jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: S. Maheswari | N. P. Swetha Menon | B. Sumathi | M. A. Mukunthan | V. Bhanu Rekha | A. Jose Anand
Source: Journal of Environmental Nanotechnology (article, 2025-01-01)
Field: Materials Science — Polymers and Plastics

Predicting and Improving the Mechanical Strength of a Modified Natural Fiber/TiO2 Polymer Nanocomposite Using Artificial Intelligence Artificial Intelligence (AI) including methods such as artificial neural networks (ANN) and machine learning has been employed to address numerous technical challenges. This study employed titanium dioxide (TiO2) and natural fibers derived from Paederia foetida (PF) to fabricate a high-performance hybrid nanocomposite reinforced for structural functions. A chemical mixture of KMnO4 in acetone was utilized to modify the fibers, enhancing the internal properties and improving bonding with the PF/polymer matrix. To enhance the tensile strength (TS) and flexural strength (FS) of the synthesized PF/TiO2 hybrid nanocomposite (PFMNC), an ANN model featuring hyper-parameter enhancement within a linear neural network of 3-5-1 was employed, incorporating 5 neurons in the internal layer, with the application of Box-Behnken Design (BBD). Scanning electron microscopic images indicate that KMnO4 alteration influenced the mechanical strength of the composite. The mechanical assessment findings indicate that variables influenced the tensile strength (TS) and flexural strength (FS) of PFMNC, as demonstrated by the analysis of variance R² = 0.9525 and R² = 0.9526, respectively. The anticipated outcomes closely resembled the experimental results. The model forecasted an ideal TS of 47.5613 MPa and FS of 38.12 MPa. To validate the empirical experimental research, TS and FS analyses were conducted at the projected optimal settings. The TS data indicated an average strength of 40.67 MPa, while the FS exhibited an average value of 52.98 MPa. This work has shown that the ANN-BBD modeling technique effectively attains suitable mechanical property values rapidly, reduce production costs, and conserves resources. Ultimate tensile strength ; Artificial neural network ; Flexural strength ; Nanocomposite ; Mechanical strength ; Izod impact strength test Natural Fiber Reinforced Composites ; Polymer Nanocomposites and Properties ; Electrospun Nanofibers in Biomedical Applications
Authors: Nishamol Kuriakose | Lucía Morán‐González | Hannes Kneiding | Jørn Eirik Betten | David Balcells
Source: ChemRxiv (preprint, 2025-07-06)
Field: Chemistry — Inorganic Chemistry

Machine Learning the Hydrogen Adsorption Capacity of Metal Organic Frameworks High-throughput virtual screening and machine learning (ML) are powerful tools for accelerating the discovery of nanoporous adsorbents for gas storage applications, including metal-organic frameworks (MOFs). Besides the nature of the data and the models, ML performance is strongly dependent on the MOF representation. In this work, we extended the molecular atom-atom, bond-bond, bond-atom (AABBA) autocorrelations to generate vector representations of MOFs for ML models. AABBA vectors encoded both the atomic and bond properties of the MOF metal nodes and linkers, enhancing accuracy in the prediction of gas storage properties. In particular, we computed the gravimetric and volumetric adsorption capacities for the adsorption of hydrogen into 11,500 MOFs. The data, which is made openly available together with the ML models code, was computed with Grand Canonical Monte Carlo simulations to train neural networks predicting the isothermal hydrogen deliverable capacity at room temperature. Further, feature engineering based on a gradient boosting algorithm highlighted the benefits of reducing the dimensionality of the AABBA representation, while keeping the autocorrelation of the bond properties. Adsorption ; Metal-organic framework ; Metal ; Hydrogen ; Materials science ; Environmental science ; Chemistry ; Metallurgy ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Enhanced Oil Recovery Techniques ; Machine Learning in Materials Science
Authors: N. Haridharan | Venkata Shivakumar Remella
Source: Journal of Applied Polymer Science (article, 2025-08-08)
Field: Chemistry — Organic Chemistry

Polymerization Rate Control in <scp>ATRP</scp> : Experimental Kinetics and Machine Learning Predictions of Initiator Efficiency in Quinol Ester, Phenolic Ester‐Amide, and Diamide‐Based Systems ABSTRACT The effect of initiator structure and configuration on the initiation and kinetics was determined for a series of amide‐and ester‐linked initiators in copper‐mediated atom transfer radical polymerization. In this study, we adopt a hybrid approach, integrating experimental polymerization data with computational modeling to enhance predictive accuracy and mechanistic understanding. Experimentally, polymerization rates were measured for various initiator‐monomer systems, using different initiators. The influence of molecular structure, steric effects, and electronic properties on R p was systematically analyzed. The lower polymerization rate observed with the amide‐based initiator may be attributed to the coordination of the copper catalyst with the nitrogen of the amide group through the lone pair of electrons on nitrogen. The efficiency of an initiator is attributed to the competing effect of the substituents attached next to the CX (X = halogen) bond. Computationally, we employed Chemical Graph Theory (CGT) descriptors, including Wiener index, Hosoya index, Randic index, Electronegativity index, Flexibility index, and steric hindrance, to correlate molecular structure with polymerization kinetics. A multiple linear regression (MLR) model was initially developed to establish a mathematical relationship between topological indices and experimentally measured polymerization rates. Our findings indicate that Randic index and Electronegativity index significantly influence polymerization rates, while steric hindrance exhibits a strong negative correlation with R p . Polymerization ; Chemistry ; Steric effects ; Electronegativity ; Amide ; Polymer chemistry ; Radical polymerization ; Monomer ; Atom-transfer radical-polymerization ; Stereochemistry ; Organic chemistry ; Polymer Advanced Polymer Synthesis and Characterization ; Machine Learning in Materials Science ; Conducting polymers and applications
Authors: Elena-Emilia Oprescu | Maria Tănase | Bianca-Ana-Maria Burdusel | Cătălina Călin | Cristina Popa
Source: International Multidisciplinary Scientific GeoConference SGEM ... (article, 2025-08-15)
Field: Materials Science — Biomaterials

PREDICTION THE SOLUBILITY AND SWELLING INDEX OF STARCH FILMS APPLYING MACHINE LEARNING METHODS The utilization of renewable materials has raised worldwide interest regarding biodegradable polymers, proving important scientific, ecological, and economic relevance. Biopolymers are versatile materials used in various applications due to their high strength, low weight, and minimal environmental impact. However, their characterization for understanding their properties requires extensive experimental measurement. In this context, conventional research is improved by the integration of artificial intelligence (AI) and machine learning (ML) for predicting material properties. This research studies the optimization of starch extraction from potato waste and the influence of plasticizers and fillers on some physical properties, such as swelling index, water solubility and thermal stability. Starch films contain glycerol, sorbitol and biochar in varying percents were characterized by Fourier-transform infrared spectroscopy (FTIR), thermogravimetric analysis and tensile strength. The results indicated that films with 20% glycerol content showed enhanced flexibility and solubility, whereas films with 30% sorbitol showed superior thermal stability, exhibiting a degradation temperature by 12 degrees, compared with standard sample which doesn�t have additives. It has been observed that a higher percentage of biochar about 15 % reduces the solubility of polymer films, a very important characteristic when starch polymer films are used as food packaging. The experimental data obtained was used to predict the solubility and swelling index of starch films applying three machine learning methods. Starch ; Solubility ; Plasticizer ; Swelling ; Thermogravimetric analysis ; Sorbitol ; Ultimate tensile strength ; Polymer Nanocomposite Films for Food Packaging ; Polymer Science and PVC ; biodegradable polymer synthesis and properties
Authors: Fanzhi Meng | Wei Xu | Yanan Qian | Feng Sun | Bing Sun | Zhe Yang
Source: Journal of Thermal Analysis and Calorimetry (article, 2025-10-21)
Field: Chemistry — Analytical Chemistry

AI-based accurate and efficient flash point prediction for structurally diverse hydrocarbons via Bayesian-optimized deep neural networks Artificial neural network ; Generalization ; Pattern recognition (psychology) ; Stability (learning theory) ; Flash point ; Feature (linguistics) ; Regression ; Deep learning Spectroscopy and Chemometric Analyses ; Petroleum Processing and Analysis ; Atmospheric and Environmental Gas Dynamics
Authors: Sonaldeep Halder (21561813) | Kartikey Anand (21561816) | Rahul Maitra (9327565)
Source: Figshare (article, 2025-06-18)
Field: Materials Science — Materials Chemistry

Construction ofChemistry-Inspired Dynamic AnsatzUtilizing Generative Machine Learning Generative machine learning models like the Restricted Boltzmann Machine (RBM) provide a practical approach for ansatz construction within the quantum computing framework. This work introduces a method that efficiently leverages RBM and many-body perturbative measures to build a compact chemistry-inspired ansatz for determining accurate molecular energetics. By training on low-rank determinants derived from an approximate wave function, RBM predicts the key high-rank determinants that dominate the ground-state wave function. A shallow depth ansatz is constructed to explicitly incorporate these dominant determinants after dynamically decomposing them into low-rank components and applying many-body perturbative measures for further screening. The method requires no additional measurements beyond the initial training phase. Moreover, it incorporates Bayesian hyperparameter optimization for the RBM, ensuring efficient performance with minimal training data during its limited usage. This approach facilitates the efficient computation of molecular properties, paving the way for exploring new chemical phenomena with near-term quantum computers. Ansatz ; Hyperparameter ; Generative grammar ; Key (lock) ; Computation ; Bayesian network ; Quantum computer ; Bayesian optimization ; Bayesian probability Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Quantum many-body systems
Authors: Jae Young Kim (1575856) | Dionisios G. Vlachos (261715)
Source: Figshare (article, 2025-06-23)
Field: Materials Science — Materials Chemistry

Distance-AwareMolecular Property Prediction in NonlinearStructure–Property Space Molecular property prediction with limited data in novel chemical domains remains challenging. We introduce an approach based on the hypothesis that prediction difficulty increases systematically with distance from well-characterized regions in an appropriately defined structure–property space. Our framework combines nonlinear structure–property space embedding with distance-aware domain classification and uncertainty quantification. We create a structure–property embedding connecting molecular similarity with prediction difficulty, implement distance-aware classification balancing precision and true positive rate, and provide distance-based uncertainty estimates scaled by molecular similarity. Across four ecotoxicity data sets, our local models reduced root mean squared error by 28–48% for truly in-domain molecules compared to global models, with strong correlations (<i>r</i> = 0.40–0.62) between distance and prediction error. In a biolubricant base oil property application, our approach reduced prediction error by 29% compared to a global model and outperformed transfer learning and standard machine learning approaches. This framework’s focus on relevant domains and distance-calibrated uncertainty estimates for limited, heterogeneous chemical data makes it broadly applicable across applications, such as toxicity prediction, drug discovery, and materials design. Embedding ; Property (philosophy) ; Nonlinear system ; Similarity (geometry) ; Domain (mathematical analysis) ; Space (punctuation) ; Uncertainty quantification ; Convergence (economics) ; Focus (optics) ; Range (aeronautics) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: G. Zhao | Taiping Hu | Yingfeng Zhang | Hongshuai Wang | Lirong Wu | S.C. Lu | Fan Yang | Shangqian Chen | Zhifeng Gao | Xinyan Wang | Zheng Cheng
Source: Journal of Chemical Information and Modeling (article, 2025-07-04)
Field: Materials Science — Materials Chemistry

Data-Driven Parametrization of All-Atom Force Fields for Organic Semiconductors Organic semiconductors (OSCs) composed of π conjugated molecules have attracted significant interest in studying bulk properties such as molecular arrangements and electron mobility. However, current traditional force fields (FFs) offer limited torsion types, failing to cover the full chemical space of π conjugated molecules and hindering further molecular dynamics simulation in deducing bulk properties through statistical mechanics. In this study, we introduce OSCFF, a GAFF2-compatible FF that supports diverse torsions for conjugated molecules and enables high-accuracy RESP charge prediction through a neural network (NN). To develop the OSCFF, we construct two expansive and diverse molecular data sets: one consists of around 56,000 fragment geometries with torsion profiles and another consists of around 472,000 optimized molecular geometries with RESP charges. Using these data sets, we train NN models to predict RESP charges and fit the missing dihedral parameters in GAFF2 through automatic differentiation techniques. We further demonstrate that OSCFF achieves high accuracy in predicting torsional energy profiles, RESP charges, and radial distribution functions for conjugated systems. Additionally, we release the data sets, dihedral parameters, and RESP model as open-source resources. We believe OSCFF will serve as a valuable tool for advancing the study of bulk properties in OSCs. Parametrization (atmospheric modeling) ; Organic semiconductor ; Atom (system on chip) ; Semiconductor ; Force field (fiction) ; Physics ; Chemical physics ; Statistical physics ; Quantum mechanics ; Computer science Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Semiconductor materials and devices
Authors: Masahiko Nakase | Takahiro Nishihara | Fauzia Hanum Ikhwan | Tomohiro Okamura | Kazunori Matsui
Source: EPJ Web of Conferences (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Development of Integrated Actinide Chemistry Application, AACE, for Acceleration of Actinide Chemistry Experiments Efficient separation of Minor Actinide (MA) from High-Level Liquid Waste (HLLW) is essential in next generation reprocessing systems. To enable this, finding an efficient separation system is inevitable. We employ a chemoinformatic approach to find suitable diluents, particularly fluorinated diluents, due to some advantages. To efficiently find the candidate molecules we developed Acceleration of Actinide Chemistry Experiment (AACE), which can deploy transfer learning (TL) and human-in-the-loop machine learning (HITL-ML). Our approach utilizes Hansen’s solubility parameters derived from molecular structures to predict solubility and extractability, create extraction models for MA surrogate Lanthanide (Ln) and MA. This manuscript outlines the methodology for diluent exploration and the function of AACE. Actinide ; Acceleration ; Chemistry ; Physics ; Nuclear chemistry Nuclear Materials and Properties ; Radioactive element chemistry and processing ; Nuclear reactor physics and engineering
Authors: Guojiang Zhao (21662645) | Taiping Hu (6012947) | Yingfeng Zhang (6695606) | Hongshuai Wang (3225189) | Lirong Wu (3512831) | Shuqi Lu (12876056) | FengTianCi Yang (21662648) | Shangqian Chen (11864733) | Zhifeng Gao (11229156) | Xinyan Wang (170927) | Zheng Cheng (426625)
Source: Figshare (article, 2025-07-04)
Field: Materials Science — Materials Chemistry

Data-Driven Parametrizationof All-Atom Force Fieldsfor Organic Semiconductors Organic semiconductors (OSCs) composed of π conjugated molecules have attracted significant interest in studying bulk properties such as molecular arrangements and electron mobility. However, current traditional force fields (FFs) offer limited torsion types, failing to cover the full chemical space of π conjugated molecules and hindering further molecular dynamics simulation in deducing bulk properties through statistical mechanics. In this study, we introduce OSCFF, a GAFF2-compatible FF that supports diverse torsions for conjugated molecules and enables high-accuracy RESP charge prediction through a neural network (NN). To develop the OSCFF, we construct two expansive and diverse molecular data sets: one consists of around 56,000 fragment geometries with torsion profiles and another consists of around 472,000 optimized molecular geometries with RESP charges. Using these data sets, we train NN models to predict RESP charges and fit the missing dihedral parameters in GAFF2 through automatic differentiation techniques. We further demonstrate that OSCFF achieves high accuracy in predicting torsional energy profiles, RESP charges, and radial distribution functions for conjugated systems. Additionally, we release the data sets, dihedral parameters, and RESP model as open-source resources. We believe OSCFF will serve as a valuable tool for advancing the study of bulk properties in OSCs. Dihedral angle ; Conjugated system ; Torsion (gastropod) ; Parametrization (atmospheric modeling) ; Molecule ; Molecular dynamics ; Organic semiconductor ; Expansive Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Crystallography and molecular interactions
Authors: Bharath Somalapura Prakasha (20969159) | Peng Xiao (172430) | María José Esplandiu (20969162) | JiaQi Yang (20969165) | Daniel Navarro-Urrios (4523476) | Javier Rodríguez-Viejo (4581652) | Marianna Sledzinska (1357317)
Source: Figshare (article, 2025-03-31)
Field: Materials Science — Materials Chemistry

Impedance-AssistedMultivariate Analysis Techniquefor Enhanced Gas Sensing with 2D Dichalcogenides Semiconducting two-dimensional (2D) materials have emerged as promising candidates for gas sensors due to their exceptional sensitivity and rapid response/recovery times. However, these sensors often face significant challenges, including baseline drift, nonlinearity, cross-sensitivity to multiple gases, and early response saturation, all of which compromise their accuracy and reliability. Conventional resistive sensing approaches, which rely on a single output signal for gas concentration estimation, fail to capture the complex interactions inherent to 2D materials, such as charge carrier generation, transport, and polarization. This work addresses these limitations by utilizing impedance measurements across multiple frequencies for MoS<sub>2</sub>- and WS<sub>2</sub>-based sensors, coupled with machine learning-assisted data processing for accurate relative humidity (RH) quantification. By leveraging the impedance domain, we effectively mitigated baseline drift over extended periods and identified mutually exclusive phase behavior for the WS<sub>2</sub>-based sensor. The MoS<sub>2</sub>-based sensor exhibited long-term stability, motivating the application of a neural network-based multilayer perceptron (MLP), one-dimensional convolutional network (1D-CNN), and long short-term memory (LSTM) models to interpret multifrequency impedance data for precise RH measurements. Our approach enabled robust humidity sensing over a wide range (0–90%) with significantly faster response and recovery times than commercial sensors. Additionally, the neural network-assisted WS<sub>2</sub> sensor effectively minimized cross-sensitivity between humidity and CO<sub>2</sub>. This work showcases the potential of multifrequency impedance-based sensing, combined with machine learning, to overcome the traditional limitations of 2D material-based sensors, offering a pathway toward more reliable, stable, and precise gas-sensing technologies. Resistive touchscreen ; Sensitivity (control systems) ; Electrical impedance ; Convolutional neural network ; Multilayer perceptron ; Signal processing ; Artificial neural network ; SIGNAL (programming language) 2D Materials and Applications ; Gas Sensing Nanomaterials and Sensors ; Nanowire Synthesis and Applications
Authors: Pedro H. M. Zanineli | Mário Luiz Ribeiro Monteiro | Vinícius Francisco Wasques | Francielle Santo Pedro Simões | Gabriel R. Schleder
Source: ArXiv.org (preprint, 2025-11-07)
Field: Materials Science — Materials Chemistry

Fuzzy Neural Network Performance and Interpretability of Quantum Wavefunction Probability Predictions Predicting quantum wavefunction probability distributions is crucial for computational chemistry and materials science, yet machine learning (ML) models often face a trade-off between accuracy and interpretability. This study compares Artificial Neural Networks (ANNs) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS) in modeling quantum probability distributions for the H$_{2}^+$ ion, leveraging data generated via Physics-Informed Neural Networks (PINNs). While ANN achieved superior accuracy (R$^2$ = 0.99 vs ANFIS's 0.95 with Gaussian membership functions), it required over 50x more parameters (2,305 vs 39-45). ANFIS, however, provided unique interpretability: its Gaussian membership functions encoded spatial electron localization near proton positions ($μ= 1.2 A$), mirroring Born probability densities, while fuzzy rules reflected quantum superposition principles. Rules prioritizing the internuclear direction revealed the system's 1D symmetry, aligning with Linear Combination of Atomic Orbitals theory--a novel data-driven perspective on orbital hybridization. Membership function variances ($σ$) further quantified electron delocalization trends, and peak prediction errors highlighted unresolved quantum cusps. The choice of functions critically impacted performance: Gaussian/Generalized Bell outperformed Sigmoid, with errors improving as training data increased, showing scalability. This study underscores the context-dependent value of ML: ANN for precision and ANFIS for interpretable, parameter-efficient approximations that link inputs to physical behavior. These findings advocate hybrid approaches in quantum simulations, balancing accuracy with explainability to accelerate discovery. Future work should extend ANFIS to multi-electron systems and integrate domain-specific constraints (e.g., kinetic energy terms), bridging data-driven models and fundamental physics. Interpretability ; Artificial neural network ; Wave function ; Adaptive neuro fuzzy inference system ; Quantum ; Gaussian ; Superposition principle ; Fuzzy logic ; Probability distribution Machine Learning in Materials Science ; Quantum many-body systems ; Quantum, superfluid, helium dynamics