jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yingying Zhang | Yunjin Li | Kehu Yang | Weiping Liu | Sha Zhao | Yuan Tang | Zihang Chen
Source: Journal of Translational Medicine (review, 2025-12-09)
Field: Materials Science — Biomaterials

Synergistic innovations of nanomedicine in lymphoma treatment: a systematic review Lymphoma therapy faces persistent challenges, including tumor heterogeneity, drug resistance, and immunosuppressive microenvironments, particularly in relapsed or refractory cases. Current treatments, such as chemotherapy, targeted therapy, and cell-based therapies, are limited by suboptimal targeting, systemic toxicity, and manufacturing complexities, highlighting the urgent need for innovative solutions. Nanomedicine has emerged as a transformative approach, integrating material design with therapeutic strategies to address these barriers. This review of 133 preclinical studies highlights key advancements: the dominance of lipid- and polymer-based nanoparticles, increasing use of natural materials, and the combination of passive EPR-based targeting with active strategies like CD20-mediated approaches. Stimuli-responsive systems, particularly pH-sensitive platforms, further enhance precision drug delivery, improving efficacy while reducing toxicity. Artificial intelligence accelerates progress by integrating multi-omics data and utilizing machine learning to optimize nanoparticle design, enhancing precision and personalization. Additionally, nanotechnology has advanced imaging, minimized chemotherapy-induced toxicity, and enabled in vivo CAR-T generation, offering safer and scalable therapeutic options. However, clinical translation faces hurdles, including scalable manufacturing, single-cell omics-guided nanoparticle design, and humanized models to validate immune microenvironment interactions. Addressing these challenges is essential to fully realize the potential of nanomedicine and AI integration, driving next-generation platforms for precision lymphoma therapy. Nanomedicine ; Drug delivery ; Precision medicine ; Immune system ; Preclinical testing ; Drug ; Transformative learning ; Lymphoma Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; CAR-T cell therapy research
Authors: Fernando Arrais Romero Dias Lima | Marcellus G.F. de Moraes | Carine Menezes Rebello | Evaristo C. Biscaia | Argimiro R. Secchi | Maurício B. de Souza | Idelfonso B. R. Nogueira
Source: Chemical Engineering and Processing - Process Intensification (article, 2025-05-19)
Field: Materials Science — Materials Chemistry

Interpretable and uncertainty-aware machine learning for trustworthy prediction in batch crystallization Trustworthiness ; Computer science ; Crystallization ; Artificial intelligence ; Machine learning ; Data mining ; Engineering ; Computer security ; Chemical engineering Crystallization and Solubility Studies ; Mineral Processing and Grinding ; Hydrocarbon exploration and reservoir analysis
Authors: Okan Bayram | Uğur Özkan | İlhan Pekgözlü | Fethiye Göde
Source: Separation Science and Technology (article, 2025-05-05)
Field: Chemistry — Inorganic Chemistry

Methyl blue removal by Na <sub>2</sub> Al <sub>2</sub> B <sub>2</sub> O <sub>7</sub> modeled with artificial neural networks: Synthesis, kinetics, isotherm, thermodynamic studies Chemistry ; Kinetics ; Thermodynamics ; Artificial neural network ; Physical chemistry ; Artificial intelligence Pigment Synthesis and Properties
Authors: Dustin Broderick | Paige Bowling | Carlos A. Brandt | Scott J. Childress | Joshua Shockey | J. Mark Higley | Haden Dickerson | S. Ahmed | John M. Herbert
Source: Wiley Interdisciplinary Reviews Computational Molecular Science (article, 2025-11-01)
Field: Materials Science — Materials Chemistry

<scp>Fragme∩t</scp> : An Open‐Source Framework for Multiscale Quantum Chemistry Based on Fragmentation Fragment-based quantum chemistry offers a means to circumvent the nonlinear computational scaling of conventional electronic structure calculations by partitioning a large calculation into smaller subsystems, then considering the many-body interactions between them. Variants of this approach have been used to parameterize classical force fields and machine learning potentials, applications that benefit from interoperability between quantum chemistry codes. However, there is a dearth of software that provides interoperability yet is purpose-built to handle the combinatorial complexity of fragment-based calculations. To fill this void we introduce "Fragme∩t", an open-source software application that provides a tool for community validation of fragment-based methods, a platform for developing new approximations, and a framework for analyzing many-body interactions. Fragme∩t includes algorithms for automatic fragment generation and structure modification and for distance- and energy-based screening of the requisite subsystems. Checkpointing, database management, and parallelization are handled internally and results are archived into a portable database format. Interfaces are provided to quantum chemistry engines including Q-Chem, PySCF, xTB, Orca, CP2K, MRCC, Psi4, NWChem, GAMESS, and MOPAC. Applications reported here demonstrate parallel efficiencies around 96% on more than 1,000 processors but also showcase that the code can handle large-scale protein fragmentation using only workstation hardware, all with a codebase that is designed to be usable by non-experts. Fragme∩t conforms to modern software development best practices and is built upon well established technologies including Python, SQLite, and Ray. The source code is available under the Apache 2.0 license. Codebase ; Software ; Interoperability ; Workstation ; Software framework ; USable ; Quantum chemistry ; Quantum Machine Learning in Materials Science ; Protein Structure and Dynamics ; Enzyme Structure and Function
Authors: Guangzhao Chen | Joseph C. A. Prentice | Jason M. Smith
Source: npj Computational Materials (article, 2025-05-28)
Field: Materials Science — Materials Chemistry

Simulating the dynamics of NV− formation in diamond in the presence of carbon self-interstitials Abstract This study utilises linear-scaling density functional theory (DFT) and develops a new machine-learning potential for carbon and nitrogen (GAP-CN), based on the carbon potential (GAP20), to investigate the interaction between carbon self-interstitials and nitrogen-vacancy (NV) centres in diamond, focusing on their excited states and diffusion behaviour. From the simulated excited states, 'Bright', 'Spike', and 'Dark' defect configurations are classified based on their absorption spectrum features. Furthermore, machine learning molecular dynamics simulation provides insight into the possible diffusion mechanism of C i and NV, showing that C i can diffuse away or recombine with NV. The study yields new insight into the formation of NV defects in diamond for quantum technology applications. Chemical physics ; Dynamics (music) ; Diamond ; Materials science ; Carbon fibers ; Statistical physics ; Nanotechnology ; Psychology ; Chemistry ; Physics ; Composite material Diamond and Carbon-based Materials Research ; High-pressure geophysics and materials ; Electronic and Structural Properties of Oxides
Authors: Jiahua Kong | Maziar Jafari | Yixiao Huang | Qingxi Hou | Keke Wang | Mohamed Siaj | Jianguo Tang | Zhonglin Du
Source: Solar Energy (article, 2025-07-15)
Field: Materials Science — Materials Chemistry

Low-dimensional semiconductor nanohybrids for charge transport layers towards emerging photovoltaics Photovoltaics ; Semiconductor ; Materials science ; Charge (physics) ; Optoelectronics ; Nanotechnology ; Photovoltaic system ; Engineering physics ; Physics ; Electrical engineering ; Engineering Quantum Dots Synthesis And Properties ; Chalcogenide Semiconductor Thin Films ; Perovskite Materials and Applications
Authors: Jiale Guo | Xiaoyu Zhang | Yanhong Liu | Jie Wu | Huihuang Xu | Hong‐Wei Xiao | Ziping Ai | Peng Gong
Source: Food Control (article, 2025-08-22)
Field: Chemistry — Analytical Chemistry

Intelligent monitoring, predicting, and control technology of food drying: Recent advances, challenges, and future prospects Monitoring and control ; Biochemical engineering ; Computer science ; Environmental science ; Risk analysis (engineering) ; Business ; Engineering Spectroscopy and Chemometric Analyses ; Food Drying and Modeling ; Food Supply Chain Traceability
Authors: Junlan Liu | Qian Yin | Mengshu He | Jun Zhou
Source: Computational Materials Science (article, 2025-02-06)
Field: Materials Science — Materials Chemistry

Constructing accurate machine learned potential and performing highly efficient atomistic simulation to predict structural and thermal properties: The Case of Cu7PS6 Thermal ; Computer science ; Molecular dynamics ; Statistical physics ; Materials science ; Chemistry ; Computational chemistry ; Thermodynamics ; Physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electronic and Structural Properties of Oxides
Authors: Natasha Videcrantz Faurschou | Victor Friis | Priyanka Raghavan | Christian Pedersen | Connor W. Coley
Source: Journal of the American Chemical Society (article, 2025-09-25)
Field: Chemistry — Organic Chemistry

Anomeric Selectivity of Glycosylations through a Machine Learning Lens Predicting the stereoselectivity of glycosylations is a major challenge in carbohydrate chemistry. Herein we show that it is possible to build machine learning models that can predict the major anomer of a glycosylation, whether the other anomer is observed as the minor product, and the anomeric ratio of the two anomers. The three models are integrated into a publicly available tool, GlycoPredictor. From a statistical analysis of literature data, we analyze glycosylation trends and compare them to known trends in the field of carbohydrate chemistry, making it possible to elucidate a hierarchy of rules governing the stereoselectivity of glycosylations and discover promising new trends that complement expert intuition, which are tested in novel glycosylation methods. Anomer ; Glycosylation ; Stereoselectivity ; Complement (music) ; Selectivity ; Training set Carbohydrate Chemistry and Synthesis ; Glycosylation and Glycoproteins Research ; Chemical Synthesis and Analysis
Authors: Deepu Kumar Jha
Source: Next Energy (article, 2025-09-23)
Field: Chemistry — Inorganic Chemistry

Smart screening of hydrogen storage capacities in MOFs using a tailored machine learning Metal-organic frameworks (MOFs) have emerged as promising candidates for solid-state hydrogen storage owing to their exceptional specific surface area, high pore volume, and chemically tunable structural properties. In this work, a diverse set of experimentally synthesized MOFs were evaluated to model and predict hydrogen storage capacity (wt%), using 4 key descriptors which are Brunauer–Emmett–Teller (BET) surface area, pore volume, operating pressure, and temperature. Correlation analysis revealed positive associations between BET surface area, pressure, and pore volume with storage capacity, and a negative association with temperature, consistent with physisorption mechanism. Six machine learning models were developed: support vector regression (SVR), artificial neural networks (ANN), random forest (RF), Gaussian process regression (GPR), gradient boosting (GB), and a Committee of Expert Systems (CES) integrating all base learners. While GB was the top-performing standalone model, the CES delivered the highest predictive fidelity (R 2 = 0.9958, MSE = 0.0094), as confirmed by parity plots and residual analysis. SHapley Additive exPlanations (SHAP) corroborated the statistical feature rankings, consistently identifying BET surface area and pressure as the most influential positive contributors in alignment with adsorption thermodynamics. Paired t -tests on root-mean-square error (RMSE) values confirmed statistically significant CES improvements over all individual models. The CES framework thus offers a data-efficient, accurate, and interpretable approach for rapid MOF screening, with straightforward adaptability to other porous materials and adsorption-based energy storage systems. • CES combines ANN, SVR, RF, GPR, and GB models as base learners. • BCES outperforms all standalone models with R 2 = 0.9958 and RMSE = 0.0969. • SHAP reveals pressure and BET surface area as the dominant features. • Robust validation using parity/residual plots with R 2 , RMSE and MAE. Random forest ; Support vector machine ; Artificial neural network ; Mean squared error ; Feature selection ; Physisorption ; Desiccant ; Hydrogen storage ; Boosting (machine learning) ; Residual Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Gas Sensing Nanomaterials and Sensors
Authors: P. Hariharasakthisudhan | M. Kandasamy | K. Logesh | K. Sathickbasha
Source: Fibers and Polymers (article, 2025-05-22)
Field: Materials Science — Polymers and Plastics

Machine Learning-Guided Evolutionary Optimization of Compression Molding Parameters for Graphene-Enhanced Jute Fiber Composites Materials science ; Composite material ; Compression molding ; Graphene ; Compression (physics) ; Fiber ; Composite number ; Molding (decorative) ; Nanotechnology ; Mold Natural Fiber Reinforced Composites ; Epoxy Resin Curing Processes ; Fiber-reinforced polymer composites
Authors: Hiroto Arima | Yuichiro Yamashita | Takashi Yagi
Source: Science and Technology of Advanced Materials (article, 2025-06-26)
Field: Materials Science — Materials Chemistry

Time-domain thermoreflectance technique using multiple delayed probe pulses for high-throughput data acquisition and analysis To advance thermal control technology, improve thermal reuse efficiency, and further enhance device performance, it is crucial to understand microscopic spatial and temporal heat transport in materials. In this study, we developed a high-throughput time-domain thermoreflectance (HT-TDTR) technique that accelerates the measurement speed of thermophysical properties. The fundamental concept involves decomposing supercontinuum light into a pump pulse (1064 nm) and multiple delayed probe pulses (900 nm-730 nm) with different delays, enabling simultaneous acquisition of thermoreflectance signals at multiple delay times. Quartz glass, SrTiO<sub>3</sub> (100) single crystal, and c-plane sapphire were heated with picosecond pulsed light, and the temporal temperature decrease at six delay times was simultaneously measured. The thermal effusivities analyzed based on heat diffusion equation were consistent with the literature values. Furthermore, we applied machine learning-based analysis and demonstrated the ability to determine thermophysical properties from measurement data consisting of only a few delay points. With sufficient signal strength, machine learning can predict a reasonable thermal effusivity based on experimental data obtained in less than a second. HT-TDTR enables rapid and accurate measurement of samples based on information about thermal relaxation dynamics, facilitating more efficient characterization of thermophysical properties. Materials science ; Picosecond ; Throughput ; Time domain ; Thermal ; Optoelectronics ; Optics ; Computer science ; Laser ; Physics Thermal properties of materials ; Thermal Radiation and Cooling Technologies ; Thermography and Photoacoustic Techniques
Authors: Wenhui Geng | Ying Zhou | Kang Huang | Yibin Ying | Lijuan Xie
Source: Device (article, 2025-02-13)
Field: Chemistry — Organic Chemistry

A colorimetric-bionic sensor for multimodal molecular sensing using hierarchical metamaterials Metamaterial ; Computer science ; Nanotechnology ; Materials science ; Optoelectronics Polydiacetylene-based materials and applications ; Gas Sensing Nanomaterials and Sensors ; Advanced Chemical Sensor Technologies
Authors: Yuhui Jin | Ji Ma | Yuandong Lin | Jun‐Hu Cheng | Da‐Wen Sun
Source: Food Engineering Reviews (article, 2025-07-26)
Field: Chemistry — Analytical Chemistry

Intelligent Mobile Diagnostic Platforms for Food Safety Control: Functions, Classifications and Applications Abstract The rapid detection of food contaminants is crucial for food safety and human health. Portable sensing platforms are widely used, especially in resource-limited areas. Intelligent mobile diagnostic platforms (IMDPs) integrate multifunctionality, computing power, and data transfer, offering portability, scalability, and cost-effectiveness. However, challenges in detection reliability remain. This review introduces IMDP characteristics, components, and design, covering signal detection, data analysis, visualization, and power management. It explores diverse types of IMDPs, including paper-based, chip-based, hydrogel-based, and wearable devices, and their applications in detecting food freshness, contaminants, and pathogens. The review highlights that integrating stretchable materials, lab-on-a-chip technology, and smart devices enhances detection accuracy, while advanced algorithms like machine learning improve reliability. Consequently, IMDPs provide cost-effective, portable, and rapid food safety monitoring. Control (management) ; Computer science ; Artificial intelligence Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Identification and Quantification in Food
Authors: Yuhai Li | Tianmu Li | Longwen Tang | Shenggang MA | Q. M. Jonathan Wu | Puneet Gupta | Mathieu Bauchy
Source: Materials Science and Engineering A (article, 2025-03-08)
Field: Materials Science — Materials Chemistry

ConvFeatNet ensemble: Integrating microstructure and pre-defined features for enhanced prediction of porous material properties Microstructure ; Porosity ; Materials science ; Porous medium ; Biological system ; Computer science ; Composite material ; Biology Machine Learning in Materials Science ; Injection Molding Process and Properties ; Advanced materials and composites
Authors: Rayan Y. Mushtaq | H. P. S. Abdul Khalil
Source: International Journal of Pharmaceutics (review, 2025-09-27)
Field: Materials Science — Biomaterials

Green nanofiber biocomposites for drug delivery: A sustainable approach to advanced pharmaceutical systems Drug delivery ; Biocompatible material ; Nanofiber ; Drug ; Targeted drug delivery ; Pharmaceutical industry ; Drug development Electrospun Nanofibers in Biomedical Applications ; biodegradable polymer synthesis and properties ; Graphene and Nanomaterials Applications
Authors: Kang Xiang | Ling Qin | Shi Huang | Hongyuan Song | Vasilii V. Bazhenov | Valerio Bellucci | Šarlota Birnšteinová | Raphaël de Wijn | Jayanath Koliyadu | F. Koua | Adam Round | Ekaterina Round | Abhisakh Sarma | Tokushi Sato | Marcin Sikorski | Yuhe Zhang | E. M. Asimakopoulou | Pablo Villanueva‐Perez | Kyriakos Porfyrakis | Iakovos Tzanakis | Dmitry Eskin | Nicole Grobert | Adrian P. Mancuso⋈ | Richard Bean | Patrik Vagovič | Jiawei Mi
Source: Science Advances (article, 2025-11-28)
Field: Materials Science — Materials Chemistry

Ultrasonic cavitation shock wave exfoliation dynamics of 2D materials revealed in situ by MHz XFEL imaging and multiphysics modeling Using megahertz x-ray free electron laser imaging with x-ray pulses of ~25 femtoseconds and a machine-learning strategy, we have conducted comprehensive in situ imaging studies on the dynamics of cavitation bubble clouds in ultrasound fields at the SPB/SFX beamline of the European XFEL. The research unambiguously revealed the quasi-simultaneous implosion of multiple bubbles and simultaneous collapse of bubble cloud in nanosecond scale and their dynamic impacts onto two-dimensional (2D) materials for layer exfoliation. We have also performed multiphysics modeling to simulate the shock wave emission, propagation, impact, and stresses produced. We elucidated the critical conditions for producing instant or fatigue exfoliation and the effects of bonding strengths and structural defects on the exfoliation rate. The discoveries have filled the long-standing missing knowledge gaps in the underlying physics of exfoliating 2D materials in ultrasound fields, providing a solid theoretical foundation for optimizing and scaling-up operation to produce 2D materials in a much more cost-effective and sustainable way. Multiphysics ; Implosion ; Bubble ; Exfoliation joint ; Shock wave ; Cavitation ; Nanosecond ; Shock (circulatory) ; Femtosecond Ultrasound and Cavitation Phenomena ; Ultrasound and Hyperthermia Applications ; Advanced X-ray Imaging Techniques
Authors: Oliver Knitter | Dan Zhao | James Stokes | Martin Ganahl | Stefan Leichenauer | Shravan Veerapaneni
Source: Machine Learning Science and Technology (article, 2025-04-10)
Field: Chemistry — Spectroscopy

Retentive neural quantum states: efficient ansätze for <i>ab initio</i> quantum chemistry Abstract Neural-network quantum states (NQS) has emerged as a powerful application of quantum-inspired deep learning for variational Monte Carlo methods, offering a competitive alternative to existing techniques for identifying ground states of quantum problems. A significant advancement toward improving the practical scalability of NQS has been the incorporation of autoregressive models, most recently transformers, as variational ansätze. Transformers learn sequence information with greater expressiveness than recurrent models, but at the cost of increased time complexity with respect to sequence length. We explore the use of the retentive network (RetNet), a recurrent alternative to transformers, as an ansatz for solving electronic ground state problems in ab initio quantum chemistry. Unlike transformers, RetNets overcome this time complexity bottleneck by processing data in parallel during training, and recurrently during inference. We give a simple computational cost estimate of the RetNet and directly compare it with similar estimates for transformers, establishing a clear threshold ratio of problem-to-model size past which the RetNet’s time complexity outperforms that of the transformer. Though this efficiency comes at the expense of decreased expressiveness relative to the transformer, we overcome this gap through training strategies that leverage the autoregressive structure of the model—namely, variational neural annealing. Our findings support the RetNet as a means of improving the time complexity of NQS without sacrificing accuracy. We provide further evidence that the ablative improvements of neural annealing extend beyond the RetNet architecture, suggesting it would serve as an effective general training strategy for autoregressive NQS. Ab initio ; Quantum ; Quantum chemistry ; Quantum chemical ; Computational chemistry ; Chemistry ; Ab initio quantum chemistry methods ; Quantum mechanics ; Physics ; Organic chemistry ; Molecule ; Catalysis ; Reaction mechanism Molecular spectroscopy and chirality
Authors: Ninghan Tang | P. Hao | Juan Miguel Tiscar | F.A. Gilabert
Source: International Journal of Mechanical Sciences (article, 2025-09-02)
Field: Materials Science — Polymers and Plastics

Hybrid FEM-NN modeling of rate-dependent polymer mechanics across glass transition Glass transition ; Finite element method ; Polymer ; Materials science ; Composite material ; Mechanics ; Mechanical engineering ; Statistical physics ; Thermodynamics ; Engineering ; Physics Polymer crystallization and properties ; Polymer Nanocomposites and Properties ; Structural Analysis of Composite Materials