jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xiaoxuan Zhang | Ming Zhang | Yihao Li | Ping Deng
Source: Current Issues in Molecular Biology (review, 2025-01-06)
Field: Materials Science — Materials Chemistry

Identification of Potential Selective PAK4 Inhibitors Through Shape and Protein Conformation Ensemble Screening and Electrostatic-Surface-Matching Optimization P21-activated kinase 4 (PAK4) plays a crucial role in the proliferation and metastasis of various cancers. However, developing selective PAK4 inhibitors remains challenging due to the high homology within the PAK family. Therefore, developing highly selective PAK4 inhibitors is critical to overcoming the limitations of existing inhibitors. We analyzed the structural differences in the binding pockets of PAK1 and PAK4 by combining cross-docking and molecular dynamics simulations to identify key binding regions and unique structural features of PAK4. We then performed screening using shape and protein conformation ensembles, followed by a re-evaluation of the docking results with deep-learning-driven GNINA to identify the candidate molecule, STOCK7S-56165. Based on this, we applied a fragment-replacement strategy under electrostatic-surface-matching conditions to obtain Compd 26. This optimization significantly improved electrostatic interactions and reduced binding energy, highlighting its potential for selectivity. Our findings provide a novel approach for developing selective PAK4 inhibitors and lay the theoretical foundation for future anticancer drug design. Docking (animal) ; Homology modeling ; Computational biology ; Molecular dynamics ; Chemistry ; Electrostatic interaction ; Biophysics ; Materials science ; Biochemistry ; Biology ; Enzyme ; Computational chemistry ; Medicine ; Chemical physics Enzyme Structure and Function ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Sourabh Singhal | Prateek Srivastava | Jyoti Verma
Source: PhytoTalks (article, 2025-10-15)
Field: Materials Science — Biomaterials

Diatom-Based Multifunctional Biogenic Nanostructures in Water Purification: From Mechanistic Understanding to Innovative Applications The intensifying global water crisis, driven by industrial expansion, population pressures, and climate variability, necessitates transformative and sustainable purification technologies. Diatoms—unicellular microalgae encased in intricately nano- and micro-structured silica frustules—and their fossilized derivative, diatomaceous earth (DE), have emerged as multifunctional platforms for water treatment. Their hierarchical porosity, exceptional surface-to-volume ratio, and chemical tunability underpin the removal of a wide spectrum of contaminants, including heavy metals, synthetic dyes, pathogenic microorganisms, and emerging pollutants such as microplastics and per- and polyfluoroalkyl substances (PFAS). This review consolidates current advances in understanding the physicochemical and biological pathways of diatom-mediated remediation, encompassing adsorption, ion exchange, photocatalysis, and biofilm-driven processes. Recent progress in surface functionalization, nanocomposite integration, and photocatalytic coupling is critically evaluated, along with innovations that link diatom systems to constructed wetlands, phytoremediation frameworks, and circular bioeconomy models. Case studies across global regions, with emphasis on Indian contexts, highlight techno-economic feasibility and socio-environmental relevance. Emerging frontiers, including nanotechnology-enabled hybrids, machine learning-assisted predictive modeling, and life cycle sustainability assessment, are identified as accelerators of translational deployment. Persistent challenges, notably regeneration efficiency, safe disposal of contaminant-loaded DE, and competition with membrane technologies, are also examined. Collectively, this synthesis positions diatoms as both biological sentinels and material platforms, charting strategic pathways for next-generation, climate-resilient water purification systems. This review provides a critical assessment of the physicochemical processes governing contaminant removal, highlights recent innovations in diatom functionalization that enhance reactivity, and explores the incorporation of diatom-derived biosilica into nanocomposite materials and advanced filtration platforms. Additionally, it assesses the scalability, economic viability, and ecological implications of implementing diatom-based technologies in real-world water treatment applications. By pinpointing existing research gaps and technological constraints, the article proposes strategic pathways for interdisciplinary investigations aimed at fully leveraging the potential of diatoms in shaping next-generation water purification solutions. Sustainability ; Population ; Global population ; Life-cycle assessment ; Microplastics ; Transformative learning ; Nanocomposite ; Diatom Diatoms and Algae Research ; Algal biology and biofuel production ; Aquatic Ecosystems and Phytoplankton Dynamics
Authors: Álmos Orosz | Monika Neal | Rekha R. Rao | Christine Cardinal Roberts | Botond Szilágyi | Zoltán K. Nagy
Source: AIChE Journal (article, 2025-10-03)
Field: Materials Science — Materials Chemistry

<scp>2D</scp> population balance modeling and <scp>ML</scp> ‐based multi‐objective optimization for the crystallization process of resveratrol Abstract Crystallization is critical in pharmaceutical manufacturing, influencing active pharmaceutical ingredient (API) purity and processability. This study models the cooling crystallization of resveratrol in a water‐ethanol solvent using a two‐dimensional population balance model (2D‐PBM). Experimental data from Focused Beam Reflectance Measurement (FBRM), UV/Vis spectroscopy, and microscopy supported model calibration via design of experiments. The well‐calibrated model enabled multi‐objective optimization (MOO) to (1) maximize yield and minimize batch time, and (2) explore the relationship between aspect ratio and median crystal size. While the first scenario showed minimal trade‐offs, the second revealed a balance between aspect ratio and size/yield. A hybrid approach combining mechanistic modeling with machine learning drastically accelerated simulations and enabled efficient prediction of Pareto‐optimal solutions. This integration offers a scalable and accurate optimization strategy for complex crystallization processes. Crystallization ; Process analytical technology ; Scalability ; Process (computing) ; Population ; Calibration ; Active ingredient ; Process optimization Crystallization and Solubility Studies ; Analytical Chemistry and Chromatography ; Field-Flow Fractionation Techniques
Authors: Khaled Aliqab | Dhruvik Agravat | Ammar Armghan | Meshari Alsharari | Naim Ben Ali | Shobhit K. Patel
Source: Case Studies in Thermal Engineering (article, 2025-10-25)
Field: Materials Science — Materials Chemistry

Tunable graphene-based metamaterial thermal absorber design for thermal sensing applications with behaviour prediction using machine learning Abstract : This investigated Graphene Monolayer Nanostructure as a Narrow Band (GMNNB) absorber. Which used AuAl 2 as a substrate and SiO 2 as a resonator material with graphene monolayer-based structure. It presents the narrow band of 10 nm with more than 97.81 % absorption peak at 1880 nm wavelength. Also, a narrow band region 0.5 μm to 0.6 μm. To make the process faster, we utilized a machine learning approach based on the K-Nearest Neighbors (KNN) model which reduces the process time by 1/8 then the conventional method. A low mean squared error (4.12652 x 10 -5 ) and high R 2 values (ranging from 0.944 to 0.986) in the prediction indicate that the model is very accurate and generalizable in estimating GMNNB structure. By varying the resonator thickness, substrate thickness, and structure breadth to target certain absorption wavelengths, the GMNNB structure may be tuned to operate as a metamaterial absorber and sensor. Its exceptional selectivity makes it valuable for various applications like optical filtering, gas/chemical sensing, stealth tech, and thermal energy harvesting. Substrate (aquarium) ; Metamaterial ; Absorption (acoustics) ; Resonator ; Thermal ; Graphene ; Process (computing) ; Nanostructure Advanced Thermoelectric Materials and Devices ; Thermal Radiation and Cooling Technologies ; Advanced Sensor and Energy Harvesting Materials
Authors: Shinto Mundackal Francis | Andrew Ferebee | Sajib Kumar Mohonta | A Yagız Sen | Pooja Puneet | Yi Ding | Ramakrishna Podila
Source: ACS Applied Materials & Interfaces (article, 2025-12-04)
Field: Materials Science — Materials Chemistry

Physics-Informed Machine Learning for the Prediction of Thermal Conductivity from IR Images Polymer-composite thermal interface materials (PC-TIMs) are essential for heat dissipation in electronics and energy storage systems, yet their cross-plane thermal conductivity (κ) is difficult to measure rapidly with conventional techniques. Here, we present a physics-driven machine learning (ML) framework that integrates infrared (IR) thermography with feature-engineered models to predict κ in the low-conductivity regime (<5 W m<sup>1-</sup> K<sup>-1</sup>). More than 200 thermal images, obtained from experiments and Multiphysics simulations, were converted into universal temperature fields and transformed into structured feature vectors encoding gradients, Laplacian variance, and thermal extrema. Random forest regressors achieved robust predictions (<i>R</i><sup>2</sup> = 0.905, MAE = 0.169) on experimental test sets, outperforming linear and boosting models. Domain adaptation via Gaussian-perturbed simulations enhanced transferability, while SHapley Additive exPlanations or SHAP analysis confirmed the physical relevance of density and gradient features. This work establishes IR thermography coupled with interpretable ML as a rapid and scalable diagnostic for PC-TIM development and manufacturing quality control. Thermography ; Thermal conductivity ; Support vector machine ; Multiphysics ; Boosting (machine learning) ; Thermal ; Curse of dimensionality ; Infrared ; Random forest ; MXenes Thermal properties of materials ; Machine Learning in Materials Science ; Thermography and Photoacoustic Techniques
Authors: Zhiqing Wen | Wenyuan Jiang | Long Yu | Jia Liu | Ziqing Liu | Xin Yang | Qiushuo Zheng | Xin Li | Yuning Wang | Suhua Wang
Source: Sensors and Actuators B Chemical (article, 2025-12-31)
Field: Chemistry — Inorganic Chemistry

Intelligent visual recognition of UO₂²⁺ ions using a machine learning-based metal-organic framework ratiometric fluorescent probe Fluorescence ; Uranyl ; Detection limit ; Ion ; Quenching (fluorescence) ; Uranium ; Selectivity Radioactive element chemistry and processing ; Metal-Organic Frameworks: Synthesis and Applications ; Analytical chemistry methods development
Authors: Yanxiao Hu | Sheng Ye | Jing Huang | Xiaoxin Xu | Yuyan Yang | Mingqiang Zhang | Yabei Wu | Caichao Ye | Jiong Yang | Wenqing Zhang
Source: Proceedings of the National Academy of Sciences (article, 2025-06-20)
Field: Materials Science — Materials Chemistry

Global universal scaling and ultrasmall parameterization in machine-learning interatomic potentials with superlinearity Using machine learning (ML) to construct interatomic interactions and thus potential energy surface (PES) has become a common strategy for materials design and simulations. However, those current models of machine-learning interatomic potential (MLIP) consider no relevant physical constraints or global scaling and thus may owe intrinsic out-of-domain difficulty which underlies the challenges of model generalizability and physical scalability. Here, by incorporating the global universal scaling law, we develop an ultrasmall parameterized MLIP with superlinear expressive capability, named SUS<sup>2</sup>-MLIP. Due to the global scaling derived from the universal equation of state (UEOS), SUS<sup>2</sup>-MLIP not only has significantly reduced parameters by decoupling the element space from coordinate space but also naturally outcomes the out-of-domain difficulty and endows the model with inherent generalizability and scalability even with relatively small training dataset. The non-linearity-embedding transformation in radial function endows the model with superlinear expressive capability. SUS<sup>2</sup>-MLIP outperforms the state-of-the-art MLIP models with its exceptional computational efficiency, especially for multiple-element materials and physical scalability in property prediction. This work not only presents a highly efficient universal MLIP model but also sheds light on incorporating physical constraints into AI-aided materials simulation. Scalability ; Generalizability theory ; Scaling ; Computer science ; Embedding ; Parameterized complexity ; Decoupling (probability) ; Physical system ; Theoretical computer science ; Domain (mathematical analysis) ; Artificial intelligence ; Statistical physics ; Algorithm ; Topology (electrical circuits) ; Physics ; Mathematics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Ferroelectric and Negative Capacitance Devices
Authors: Antoine Loew | Jonathan Schmidt | Silvana Botti | Miguel A L Marques
Source: Journal of Physics Materials (article, 2025-12-11)
Field: Materials Science — Materials Chemistry

Universal machine learning potentials under pressure Abstract Universal machine learning interatomic potentials (uMLIPs) represent arguably the most successful application of machine learning to materials science, demonstrating remarkable performance across diverse applications. However, critical blind spots in their reliability persist. Here, we address one such significant gap by systematically investigating the accuracy of uMLIPs under extreme pressure conditions from 0 to 150 GPa. Our benchmark reveals that while these models excel at standard pressure, their predictive accuracy deteriorates considerably as pressure increases. This decline in performance originates from fundamental limitations in the training data rather than in algorithmic constraints. In fact, we show that through targeted fine-tuning on high-pressure configurations, the robustness of the models can be easily increased. These findings underscore the importance of identifying and addressing overlooked regimes in the development of the next generation of truly universal interatomic potentials. Robustness (evolution) ; Benchmark (surveying) ; Training set ; Reliability (semiconductor) ; Experimental data ; Extreme learning machine Machine Learning in Materials Science ; Quantum many-body systems ; Model Reduction and Neural Networks
Authors: Sameh Fuqaha | Guntur Nugroho | Ahmad Zaki
Source: Diyala Journal of Engineering Sciences (article, 2025-09-01)
Field: Materials Science — Polymers and Plastics

Interpretable AI-Based Prediction of Elastic Modulus in Bamboo-Reinforced Polypropylene Using Mori–Tanaka and Neural Networks This study presents a hybrid and interpretable modeling framework that integrates the Mori–Tanaka micromechanical model with artificial neural networks (ANNs) to predict the elastic modulus of bamboo-reinforced polypropylene composites. A synthetic dataset was created encompassing bamboo fiber volume fractions from 5% to 25%, enabling the ANN to generalize proficiently across diverse reinforcement setups. The ideal network architecture (2–15–1) attained superior predictive performance, with mean squared errors under 20 and regression coefficients surpassing 0.98, so validating the model's accuracy and robustness. To guarantee reliability, the model was evaluated on intermediate components not encountered during training, exhibiting consistent performance and resilience to overfitting. The interpretability of the black-box AI model was improved via sensitivity analysis and SHAP (Shapley Additive Explanations), which revealed that bamboo modulus was the primary factor affecting composite stiffness, contributing around 72% of predictive influence, whereas polypropylene accounted for 28%. These findings correspond with micromechanical theory and offer insights into material design methodologies. Combining physics-based modeling with artificial intelligence improves the accuracy of predictions and helps engineers make smart choices during the early stages of bio-composite development. This research enhances sustainable material innovation by offering a transparent, efficient, and scalable modeling tool suitable for comprehensive mechanical property forecasts and practical composite design. Bamboo ; Polypropylene ; Artificial neural network ; Composite material ; Polymer science ; Materials science ; Elastic modulus ; Structural engineering ; Artificial intelligence ; Computer science ; Engineering Natural Fiber Reinforced Composites ; Mechanical Engineering and Vibrations Research ; Material Properties and Processing
Authors: Rui Fang | Tuo Zhou | Zhuangzhuang Xu | Xiannan Hu | Man Zhang | Hairui Yang
Source: Energies (article, 2025-06-17)
Field: Materials Science — Materials Chemistry

Energy-Efficient Prediction of Carbon Deposition in DRM Processes Through Optimized Neural Network Modeling Methane dry reforming (DRM) offers a promising route by converting two greenhouse gases into syngas, but catalyst deactivation through carbon deposition severely reduces energy efficiency. While neural networks offer potential for predicting carbon deposition and reducing experimental burdens, conventional random data partitioning in small-sample regimes compromises model accuracy, stability, and generalizability. To overcome these limitations, we conducted a systematic comparison between backpropagation (BP) and radial basis function (RBF) neural network models. Throughout 10 model trials with random trainset splits, the RBF model demonstrated superior performance and was consequently selected for further optimization. Then, we developed a K-fold cross-validation framework to enhance model selection, resulting in an optimized RBF model (RBF-Imp). The final model achieved outstanding performance on unseen test data (MSE = 0.0018, R² = 0.9882), representing a 64% reduction in MSE and a 4.3% improvement in R² compared to the mean performance across 10 independent validations. These results demonstrated significant improvements in the prediction accuracy, model stability, and generalization capability of the small-sample data model, providing intelligent decision-making support for the removal of carbon deposition. Artificial neural network ; Deposition (geology) ; Energy (signal processing) ; Computer science ; Efficient energy use ; Environmental science ; Artificial intelligence ; Biological system ; Biochemical engineering ; Process engineering ; Engineering ; Electrical engineering ; Geology ; Physics Catalytic Processes in Materials Science ; Machine Learning in Materials Science ; Injection Molding Process and Properties
Authors: Gerardo Valadez Huerta | Yūsuke Nanba | Michihisa Koyama
Source: Surfaces and Interfaces (article, 2025-04-23)
Field: Materials Science — Materials Chemistry

Universal neural network potential study of the Pt/CO heterogeneous catalytic system The high computational costs of DFT methods limit their applicability in studying heterogeneous catalytic systems. Neural network potentials present a promising approach for modelling such systems. In this study, we first validate the use of a universal neural network potential for the Pt(111)/CO system, whose theoretical approach is still under debate, by reproducing experimental CO IR spectra and providing insights into the vibrational modes of adsorbed CO. We further study temperature-dependent single CO adsorption dynamics and oxidation barriers on a Pt 201 nanoparticle . The results show that low barriers require on-top CO and O adsorption at ridge sites, which may be hindered by CO poisoning or strong bridge oxygen adsorption, providing a possible explanation for the experimentally observed slower reaction kinetics at ridges compared to facets. These findings underscore the need for consideration of site distribution on nanoparticles beyond the adsorption and activation properties calculated from slab systems. Materials science ; Catalysis ; Artificial neural network ; Chemical engineering ; Artificial intelligence ; Computer science ; Organic chemistry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Neural Networks and Applications
Authors: Hiroshi Nada | Osama A. Omer | Hamada A. H. Esmaiel | A. A. Arafa | M. Ashour
Source: Multimedia Tools and Applications (article, 2025-10-01)
Field: Chemistry — Analytical Chemistry

Identification of irradiated food through hyperspectral imaging assisted by deep learning techniques Hyperspectral imaging ; Deep learning ; Pixel ; Pattern recognition (psychology) ; Principal component analysis ; Endmember ; Sample (material) Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Identification and Quantification in Food
Authors: Sandro Wieser | Yu-Jie Cen | Georg K. H. Madsen | Jesús Carrete
Source: Journal of Chemical Theory and Computation (article, 2025-12-19)
Field: Materials Science — Materials Chemistry

Accelerating First-Principles Molecular-Dynamics Thermal Conductivity Calculations for Complex Systems Atomistic simulations of heat transport in complex materials are costly and hard to converge. This has led to the development of several noise-reduction techniques applicable to equilibrium molecular-dynamics (MD) simulations. We analyze the performance of those strategies, taking InAs nanowires as our benchmark due to the diverse structures and complex phonon spectra of these quasi-1D systems. We demonstrate how, for low-thermal-conductivity systems, cepstral analysis can reduce computational demands while still delivering accurate results that do not require discarding arbitrary parts of the data set. However, issues with this approach are revealed when treating high-thermal-conductivity systems, where the thermal conductivity is significantly underestimated. We discuss alternative methods to be used in that situation, relying on uncertainty propagation from independent simulations. We show that the contributions of the covariance matrix have to be included for a quantitative assessment of the error. The combination of these strategies with machine-learning interatomic potentials (MLIPs) provides an accelerated, robust workflow applicable to a diverse set of systems, as our examples using a highly transferable MACE potential illustrate. Thermal conductivity ; Complex system ; Workflow ; Benchmark (surveying) ; Set (abstract data type) ; Nanowire ; Covariance ; Interatomic potential Thermal properties of materials ; Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices