The emergence of high-entropy materials has inspired the exploration of novel materials in diverse technologies. In electrochemical energy storage, high-entropy design has shown advantageous impacts on battery materials such as suppressing undesired short-range order, frustrating energy landscape, decreasing volumetric change and reducing the reliance on critical metals. This comment addresses the definition and potential improper use of the term “high entropy” in the context of battery materials design, highlights the unique properties of high-entropy materials in battery applications, and outlines the remaining challenges in the synthesis, characterization, and computational modeling of high-entropy battery materials.

Autonomous experimentation driven by artificial intelligence (AI) provides an exciting opportunity to revolutionize inorganic materials discovery and development. Herein, we review recent progress in the design of self-driving laboratories, including robotics to automate materials synthesis and characterization, in conjunction with AI to interpret experimental outcomes and propose new experimental procedures. We focus on efforts to automate inorganic synthesis through solution-based routes, solid-state reactions, and thin film deposition. In each case, connections are made to relevant work in organic chemistry, where automation is more common. Characterization techniques are primarily discussed in the context of phase identification, as this task is critical to understand what products have formed during synthesis. The application of deep learning to analyze multivariate characterization data and perform phase identification is examined. To achieve "closed-loop" materials synthesis and design, we further provide a detailed overview of optimization algorithms that use active learning to rationally guide experimental iterations. Finally, we highlight several key opportunities and challenges for the future development of self-driving inorganic materials synthesis platforms.

Solid-state synthesis plays an important role in the development of new materials and technologies. While in situ characterization and ab-initio computations have advanced our understanding of materials synthesis, experiments targeting new compounds often still require many different precursors and conditions to be tested. Here we introduce an algorithm (ARROWS³) designed to automate the selection of optimal precursors for solid-state materials synthesis. This algorithm actively learns from experimental outcomes to determine which precursors lead to unfavorable reactions that form highly stable intermediates, preventing the target material's formation. Based on this information, ARROWS³ proposes new experiments using precursors it predicts to avoid such intermediates, thereby retaining a larger thermodynamic driving force to form the target. We validate this approach on three experimental datasets, containing results from over 200 synthesis procedures. In comparison to black-box optimization, ARROWS³ identifies effective precursor sets for each target while requiring substantially fewer experimental iterations. These findings highlight the importance of domain knowledge in optimization algorithms for materials synthesis, which are critical for the development of fully autonomous research platforms.

Machine learning (ML) has become a valuable tool to assist and improve materials characterization, enabling automated interpretation of experimental results with techniques such as X-ray diffraction (XRD) and electron microscopy. Because ML models are fast once trained, there is a key opportunity to bring interpretation in-line with experiments and make on-the-fly decisions to achieve optimal measurement effectiveness, which creates broad opportunities for rapid learning and information extraction from experiments. Here, we demonstrate such a capability with the development of autonomous and adaptive XRD. By coupling an ML algorithm with a physical diffractometer, this method integrates diffraction and analysis such that early experimental information is leveraged to steer measurements toward features that improve the confidence of a model trained to identify crystalline phases. We validate the effectiveness of an adaptive approach by showing that ML-driven XRD can accurately detect trace amounts of materials in multi-phase mixtures with short measurement times. The improved speed of phase detection also enables in situ identification of short-lived intermediate phases formed during solid-state reactions using a standard in-house diffractometer. Our findings showcase the advantages of in-line ML for materials characterization and point to the possibility of more general approaches for adaptive experimentation.

Autonomous synthesis and characterization of inorganic materials require the automatic and accurate analysis of X-ray diffraction spectra. For this task, we designed a probabilistic deep learning algorithm to identify complex multi-phase mixtures. At the core of this algorithm lies an ensemble convolutional neural network trained on simulated diffraction spectra, which are systematically augmented with physics-informed perturbations to account for artifacts that can arise during experimental sample preparation and synthesis. Larger perturbations associated with off-stoichiometry are also captured by supplementing the training set with hypothetical solid solutions. Spectra containing mixtures of materials are analyzed with a newly developed branching algorithm that utilizes the probabilistic nature of the neural network to explore suspected mixtures and identify the set of phases that maximize confidence in the prediction. Our model is benchmarked on simulated and experimentally measured diffraction spectra, showing exceptional performance with accuracies exceeding those given by previously reported methods based on profile matching and deep learning. We envision that the algorithm presented here may be integrated in experimental workflows to facilitate the high-throughput and autonomous discovery of inorganic materials.

Epidermal growth factor receptor (EGFR) inhibitors are approved by the Food and Drug Administration (FDA) but remain under active clinical investigation for the treatment of both newly diagnosed and recurrent/metastatic head and neck squamous cell carcinoma (HNSCC). Despite EGFR expression in the majority of HNSCC tumors, the levels of total or phosphorylated EGFR have not consistently been correlated with a response to EGFR targeting agents. The lack of predictive biomarkers represents a major obstacle to successful use of these drugs. Activation of phosphatidylinositol 3-kinase (PI3K) signaling by mutation of the PIK3CA oncogene represents a plausible mechanism for EGFR inhibitor drug resistance. We compared the impact of EGFR inhibitors, alone or in combination with non-steroidal anti-inflammatory drugs (NSAIDs), in preclinical HNSCC models harboring mutant versus wild-type PIK3CA. Our results demonstrate additive or synergistic effects of NSAIDs and EGFR inhibitors in vitro and in vivo in PIK3CA-mutated HNSCC models. These findings suggest that the addition of NSAIDs to EGFR inhibitors for the treatment of HNSCC may represent a promising therapeutic strategy in PIK3CA-mutated cancers.

Electron microscopy is indispensable for examining the morphology and composition of solid materials at the sub-micron scale. To study the powder samples that are widely used in materials development, scanning electron microscopes (SEMs) are increasingly used at the laboratory scale to generate large datasets with hundreds of images. Parsing these images to identify distinct particles and determine their morphology requires careful analysis, and automating this process remains challenging. In this work, we enhance the Mask R-CNN architecture to develop a method for automated segmentation of particles in SEM images. We address several challenges inherent to measurements, such as image blur and particle agglomeration. Moreover, our method accounts for prediction uncertainty when such issues prevent accurate segmentation of a particle. Recognizing that disparate length scales are often present in large datasets, we use this framework to create two models that are separately trained to handle images obtained at low or high magnification. By testing these models on a variety of inorganic samples, our approach to particle segmentation surpasses an established automated segmentation method and yields comparable results to the predictions of three domain experts, revealing comparable accuracy while requiring a fraction of the time. These findings highlight the potential of deep learning in advancing autonomous workflows for materials characterization.

Epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) have not been effective in unselected head and neck squamous cell carcinoma (HNSCC) populations. We previously reported an exceptional response to a brief course of erlotinib in a patient with advanced HNSCC whose tumor harbored a MAPK1E322K somatic mutation. MAPK1E322Kwas associated with increased p-EGFR, increased EGFR downstream signaling and increased sensitivity to erlotinib. In this study, we investigated the mechanism of MAPK1E322K-mediated EGFR activation in the context of erlotinib sensitivity. We demonstrated increased AREG secretion in HNSCC cell lines harboring endogenous or exogenous MAPK1E322K compared to wild type MAPK1. We found inhibition or knockdown of MAPK1 with siRNA resulted in reduced secretion of AREG and decreased sensitivity to erlotinib in the setting of MAPK1E322K. MAPK1E322K was associated with increased AREG secretion leading to an autocrine feedback loop involving AREG, EGFR and downstream signaling. Knockdown of AREG in HNSCC cells harboring MAPK1E322K abrogated EGFR signaling and decreased sensitivity to erlotinib in vitro and in vivo. These cumulative findings implicate increased AREG secretion and EGFR activation as contributing to increased erlotinib sensitivity in MAPK1E322K HNSCC.

With increasing battery demand comes a need for diversified Li sources beyond brines. Among all Li-bearing minerals, spodumene is most often used for its high Li content and natural abundance. However, the traditional approach to process spodumene is costly and energy-intensive, requiring the mineral be transformed from its natural α to β phase at >1000 °C. Acid leaching is then applied, followed by neutralization to precipitate Li₂CO₃. In this work, we report an alternative method to extract Li directly from α-spodumene, which is performed at lower temperatures and avoids the use of acids. It is shown that Li₂CO₃ is formed with >90% yield at 750 °C by reacting α-spodumene with Na₂CO₃ and Al₂O₃. The addition of Al₂O₃ is critical to reduce the amount of Li₂SiO₃ that forms when only Na₂CO₃ is used, instead providing increased thermodynamic driving force to form NaAlSiO₄ and Li₂CO₃ as the sole products. We find that this reaction is most effective at 4 h, after which volatility limits the yield. Following its extraction, Li₂CO₃ can be isolated by washing the sample using deionized water. This energy-saving and acid-free route to obtain Li₂CO₃ directly from spodumene can help meet the growing demand for Li.