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Super-ionic conductor materials have great potential to enable novel technologies in energy storage and conversion. However, it is not yet understood why only a few materials can deliver exceptionally higher ionic conductivity than typical solids or how one can design fast ion conductors following simple principles. Using ab initio modelling, here we show that fast diffusion in super-ionic conductors does not occur through isolated ion hopping as is typical in solids, but instead proceeds through concerted migrations of multiple ions with low energy barriers. Furthermore, we elucidate that the low energy barriers of the concerted ionic diffusion are a result of unique mobile ion configurations and strong mobile ion interactions in super-ionic conductors. Our results provide a general framework and universal strategy to design solid materials with fast ionic diffusion. Ab initio atomistic modelling reveals that fast ion diffusion in super-ionic conductors is mediated by concerted migration of multiple ions. On the basis of this understanding, a universal strategy for designing fast ion conductors is proposed and demonstrated.

Ab initio molecular dynamics (AIMD) simulation is widely employed in studying diffusion mechanisms and in quantifying diffusional properties of materials. However, AIMD simulations are often limited to a few hundred atoms and a short, sub-nanosecond physical timescale, which leads to models that include only a limited number of diffusion events. As a result, the diffusional properties obtained from AIMD simulations are often plagued by poor statistics. In this paper, we re-examine the process to estimate diffusivity and ionic conductivity from the AIMD simulations and establish the procedure to minimize the fitting errors. In addition, we propose methods for quantifying the statistical variance of the diffusivity and ionic conductivity from the number of diffusion events observed during the AIMD simulation. Since an adequate number of diffusion events must be sampled, AIMD simulations should be sufficiently long and can only be performed on materials with reasonably fast diffusion. We chart the ranges of materials and physical conditions that can be accessible by AIMD simulations in studying diffusional properties. Our work provides the foundation for quantifying the statistical confidence levels of diffusion results from AIMD simulations and for correctly employing this powerful technique.

Lithium metal battery is a promising candidate for high‐energy‐density energy storage. Unfortunately, the strongly reducing nature of lithium metal has been an outstanding challenge causing poor stability and low coulombic efficiency in lithium batteries. For decades, there are significant research efforts to stabilize lithium metal anode. However, such efforts are greatly impeded by the lack of knowledge about lithium‐stable materials chemistry. So far, only a few materials are known to be stable against Li metal. To resolve this outstanding challenge, lithium‐stable materials have been uncovered out of chemistry across the periodic table using first‐principles calculations based on large materials database. It is found that most oxides, sulfides, and halides, commonly studied as protection materials, are reduced by lithium metal due to the reduction of metal cations. It is discovered that nitride anion chemistry exhibits unique stability against Li metal, which is either thermodynamically intrinsic or a result of stable passivation. The results here establish essential guidelines for selecting, designing, and discovering materials for lithium metal protection, and propose multiple novel strategies of using nitride materials and high nitrogen doping to form stable solid‐electrolyte‐interphase for lithium metal anode, paving the way for high‐energy rechargeable lithium batteries. Novel stabilization strategies for Li metal anode are proposed by uncovering lithium‐stable materials chemistry across the periodic table using first‐principles calculations. Nitride anion chemistry exhibits unique lithium stability, which is thermodynamically intrinsic or a result of stable passivation. Applying nitride interphase and nitrogen doping provides ultimate stability to protect lithium metal anode.

Although machine learning has gained great interest in the discovery of functional materials, the advancement of reliable models is impeded by the scarcity of available materials property data. Here we propose and demonstrate a distinctive approach for materials discovery using unsupervised learning, which does not require labeled data and thus alleviates the data scarcity challenge. Using solid-state Li-ion conductors as a model problem, unsupervised materials discovery utilizes a limited quantity of conductivity data to prioritize a candidate list from a wide range of Li-containing materials for further accurate screening. Our unsupervised learning scheme discovers 16 new fast Li-conductors with conductivities of 10 −4 –10 −1  S cm −1 predicted in ab initio molecular dynamics simulations. These compounds have structures and chemistries distinct to known systems, demonstrating the capability of unsupervised learning for discovering materials over a wide materials space with limited property data. Predictions of new solid-state Li-ion conductors are challenging due to the diverse chemistries and compositions involved. Here the authors combine unsupervised learning techniques and molecular dynamics simulations to discover new compounds with high Li-ion conductivity.

Garnet-type solid-state electrolytes have attracted extensive attention due to their high ionic conductivity, approaching 1 mS cm −1 , excellent environmental stability, and wide electrochemical stability window, from lithium metal to ∼6 V. However, to date, there has been little success in the development of high-performance solid-state batteries using these exceptional materials, the major challenge being the high solid–solid interfacial impedance between the garnet electrolyte and electrode materials. In this work, we effectively address the large interfacial impedance between a lithium metal anode and the garnet electrolyte using ultrathin aluminium oxide (Al 2 O 3 ) by atomic layer deposition. Li 7 La 2.75 Ca 0.25 Zr 1.75 Nb 0.25 O 12 (LLCZN) is the garnet composition of choice in this work due to its reduced sintering temperature and increased lithium ion conductivity. A significant decrease of interfacial impedance, from 1,710 Ω cm 2 to 1 Ω cm 2 , was observed at room temperature, effectively negating the lithium metal/garnet interfacial impedance. Experimental and computational results reveal that the oxide coating enables wetting of metallic lithium in contact with the garnet electrolyte surface and the lithiated-alumina interface allows effective lithium ion transport between the lithium metal anode and garnet electrolyte. We also demonstrate a working cell with a lithium metal anode, garnet electrolyte and a high-voltage cathode by applying the newly developed interface chemistry. Garnet-type electrolytes are attractive for lithium metal batteries due to their high ionic conductivity. A strategy to decrease interfacial impedance between a lithium metal anode and garnet electrolyte is found promising for all-solid-state batteries.

Machine learning interatomic potentials (MLIPs) are a promising technique for atomic modeling. While small errors are widely reported for MLIPs, an open concern is whether MLIPs can accurately reproduce atomistic dynamics and related physical properties in molecular dynamics (MD) simulations. In this study, we examine the state-of-the-art MLIPs and uncover several discrepancies related to atom dynamics, defects, and rare events (REs), compared to ab initio methods. We find that low averaged errors by current MLIP testing are insufficient, and develop quantitative metrics that better indicate the accurate prediction of atomic dynamics by MLIPs. The MLIPs optimized by the RE-based evaluation metrics are demonstrated to have improved prediction in multiple properties. The identified errors, the evaluation metrics, and the proposed process of developing such metrics are general to MLIPs, thus providing valuable guidance for future testing and improvements of accurate and reliable MLIPs for atomistic modeling.

Background LncRNA-PACERR plays critical role in the polarization of tissue-associated macrophages (TAMs). In this study, we found the function and molecular mechanism of PACERR in TAMs to regulate pancreatic ductal adenocarcinoma (PDAC) progression. Methods We used qPCR to analyse the expression of PACERR in TAMs and M1-tissue-resident macrophages (M1-NTRMs) which were isolated from 46 PDAC tissues. The function of PACERR on macrophages polarization and PDAC proliferation, migration and invasion were confirmed through in vivo and in vitro assays. The molecular mechanism of PACERR was discussed via fluorescence in situ hybridization (FISH), RNA pull-down, ChIP-qPCR, RIP-qPCR and luciferase assays. Results LncRNA-PACERR was high expression in TAMs and associated with poor prognosis in PDAC patients. Our finding validated that LncRNA-PACERR increased the number of M2-polarized cells and facilized cell proliferation, invasion and migration in vitro and in vivo. Mechanistically, LncRNA-PACERR activate KLF12/p-AKT/c-myc pathway by binding to miR-671-3p. And LncRNA-PACERR which bound to IGF2BP2 acts as an m6A-dependent manner to enhance the stability of KLF12 and c-myc in cytoplasm. In addition, the promoter of LncRNA-PACERR was a target of KLF12 and LncRNA-PACERR recruited EP300 to increase the acetylation of histone by interacting with KLF12 in nucleus. Conclusions This study found that LncRNA-PACERR functions as key regulator of TAMs in PDAC microenvironment and revealed the novel mechanisms in cytoplasm and in nucleus.

There is no effective method to predict chemotherapy response and postoperative prognosis of colorectal cancer liver metastasis (CRLM) patients. Patient‐derived organoid (PDO) has become an important preclinical model. Herein, a living biobank with 50 CRLM organoids derived from primary tumors and paired liver metastatic lesions is successfully constructed. CRLM PDOs from the multiomics levels (histopathology, genome, transcriptome and single‐cell sequencing) are comprehensively analyzed and confirmed that this organoid platform for CRLM could capture intra‐ and interpatient heterogeneity. The chemosensitivity data in vitro reveal the potential value of clinical application for PDOs to predict chemotherapy response (FOLFOX or FOLFIRI) and clinical prognosis of CRLM patients. Taken together, CRLM PDOs can be utilized to deliver a potential application for personalized medicine. A living biobank of patient‐derived organoid (PDO) is derived from primary colorectal cancer and paired liver metastatic lesions, which captures intra‐ and interpatient molecular fingerprint heterogeneity. PDO chemosensitivity measured in vitro may be used as a predictive tool for clinical chemotherapeutic efficacy, and guide the formulation of precise treatment strategies for colorectal cancer patients with liver metastases.

Introduction: Most advanced colorectal cancers are aggressive, and there is a lack of effective methods for selecting appropriate anticancer regimens. Patient-derived organoids (PDOs) have emerged as preclinical platforms for modeling clinical responses to cancer therapy. Methods: In this study, we successfully constructed a living biobank with 42 organoids derived from primary and metastatic lesions of metastatic colorectal cancer patients. Tumor tissue was obtained from patients undergoing surgical resection of the primary or metastatic lesion and then used to establish PDOs. Immunohistochemistry (IHC) and drug sensitivity assays were performed to analyze the properties of these organoids. Results: The mCRC organoids were successfully established with an 80% success rate. The PDOs maintained the genetic and phenotypic heterogeneity of their parental tumors. The IC50 values of5-fluorouracil (5-FU), oxaliplatin, and irinotecan (CPT11) were determined for mCRC organoids using drug sensitivity assays. The in vitro chemosensitivity data revealed the potential value of PDOs for clinical applications in predicting chemotherapy response and clinical outcomes in mCRC patients. Discussion: In summary, the PDO model is an effective platform for in vitro assessment of patient-specific drug sensitivity, which can guide personalized treatment decisions for patients with end-stage CRC.

Background Organoids are three-dimensional structures that closely recapitulate tissue architecture and cellular composition, thereby holding great promise for organoid-based drug screening. Although growing in three-dimensional provides the possibility for organoids to recapitulate main features of corresponding tissues, it makes it incommodious for imaging organoids in two-dimensional and identifying surviving organoids from surrounding dead cells after organoids being treated by irradiation or chemotherapy. Therefore, significant work remains to establish high-quality controls to standardize organoid analyses and make organoid models more reproducible. Methods In this study, the Z-stack imaging technique was used for the imaging of three-dimensional organoids to gather all the organoids’ maximum cross sections in one imaging. The combination of live cell staining fluorescent dye Calcein-AM and ImageJ assessment was used to analyze the survival of organoids treated by irradiation or chemotherapy. Results We have established a novel quantitative high-throughput imaging assay that harnesses the scalability of organoid cultures. Using this assay, we can capture organoid growth over time, measure multiple whole-well organoid readouts, and show the different responses to drug treatments. Conclusions In summary, combining the Z-stack imaging technique and fluorescent labeling methods, we established an assay for the imaging and analysis of three-dimensional organoids. Our data demonstrated the feasibility of using organoid-based platforms for high-throughput drug screening assays. Graphical Abstract