jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Arun Sebastian | Asaletha Raghavan
Source: Suranaree Journal of Science and Technology (article, 2025-02-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

BIO-INSPIRED PREDICTIVE INSIGHT AND OPTIMIZATION STRATEGY FOR ELECTROMAGNETIC SHIELDING IN HYBRID POLYMER COMPOSITES The Machine learning-based EMI shielding effect prediction on polymer composite materials is of supreme importance in modern electronic applications as this shielding serves as a robust protective barrier, safeguarding electronic components from electromagnetic interference. But, capturing the multifactorial nature of the composites and handling multi-objective optimization is still difficult to achieve with the existing machine-learning approaches. Hence, this research proposes a novel “Bio-inspired Predictive Insight and Optimization Strategy” for improving the efficacy of prediction and optimization of the EMI shielding effect of polymer composites. Polymer composites have complex structures with multiple components, leading to nonlinear relationships with EMI shielding effectiveness. This results in a high-dimensional search space, making the search for effective solutions computationally demanding and time-consuming. Here, the “Bayesian-Enriched Genetic Programming” approach captures the multiple parameters such as filler loading, matrix type, and processing conditions effectively to reduce this high-dimensional space complexity. The “Multi-objective Dominant Crowding Seagull (MDCS) optimization” model effectively addresses the challenges of traditional optimization algorithms by optimizing multiple conflicting objectives and EMI shielding. The comparison results show that the proposed method outperforms other methods with a higher prediction accuracy of 98.7%, faster training time of 19 seconds and quicker prediction time of 6 seconds. Electromagnetic shielding ; Composite material ; Materials science ; Polymer Electromagnetic wave absorption materials ; Natural Fiber Reinforced Composites ; Material Properties and Applications
Authors: Jieying Li | Peilong Li | Amin Zarei | Leila Khazdooz | Amin Zadehnazari | Arkaye Kierulf | James Smoot | Mohammad Yaghoobi | Alireza Abbaspourrad
Source: Food Hydrocolloids (article, 2025-10-21)
Field: Materials Science — Biomaterials

Crafting surface-active composite fibers from protein-fortified starch via synergistic Co-electrospinning and deep learning Starch ; Whey protein ; Composite number ; Nanofiber ; Fiber ; Modified starch ; Whey protein isolate Electrospun Nanofibers in Biomedical Applications ; Silk-based biomaterials and applications ; Advanced Sensor and Energy Harvesting Materials
Authors: Rong-rong Zheng (10132278) | Wen-jia Huo (21820580) | Bo-yang Liang (21820583) | Xiang Wu (1341072)
Source: Figshare (article, 2025-07-30)
Field: Materials Science — Materials Chemistry

Accelerating theDiscovery of Low-Dielectric PolyimidesBased on Interpretable Machine Learning Rapidly advancing computer technology has demonstrated great potential in the development of promising materials. Polyimide (PI), an important dielectric material, is widely applied in modern industries. The demand for devices with low dielectric and low energy consumption in the microelectronics industry has led to a growing need for low dielectric constant (<i>D</i><sub>k</sub>) PI. Developing <i>D</i><sub>k</sub> prediction models using machine learning (ML) algorithms can help construct quantitative structure–property relationships and deeply understand the molecular nature that affects the dielectric properties of PI. In this study, we assembled a data set comprising 970 PIs and extracted key structural features using RDKit. Among the 12 popular ML algorithms, the Extra Trees-based model yielded the most accurate results, achieving a coefficient of determination of 0.897 for the test set, a mean absolute error of 0.194, and a root-mean-square error of 0.267. SHapley Additive exPlanation analysis was then employed to explain the optimal model for <i>D</i><sub>k</sub> prediction from a physicochemical point of view and structural aspects. The study identified the BCUT2D_LOGPHI descriptor as being particularly influential on <i>D</i><sub>k</sub>, showing a negative correlation. More importantly, eight potential PI candidates with low <i>D</i><sub>k</sub> values were designed according to the chemical insights of the key descriptors, which were verified through all-atom molecular dynamics (MD) simulation. By comparison of the calculated and predicted <i>D</i><sub>k</sub> values, the lowest prediction deviation was found to be approximately 1.06%. The proposed methodology achieved a prediction accuracy comparable to that of traditional MD simulation, but the computational time and resource consumption were dramatically reduced. And Schuffenhauer’s synthetic accessibility scores were used to evaluate the ease of synthesis of each PI before the experiment. This research demonstrates the feasibility of using ML methods to accelerate the property prediction and molecular design and provides a powerful tool for discovering high-performance dielectric materials in the future. Dielectric ; Microelectronics ; Standard deviation ; Construct (python library) ; Test set ; Key (lock) ; Set (abstract data type) ; Absolute deviation ; Predictive modelling Machine Learning in Materials Science ; Synthesis and properties of polymers ; Dielectric materials and actuators
Authors: Hui Li | Yichen Yang | Shuting Hou | Lujia Zhang | Jiaying Yang | Bei Gao
Source: BioFactors (article, 2025-09-01)
Field: Chemistry — Organic Chemistry

Deep Learning‐Driven Enzyme Engineering in <i>Pichia pastoris</i> : A Sustainable Platform for High‐Yield Nootkatone Biosynthesis Nootkatone, a valuable sesquiterpene with broad bioactivities and application potential, faces yield limitations in microbial synthesis due to metabolic and enzymatic inefficiencies. In this study, we present an advanced strategy combining metabolic engineering and deep learning-guided enzyme design to optimize nootkatone production in Pichia pastoris. By systematically modifying the mevalonate pathway, optimizing cofactor supply, and minimizing competing metabolic pathways, a robust yeast strain producing 702.15 mg/L valencene was developed. To facilitate the efficient conversion of valencene to nootkatone, we applied ancestral sequence reconstruction (ASR) to identify hotspot amino acid residues, guiding the design of a variant library. The deep learning model DLKcat was then used to conduct virtual saturation mutagenesis screening on library sites, predicting their enzyme turnover number (k<sub>cat</sub>). The engineered cytochrome P450 (HPO) variant H54A exhibited the highest activity, with catalytic performance 2.3 times that of the initial. Furthermore, the implementation of intermittent feeding fermentation significantly elevated the final nootkatone yield to 3365.36 mg/L, the highest reported to date. This study provided a green platform for an alternative sustainable access of high-value nootkatone, and exemplifies the potential of machine learning in optimizing metabolic pathway enzymes for efficient biosynthesis of other bioactive terpenoids in microbial systems. Metabolic engineering ; Metabolic pathway ; Saturated mutagenesis ; Enzyme ; Protein engineering ; Mevalonate pathway ; Synthetic biology ; Biocatalysis ; Fermentation Chemical synthesis and alkaloids ; Phytochemistry and Bioactivity Studies ; Phytochemical compounds biological activities
Authors: Rui Zhao | Junjie Ning | Shumin Chen | Chengfeng Liang | Kun Shen | Yuxuan Chen | Longqiang Xiao | Jingyu Cai | Linxi Hou
Source: Industrial & Engineering Chemistry Research (article, 2025-08-27)
Field: Chemistry — Inorganic Chemistry

High-Throughput and Machine Learning for Predicting the Separation Performance of Metal–Organic Frameworks for SF<sub>6</sub>/N<sub>2</sub> In view of the greenhouse effect of SF6 and its recycling benefits, the development of highly efficient SF6/N2 separation is extremely urgent. In this study, high-throughput screening and machine learning methods were used to analyze the relationship between features and performance, and a machine learning model capable of predicting materials with high SF6/N2 adsorption and separation performance was trained. By implementing a database cleaning strategy, unreasonable structural data were systematically removed, improving the prediction accuracy of the model. Feature construction was carried out to obtain characteristic information covering aspects of structure, chemistry, force field, and energy. The adsorption data of the SF6/N2 mixed gas were calculated based on the RASPA2 software. The importance analysis shows that the Henry’s coefficient of SF6 plays a decisive role in the adsorption performance score. And cross-database prediction was conducted on the Tobacco database. The coefficient of determination of the logarithmic selectivity reaches 0.969. Metal-organic framework ; Throughput ; Separation (statistics) ; Computer science ; Materials science ; Chemistry ; Machine learning ; Physical chemistry ; Adsorption ; Operating system Metal-Organic Frameworks: Synthesis and Applications ; Inorganic Fluorides and Related Compounds ; Nuclear Physics and Applications
Authors: Xue Fang | Ulzhalgas Karatayeva | John Worth | Merve Gumussoy Girgin | Safa Ali Al Siyabi | Dauren Mukhanov | Ella Gale | Natalie Fey | Charl F. J. Faul
Source: ChemRxiv (preprint, 2025-03-04)
Field: Materials Science — Materials Chemistry

A solvent selection framework for porous organic polymers The relationship between solvent, morphology, and properties is crucial for developing functional materials. However, selecting suitable solvents for novel systems remains a significant challenge due to the lack of prior knowledge to guide solvent selections. In this work, we present a solvent selection toolkit for functional porous organic polymers. We have developed an interpretable machine-learning algorithm, MLoc, for the fast prediction of Hansen solubility parameters (HSPs) of novel target materials. This workflow is accessible to non-specialists, operates at negligible computing costs and relies simply on ultraviolet and visible (UV/Vis) absorbance data that can be measured using a standard laboratory setup. We demonstrate successful tuning of both morphology and carbon capture performance for target polymers using MLoc. We describe our vision for MLoc’s broader application in the sustainable development of functional materials, without the need for extensive screening, i.e., in a low data regime. Using MLoc, we report the first HSP database for porous organic polymers, which will serve as a valuable resource for future data-driven research. Polymer ; Selection (genetic algorithm) ; Porosity ; Solvent ; Organic solvent ; Organic polymer ; Materials science ; Chemical engineering ; Chemistry ; Computer science ; Organic chemistry ; Artificial intelligence ; Engineering Covalent Organic Framework Applications ; Chemical Synthesis and Reactions ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Boram Kim (361934) | Jaeyong Ahn (6375) | Ke Gao (1521607) | Xiaobo Shang (5043152) | Joon Hak Oh (1534867)
Source: Figshare (article, 2025-07-28)
Field: Chemistry — Organic Chemistry

CircularlyPolarized Light-Responsive Flexible SynapsesBased on Supramolecular <i>n</i>‑Type Chiral OrganicSingle Crystal/<i>p</i>‑Type Polymer Heterojunctions Chiral neuromorphic devices that detect both circularly polarized light and digitized electrical signals are cutting-edge combinations of neuromorphic engineering and chiral optoelectronics that may advance both computing and sensing. In this work, organic electrochemical transistors (OECTs) based on <i>n</i>-type 2D organic single-crystal/<i>p</i>-type polymer heterojunctions are described. The supramolecular characteristics and molecular packing modes of the single crystals endowed the system with a high polarization selectivity. Furthermore, the integration of a <i>p–n</i> heterojunction facilitated modulation of charge trapping and separation at the interface, leading to improved chiroptical sensitivity. The devices emulate key features of biological synapses, including paired-pulse facilitation (PPF) and synaptic plasticity according to number, voltage, and frequency of spikes (SNDP, SVDP, and SFDP) under both electrical and optical stimulation. Leveraging these properties, the biocompatibility and flexibility of these synapse-like devices enabled the development of wearable chiral neuromorphic devices on flexible polyethylene naphthalate (PEN) substrates, highlighting their potential for advanced bioinspired applications such as humanoid robots. Additionally, the artificial nervous system based on a trained convolutional neural network successfully performs image classification work. These findings in chiral single-crystal-based artificial synapses suggest potential strategies for advanced opto-neuromorphic computing depending on the wavelength and circular polarization state. Neuromorphic engineering ; Heterojunction ; Transistor ; Photonics ; Circular polarization ; Supramolecular chemistry ; Organic semiconductor ; Nanophotonics ; Polarization (electrochemistry) Synthesis and Properties of Aromatic Compounds ; Advanced Memory and Neural Computing ; Covalent Organic Framework Applications
Authors: Li-Qun Shen | Hao-Jin Wang | Mengzhao Sun | Yang Xiang | Xin-Ning Tian | Yue Chai | Yue Yang | Feng Ding | Xiao Kong | Marc‐Georg Willinger | Zhu‐Jun Wang
Source: arXiv (Cornell University) (preprint, 2025-12-30)
Field: Materials Science — Materials Chemistry

Non-Euclidean interfaces decode the continuous landscape of graphene-induced surface reconstructions Interfacial reconstruction between two-dimensional (2D) materials and metal substrates fundamentally governs heterostructure properties, yet conventional flat substrates fail to capture the continuous crystallographic landscape. Here, we overcome this topological limitation using non-Euclidean interfaces-curved 2D graphene-copper surfaces as a model system-to traverse the infinite spectrum of lattice orientations. By integrating multimodal microscopy with a deep-learning-enhanced dimensional upscaling framework, we translate 2D scanning electron microscopy (SEM) contrast into quantitative three-dimensional (3D) morphologies with accurate facet identification. Coupling these observations with machine-learning-assisted density functional theory, we demonstrate that reconstruction is governed by a unified thermodynamic mechanism where high-index facets correspond to specific local minima in the surface energy landscape. This work resolves the long-standing complexity of graphene-copper faceting and establishes non-Euclidean surface topologies as a generalizable paradigm for decoding and controlling interfacial reconstruction in diverse metal-2D material systems. Faceting ; Surface reconstruction ; Maxima and minima ; Surface (topology) ; Lattice (music) ; Traverse ; Decoding methods ; Topology (electrical circuits) Graphene research and applications ; Topological Materials and Phenomena ; Surface and Thin Film Phenomena
Authors: Viktor V. Nikolaev | Tatiana B. Lepekhina | Georgii V. Malkin | Н. А. Кривова | Dmitry Grigoriev | А. С. Гарин | Ekaterina S. Marchenko | Maxim Khomenko | Yury V. Kistenev
Source: Journal of Biomedical Photonics & Engineering (article, 2025-09-28)
Field: Materials Science — General Materials Science

Construction of a Predictive Model for Assessing the Porosity of TiNi Materials Based on Sintering Parameters of Samples Additive manufacturing of TiNi alloys has developed significantly in recent years. Such growth is due to the unique properties of the resulting designs − superelasticity, bending strength − stretching, corrosion, chemical and thermodynamic stability. In addition, three-dimensional (3D) printing technology significantly expanded the scope of use of this material in various applied areas from biomedicine (for printing implants and bone tissue engineering), to, for example, the automotive and aerospace industries. Despite the fact that 3D printing technology already has commercial solutions that allows producing complex-shaped structures, this area is in the stage of development. Of a particular interest is the optimization of printing process and the development of a quality control system (achieving the required parameters of porosity, roughness, minimizing defects such as cracks, contamination, delamination, surface and subsurface heterogeneities, surface quality, shrinkage, etc.). In this paper, an original non-destructive method for assessing the sintering temperature as an indirect measure of porosity of a material using optical coherence tomography (OCT) and machine learning methods is proposed. Random Forest Regression approach was used to predict depth maps and spatial statistics from the sintering temperature. The Random Forest model based on the first- and the second-order texture metrics of OCT images provided R ² of about 0.95 for sintering temperature prediction. Sintering ; Porosity ; Random forest ; 3D printing ; Selective laser sintering ; Texture (cosmology) ; Powder metallurgy ; Process (computing) ; Material properties Material Properties and Applications ; Additive Manufacturing and 3D Printing Technologies ; Laser and Thermal Forming Techniques
Authors: Mehrdad Jalali (17133516) | A. D. Dinga Wonanke (11007888) | Pascal Friederich (1753135) | Christof Wöll (1326411)
Source: Figshare (article, 2025-10-01)
Field: Materials Science — Materials Chemistry

The Black HoleStrategy: Gravity-Based RepresentativeSampling for Frugal Graph Learning on Metal–Organic FrameworkNetworks The expansion of large-scale materials databases has facilitated the development of graph-based representations, encoding structural and functional similarities as edges in data-driven networks. These enable machine learning models to leverage both local features and global relationships. However, densely connected datasets often introduce redundancy and noise, escalating computational complexity without improving performance. Here, we introduce the Black Hole Strategy, a gravity-based representative sampling method that constructs compact, informative subsets from large materials datasets while preserving essential structural and property diversity. Using metal–organic frameworks (MOFs) as a case study, we demonstrate that graph neural networks (GraphSAGE, GCN, and GAT) trained on Black Hole–sparsified datasets achieve comparable or superior classification and regression performance compared to full-dataset models, despite utilizing significantly fewer data points and reduced memory and training time requirements. Analysis of class-level confusion matrices confirms that critical structure–property relationshipssuch as pore-limiting diameter (PLD)persist under substantial sparsification. An ablation study on gravity score weights validates the balanced formulation and robustness of the approach. Topological and efficiency benchmarks further demonstrate that the method preserves modularity, diversity, and connectivity across sparsification levels. These findings establish the Black Hole Strategy as a principled and frugal approach for machine learning in materials science, enabling efficient, interpretable, and scalable discovery workflows. Importantly, this work contributes to the objectives of the FAIRmat consortium, which aims to develop a FAIR data infrastructure for condensed matter physics and materials science. Our approach advances FAIR (Findable, Accessible, Interoperable, Reusable) data practices through optimized sampling techniques that enhance data management, reusability, and interoperability in materials informatics. Leverage (statistics) ; Scalability ; Graph ; Redundancy (engineering) ; Robustness (evolution) ; Big data ; Inference ; Interoperability Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Advanced Graph Neural Networks
Authors: Sanam Pudasaini (12406697) | Amrutha S.
V. (22271418) | Oliver Steinbock (1471618) | Beni B. Dangi (1652647)
Source: Figshare (article, 2025-09-18)
Field: Materials Science — Materials Chemistry

Morphological Signaturesof Salt Crystals under ControlledHumidity Using Advanced Image Analysis We designed, fabricated, and utilized a humidity control chamber to investigate how controlled relative humidity (RH) affects crystallization patterns of two different salts; sodium chloride (NaCl) and ammonium chloride (NH<sub>4</sub>Cl) on glass slides. We also performed high-resolution imaging and MATLAB-based analysis to extract morphological and textural features of dried salt deposits, which were then assessed using Principal Component Analysis (PCA) technique. Results showed that humidity significantly impacted drying times and crystal morphologies. NH<sub>4</sub>Cl formed dendritic structures that grew more complex with higher humidity, while NaCl produced cubic/hopper crystals, whose size and aggregation varied with humidity. PCA analysis showed humidity-specific patterns, with NH<sub>4</sub>Cl displaying greater sensitivity. Our results demonstrate that the controlled humidity systematically alters salt crystallization dynamics, and advanced image analysis can precisely quantify these morphological signatures. Deep learning neural network models predict the correct salts from their image morphologies with over 97% accuracy. Crystallization ; Humidity ; Principal component analysis ; Relative humidity ; Salt (chemistry) ; Crystal (programming language) Crystallization and Solubility Studies ; Nanomaterials and Printing Technologies ; Calcium Carbonate Crystallization and Inhibition
Authors: Roman Stratiichuk | Наталія Анатоліївна Шевчук | Roman Kyrylenko | Volodymyr Vozniak | Ihor Koleiev | Taras Voitsitskyi | Sasha Korogodski | Zakhar Ostrovsky | Ivan Khropachov | Valerii Vasylevskyi | Vladyslav Husak | Sergiy A. Starosyla | Semen Yesylevskyy | Alan Nafiiev
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-07-18)
Field: Materials Science — Materials Chemistry

Improving ADMET prediction with descriptor augmentation of Mol2Vec embeddings The accurate prediction of ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) properties is crucial for early-stage drug development, enabling the reduction of late-stage attrition and guiding compound prioritization. In recent years, machine learning models have emerged as powerful tools for ADMET prediction, leveraging diverse molecular representations ranging from handcrafted descriptors to graph neural networks and language model embeddings. Despite these advances, balancing predictive performance with computational efficiency remains a key challenge, particularly for high-throughput screening scenarios. Among unsupervised embedding methods, Mol2Vec has shown promise by capturing chemical substructure context analogously to word embeddings in natural language processing. However, its performance on comprehensive ADMET benchmarks has not been systematically assessed. In this work, we reimplement Mol2Vec with an expanded training corpus and higher embedding dimensionality, and evaluate its utility across 16 ADMET prediction tasks from the Therapeutics Data Commons (TDC). We show that while Mol2Vec embeddings alone are competitive, combining them with classical molecular descriptors and applying feature selection significantly improves performance. Our final MLP models with enhanced Mol2Vec embeddings achieved top-1 results in 10 of 16 benchmarks, outperforming all previously reported models on the TDC leaderboard in this regard, demonstrating that descriptor-enriched representations, paired even with relatively simple MLPs, can rival or exceed the performance of more complex models. Computer science ; Artificial intelligence Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning in Bioinformatics