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To reliably deploy lithium-ion batteries, a fundamental understanding of cycling and aging behavior is critical. Battery aging, however, consists of complex and highly coupled phenomena, making it challenging to develop a holistic interpretation. In this work, we generate a diverse battery cycling dataset with a broad range of degradation trajectories, consisting of 363 high energy density commercial Li(Ni,Co,Al)O$_2$/Graphite + SiO$_x$ cylindrical 21700 cells cycled under 218 unique cycling protocols. We consolidate aging via 16 mechanistic state-of-health (SOH) metrics, including cell-level performance metrics, electrode-specific capacities/state-of-charges (SOCs), and aging trajectory descriptors. Through the use of interpretable machine learning and explainable features, we deconvolute the underlying factors that contribute to battery degradation. This generalizable data-driven framework reveals the complex interplay between cycling conditions, degradation modes, and SOH, representing a holistic approach towards understanding battery aging.

Exploratory synthesis has been the main generator of new inorganic materials for decades. However, our Edisonian and bias-prone processes of synthetic exploration alone are no longer sufficient in an age that demands rapid advances in materials development. In this work, we demonstrate one of the first end-to-end attempts towards systematic, computer-aided discovery and laboratory synthesis of inorganic crystalline compounds as a modern alternative to purely exploratory synthesis. Our approach initializes materials discovery campaigns by autonomously mapping the synthetic feasibility of a chemical system using density functional theory with AI feedback. Following expert-driven down-selection of newly generated phases, we use solid-state synthesis and in situ characterization via hot-stage X-ray diffraction in order to realize new ternary oxide phases experimentally. We applied this strategy in six ternary transition-metal oxide chemistries previously considered well-explored, one of which culminated in the discovery of two novel phases of calcium ruthenates. Detailed characterization using room temperature X-ray powder diffraction, 4D-STEM and SQUID measurements identify the structure, composition and confirm distinct properties, including distinct defect concentrations, of one of the new phases formed in our experimental campaigns. While the discovery of a new material guided by AI and DFT theory represents a milestone, our procedure and results also highlight a number of critical gaps in the process that can inform future efforts towards the improvement of AI-coupled methodologies, which are discussed.

Lithium-ion batteries (LIBs) have attracted widespread attention as an efficient energy storage device on electric vehicles (EV) to achieve emission-free mobility. However, the performance of LIBs deteriorates with time and usage, and the state of health of used batteries are difficult to quantify and to date are poorly understood. Having accurate estimations of a battery's remaining life across different life stages would benefit maintenance, safety, and serve as a means of qualifying used batteries for second-life applications. Since the full history of a battery may not always be available in downstream applications, in this study, we demonstrate a deep learning framework that enables dynamic degradation trajectory prediction, while requiring only the most recent battery usage information. Specifically, our model takes a rolling window of current and voltage time-series inputs, and predicts the near-term and long-term capacity fade via a recurrent neural network. We exhaustively benchmark our model against a naive extrapolating model by evaluating the error on reconstructing the discharge capacity profile under different settings. We show that our model's performance in accurately inferring the battery's degradation profile is "agnostic" with respect to cell cycling history and its current state of health.

This study is motivated by the desire to disseminate a low-cost, high-precision, high-throughput environmental chamber to test materials and devices under elevated humidity, temperature, and light. This paper documents the creation of an open-source tool with a bill of materials as low as US$2,000, and the subsequent evolution of three second-generation tools installed at three different universities spanning thin films, bulk crystals, and thin-film solar-cell devices. We introduce an optical proxy measurement to detect real-time phase changes in materials. We present correlations between this optical proxy and standard X-ray diffraction measurements, describe some edge cases where the proxy measurement fails, and report key learnings from the technology-translation process. By sharing lessons learned, we hope that future open-hardware development and translation efforts can proceed with reduced friction. Throughout the paper, we provide examples of scientific impact, wherein participating laboratories used their environmental chambers to study and improve the stabilities of halide-perovskite materials. All generations of hardware bills of materials, assembly instructions, and operating codes are available in open-source repositories.

Summary Search for resource-efficient materials in vast compositional spaces is an outstanding challenge in creating environmentally stable perovskite semiconductors. We demonstrate a physics-constrained sequential learning framework to subsequently identify the most stable alloyed organic-inorganic perovskites. We fuse data from high-throughput degradation tests and first-principle calculations of phase thermodynamics into an end-to-end Bayesian optimization algorithm using probabilistic constraints. By sampling just 1.8% of the discretized CsxMAyFA1−x−yPbI3 (MA, methylammonium; FA, formamidinium) compositional space, perovskites centered at Cs0.17MA0.03FA0.80PbI3 show minimal optical change under increased temperature, moisture, and illumination with >17-fold stability improvement over MAPbI3. The thin films have 3-fold improved stability compared with state-of-the-art multi-halide Cs0.05(MA0.17FA0.83)0.95Pb(I0.83Br0.17)3, translating into enhanced solar cell stability without compromising conversion efficiency. Synchrotron-based X-ray scattering validates the suppression of chemical decomposition and minority phase formation achieved using fewer elements and a maximum of 8% MA. We anticipate that this data fusion approach can be extended to guide materials discovery for a wide range of multinary systems.

Sepsis is recognized as an inflammation-related syndrome in response to invading pathogens. Many patients suffer from sepsis including transplant recipients. Lipopolysaccharide (LPS) is known to trigger sepsis-related organ dysfunction. This study expounded on the possible effect of microRNA (miR)-338-3p in sepsis-induced acute lung injury (ALI). Firstly, human bronchial epithelial cell line 16HBE received LPS treatment to establish the cell models of sepsis-induced ALI. The expression patterns of miR-338-3p, long non-coding RNA OPA-interacting protein 5 antisense transcript 1 (lncRNA OIP5-AS1), and activating transcription factor 4 (ATF4) in 16HBE cells were examined. Afterwards, 16HBE cell viability, the apoptosis rate, and the levels of inflammation and lactate dehydrogenase (LDH) were determined to assess the degree of cell injury. We disclosed that LPS treatment triggered 16HBE cell injury, downregulated miR-338-3p, and upregulated OIP5-AS1 and ATF4. miR-338-3p overexpression repressed LPS-induced 16HBE cell injury. miR-338-3p diminished OIP5-AS1 stability via binding to OIP5-AS1 and downregulated OIP5-AS1 expression and OIP5-AS1 can enhance ATF4 mRNA stability and upregulate ATF4 mRNA level. The rescue experiments showed that ATF4 overexpression aggravated LPS-induced 16HBE cell injury. Overall, miR-338-3p overexpression decreased OIP5-AS1 expression and stability and further downregulated ATF4 mRNA level, thereby mitigating LPS-induced 16HBE cell injury.

Incorporating a low dimensional LD perovskite capping layer on top of a perovskite absorber, improves the stability of perovskite solar cells PSCs . However, in the case of mixed halide perovskites, which can undergo halide segregation into single halide perovskites, a systematic study of the capping layer s effect on mixed halide perovskite absorber is still lacking. This study bridges this gap by investigating how the 1D perovskite capping layers on top of MAPb IxBr1 amp; 8722;x 3 x 0, 0.25, 0.5, 0.75, 1 absorbers affect the films stability. We utilize a new method, dissimilarity matrix, to investigate the image based stability performance of capping absorber pair compositions across time. This method overcomes the challenge of analyzing various film colors due to bandgap difference in mixed halide perovskites. We also discover that the intrinsic absorber stability plays an important role in the overall stability outcome, despite the capping layer s support. Within the 55 unique capping absorber pairs, we observe a notable 1D perovskite material, 1 methoxynaphthalene 2 ethylammonium chloride 2MeO NEA Cl or 9 Cl , that improves the stability of MAPbI3 and MAPb I0.5Br0.5 3 by at least 8 and 1.5 times, respectively, compared to bare films under elevated humidity and temperature. Surface photovoltage results also show that the accumulation of electrostatic charges on the film surface depends on the capping layer type, which could contribute to the acceleration deceleration of degradation

Realizing general inverse design could greatly accelerate the discovery of new materials with user-defined properties. However, state-of-the-art generative models tend to be limited to a specific composition or crystal structure. Herein, we present a framework capable of general inverse design (not limited to a given set of elements or crystal structures), featuring a generalized invertible representation that encodes crystals in both real and reciprocal space, and a property-structured latent space from a variational autoencoder (VAE). In three design cases, the framework generates 142 new crystals with user-defined formation energies, bandgap, thermoelectric (TE) power factor, and combinations thereof. These generated crystals, absent in the training database, are validated by first-principles calculations. The success rates (number of first-principles-validated target-satisfying crystals/number of designed crystals) ranges between 7.1% and 38.9%. These results represent a significant step toward property-driven general inverse design using generative models, although practical challenges remain when coupled with experimental synthesis. Agency for Science, Technology and Research (A*STAR) National Research Foundation (NRF) This research is supported by the National Research Foundation, Prime Minister’s Office, Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) program through the Singapore Massachusetts Institute of Technology (MIT) Alliance for Research and Technology’s Low Energy Electronic Systems (LEES) research program. F.O., S.S., and Q. Liang acknowledge support from Total Energies Research grant funded through MITei. Y.J. acknowledges the support from the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (No. 2021-0-02068, Artificial Intelligence Innovation Hub). J.L. and X.W. acknowledge support from the Ministry of Education Academic Research Fund R-279-000-532-114,. Y.L. is supported by the National Key Research and Development Program of China (Grant Nos. 2017YFB0702901 and 2017YFB0701502) and the National Natural Science Foundation of China (Grant No. 91641128). S.J. and K.H. acknowledge funding from the Accelerated Materials Development for Manufacturing Program at A*STAR via the AME Programmatic Fund by the Agency for Science, Technology and Research under Grant No. A1898b0043. G.X. is grateful for the support by the Scientific Computing and Data Analysis section of the Research Support Division at Okinawa Institute of Science and Technology Graduate University (OIST). Q.Li is supported by the National Research Foundation (NRF) fellowship grant NRFF13-2021-0106. A.G.A. acknowledges support from Solar Energy Research Institute of Singapore (SERIS). SERIS is a research institute at the National University of Singapore (NUS). SERIS is supported by the National University of Singapore (NUS), the National Research Foundation Singapore (NRF), the Energy Market Authority of Singapore (EMA), and the Singapore Economic Development Board (EDB).

The volatile organic compounds (VOCs) emitted from wood-based panels are hazardous to indoor air quality. Usually, the VOCs are derived from the adhesive, chemical compounds, and wood components. However, there has been little research focusing on the effects of manufacture conditions on the VOC emissions. In this study, the effects of density, thickness, and resin content on total VOC (TVOC) and individual VOCs were investigated by the small chamber method and gas chromatography and mass spectrometry (GC/MS). The TVOC emission from the particleboard of each manufacturing condition decreased with extended exposure time. The higher density, thickness, and resin content of particleboard at each measured time caused higher concentrations of TVOC emissions. Most of the detected VOCs were aromatics. The esters, aldehydes, and ketones showed a high increasing level with increasing particleboard density, thickness, and resin content. This result indicated that these chemical compounds were most sensitive to changes in manufacturing conditions.

Doping is an effective strategy for tuning metal oxide-based semiconductors for solar-driven photoelectrochemical (PEC) water splitting. Despite decades of extensive research effort, the dopant selection is still largely dependent on a trial-and-error approach. Machine learning (ML) is promising in providing predictable insights on the dopant selection for high-performing PEC systems because it can uncover correlations from the seemingly ambiguous linkages between vast features of dopants and the PEC performance of doped photoelectrodes. Herein, the authors successfully build ML model to predict the doping effect of 17 metal dopants into hematite (Fe

Pulmonary arterial hypertension is a progressive and fatal disease. Recent studies suggest that circular RNA (circRNAs/circs) can regulate various biological processes, including cell proliferation. Therefore, it is possible that circRNA may have important roles in pulmonary artery smooth muscle cell proliferation in hypoxic pulmonary hypertension (HPH). The aim of the present study was to determine the role and mechanism of circRNA‑glutamate metabotropic receptor 1 (circ‑Grm1; mmu_circ_0001907) in pulmonary artery smooth muscle cell (PASMC) proliferation and migration in HPH. High‑throughput transcriptome sequencing was used to screen circRNAs and targeted genes involved in HPH. Cell Counting Kit‑8 (CCK‑8), 5‑ethynyl‑2‑deoxyuridine and wound healing assays were employed to assess cell viability and migration. Reverse transcription‑quantitative PCR and western blotting were used to detect target gene expression in different groups. Bioinformatical approaches were used to predict the interaction probabilities of circ‑Grm1 and Grm1 with FUS RNA binding protein (FUS). The interactions of circ‑Grm1, Grm1 and FUS were evaluated using RNA silencing and RNA immunoprecipitation assays. The results demonstrated that circ‑Grm1 was upregulated in hypoxic PASMCs. Further experiments revealed that the knockdown of circ‑Grm1 could suppress the proliferation and migration of hypoxic PASMCs. Transcriptome sequencing revealed that Grm1 could be the target gene of circ‑Grm1. It was found that circ‑Grm1 could competitively bind to FUS and consequently downregulate Grm1. Moreover, Grm1 could inhibit the function of circ‑Grm1 by promoting the proliferative and migratory abilities of hypoxic PASMCs. The results also demonstrated that circ‑Grm1 influenced the biological functions of PASMCs via the Rap1/ERK pathway by regulating Grm1. Overall, the current results suggested that circ‑Grm1 was associated with HPH and promoted the proliferation and migration of PASMCs via suppression of Grm1 expression through FUS.

Halide perovskite materials have attracted great interest for applications in low-cost, solution-processed solar cells and other optoelectronics applications. The role of moisture in perovskite device degradation and crystal formation processes remains poorly understood. Here, we use a data-driven approach to discover the influence of trace amounts of water on perovskite crystal formation by analyzing a comprehensive dataset of 8470 inverse-temperature crystallization lead iodide perovskite synthesis reactions, performed over 20 months using a robotic system. We identified discrepancies between the empirical crystal formation rates in batches of experiments conducted under different ambient relative humidity conditions for each organoammonium cation. We prioritized these using a statistical model and then used the robotic system to conduct 1296 controlled interventional experiments, in which small amounts of water were deliberately introduced to the reactions. The addition of trace amounts of water promotes crystal formation for 4-methoxyphenylammonium lead iodide and iso-propylammonium lead iodide and inhibits crystal formation for dimethylammonium lead iodide and acetamidinium lead iodide. We also performed thin-film syntheses of these four materials and determined the grain size distributions using scanning electron microscopy. The addition of water results in smaller grain sizes for dimethylammonium and larger grain sizes for iso-propylammonium, consistent with earlier or delayed nucleation, respectively. The agreement between the inverse temperature crystallization and thin film results indicates that this is a feature of the organoammonium-water interaction that persists despite differences in the synthesis method.

While machine learning (ML) in experimental research has demonstrated impressive predictive capabilities, inductive reasoning and knowledge extraction remain elusive tasks, in part because of the difficulty extracting fungible knowledge representations from experimental data. In this manuscript, we use ML to infer the underlying dynamical differential equation (DE) from experimental data of degrading organic-inorganic methylammonium lead iodide (MAPI) perovskite thin films under environmental stressors (elevated temperature, humidity, and light). We apply a sparse regression algorithm that automatically identifies the differential equation describing the dynamics from time-series data. We find that the underlying DE governing MAPI degradation across a broad temperature range of 35 to 85{\deg}C is described minimally with three terms (specifically, a second-order polynomial), and not a simple single-order reaction (i.e. 0th, 1st, or 2nd-order reaction). We demonstrate how computer-derived results can aid the researcher to develop profound mechanistic insights. This DE corresponds to the Verhulst logistic function, which describes reaction kinetics analogous in functional form to autocatalytic or self-propagating reactions, suggesting future strategies to suppress MAPI degradation. We examine the robustness of our conclusions to experimental luck-of-the-draw variance and Gaussian noise using a combination of experiment and simulation, and describe the experimental limits within which this methodology can be applied. Our study demonstrates the application of scientific ML in experimental chemical and materials systems, highlighting the promise and challenges associated with ML-aided scientific discovery.

Modern machine learning and autonomous experimentation schemes in materials science rely on accurate analysis of the data ingested by these models. Unfortunately, accurate analysis of the underlying data can be difficult, even for domain experts, complicating the training of the models intended to drive experiments. This is especially true when the goal is to identify the presence of weak signatures in diffraction or spectroscopic datasets. In this work, we examine a set of as-obtained diffraction data that track the phase transition from monoclinic to tetragonal in a Nb-doped VO2 film as a function of temperature and dopant concentration. We then task a set of domain experts and a set of machine learning experts with identifying which phase is present in each diffraction pattern manually and algorithmically, respectively; in both cases, the labels can vary dramatically, especially at the phase boundaries. We use the mode of the labels and the Shannon entropy as a method to capture, preserve and propagate consensus labels and their variance. Further we use the expert labels as a benchmark and demonstrate the use of Shannon entropy weighted scoring to test the performance of machine learning generated labels. Finally, we propose a material data challenge centered around generating improved labeling algorithms. This real-world dataset curated with expert labels can act as test bed for new algorithms. The raw data, annotations and code used in this study are all available online at data.gov and the interested reader is encouraged to replicate and improve the existing models

In the field of machine learning (ML) for materials optimization, active learning algorithms, such as Bayesian Optimization (BO), have been leveraged for guiding autonomous and high-throughput experimentation systems. However, very few studies have evaluated the efficiency of BO as a general optimization algorithm across a broad range of experimental materials science domains. In this work, we evaluate the performance of BO algorithms with a collection of surrogate model and acquisition function pairs across five diverse experimental materials systems, namely carbon nanotube polymer blends, silver nanoparticles, lead-halide perovskites, as well as additively manufactured polymer structures and shapes. By defining acceleration and enhancement metrics for general materials optimization objectives, we find that for surrogate model selection, Gaussian Process (GP) with anisotropic kernels (automatic relevance detection, ARD) and Random Forests (RF) have comparable performance and both outperform the commonly used GP without ARD. We discuss the implicit distributional assumptions of RF and GP, and the benefits of using GP with anisotropic kernels in detail. We provide practical insights for experimentalists on surrogate model selection of BO during materials optimization campaigns.

Hybrid organic-inorganic perovskites (HOIPs) have attracted significant attention in recent years attributed to their outstanding physical properties and striking application potentials in the fields of photovoltaic and optoelectronic devices. For the commercialization of devices employing hybrid perovskite materials, their mechanical properties critically influence the manufacturing, processing as well as the device durability, and hence need to be well understood. In this review, we summarize recent mechanical studies of the known HOIPs. Firstly, we present a comprehensive account of the relationship between crystal structures and properties, where the influence of different chemical and bonding factors on the load response of HOIPs are extensively discussed. The functional abnormalities of HOIP materials under the stress stimuli are also reviewed, addressing the effects of negative linear compressibility, negative Poisson’s ratio and barocaloric effect. Finally, we briefly summarize the significant effect of strain and stress in thin-film halide perovskite materials, which have been widely employed in solar cells and Light-emitting diodes. This review aims to raise more awareness about the fundamental understanding of the mechanical properties of hybrid perovskite materials, and attract more research attention to this interdisciplinary field.

Summary Accelerating the experimental cycle for new materials development is vital for addressing the grand energy challenges of the 21st century. We fabricate and characterize 75 unique perovskite-inspired compositions within a 2-month period, with 87% exhibiting band gaps between 1.2 and 2.4 eV, which are of interest for energy-harvesting applications. We utilize a fully connected deep neural network to classify compounds based on experimental X-ray diffraction data into 0D, 2D, and 3D structures, more than 10 times faster than human analysis and with 90% accuracy. We validate our methods using lead-halide perovskites and extend the application to lead-free compositions. The wider synthesis window and faster cycle of learning enables the realization of a multi-site lead-free alloy series, Cs3(Bi1-xSbx)2(I1-xBrx)9. We reveal the non-linear band-gap behavior and transition in dimensionality upon simultaneous alloying on the B-site and X-site of Cs3Bi2I9 with Sb and Br.

The Brucella spp encounter stressful environment inside their host cells. The Lon protein is an important protease related to cellular protein degradation and resistance to stress in Brucella. However, the molecular mechanism between Lon protein and stress response was still unknown. In this study, it was found that the lon mutant exhibited obvious growth defect in TSB medium, compared with its parent strain. In addition, our results indicated that Lon protein was involved in resistance to various stress conditions and all the β-lactam antibiotics tested. Although deletion of this protease did not affect Brucella virulence in macrophage, the mutant strain was significantly attenuated in mice infection model at 1 week post infection, and the expression level of several cytokine genes was significantly changed in vivo. To gain insight into the genetic basis for the distinctive phenotypic properties exhibited by the lon mutant strain, RNA-seq was performed, and the result showed that various genes involved in stress response, quorum sensing and transcriptional regulation were significantly altered in Δlon strain. Overall, these studies have preliminary uncovered the molecular mechanism between Lon protease, stress response and bacterial virulence.

The mechanism and elemental composition that form the basis for the improved optical and electronic properties in mixed-ion lead halide perovskite solar cells are still not well understood compared...

The role electron-transport layers (ETLs) play in perovskite solar cells (PSCs) is still widely debated. Conduction band alignment at the perovskite/ETL interface has been suggested to be an import...

Pure metal-organic framework (MOF) layers deposited on porous supports are important candidates for molecular sieving membranes, but their performance usually deviates from theoretical estimations. Here, we combine step-wise scanning electron microscopy imaging, time-resolved synchrotron X-ray scattering, terahertz infrared spectroscopy, and density functional theory calculation to investigate the ZIF-8 membrane formation on two types (polydopamine and TiO2) of functionalized porous supports. Though molecular sieving of ZIF-8 membranes for smaller gases (He, H2, and CO2) can be achieved with both types of functionalized supports, we unravel that the strong interaction between MOF and polydopamine can disrupt the formation of "perfect" MOF crystals at the interface, leading to a "contracted" MOF structure with partially uncoordinated imidazolate ligands. This further affects the low-frequency dynamical parameters of the framework and inhibits the effective seeded growth. Eventually, it leads to an unexpected loss of selectivity for the bulkier gases (N2 and CH4) for ZIF-8 on polydopamine-functionalized supports. This work links the dynamical aspects of MOFs with their gas transport behavior and highlights the importance of regulating the interfacial weak forces to preserve the ideal molecular sieving efficiency of MOF membranes, which also provides guidance for defect engineering of MOF film fabrication for sensing and electronic devices beyond membranes.

In a search for Pb-free photovoltaic materials, a double perovskite Cs2AgSbBr6 with an indirect optical bandgap of 1.64 eV has been synthesized. Single crystal X-ray diffraction determined the space group as Fmm with a = 11.1583(7) A. The black, as-synthesised compound turned brown after heat treatment at 480 K while the symmetry and crystallinity were preserved. X-ray photoelectron spectroscopy indicated the existence of Sb5+ in the black crystals, suggesting that the dark colour arises from the Sb3+–Sb5+ charge transfer. Furthermore, UV visible spectroscopy and density functional theory calculations have been applied to probe the optical properties and electronic structure.

A key debate involving mixed-cation lead mixed-halide perovskite thin-films relates to the effects of process conditions on film morphology and local performance of perovskite solar cells. In this contribution, we investigate the influence of precursor concentration on the film thickness, grain size, and orientation of these polycrystalline thin-films. We vary the molar concentration of the perovskite precursor containing Rb, Cs, MA, FA, Pb, I, and Br from 0.4 to 1.2 M. We use optical and electrical probes to measure local properties and correlate the effect of crystallographic orientation on the inter- and intragrain charge-carrier transport. We find that, with increasing precursor concentration, the grain size of the polycrystalline thin-films becomes larger and more faceted. Films with small grains show mostly random grain orientation angles, whereas films with large grains are oriented with {100} planes around an angle of 20° relative to the surface normal. These films with oriented large grains also ...