in chemistry and materials discovery. This review provides an in-depth analysis of the state-of-the-art in SDL technology, its applications across various scientific disciplines, and the potential implications for research and industry. This review additionally provides an overview of the enabling technologies for SDLs, including their hardware, software, and integration with laboratory infrastructure. Most importantly, this review explores the diverse range of scientific domains where SDLs have made significant contributions, from drug discovery and materials science to genomics and chemistry. We provide a comprehensive review of existing real-world examples of SDLs, their different levels of automation, and the challenges and limitations associated with each domain.

Synthesis of Sulfonated Phenylsilsesquioxanes Guided by Machine Learning

Sulfonated octaphenylsilsesquioxane (SPOSS) has garnered significant interest due to its unique structural properties of containing the -SO3H group and its wide range of applications. This study introduces a novel approach to the synthesis of SPOSS, leveraging machine learning algorithms to explore new recipes and achieve higher -SO3H functionality. The focus was on synthesizing SPOSS with 2, 4, 6, and 8-SO3H functional groups on the phenyl group, marked as SPOSS-2, SPOSS-4, SPOSS-6, and SPOSS-8, respectively. The successful synthesis of SPOSS-8 was achieved by 5 training outputs based on the recipes of 21 sets of low-functionality (<4) SPOSS. The structure of SPOSS was confirmed using Fourier transform infrared (FTIR) spectroscopy, nuclear magnetic resonance (NMR) spectroscopy, and time-of-flight mass spectrometry (MALDI-TOF MS). Machine learning analysis revealed that K2SO4 is an important additive to improve the functionality of SPOSS. A synthetic mechanism was proposed and validated that K2SO4 participated in the reaction to generate sulfur trioxide (SO3), a sulfonating agent with high reactivity. SPOSS shows thermal stability superior to octaphenylsilsesquioxane (OPS) according to thermogravimetric analysis (TGA) and TG-FTIR.

Quantifying the regime of thermodynamic control for solid-state reactions during ternary metal oxide synthesis

The success of solid-state synthesis often hinges on the first intermediate phase that forms, which determines the remaining driving force to produce the desired target material. Recent work suggests that when reaction energies are large, thermodynamics primarily dictates the initial product formed, regardless of reactant stoichiometry. Here, we validate this principle and quantify its constraints by performing in situ characterization on 37 pairs of reactants. These experiments reveal a threshold for thermodynamic control in solid-state reactions, whereby initial product formation can be predicted when its driving force exceeds that of all other competing phases by ≥60 milli-electron volt per atom. In contrast, when multiple phases have a comparable driving force to form, the initial product is more often determined by kinetic factors. Analysis of the Materials Project data shows that 15% of possible reactions fall within the regime of thermodynamic control, highlighting the opportunity to predict synthesis pathways from first principles.

Text Mining the Literature to Inform Experiments and Rationalize Impurity Phase Formation for BiFeO3

We used data-driven methods to understand the formation of impurity phases in BiFeO3 thin-film synthesis through the sol-gel technique. Using a high-quality dataset of 331 synthesis procedures and outcomes extracted manually from 177 scientific articles, we trained decision tree models that reinforce important experimental heuristics for the avoidance of phase impurities but ultimately show limited predictive capability. We find that several important synthesis features, identified by our model, are often not reported in the literature. To test our ability to correctly impute missing synthesis parameters, we attempted to reproduce nine syntheses from the literature with varying degrees of "missingness". We demonstrate how a text-mined dataset can be made useful by informing new controlled experiments and forming a better understanding for impurity phase formation in this complex oxide system.

An autonomous laboratory for the accelerated synthesis of novel materials

To close the gap between the rates of computational screening and experimental realization of novel materials1,2, 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 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.