High-Energy Laser Protection Performance of Fibrous Felt-Reinforced Aerogels with Hierarchical Porous Architectures.

Continuous-wave lasers can cause irreversible damage to structured materials in a very short time. Modern high-energy laser protection materials are mainly constructed from ceramic, polymer, and metal constitutions. However, these materials are protected by sacrificing their structural integrity under the irradiation of high-energy lasers. In this contribution, we reported multilayer fibrous felt-reinforced aerogels that can sustain the continuous irradiation of a laser at a power density of 120 MW·m-2 without structural damage. It is found that the exceptional high-energy laser protection performance and the comparable mechanical properties of aerogel nanocomposites are attributed to the unique characteristics of hierarchical porous architectures. In comparison with various preparation methods and other aerogel materials, multilayer fibrous felt-reinforced aerogels exhibit the best performance in high-energy laser protection, arising from the gradual interception and the Raman-Rayleigh scattering cycles of a high-energy laser in the porous aerogels. Furthermore, a near-zero thermal expansion coefficient and extremely low thermal conductivity at high temperature allow the lightweight felt-reinforced aerogels to be applied in extreme conditions. The felt-reinforced aerogels reported herein offer an attractive material that can withstand complex thermomechanical stress and retain excellent insulation properties at extremely high temperature.

The association between diverse serum folate with MAFLD and liver fibrosis based on NHANES 2017-2020.

Background: Metabolically Associated Fatty Liver Disease (MAFLD) marks a progression from the previous paradigm of Non-Alcoholic Fatty Liver Disease (NAFLD), presenting a redefined diagnostic framework that accentuates metabolic factors while recognizing non-alcoholic contributors. In our investigation, our principal aim was to scrutinize the conceivable correlation between diverse serum folate levels and the prevalence of MAFLD and liver fibrosis. Methods: In our investigation, we conducted an extensive analysis utilizing data derived from the National Health and Nutrition Examination Survey (NHANES) across the years 2017-2020. We aimed to investigate the association between different serum folate concentrations and the prevalence of MAFLD and liver fibrosis by comprehensive multivariate analysis. This analytical approach considered various variables, encompassing sociodemographic characteristics, lifestyle factors, hypertension, and diabetes. By including these potential confounders in our analysis, we aimed to ensure the stability of the findings regarding the association between different serum folate concentrations and the development of MAFLD and liver fibrosis. Results: In our investigation, we utilized multiple linear regression models to thoroughly analyze the data, revealing noteworthy insights. Evidently, elevated levels of both total folate and 5-MTHF exhibited a distinct negative correlation with CAP, while 5-MTHF demonstrated a notable negative correlation with LSM. Furthermore, multiple logistic regression models were employed for an in-depth examination of the data. As the concentrations of total folate and 5-MTHF in the serum increased, a substantial decrease in the likelihood of MAFLD and liver fibrosis occurrence was observed. Conclusion: The findings of this investigation robustly suggest the prevalence of MAFLD and liver fibrosis decreased significantly with the increase of serum concentrations of total folate and 5-MTHF.

Effect of circadian rhythm change on gut microbiota and the development of nonalcoholic fatty liver disease in mice.

BACKGROUND: This study was to investigate the effect and possible mechanism of circadian rhythm change on the development of nonalcoholic fatty liver disease (NAFLD) in mice. METHODS: A total of 80 male SPF-grade 4-week C57BL/6J mice were randomly divided into normal diet normal light/dark cycle (ND-LD) and high-fat diet all dark (HFD-DD) groups. Weight measurements were taken weekly, and after 24 weeks of intervention, 24 mice from both groups were randomly selected and analyzed. Additionally, the remaining mice in the HFD-DD group were divided into two groups: one group continued the high-fat all-dark treatment (HFD-DD-DD), and the other group was restored to normal light/dark cycle treatment (HFD-DD-LD). Mice were euthanized after a total of 48 weeks of intervention. Measurements were taken for each mouse including liver function serum indicators, liver tissue pathological sections, rhythm-related proteins, and determination of the gut microbiota community. RESULTS: The HFD induced NAFLD in mice, exhibiting symptoms such as obesity, lipid and glucose metabolism disorders, elevated liver enzymes, and decreased gut microbiota diversity. The composition of the gut microbiota was significantly different from that of the normal diet group, with a significant increase in the ratio of Firmicutes to Bacteroides. Restoration of normal light/dark cycles exacerbated the disorder of lipid metabolism, liver steatosis, and the expression of BMAL1 in mice and significantly reduced the diversity of gut microbiota. CONCLUSIONS: Circadian rhythm changes aggravate the development of NAFLD induced by a high-fat diet by affecting glucose metabolism, liver steatosis, and gut microbiota diversity. Restoration of normal circadian rhythm did not improve NAFLD. Our findings open up new avenues for the prevention, diagnosis, and treatment of NAFLD.

Data-independent acquisition (DIA) mass spectrometry reveals related proteins involved in the occurrence of early intestinal-type gastric cancer.

Identifying proteins associated with the onset of early intestinal-type gastric cancer (EIGC) can yield valuable insights into the pathogenesis of this specific subtype of gastric cancer. Data-independent acquisition mass spectroscopy (DIA-MS) was utilized to identify the differential protein between 10 cases of EIGC and atrophic gastritis with intestinal metaplasia (NGC). The expressions of IPO4, TBL1XR1, p62/SQSTM1, PKP3, and CRTAP were verified by immunohistochemistry (IHC) in 20 EIGC samples, 17 gastric low-grade intraepithelial neoplasia (LGIN) samples, and 21 healthy controls. The prognostic values of the five genes were validated in the transcriptome data by survival analysis. A total of 4,028 proteins were identified using DIA-MS and a total of 177 differential proteins were screened with log2(fold change) > 1.5. Among them, 113 proteins were significantly up-regulated, and 64 proteins were significantly down-regulated in EIGC tissues. IHC results showed that proteins IPO4, TBL1XR1, p62/SQSTM1, PKP3, and CRTAP were highly expressed in the cytoplasm of EIGC and LGIN, which was consistent with the results of DIA-MS. Among them, p62/SQSTM1 may undergo nuclear-cytoplasmic transfer. The five protein-coding genes were associated with intestinal-type gastric cancer survival and exhibited differential expression across various disease stages. The study successfully identified differentially expressed proteins between EIGC and NGC, providing potential biomarkers and valuable insights into the mechanism underlying intestinal-type gastric cancer.

Prediction and Interpretability of Glass Transition Temperature of Homopolymers by Data-Augmented Graph Convolutional Neural Networks.

Establishing the structure-property relationship by machine learning (ML) models is extremely valuable for accelerating the molecular design of polymers. However, existing ML models for the polymers are subject to scarcity issues of training data and fewer variations of graph structures of molecules. In addition, limited works have explored the interpretability of ML models to infer the latent knowledge in the field of polymer science that could inspire ML-assisted molecular design. In this contribution, we integrate graph convolutional neural networks (GCNs) with data augmentation strategy to predict the glass transition temperature Tg of polymers. It is demonstrated that the data-augmented GCN model outperforms the conventional models and achieves a higher accuracy for the prediction of Tg despite a small amount of training data. Furthermore, taking advantage of molecular graph representations, the data-augmented GCN model has the capability to infer the importance of atoms or substructures from the understanding of Tg, which generally agrees with the experimental findings in the field of polymer science. The inferred knowledge of the GCN model is used to advise on the design of functional polymers with specific Tg. The data-augmented GCN model possesses prominent superiorities in the establishment of structure-property relationship and also provides an efficient way for accelerating the rational design of polymer molecules.

Designing Multimodal ON-OFF Nanoswitches of DNA-Functionalized Nanoparticles by Stimuli-Responsive Polymers.

It is a challenging task to realize highly reversible ON-OFF nanoswitches over a wide range of temperatures, which emerge as a versatile toolbox for use in nanobiotechnology. Herein, nanoparticles (NPs) bifunctionalized by DNA strands and stimuli-responsive polymers are proposed to construct multimodal ON-OFF nanoswitches by the coarse-grained model. The successful achievement of multimodal ON-OFF nanoswitches for bifunctionalized NPs at lower temperatures is attributed to the synergistic effects of the contraction and expansion configurations of stimuli-responsive polymers, combined with the hybridization-dehybridization event of DNA strands. Importantly, our simulations isolate the conditions of programmable self-assembly of bifunctionalized NPs to realize the multimodal ON-OFF nanoswitches by the changes of temperature and chain rigidity. In addition, it is found that the bifunctionalized NPs in the ON state display anisotropic and patchy features due to an introduction of stimuli-responsive polymers. Our simulation results provide fundamental insights on qualitative predictions of ON/OFF states of DNA-based NPs, which can aid in realizing a set of ON-OFF nanoswitches by the rational design of functionalization molecules.

Emulsion-Directed Synthesis of Poly-Porphyrin Nanoparticles as Heterogeneous Photocatalysts for PET-RAFT Polymerization.

Heterogeneous photocatalysts have attracted extensive attention in photo-induced electron transfer-reversible addition-fragmentation chain transfer (PET-RAFT) polymerization due to their remarkable advantages such as easy preparation, tunable photoelectric properties, and recyclability. In this study, zinc (II) 5,10,15,20-tetrakis(4-aminophenyl)porphyrin (ZnTAPP)-based poly-porphyrin nanoparticles (PTAPP-Zn) are constructed by an emulsion-directed approach. It is investigated as a heterogeneous photocatalyst for PET-RAFT polymerization of various methacrylate monomers under visible light exposure, and the reactions show refined polymerization control with high monomer conversions. Furthermore, it is demonstrated that the PTAPP-Zn nanoparticles with the larger pore size enhance photocatalytic activity in PET-RAFT polymerization. In addition, the capabilities of oxygen tolerance and temporal control are demonstrated and PTAPP-Zn particles can be easily recycled and reused without an obvious decrease in catalytic efficiency.

Harnessing Data Augmentation and Normalization Preprocessing to Improve the Performance of Chemical Reaction Predictions of Data-Driven Model.

As a template-free, data-driven methodology, the molecular transformer model provides an alternative by which to predict the outcome of chemical reactions and design the route of the retrosynthetic plane in the field of organic synthesis and polymer chemistry. However, in consideration of the small datasets of chemical reactions, the data-driven model suffers from the difficulty of low accuracy in the prediction tasks of chemical reactions. In this contribution, we integrate the molecular transformer model with the strategies of data augmentation and normalization preprocessing to accomplish the three tasks of chemical reactions, including the forward predictions of chemical reactions, and single-step retrosynthetic predictions with and without the reaction classes. It is clearly demonstrated that the prediction accuracy of the molecular transformer model can be significantly raised by the use of proposed strategies for the three tasks of chemical reactions. Notably, after the introduction of the 40-level data augmentation and normalization preprocessing, the top-1 accuracy of the forward prediction increases markedly from 71.6% to 84.2% and the top-1 accuracy of the single-step retrosynthetic prediction with additional reaction class increases from 53.2% to 63.4%. Furthermore, it is found that the superior performance of the data-driven model originates from the correction of the grammatical errors of the SMILES strings, especially for the case of the reaction classes with small datasets.

The Process-Directed Self-Assembly of Block Copolymer Particles.

The kinetic paths of structural evolution and formation of block copolymer (BCP) particles are explored using dynamic self-consistent field theory (DSCFT). It is shown that the process-directed self-assembly of BCP immersed in a poor solvent leads to the formation of striped ellipsoids, onion-like particles and double-spiral lamellar particles. The theory predicts a reversible path of shape transition between onion-like particles and striped ellipsoidal ones by regulating the temperature (related to the Flory-Huggins parameter between the two components of BCP, χAB ) and the selectivity of solvent toward one of the two BCP components. Furthermore, a kinetic path of shape transition from onion-like particles to double-spiral lamellar particles, and then back to onion-like particles is demonstrated. By investigating the inner-structural evolution of a BCP particle, it is identified that changing the intermediate bi-continuous structure into a layered one is crucial for the formation of striped ellipsoidal particles. Another interesting finding is that the formation of onion-like particles is characterized by a two-stage microphase separation. The first is induced by the solvent preference, and the second is controlled by the thermodynamics. The findings lead to an effective way of tailoring nanostructure of BCP particles for various industrial applications.

The association between sleep duration, quality, and nonalcoholic fatty liver disease: A cross-sectional study.

Sleep can affect nonalcoholic fatty liver disease (NAFLD). We investigated the association between sleep duration, sleep quality, and NAFLD. From January to December 2018, 1,073 patients (age: 37.94 ± 10.88, Body Mass Index (BMI): 22.85 ± 3.27) were enrolled. Pittsburgh Sleep Quality Index Questionnaire and Munich Chronotype Questionnaire were used to assess sleep duration, quality, and habits. Ultrasonography was used to diagnose NAFLD. Multivariate logistic regression models were used to calculate the odds ratio (OR) and 95% confidence interval (CI) of the risk of NAFLD by different types of sleep duration and sleep quality. No significant differences in sleep time, sleep quality, and sleep habits between the NAFLD and the non-NAFLD groups were observed (P > 0.05). There was no correlation between sleep duration and NAFLD in the whole cohort. After adjusting for age, exercise, fasting plasma glucose, and BMI, the group with long sleep duration showed a decreased risk of NAFLD in men (OR = 0.01, 95% CI: 0.001-0.27, P = 0.032). However, in all four adjusted models, no correlation between sleep duration, quality, and NAFLD was found in women. In conclusion, sleep duration was significantly and negatively associated with NAFLD in men but not women. Prospective studies are required to confirm this association.

Inverse Design of Complex Block Copolymers for Exotic Self-Assembled Structures Based on Bayesian Optimization.

Variable chain topologies of multiblock copolymers provide great opportunities for the formation of numerous self-assembled nanostructures with promising potential applications. However, the consequent large parameter space poses new challenges for searching the stable parameter region of desired novel structures. In this Letter, by combining Bayesian optimization (BO), fast Fourier transform-assisted 3D convolutional neural network (FFT-3DCNN), and self-consistent field theory (SCFT), we develop a data-driven and fully automated inverse design framework to search for the desired novel structures self-assembled by ABC-type multiblock copolymers. Stable phase regions of three exotic target structures are efficiently identified in high-dimensional parameter space. Our work advances the new research paradigm of inverse design in the field of block copolymers.

Enhanced dielectric permittivity of hierarchically double-gyroid nanocomposites via macromolecular engineering of block copolymers.

It is a challenging task to realize the periodically bicontinuous gyroid nanostructures of flexible nanocomposites with high loading of functionalized nanoparticles, which could exhibit high dielectric permittivity for energy storage and electronic devices. Herein, with the aid of the concept of macromolecular engineering, we propose novel nanocomposites, composed of A'(A''B)n miktoarm star copolymers and nanoparticles, to obtain a double-gyroid structure through self-consistent field theory coupled with density functional theory. By tailoring the architecture of this copolymer, a large window of the double-gyroid phase extending to a high loading concentration of nanoparticles is achieved, leading to a hierarchical structure of a percolation network of nanoparticles within the gyroid channels. Furthermore, the finite difference quasielectrostatic method is integrated to reveal an enhanced dielectric permittivity of the structured nanocomposites by increasing the loading concentration of nanoparticles. The simultaneous achievement of an ordered double-gyroid phase and high loading nanoparticles represents a crucial step toward the realization of fully three-dimensional network-like metamaterials via a rational molecular design of nanocomposites.

Kinetically Programming Copolymerization-like Coassembly of Multicomponent Nanoparticles with DNA.

Programmable coassembly of multicomponent nanoparticles (NPs) into heterostructures has the capability to build upon nanostructured metamaterials with enhanced complexity and diversity. However, a general understanding of how to manipulate the sequence-defined heterostructures using straightforward concepts and quantitatively predict the coassembly process remains unreached. Drawing inspiration from the synthetic concepts of molecular block copolymers is extremely beneficial to achieve controllable coassembly of NPs and access mesoscale structuring mechanisms. We herein report a general paradigm of kinetic pathway guidance for the controllable coassembly of bivalent DNA-functionalized NPs into regular block-copolymer-like heterostructures via the stepwise polymerization strategy. By quantifying the coassembly kinetics and structural statistics, it is demonstrated that the coassembly of multicomponent NPs, through directing the specific pathways of prepolymer intermediates, follows the step-growth copolymerization mechanism. Meanwhile, a quantitative model is developed to predict the growth kinetics and outcomes of heterostructures, all controlled by the designed elements of the coassembly system. Furthermore, the stepwise polymerization strategy can be generalized to build upon a great variety of regular nanopolymers with complex architectures, such as multiblock terpolymers and ladder copolymers. Our theoretical and simulation results provide fundamental insights on quantitative predictions of the coassembly kinetics and coassembled outcomes, which can aid in realizing a diverse set of supramolecular DNA materials by the rational design of kinetic pathways.

RAFT Polymerization of Semifluorinated Monomers Mediated by a NIR Fluorinated Photocatalyst.

Near-infrared (NIR) light plays an increasingly important role in the field of photoinduced electron/energy transfer-reversible addition-fragmentation chain transfer (PET-RAFT) polymerization due to its unique properties. Yet, the NIR photocatalyst with good stability for PET-RAFT polymerization remains promising. Here, a strategy of NIR PET-RAFT polymerization of semifluorinated monomers using fluorophenyl bacteriochlorin as a photocatalyst with strong absorption at the NIR light region (710-780 nm) is reported. In which, the F atoms are used to modify reduced tetraphenylporphyrin structure with enhanced photostability of photocatalyst. Under the irradiation of NIR light (λmax = 740 nm), the PET-RAFT polymerization of semifluorinated methylacrylic monomers presents living/control characteristics and temporal modulation. By the PET-RAFT polymerization-induced self-assembly (PISA) strategy, stable fluorine-containing micelles are constructed in various solvents. In addition, the fluorinated hydrophobic surface is fabricated via a surface-initiated PET-RAFT (SI-PET-RAFT) polymerization using silicon wafer bearing RAFT agents with tunable surface hydrophobicity. This strategy not only enlightens the application of further modified compounds based on porphyrin structure in photopolymerization, but also shows promising potential for the construction of well-defined functional fluoropolymers.

Gene Network Analysis of Hepatocellular Carcinoma Identifies Modules Associated with Disease Progression, Survival, and Chemo Drug Resistance.

BACKGROUND: Hepatocellular carcinoma (HCC) is the second leading cause of cancer-related mortality worldwide. HCC transcriptome has been extensively studied; however, the progress in disease mechanisms, prognosis, and treatment is still slow. METHODS: A rank-based module-centric workflow was introduced to analyze important modules associated with HCC development, prognosis, and drug resistance. The currently largest HCC cell line RNA-Seq dataset from the LIMORE database was used to construct the reference modules by weighted gene co-expression network analysis. RESULTS: Thirteen reference modules were identified with validated reproducibility. These modules were all associated with specific biological functions. Differentially expressed module analysis revealed the crucial modules during HCC development. Modules and hub genes are indicative of patient survival. Modules can differentiate patients in different HCC stages. Furthermore, drug resistance was revealed by drug-module association analysis. Based on differentially expressed modules and hub genes, six candidate drugs were screened. The hub genes of those modules merit further investigation. CONCLUSION: We proposed a reference module-based analysis of the HCC transcriptome. The identified modules are associated with HCC development, survival, and drug resistance. M3 and M6 may play important roles during HCV to HCC development. M1, M3, M5, and M7 are associated with HCC survival. High M4, high M9, low M1, and low M3 may be associated with dasatinib, doxorubicin, CD532, and simvastatin resistance. Our analysis provides useful information for HCC diagnosis and treatment.

Gene co-expression analysis identifies modules related to insufficient sleep in humans.

BACKGROUND: Insufficient sleep and circadian rhythm disruption may cause cancer, obesity, cardiovascular disease, and cognitive impairment. The underlying mechanisms need to be elucidated. METHOD: Weighted gene co-expression network analysis (WGCNA) was used to identify co-expressed modules. Connectivity Map tool was used to identify candidate drugs based on top connected genes. R ptestg package was utilized to detected module rhythmicity alteration. A hypergeometric test was used to test the enrichment of insomnia SNP signals in modules. Google Scholar was used to validate the modules and hub genes by literature. RESULTS: We identified a total of 45 co-expressed modules. These modules were stable and preserved. Eight modules were correlated with sleep restriction duration. Module rhythmicity was disrupted in sleep restriction subjects. Hub genes that involve in insufficient sleep also play important roles in sleep disorders. Insomnia GWAS signals were enriched in six modules. Finally, eight drugs associated with sleep disorders were identified. CONCLUSION: Systems biology method was used to identify sleep-related modules, hub genes, and candidate drugs. Module rhythmicity was altered in sleep insufficient subjects. Thiamphenicol, lisuride, timolol, and piretanide are novel candidates for sleep disorders.

A pH/H2O2 dual triggered nanoplatform for enhanced photodynamic antibacterial efficiency.

Bacterial infection and biofilms cause non-healing chronic wounds and threaten human health. Although antibiotics still play an irreplaceable role to treat infectious diseases in clinics, increasing attention has been paid to the problem of multidrug resistance (MDR). As a novel strategy to deal with bacterial infection, photodynamic antimicrobial therapy (PDAT) has shown promising potential to reduce bacterial infection, and stimuli-responsive nanomaterials have been shown to enhance the antibacterial efficiency and postpone the emergence of drug-resistant bacteria. In this work, we developed a bacterial microenvironment-responsive nanoplatform to eliminate bacteria and bacterial biofilms under 650 nm laser irradiation. Reversible addition-fragmentation chain transfer (RAFT) polymerization was applied to synthesize an H2O2 responsive block copolymer of POEGMA-b-PBMA, and the antibacterial drug of porphyrin TAPP was loaded to form nanoparticles (PT) by a co-assembled approach. At the infection area with overexpressed peroxide, nanoparticles were disintegrated due to the cleaved boronic ester leading to the release of TAPP. Furthermore, the released TAPP became protonated in the acidic infection area (pH = 5.5) and then enhanced its photodynamic antibacterial efficacy by producing higher singlet oxygen (1O2) levels under light irradiation. Both in vitro and in vivo antimicrobial and biofilm elimination experiments demonstrated that the responsive nanoplatform combined with PDAT has tremendous potential for the treatment of infections.

Distinctive Dielectric Permittivity of Hierarchical Nanostructures with Ordered Nanoparticle Networks Self-Assembled from AB-g-NP/AC Block Copolymer Mixtures.

Directing nanoparticles into ordered organization in polymer matrix to improve macroscopic properties of nanocomposites remains a challenge. Herein, by means of theoretical simulations, we show the high permittivity of hybrid nanostructures designed with mixtures of AB block copolymer-grafted nanoparticles and lamella-forming AC diblock copolymers. The grafted nanoparticles self-assemble into parallel stripes or highly ordered networks in the lamellae of the AC diblock copolymers. The ordered nanoparticle networks, including honeycomb-like and kagomé networks, provide bending and conductive pathways for concentrating electric fields, which results in the improvement of the permittivity. We envisage that this strategy will open a gateway to prepare hierarchically ordered functional nanocomposites with distinctive dielectric properties.

An Antifouling and Antimicrobial Zwitterionic Nanocomposite Hydrogel Dressing for Enhanced Wound Healing.

Antibacterial hydrogels have received intensive interest in soft tissue repair, especially for preventing infections associated with impaired wound healing. However, developing an inherent antibacterial hydrogel dressing with antifouling ability without causing secondary damage to repaired tissues has proven to be promising and challenging. In this work, a mussel-inspired zwitterionic sulfobetaine acrylamide hydrogel incorporated with laponite (LAP) nanoplatelets and methacrylamide dopamine (DMA) has been developed for effective wound dressings, where LAP nanoplatelets and DMA endow the hydrogel with enhanced mechanical strength and substance adhesiveness, respectively. Moreover, LAP nanoplatelets could immobilize hydrophobic curcumin to form complexes, realizing the controlled release of curcumin to provide antimicrobial activities. In vitro results showed that hydrogels did not cause obvious cytotoxicity and hemolysis, but they still can well resist bovine serum albumin (BSA) adsorption. Wound closure and histopathological experiments have been performed in vivo to evaluate the therapeutic effects of the hydrogel by a full-thickness skin defect mouse model, and the results demonstrated that infected wounds could be well closed after being treated with the hydrogel for 15 days. Meanwhile, the full re-epithelialization and total formation of new connective tissues can be clearly observed by histological analysis. Moreover, the hydrogel could be easily removed from recovered tissues without causing secondary damage. Therefore, this antifouling and antimicrobial hydrogel dressing with suitable adhesiveness would provide a new strategy for wound healing without causing secondary damage.

Autonomous Construction of Phase Diagrams of Block Copolymers by Theory-Assisted Active Machine Learning.

Equilibrium phase diagrams serve as blueprints for rational design of nanostructured materials of block copolymers, but their construction is time-consuming and requires profound expertise. Herein, by virtue of the knowledge of self-consistent field theory (SCFT), the active-learning method is developed to autonomously construct the phase diagrams of block copolymers. Without human intervention, the SCFT-assisted active-learning method can rapidly search the undetected phases and efficiently reproduce the complicated phase diagrams of diblock copolymers and multiblock terpolymers via decreasing the number of sampling points to about 20%. It is clearly demonstrated that the combined uncertainty sampling/random selection scheme in the active-learning method shows the outperformance in spite of a small amount of initial data set. This work highlights the promising integration of theoretical modeling with machine learning and represents a crucial step toward rational design of nanostructured materials.