jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xiang Li | Feina Cao | Minle Peng | Junwei Ai | Yong Jian Zhang | Chao Ding | YE Nan-biao | Pingxu Chen | Haibo Zhao
Source: Journal of Applied Polymer Science (article, 2025-11-08)
Field: Materials Science — Polymers and Plastics

Recent Progress in Flame Retardant Technology for Polycarbonate: Advancing From Halogenated to Halogen‐Free and <scp>PFAS</scp> ‐Free Solutions ABSTRACT Polycarbonate (PC), a high‐performance engineering thermoplastic widely used in aerospace, electronics, and automotive industries, has inherent flammability. Traditional halogen‐based flame retardants and PFAS‐derived anti‐dripping agents (e.g., PTFE) pose environmental and health risks via hazardous emissions and bioaccumulation. This review examines recent advances in transitioning PC to halogen‐free, PFAS‐free flame‐retardant technologies. Key PFAS‐free innovations highlighted include: (1) synergistic multi‐element systems and bio‐derived additives that achieve UL‐94 V‐0 ratings at low loadings; (2) copolymerization and multilayer co‐extrusion technologies that enable PFAS‐free anti‐dripping performance. The review further identifies unresolved challenges (e.g., balancing transparency/strength with flame‐retardant efficiency, scaling bio‐based additives) and future pathways (machine learning for material discovery, plasma surface functionalization). Ultimately, it emphasizes that cross‐sector collaboration (academia‐industry) is essential to harmonize fire safety, environmental sustainability, and material functionality—providing an actionable framework for developing next‐generation high‐performance PC aligned with global sustainability goals. Fire retardant ; Hazardous waste ; Sustainability ; Automotive industry ; Key (lock) ; Copolymer ; Thermoplastic Flame retardant materials and properties ; Fire dynamics and safety research ; Polymer Foaming and Composites
Authors: Seungjun Lee (2000281) | Eng Hock Lee (18208510) | Young-Kyun Kwon (106168) | Steven J. Koester (1473982) | Phaedon Avouris (1640662) | Vladimir Cherkassky (779478) | Jerry Tersoff (1467274) | Tony Low (1233717)
Source: Figshare (article, 2025-10-22)
Field: Materials Science — Materials Chemistry

Generalized EnergyBand Alignment Model for van derWaals Heterostructures with a Charge Spillage Dipole The energy band alignment at the interface of van der Waals heterostructures (vdWHs) is a key design parameter for next-generation electronic and optoelectronic devices. Although the Anderson and midgap models have been widely adopted for bulk semiconductor heterostructures, they exhibit severe limitations when applied to vdWHs, particularly for type-III systems. Based on first-principles calculations for approximately 10<sup>3</sup> vdWHs, we demonstrate that these traditional models miss a critical dipole arising from interlayer charge spillage. We introduce a generalized linear response (gLR) model that includes this dipole through a quantum capacitance term while remaining analytically compact. With only two readily computed inputs, the charge neutrality level offset and the sum of the isolated-layer bandgaps, the gLR reproduces density functional theory (DFT) band line-ups with <i>r</i><sup>2</sup> ∼ 0.9 across type-I, -II, and -III stacks. Machine learning feature analysis confirms that these two descriptors dominate the underlying physics, indicating that the model is near-minimal and broadly transferable. The gLR framework therefore provides both mechanistic insight and a fast and accurate surrogate for high-throughput screening of the vast vdW heterostructure design space. Heterojunction ; van der Waals force ; Band offset ; Dipole ; Charge (physics) ; Density functional theory ; Semiconductor ; Electronic band structure 2D Materials and Applications ; Boron and Carbon Nanomaterials Research ; Graphene research and applications
Authors: Miguel Ángel de Carvalho Servia
Source: Open MIND (article, 2025-09-17)
Field: Materials Science — Materials Chemistry

Automated knowledge discovery for reaction engineering Accurate, interpretable kinetic models are essential for designing, optimizing, and controlling chemical processes. Traditional mechanistic modeling demands domain expertise, whereas purely data-driven approaches often lack transparency and physical consistency. This thesis presents automated knowledge discovery frameworks that address this by combining symbolic regression, information-theoretic model selection, and physicochemical constraints. Symbolic regression denotes machine learning methods that infer expressions directly from data, rather than assuming a fixed model form, aiming to extract plausible kinetic models with minimal expert intervention. Chapter 3 examines model selection. A range of information criteria -- including Akaike, Bayesian, generalizable, and Hannan-Quinn -- are assessed to discriminate among kinetic models under noise, limited data, and data richness. A case study illustrates their practical behavior, and model-based design of experiments for model discrimination is introduced. Chapter 4 introduces two symbolic regression frameworks, ADoK-S and ADoK-W (Automated Discovery of Kinetics, strong and weak formulations), which automate the discovery of rate laws from rate or concentration data. Both rely on genetic programming for expression generation and information-criterion-based refinement, and recover ground-truth models from sparse, noisy datasets. Chapter 5 extends symbolic regression to mechanism discovery via SiMBA (Simplest Mechanism Builder Algorithm), a four-stage pipeline for mechanism generation, model translation, parameter estimation, and information-criterion-based model comparison. Parallelized backtracking and matrix representations enable efficient exploration of chemically feasible networks, as demonstrated on aldol condensation and fructose dehydration. Finally, Chapter 6 proposes PI-ADoK (Physics-Informed Automated Discovery of Kinetics), which embeds physicochemical constraints into symbolic regression and incorporates uncertainty quantification via Metropolis-Hastings. Across several catalytic benchmarks, PI-ADoK improves model fidelity, robustness, and data efficiency. Collectively, these contributions advance automated kinetic model discovery for reaction engineering and process systems applications. Symbolic regression ; Knowledge extraction ; Genetic programming ; Mechanism (biology) ; Pipeline (software) ; Applicability domain ; Data modeling ; Regression ; Regression analysis Machine Learning in Materials Science ; Evolutionary Algorithms and Applications ; Process Optimization and Integration
Authors: Zarna D. Ponkiya | Thanasee Thanasarnsurapong | Adisak Boonchun | Prafulla K. Jha
Source: ACS Applied Energy Materials (article, 2025-10-22)
Field: Materials Science — Materials Chemistry

Machine Learning Force Field Accelerated Prediction of Low Lattice Thermal Conductivity in Strained WS <sub>2</sub> Monolayer Due to its excellent electronic properties, the WS2 monolayer is promising for electronic and optoelectronic devices. However, its high lattice thermal conductivity (κL) limits its application in thermoelectric and thermal insulation. One effective strategy to reduce κL is the application of biaxial tensile strain. In this work, we perform an efficient approach that integrates on-the-fly machine learning force fields with the Peierls-Boltzmann transport equation to accurately and efficiently evaluate κL of the WS2 monolayer. This method reproduces density functional theory results with substantial computational savings. Extending this, we predicted the κL of the WS2 monolayer under 2–20% biaxial tensile strain, beyond which dynamical instability arises. The room temperature κL shows a nonmonotonic decrease with strain, reaching a minimum of 11.18 W/mK at 16%. Thermal transport analysis reveals that reduced phonon velocity and strong phonon–phonon scattering at 16% strain led to such low κL. The 16% strained WS2 monolayer is confirmed to be dynamically, thermally, and mechanically stable for practical applicability. The finding suggests the 16% strained WS2 monolayer as a potential energy-conversion material, offering an efficient approach to explore and engineer thermal transport in 2D materials. Monolayer ; Phonon ; Thermal conductivity ; Thermoelectric effect ; Thermoelectric materials ; Thermal ; Phonon scattering ; Lattice (music) Machine Learning in Materials Science ; Thermal properties of materials ; Advanced Thermoelectric Materials and Devices
Authors: Jianqiao Xu | Xinghao Wang | Min He | Yue Bu | Zunyao Wang | Ruijuan Qu | Juan Gao
Source: Chemical Engineering Journal (article, 2025-02-24)
Field: Chemistry — Analytical Chemistry

Natural attenuation and sulfite-mediated degradation of trichloroethylene in soil: A mechanism analysis across diverse soil types Degradation (telecommunications) ; Natural (archaeology) ; Environmental chemistry ; Mechanism (biology) ; Trichloroethylene ; Attenuation ; Environmental science ; Sulfite ; Chemistry ; Soil science ; Geology ; Engineering ; Inorganic chemistry Analytical chemistry methods development ; Radioactive element chemistry and processing ; Environmental remediation with nanomaterials
Authors: F. Garcia | Frank Naets
Source: Machine Learning (article, 2025-11-20)
Field: Materials Science — Materials Chemistry

Beyond Limits: Enhancing the Extrapolation Performance of Regression Models by Leaving the Boundary Out Extrapolation ; Regression ; Hyperparameter ; Interpolation (computer graphics) ; Regression analysis ; Boundary (topology) ; Linear regression Machine Learning in Materials Science ; Advanced Multi-Objective Optimization Algorithms ; Gaussian Processes and Bayesian Inference
Authors: Jan Stanislaw Nowak | Sune Olesen | Pengfei Tian | René L. Bærentsen | Ditlev E. Brodersen | Daniel E. Otzen
Source: Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics (article, 2025-04-11)
Field: Materials Science — Materials Chemistry

Role of electrostatics in cold adaptation: A comparative study of eury- and stenopsychrophilic triose phosphate isomerase Psychrophilic (cold-active) organisms have developed enzymes that facilitate sufficient metabolic activity at low temperatures to sustain life. This occurs through molecular adaptations that tend to increase protein flexibility at the expense of stability. However, psychrophiles also vary in their growth conditions. Eurypsychrophiles thrive over a wide temperature range and often prefer temperatures above 20 °C, while stenopsychrophiles grow optimally below 15 °C and are more narrowly adapted to cold temperatures. To elucidate differences between these two classes of enzymes, we here compare the stability and unfolding kinetics of two orthologues of the basal household enzyme triose phosphate isomerase, one from the stenopsychrophilic Antarctic permafrost bacterium Rhodonellum psychrophilum (sTPI) and the other from the eurypsychrophilic Greenland ikaite column bacterium Rhodococcus sp. JG-3 (eTPI). Remarkably, sTPI proved significantly more thermostable and resistant to chemical denaturation than its eurypsychrophilic counterpart, eTPI, in the absence of ionic components in solution, whereas inclusion of electrostatic screening agents in the form of sodium chloride or the charged denaturant guanidinium chloride largely cancelled out this difference. Thus, electrostatics play a prominent role in stabilizing the stenopsychrophilic sTPI, and a mandatory low-temperature growth environment does not preclude the development of considerable thermotolerance for individual enzymes. We were able to increase the thermostability of sTPI using an evolutionary machine learning model, which transferred several sTPI residues into the eTPI active site. While the stabilizing effect was modest, the combination of individual mutations was additive, underscoring the potential of combining multiple beneficial mutations to achieve enhanced enzyme properties. Psychrophile ; Triosephosphate isomerase ; Thermostability ; Guanidinium chloride ; Enzyme ; Biology ; Biochemistry ; Active site ; Directed evolution ; Protein engineering ; Biophysics ; Chemistry ; Mutant Enzyme Structure and Function ; Protein Structure and Dynamics ; Porphyrin Metabolism and Disorders
Authors: Sreeraj Rajan Warrier | Jayasri Dontabhaktuni
Source: ArXiv.org (preprint, 2025-07-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Hybrid Quantum-Classical Inverse Design of Metasurfaces for Tailored Narrow Band Absorption The inverse design of metasurfaces poses a considerable challenge because of the intricate interdependencies that exist between structural characteristics and electromagnetic responses. Traditional optimization methods require significant computational resources and frequently do not produce the most effective solutions. This study presents a hybrid quantum-classical machine learning approach known as Latent Style-based Quantum GAN (LaSt-QGAN). This method integrates a Variational Autoencoder (VAE) with a Quantum Generative Adversarial Network (QGAN) to enhance the optimization of metasurface designs aimed at achieving narrow-band absorption and unidirectionality. The proposed method results in a reduction of training time by 10X and a decrease in data requirements by 40X when compared to traditional GAN-based approaches. The produced metasurface designs demonstrate a high fidelity in relation to the target absorption spectra compared to the classical GAN based methods. Additionally, the integration of a material look-up table facilitates manufacturability by allowing for the substitution of predicted material properties with viable alternatives, all while preserving performance accuracy. Moreover the model is able to generate Q-factor upto the order of $10^4$, while the training dataset has Q-factor upto the order of $10^3$. Design for manufacturability ; Autoencoder ; Absorption (acoustics) ; Reduction (mathematics) ; Inverse ; Frame (networking) ; Inverse problem ; Maxima and minima ; Quantum Metamaterials and Metasurfaces Applications ; Acoustic Wave Phenomena Research ; Plasmonic and Surface Plasmon Research
Authors: Zhihan Wang | Weijie Peng | Zhengrui Tian | Lin Zhu | Mengqi Guo | Zhiwei Chen | Hewei Meng | Chunwang Dong
Source: Food Chemistry X (article, 2025-10-01)
Field: Chemistry — Analytical Chemistry

Rapid detection of synthetic pigments in black tea using hyperspectral imaging technology and machine learning In this study, we propose a novel approach that integrates hyperspectral imaging with machine learning to address the challenge of rapid and non-destructive detection of artificial pigment adulteration in Wuyi Congou tea. Compared with traditional chemical methods, this method enables the identification of pigment types, quantification of adulteration rate, and visualization of pigment distribution. A hyperspectral dataset of black tea adulterated with three synthetic pigments (Tartrazine, Carmine, and Sunset Yellow) was constructed, supporting the establishment of qualitative identification and quantitative prediction models. The qualitative model achieved an accuracy greater than 98 %, while the quantitative model yielded a predictive correlation coefficient (R<sub>p</sub>) greater than 0.95 and root mean square error of prediction (RMSEP) less than 0.025. These results validate the feasibility and effectiveness of this approach for detecting and quantifying pigment adulteration in black tea, providing new possibilities for tea quality and safety inspection. Hyperspectral imaging ; Black tea ; Pattern recognition (psychology) ; Pigment ; Correlation coefficient ; Identification (biology) ; Reflectivity Spectroscopy and Chemometric Analyses ; Tea Polyphenols and Effects ; Dye analysis and toxicity
Authors: Siaga Whiky Setia | Nu Rhahida Arini | Bima Sena Bayu Dewantara
Source: Advance Sustainable Science Engineering and Technology (article, 2025-12-23)
Field: Materials Science — Polymers and Plastics

ANN-Based Mechanical Property Prediction of Bio-Fibre for Wind Turbine Blade Materials with FEM Validation The increasing demand for renewable energy highlights the need for sustainable materials in wind turbine blade design. Conventional fiberglass blades, while effective, present environmental and disposal challenges, motivating the exploration of bio-composites as greener alternatives. This study aims to develop and validate an integrated framework that combines experimental validation, Finite Element Method (FEM) pre-screening, Artificial Neural Networks (ANN), and Rule of Mixtures (RoM) validation to evaluate the feasibility of bio-fibre wind turbine blades Mechanical properties of flax, hemp, sisal, jute, pineapple fiber, and resin are obtained from previously published experimental studies available in the literature, with resin content fixed at 90% and permutations generated for ANN training. Experimental tensile testing on a 90% resin–10% pineapple fiber composite yields 131 MPa, closely matching the permutation prediction of 118.6 MPa, confirming dataset reliability. FEM simulations are then employed to pre-screen potential maximum performance values within the dataset range, ensuring the physical feasibility of ANN input properties. Using these validated inputs, the ANN predicts feasible bio-composite compositions, which are further compared against RoM estimations. The results show that ANN predictions remain within a 7% deviation from RoM values, demonstrating consistency with micromechanical theory. This integrated framework highlights that FEM-based input screening enhances ANN prediction reliability, and pineapple-based bio-composites can serve as sustainable and technically viable alternatives for wind turbine blade applications. Finite element method ; Turbine ; Artificial neural network ; Consistency (knowledge bases) ; Renewable energy ; Matching (statistics) ; Turbine blade Natural Fiber Reinforced Composites ; Wind Energy Research and Development ; Bamboo properties and applications
Authors: Hoang-Quan Nguyen | Xuan-Bac Nguyen | S. D. Pandey | T. Faltermeier | Nicholas J. Borys | Hugh Churchill | Khoa Luu
Source: ArXiv.org (preprint, 2025-07-07)
Field: Materials Science — Materials Chemistry

$φ$-Adapt: A Physics-Informed Adaptation Learning Approach to 2D Quantum Material Discovery Characterizing quantum flakes is a critical step in quantum hardware engineering because the quality of these flakes directly influences qubit performance. Although computer vision methods for identifying two-dimensional quantum flakes have emerged, they still face significant challenges in estimating flake thickness. These challenges include limited data, poor generalization, sensitivity to domain shifts, and a lack of physical interpretability. In this paper, we introduce one of the first Physics-informed Adaptation Learning approaches to overcome these obstacles. We focus on two main issues, i.e., data scarcity and generalization. First, we propose a new synthetic data generation framework that produces diverse quantum flake samples across various materials and configurations, reducing the need for time-consuming manual collection. Second, we present $φ$-Adapt, a physics-informed adaptation method that bridges the performance gap between models trained on synthetic data and those deployed in real-world settings. Experimental results show that our approach achieves state-of-the-art performance on multiple benchmarks, outperforming existing methods. Our proposed approach advances the integration of physics-based modeling and domain adaptation. It also addresses a critical gap in leveraging synthesized data for real-world 2D material analysis, offering impactful tools for deep learning and materials science communities. Adaptation (eye) ; Deep learning ; Domain (mathematical analysis) ; Quantum ; Domain adaptation ; Quantum computer ; Qubit Machine Learning in Materials Science
Authors: Fleming Kretschmer | Eva-Maria Harrieder | Michael Witting | Sebastian Böcker
Source: ChemRxiv (preprint, 2025-05-28)
Field: Chemistry — Spectroscopy

Times are changing but order matters: Transferable prediction of small molecule liquid chromatography retention times Hundreds of models for the prediction of small molecule retention times were published during the last decades. Our goal is the transferable prediction of retention times: Our method should predict retention times for a target dataset, without the need of training data from that chromatographic system. Unfortunately, retention times may change massively, even for nominally identical chromatographic conditions. Retention order is much better retained, yet even the retention order of compounds may change if chromatographic conditions vary. We present a machine learning model that can predict retention order or, more precisely, a retention order index, taking into account chromatographic conditions. We show how to map predicted retention order indices to retention times. Disentangling these two task finally enables transferable retention time prediction across chromatographic conditions and compound classes. Our 2-step method outperforms existing methods that were trained on the target dataset. Finally, we systematically study what chromatographic conditions result in notable changes of retention order. Order (exchange) ; Chromatography ; Chemistry ; Business Analytical Chemistry and Chromatography ; Various Chemistry Research Topics
Authors: Huijia Luo | Junliang Liu | Han Wang | Songqing Zhang | Wenwu Pan | Yongling Ren | Cailei Yuan | Wen Lei
Source: Journal of Alloys and Compounds (article, 2025-11-15)
Field: Materials Science — Materials Chemistry

Data-driven strategy for size-controllable growth of tin(II) sulfide nanoplates via machine learning Precise size control during nanomaterial growth remains challenging yet crucial for optimizing their application-specific properties. This study presents a machine learning (ML) assisted framework that enables predictive, size-controllable chemical vapor deposition (CVD) growth of SnS nanoplates. A Gaussian process regression (GPR) model is trained on experimental SnS growth data, with hyperparameters fine-tuned using the Bayesian optimization algorithm (BOA) with 10-fold cross-validation. The trained GPR model exhibits high predictive accuracy on test data, outperforming alternative ML models. Experimental validation with previously unexplored parameter sets results in relative errors below 8.3 % between model predictions and experimental measurements, confirming the model’s reliability. Further application of BOA to the trained GPR model successfully identifies the maximum achievable lateral size of SnS nanoplates and its corresponding optimal growth parameters. Sensitivity analysis identifies ‘Dist’—an indicator of growth temperature—as the most influential parameter, and a classical CVD growth model is used to explain the predicted lateral size trend across different Dist values. This data-driven strategy facilitates the prediction and optimization of CVD-grown SnS nanoplate size while significantly reducing the required experimental iterations, offering a scalable and generalizable methodology for intelligent nanomaterial design and process control. • ML model predicts SnS nanoplate size based on experimental CVD growth data. • GPR model enables accurate prediction across diverse growth conditions. • BOA identifies growth parameters for maximum lateral size. • Growth temperature was revealed as the key parameter, with predicted trends supported by classical CVD theory. • Framework reduces experimental workload and enables size-controllable synthesis. Bayesian optimization ; Kriging ; Hyperparameter ; Gaussian process ; Process (computing) ; Scalability ; Support vector machine ; Nanomanufacturing ; Experimental data Machine Learning in Materials Science ; Nanowire Synthesis and Applications ; Quantum Dots Synthesis And Properties