jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Helena Käck | Tove Sjögren
Source: Journal of Synchrotron Radiation (article, 2025-01-13)
Field: Materials Science — Materials Chemistry

Macromolecular crystallography from an industrial perspective – the impact of synchrotron radiation on structure-based drug discovery Structure-based drug design has been an integral part of drug discovery for over three decades, contributing to the development of numerous approved drugs. Here we discuss the evolution, as well as the current state, of structure-based drug design within the pharmaceutical industry, using data from AstraZeneca's internal repository for crystal structures to provide additional context. Over the past 20 years, the company has transitioned from a mixed in-house and synchrotron data collection model to a `synchrotron-only' approach, enabled by technological advancements at synchrotron facilities. We provide real-world examples of structure delivery to projects, including a high-throughput project and a case where a single structure was pivotal for discovering a candidate drug. We conclude that, despite recent developments in single-particle cryo-EM and deep-learning structure prediction methods, macromolecular crystallography remains a critical tool for drug discovery. Synchrotron ; Drug discovery ; Context (archaeology) ; Synchrotron radiation ; Nanotechnology ; Computer science ; Data science ; Materials science ; Bioinformatics ; Physics ; Biology ; Optics Enzyme Structure and Function ; Protein Structure and Dynamics ; Machine Learning in Materials Science
Authors: Shengxin Li | Ziyan Zhang | Zhiran Zhang | Sen Zhou | M.L. Liu | Xichao Li | Zheng Zheng | Jie Sun
Source: Microchemical Journal (article, 2025-02-23)
Field: Chemistry — Analytical Chemistry

Comparative analysis of spectral variable selection methods for NIR-based multi-component detection of Xanthoceras sorbifolium Bunge seed kernels Selection (genetic algorithm) ; Component (thermodynamics) ; Chemistry ; Biological system ; Mathematics ; Biology ; Computer science ; Artificial intelligence ; Physics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Taoyong Cui | Chenyu Tang | Dongzhan Zhou | Yuqiang Li | Xin-Gao Gong | Wanli Ouyang | Mao Su | Shufei Zhang
Source: Nature Communications (article, 2025-02-22)
Field: Materials Science — Materials Chemistry

Online test-time adaptation for better generalization of interatomic potentials to out-of-distribution data Machine learning interatomic potentials (MLIPs) enable more efficient molecular dynamics (MD) simulations with ab initio accuracy, which have been used in various domains of physical science. However, distribution shift between training and test data causes deterioration of the test performance of MLIPs, and even leads to collapse of MD simulations. In this work, we propose an online Test-time Adaptation Interatomic Potential (TAIP) framework to improve the generalization on test data. Specifically, we design a dual-level self-supervised learning approach that leverages global structure and atomic local environment information to align the model with the test data. Extensive experiments demonstrate TAIP's capability to bridge the domain gap between training and test dataset without additional data. TAIP enhances the test performance on various benchmarks, from small molecule datasets to complex periodic molecular systems with various types of elements. TAIP also enables stable MD simulations where the corresponding baseline models collapse. Generalization ; Adaptation (eye) ; Computer science ; Distribution (mathematics) ; Statistical physics ; Biology ; Physics ; Mathematics ; Neuroscience Machine Learning in Materials Science ; Nuclear Physics and Applications ; Non-Destructive Testing Techniques
Authors: Artur Wodyński | Kilian Glodny | Martin Kaupp
Source: Journal of Chemical Theory and Computation (article, 2025-01-13)
Field: Materials Science — Materials Chemistry

Data-Driven Improvement of Local Hybrid Functionals: Neural-Network-Based Local Mixing Functions and Power-Series Correlation Functionals Local hybrid functionals (LHs) use a real-space position-dependent admixture of exact exchange (EXX), governed by a local mixing function (LMF). The systematic construction of LMFs has been hampered over the years by a lack of exact physical constraints on their valence behavior. Here, we exploit a data-driven approach and train a new type of "n-LMF" as a relatively shallow neural network. The input features are of meta-GGA character, while the W4-17 atomization-energy and BH76 reaction-barrier test sets have been used for training. Simply replacing the widely used "t-LMF" of the LH20t functional by the n-LMF provides the LH24n-B95 functional. Augmented by DFT-D4 dispersion corrections, LH24n-B95-D4 remarkably improves the WTMAD-2 value for the large GMTKN55 test suite of general main-group thermochemistry, kinetics, and noncovalent interactions (NCIs) from 4.55 to 3.49 kcal/mol. As we found the limited flexibility of the B95c correlation functional to disfavor much further improvement on NCIs, we proceeded to replace it by an optimized B97c-type power-series expansion. This gives the LH24n functional. LH24n-D4 gives a WTMAD-2 value of 3.10 kcal/mol, the so far lowest value of a rung 4 functional in self-consistent calculations. The new functionals perform moderately well for organometallic transition-metal energetics while leaving room for further data-driven improvements in that area. Compared to complete neural-network functionals like DM21, the present more tailored approach to train just the LMF in a flexible but well-defined human-designed LH functional retains the possibility of graphical LMF analyses to gain deeper understanding. We find that both the present n-LMF and the recent x-LMF suppress the so-called gauge problem of local hybrids without adding a calibration function as required for other LMFs. LMF plots show that this can be traced back to large LMF values in the small-density region between the interacting atoms in NCIs for n- and x-LMFs and low values for the t-LMF. We also find that the trained n-LMF has relatively large values in covalent bonds without deteriorating binding energies. The current approach enables fast and efficient routine self-consistent calculations using n-LMFs in Turbomole. Artificial neural network ; Valence (chemistry) ; Hybrid functional ; Scalability ; Statistical physics ; Mixing (physics) ; Computer science ; Density functional theory ; Physics ; Chemistry ; Computational chemistry ; Quantum mechanics ; Artificial intelligence Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Thermal and Kinetic Analysis
Authors: Zhi Zhang | Jianbao Gao | Shuaishuai Wei | Bo Song | Yonggang Yao | Xu Zheng | Yuanjie Zhang | Lei Zhang | Qiaojiao Li | Jiafeng Wu | Yusheng Shi
Source: Virtual and Physical Prototyping (article, 2025-01-05)
Field: Materials Science — Materials Chemistry

Lattice-inspired NiTi-based metamaterials with widely tunable mechanical-superelastic synergy Inspired by austenite and martensite crystal lattices in the NiTi microstructure with versatile performances, the bionic microlattice metamaterials with strut diameter from 0.4∼0.8 mm were constructed and prepared by the laser powder bed fusion for expanding the tailored mechanical-superelastic range, and the machine learning was utilized for mapping the relationship of various parameters. The mechanical-superelastic were highly related to orientation, the martensite-inspired microlattice metamaterial in the x-axis loading direction (M-x) possessed higher mechanical properties than that of the martensite-inspired microlattice metamaterials in the z-axis loading direction (M-z). For mechanical properties, the M-x possessed the highest Young's modulus (E=1001.5∼3720.4 MPa) and simultaneously the widest tailored range (87.32%), while the austenite-inspired microlattices metamaterial (A) exhibited a fully tailored ability for yield strength (σ). For superelastic, the austenite- and martensite-inspired microlattice metamaterial had superior superelasticity (98.10%∼99.36% recoverability) with wide volume tuning space, the M-x possessed the highest recoverability with a narrow tailored range. The relation between different parameters with mechanical and superelastic properties was established through machine learning, and multiple performance optimizations were carried out with vascular stents as typical application objectives. This research provides novel ideas for designing NiTi components, contributing to the future developments of different applications. Materials science ; Nickel titanium ; Metamaterial ; Lattice (music) ; Pseudoelasticity ; Shape-memory alloy ; Structural engineering ; Composite material ; Mechanical engineering ; Optoelectronics ; Martensite ; Microstructure ; Engineering ; Acoustics ; Physics Shape Memory Alloy Transformations ; Advanced Materials and Mechanics ; Cellular and Composite Structures
Authors: Xiaoxi Li | Yuchun Li | Yingguo Yang | Bitao Dong | Yuhang Liu | Lina Li | Linfeng Pan | Gengsheng Chen | Yue Hao | Genquan Han
Source: Advanced Science (article, 2025-02-05)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Highly Stable Electronics Based on β‐Ga<sub>2</sub>O<sub>3</sub> for Advanced Memory Applications Wide-bandgap (WBG) semiconductors are at the forefront of driving innovations in electronic technology, perpetuating Moore's Law and opening up new avenues for electronic devices. Although β-Ga<sub>2</sub>O<sub>3</sub> has attracted extensive research interest in advanced electronics, its high-temperature and high-speed volatile memory applications in harsh environment has been largely overlooked. Herein, a high-performance hexagonal boron nitride (h-BN)/β-Ga<sub>2</sub>O<sub>3</sub> heterostructure junction field-effect transistor (HJFET) is fabricated, exhibiting an off-state current as low as ≈10 fA, a high on/off current ratio of ≈10<sup>8</sup>, a low contact resistance of 5.6 Ω·mm, and an impressive field-effect electron mobility of 156 cm<sup>2</sup> （Vs）<sup>-1</sup>. Notably, the current h-BN/β-Ga<sub>2</sub>O<sub>3</sub> HJFET exhibits outstanding thermal reliability in the ultra-wide temperature range from 223 to 573 K, as well as long-term environmental stability in air, which confirms its inherent capability of operation in harsh environments. Moreover, the h-BN/β-Ga<sub>2</sub>O<sub>3</sub> HJFET demonstrates successful applications for accelerator-in-memory computing fields, including dynamic random-access memory structure and neural network computations. These superior characteristics position β-Ga₂O₃-based electronics as highly promising for applications in extreme environments, with particular relevance to the automotive, aerospace, and sensor sectors. Electronics ; Materials science ; Heterojunction ; Transistor ; Reliability (semiconductor) ; Optoelectronics ; Engineering physics ; Hexagonal boron nitride ; Nanotechnology ; Computer science ; Electrical engineering ; Physics ; Engineering ; Voltage ; Graphene Ga2O3 and related materials ; Electronic and Structural Properties of Oxides ; Semiconductor materials and devices
Authors: Haijuan Wu | Zhicheng Lin | Jinxiu Liu | Chunchi Zhang | Chao Tan | Zegao Wang
Source: ACS Applied Materials & Interfaces (article, 2025-03-18)
Field: Materials Science — Materials Chemistry

Ta<sub>2</sub>PdS<sub>6</sub>/MoS<sub>2</sub> Heterojunction Phototransistor for High-Performance Photoelectric Synapses and Graphic Identification Artificial synapses and neurons with efficient, high-speed, and highly parallel information processing capabilities are considered to be a new direction for the next generation of learning, cognition, and data storage. In this work, we have integrated photodetectors and photoelectric synapse in Ta<sub>2</sub>PdS<sub>6</sub>/MoS<sub>2</sub> van der Waals heterostructures, which can be used in photodetection and optical artificial neural networks. We have systematically studied the photoelectric characteristics of the blue-violet to near-infrared (405 ∼ 1550 nm) band. At 633 nm, the responsivity and specific detectivity are as high as 590.36 AW<sup>-1</sup> and 5.63 × 10<sup>11</sup> Jones, respectively. In addition, the heterojunction acquired a persistent photoconductivity behavior due to the presence of interfacial defect states, which was used to simulate the synaptic properties of the human brain, such as transition from short-term memory to long-term memory, paired-pulse facilitation, "learning-forgetting-relearning" behavior, and excitatory-postsynaptic current. In addition, on the basis of photoelectric synapse, the recognition of handwritten digits with different noise levels by an artificial neural network was simulated, which shows a high training accuracy (90%). This study lays a foundation for the development of high-performance heterojunctions and artificial synapses using two-dimensional materials. Materials science ; Photoelectric effect ; Heterojunction ; Photodiode ; Optoelectronics ; Identification (biology) ; Photoresistor ; Photoconductivity ; Photodetector 2D Materials and Applications ; Perovskite Materials and Applications ; Gas Sensing Nanomaterials and Sensors
Authors: Harshit Sharma | Gaurav Arora | Papiya Bhowmik | Manoj Kumar Singh | Vinod Ayyappan | Anuj Kumar Sehgal | Sanjay Mavinkere Rangappa | Suchart Siengchin
Source: Discover Mechanical Engineering (article, 2025-11-26)
Field: Materials Science — Materials Chemistry

Predictive modeling of thermoplastic nanocomposites using machine learning algorithms In this investigation, various machine learning (ML) algorithms, i.e. Support Vector Regression (SVR), Decision Tree (DT), Random Forest (RF), and k-Nearest Neighbors (kNN) were employed to estimate the mechanical properties of microwave-processed polymer nanocomposites. A universal testing machine evaluated the tensile strength, fracture toughness, and Young’s modulus of Polypropylene/Carbon Nanotubes (PPNT), High-Density Polyethylene/Carbon Nanotubes (HPNT), and Low-Density Polyethylene/Carbon Nanotubes (LPNT) composites. The results indicate that SVR is the most accurate and reliable model for estimating the mechanical properties of all composites. Its superior performance is due to its ability to minimize inaccuracies and generalize effectively, even with limited datasets. Among all models, SVR consistently delivered the best performance for all three mechanical characteristics. It achieved low values for root mean square error (RMSE), mean absolute error (MAE), and mean square error (MSE) i.e. 0.0228, 0.0189, and 0.0005, respectively along with a high coefficient of determination (R2) of 0.9875. These metrics highlight its low error, excellent generalization, low bias, and consistent performance across all datasets. Despite RF performing better than DT, it underperformed compared to SVR, with RMSE variations of 6.1% for HPNT, 47.3% for PPNT, and 32.4% for LPNT. Meanwhile, kNN proved to be the least suitable algorithm in this study due to its poor predictive accuracy and lack of stability. Mean squared error ; Support vector machine ; Random forest ; Decision tree ; Ultimate tensile strength ; Root mean square ; Mean absolute error ; Benchmark (surveying) ; Coefficient of determination Machine Learning in Materials Science ; Carbon Nanotubes in Composites ; Natural Fiber Reinforced Composites
Authors: Fariba Peytam | Parham Foroumadi | Hayrettin Ozan Gülcan | Maryam Norouzbahari | Somayeh Mojtabavi | Mohammad Ali Faramarzi | Fahimeh Ghasemi | Mohammadreza Torabi | Behnaz Bameri | Maliheh Barazandeh Tehrani | Loghman Firoozpour | Alireza Foroumadi
Source: Scientific Reports (article, 2025-05-22)
Field: Chemistry — Organic Chemistry

Design, synthesis, and evaluation of triazolo[1,5-a]pyridines as novel and potent α‑glucosidase inhibitors α-Glucosidase is a key enzyme responsible for controlling the blood glucose, making a pivotal target in the treatment of type 2 diabetes mellitus. Present work introduces<sup>1,2,4</sup>triazolo[1,5-a]pyridine as a novel, potent scaffold for α-glucosidase inhibition. A diverse scope of targeted compounds was prepared through an efficient, straightforward synthetic protocol. A series of compounds (15a-15v) were synthesized using a simple and efficient protocol, all showing notable inhibitory activity. Among them, compound 15j exhibited the best inhibition potency (IC₅₀ = 6.60 ± 0.09 µM), acting as a competitive and selective α-glucosidase inhibitor with no effect on α-amylase. Moreover, comprehensive computational studies were performed to validate the in vitro results and provide insight into compounds' binding interactions within the α-glucosidase's active site. The machine learning model, trained with the Estate fingerprint, achieved an AUC score of 0.65, demonstrating its utility in predicting α-glucosidase inhibition. Random Forest was identified as the most suitable model, and the dataset with the highest R² value was selected for further feature selection and model improvement. Molecular docking studies demonstrated that compound 15j had a strong binding affinity toward α-glucosidase, with a docking score of - 10.04 kcal/mol, and formed several remarkable interactions, particularly three key hydrogen bonds with TYR158, GLN353, and GLU411, contributing to its high inhibitory efficacy. The results of the molecular dynamics simulation demonstrated that the 15j-α-glucosidase complex exhibits high stability and effectively maintains its binding without causing significant structural changes in the enzyme, confirming the stable interaction and selective inhibition of this compound at the enzyme's active site. Combinatorial chemistry ; Chemistry ; Pharmacology ; Computer science ; Computational biology ; Medicine ; Biology Click Chemistry and Applications ; Synthesis and biological activity ; Carbohydrate Chemistry and Synthesis
Authors: Yali Sun | Li Yun | Zhou Yang | Ting Cai | Yuxuan Chen | Chao Zou | Han Song | Shenghuang Lin | Shenghua Liu
Source: Materials Futures (article, 2025-04-15)
Field: Materials Science — Polymers and Plastics

MOF-MoS<sub>2</sub> nanosheets doped PEDOT:PSS for organic electrochemical transistors in enhanced glucose sensing and machine learning-based concentration prediction Abstract Organic electrochemical transistors (OECTs) are regarded as a promising platform for chemical and biological sensing due to their biocompatibility, cost-effectiveness and flexibility. However, maintaining long-term stability of OECTs while achieving high sensitivity remains a challenge for their practical applications. One of the main reasons is the relatively low electronic and ionic conductivity of the channel material. Herein, we present a p-type OECT fabricated by incorporating metal–organic framework (MOF)-MoS 2 hybrid nanosheets into the PEDOT:PSS channel via solution-based processes. The strategy significantly improves the sensitivity of OECT, with the transconductance of the device increasing by ∼threefold to 19.34 mS. The higher transconductance is attributed to the hybrid MOF-MoS 2 dopant, which not only enhances the electronic conductivity, but also strengthens ion transport and capacitance of the PEDOT:PSS film due to the synergistic effects from high electron mobility of MoS 2 and MOF porous structure with large surface area. The fabricated OECT demonstrates high selectivity and sensitivity as a glucose biosensor across a wide concentration range in saliva. Finally, we illustrate the merits of integration machine learning algorithms to construct predictive models using the extensive datasets produced by our sensors for both classification and quantification tasks. These findings highlight the great potential of OECTs incorporating MOF-MoS 2 hybrid, as a promising candidate for ultra-sensitive biological detections, and broaden the applications of our OECT biosensors for non-invasive health monitoring and wearable electronics. PEDOT:PSS ; Materials science ; Electrochemistry ; Doping ; Transistor ; Nanotechnology ; Optoelectronics ; Chemical engineering ; Chemistry ; Electrical engineering ; Engineering ; Electrode ; Layer (electronics) ; Voltage Conducting polymers and applications ; Electrochemical sensors and biosensors ; Analytical Chemistry and Sensors
Authors: A. Arvizu-Montes | M. J. Martinez-Echevarria
Source: Materials (review, 2025-01-13)
Field: Materials Science — Polymers and Plastics

Vegetable Fibers in Cement Composites: A Bibliometric Analysis, Current Status, and Future Outlooks The use of vegetable fibers (VFs) in cement-based composites has increased in recent years owing to their minimal environmental impact and notable particular properties. VFs have aroused interest within the scientific community because of their potential as a sustainable alternative for construction. This study presents a comprehensive bibliometric analysis of VFs in cement composites using data from the Scopus database and scientometric tools to explore publication trends, influential sources, and research directions. Key findings reveal a steady increase in publications, with <i>Construction and Building Materials</i> identified as a leading journal in the field and China and Brazil as prominent contributors in terms of publications and citations. The analysis highlights a strong focus on mechanical properties and durability, reflecting the interest of the scientific community in optimizing VF composites for construction. Furthermore, this study includes a revision of the most influential studies addressing VF classification, durability improvements, and advanced applications of VFs in building applications. Finally, future research opportunities are outlined, emphasizing Life Cycle Assessment (LCA), industry integration, CO<sub>2</sub> absorption, and the application of machine learning techniques to advance the development of VF composites. This work provides a comprehensive overview of the field, suggesting future guidelines and promoting collaborative research. Scopus ; Durability ; Life-cycle assessment ; Work (physics) ; Field (mathematics) ; Architectural engineering ; Construction engineering ; Engineering ; Computer science ; Civil engineering ; Materials science ; Mechanical engineering ; Composite material ; Political science ; MEDLINE ; Mathematics Natural Fiber Reinforced Composites ; Innovations in Concrete and Construction Materials ; Hygrothermal properties of building materials