jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sauradeep Majumdar | Swagata Roy | KyuJung Jun | Miguel Steiner | Rafael Gómez‐Bombarelli
Source: Chemistry of Materials (article, 2025-10-13)
Field: Materials Science — Materials Chemistry

The Free Energy Landscape of Li- and Na-Ion Transport in Nanoconfinement with Machine Learning Interatomic Potentials Nanoconfined systems such as cation-exchanged zeolites provide tunable geometric and chemical environments at the nanoscale that may facilitate regulated ion transport for energy storage applications, and enable materials to extract Li from solutions. However, simulating ion transport in nanoporous materials with density functional theory (DFT) accuracy has traditionally been challenging because of system sizes comprising hundreds of atoms and simulation times of nanoseconds. In this work, we perform molecular dynamics (MD) simulations with a machine learning interatomic potential (MLIP) to study the free energy landscape of Li+ and Na+ diffusion in zeolites, with and without solvent (water in this work). Results from umbrella sampling show Li+ to encounter a free energy barrier of ∼14 kJ/mol during their transport along the pore channels, in the presence of solvent. The absence of solvent increases the free energy barrier by ∼3–4 times. We find lower free energy barriers for Na+ diffusion (∼11 kJ/mol) than Li+ and subsequently rationalize how the different coordination environments of the ions lead to the difference in the free energy landscape. A key finding in this work is that the trade-off between the ion coordination with solvent and framework atoms determines the free energy barriers of ion transport in nanoconfinement. We then perform unbiased MD simulations at a high temperature (800 K) after modifying the chemical environment inside the zeolite in terms of the Si/Al ratio. The mean-squared displacements and probability densities of Li+ and Na+ distributions in the confined system show Na+ to diffuse faster than Li+ at high temperatures. The combination of material modeling, MLIP-driven MD simulations, and free energy calculations in this work thus provides a systematic framework for studying ion transport in nanoporous materials. Energy landscape ; Molecular dynamics ; Nanoporous ; Ion ; Thermodynamic integration ; Energy (signal processing) ; Diffusion ; Potential of mean force Machine Learning in Materials Science ; Fuel Cells and Related Materials
Authors: Suchona Akter | Mohammad R. Momeni
Source: ArXiv.org (preprint, 2025-02-24)
Field: Materials Science — Materials Chemistry

Mechanism of Charge Transport in Mixed-Valence 2D Layered Hybrid Bronze Materials Two-dimensional layered bronze (HB) materials are a new class of mixed-valence hybrid organic-inorganic metal oxides that demonstrate great potential as advanced functional materials for next-generation electronics. Recently, new hybrid vanadium bronze materials, (EV)V8O20 and (MV)V8O20, EV = ethyl viologen and MV = methyl viologen, have been introduced, with EV having ~3 orders of magnitude higher electrical conductivity than the MV system. Given their identical inorganic V-O layers and similar reduction potentials, the observed large difference in electrical conductivities is puzzling. Here, through accurate first-principles calculations coupled with MACE machine learning molecular dynamics (MD) simulations validated by accurate ab initio MD simulations, we provide mechanistic molecular-level insights into dominant charge transport and electrical conductivity pathways in these materials. Our detailed structural and electronic properties data identifies factors contributing to this significant difference in electrical conductivities of these materials. Our findings in this work offer clues and provide valuable insights into improving the electrical conductivity of hybrid bronze and similar materials in order to guide the design of next-generation materials with enhanced properties for future electronic and thermoelectric applications. Electrical resistivity and conductivity ; Bronze ; Vanadium ; Ab initio ; Electronic structure ; Conductivity ; Ab initio quantum chemistry methods ; Metal ; Molecular dynamics Nanoporous metals and alloys ; Graphene research and applications ; Catalytic Processes in Materials Science
Authors: K. Surendra Babu | Surya Prakash | L. N. Satapathy | Saja Ali Muhsin | Manish Kumar
Source: Journal of Advanced Manufacturing Systems (article, 2025-10-09)
Field: Materials Science — Polymers and Plastics

FDM Process Using Prediction of Mechanical Properties of Borophene-Reinforced Polymer Nanocomposites Polymer nanocomposites are utilized in various technical fields, including packaging, automotive, aerospace, and production, when nanoscale reinforcement is added to the polymer matrix. This paper gives a hybrid approach for the prediction of mechanical properties (MPs) of borophene-reinforced polymer nanocomposites by the fused deposition modeling (FDM) process. The proposed hybrid technique is the joint execution of both the election optimizer algorithm (EOA) and fuzzy-stranded neural network (FSNN), termed the EOA-FSNN approach. The primary objective is to assess the mechanical property improvement of borophene-reinforced polymer nanocomposites fabricated via the FDM process. The EOA algorithm is used to optimize the rheological features of polymers and their composites for FDM. The FSNN is used to predict the MP of borophene-reinforced polymer nanocomposites. The proposed method is assessed and compared to other existing approaches using MATLAB. The proposed approach shows better results compared to existing methods such as polynomial chaos expansion (PCE), artificial neural network (ANN), and genetic algorithm (GA). The integration of EOA with FSNN enables the proposed method to achieve superior performance, yielding a higher tensile strength of 33 MPa and a lower mean square error (MSE) of 0.318 compared to existing approaches. This hybrid approach not only enhances the MP of nanocomposites but also provides a reliable and efficient predictive framework for future advancements in polymer engineering. Nanocomposite ; Ultimate tensile strength ; Polymer ; Artificial neural network ; Polymer nanocomposite ; Process (computing) ; Deposition (geology) ; Mean squared error Polymer Nanocomposite Synthesis and Irradiation ; Additive Manufacturing and 3D Printing Technologies ; Tribology and Wear Analysis
Authors: Omar Alejandro Alvarez-Gonzaga | Hector Daniel Morales-Rodriguez | Juan I. Rodríguez
Source: Research Square (preprint, 2025-08-22)
Field: Materials Science — Materials Chemistry

Improving the cheminformatics-based machine learningperformance for the prediction of organic solar cells’ efficiency Cheminformatics ; Computer science ; Artificial intelligence ; Machine learning ; Chemistry ; Computational chemistry Machine Learning in Materials Science
Authors: Takafumi Nishii (10731157) | Kaname Ichizawa (22423495) | Haruka Nagano (12861299) | Hiroya Mukai (22423498) | Daimon Sakaguchi (18332040) | Hiroaki Gotoh (1771738)
Source: Figshare (article, 2025-10-13)
Field: Materials Science — Materials Chemistry

Predicting Substrate Reactivity in Oxidative Homocouplingof Phenols Using Positive and Unlabeled Machine Learning A positive and unlabeled machine learning (PU learning) model was trained to predict substrate reactivity in the oxidative homocoupling of phenols under different conditions. We demonstrated its effectiveness by conducting validation using two descriptor sets: 28-dimensional descriptors considered to influence reactivity and extended-connectivity fingerprints. We performed parameter tuning of the model using our experimental data and determined that the optimized parameters provided excellent prediction accuracy for the existing experimental data, regardless of the reaction conditions. Furthermore, the prediction results obtained using 30 types of unlabeled data matched the experimental results for approximately 83.3–86.7% of substrates, and the prediction accuracy of the PU learning model was shown to be superior to that of a model trained with both positive and negative reactivity data. Because negative data are not required to train a PU learning model, it can be applied to reactions reported in many previous studies, informing the cost-effective synthesis of molecules based on model-predicted results. Reactivity (psychology) ; Substrate (aquarium) ; Phenols ; Experimental data ; Training set ; Predictive modelling Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Tae Jun Park | Gye-Won Lee | Jong-Il Kim | Sahn Nahm | Yoon-Suk Oh
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Deep Learning-Enhanced Wear Analysis of Fe-Cr Composite Coatings for Automotive Brake Applications Under Euro 7 Requirements Automotive industry ; Brake ; Composite number ; Materials science ; Automotive engineering ; Composite material ; Manufacturing engineering ; Metallurgy ; Engineering ; Aerospace engineering Metal Alloys Wear and Properties ; Powder Metallurgy Techniques and Materials ; Surface Treatment and Coatings
Authors: Azzam Alfarraj (22331381) | Monther Rashed Alfuraidan (22331384) | Abdul Malik P. Peedikakkal (5697728) | Ibrahim O. Sarumi (22331387)
Source: Figshare (article, 2025-09-29)
Field: Chemistry — Inorganic Chemistry

Graph-Based Machine Learning Framework for PredictingHydrogen Storage Capacity in Metal–Organic Frameworks Hydrogen is a clean and high-energy fuel, yet its safe and efficient storage remains a key obstacle to widespread adoption. Metal–organic frameworks (MOFs), with their high surface area and tunable porosity, have emerged as promising candidates for solid-state hydrogen storage. In this work, we introduce a graph-based machine learning framework for predicting hydrogen uptake in MOFs by integrating spectral graph theory with data-driven modeling. Molecular structures are represented as weighted graphs from which we extract 20 graph-based descriptorsincluding Laplacian spectral features, degree statistics, and Zagreb indicesthat capture both topological and geometric characteristics of the framework. These interpretable descriptors are used to train multiple regression models on a data set of 3300 MOFs from the Cambridge Structural Database. The XGBoost regressor achieved the highest performance in predicting hydrogen uptake, with a coefficient of determination (<i>R</i><sup>2</sup>) of 0.737, RMSE of 0.850% wt, and MAE of 0.433% wt for gravimetric uptake (UG); and a coefficient of determination (<i>R</i><sup>2</sup>) of 0.698, RMSE of 4.467 g H<sub>2</sub>/L, and MAE of 3.045 g H<sub>2</sub>/L for volumetric uptake (UV). Beyond accurate prediction, the framework enables inverse materials design by identifying graph-based motifs that contribute to improved storage capacity. This integration of chemical graph theory with machine learning provides a scalable, interpretable, and computationally efficient pathway for the discovery of next-generation MOFs tailored for hydrogen storage and other clean energy applications. Mean squared error ; Graph ; Obstacle ; Graph theory ; Hydrogen storage ; Linear regression ; Key (lock) ; Regression Metal-Organic Frameworks: Synthesis and Applications ; X-ray Diffraction in Crystallography ; Hydrogen Storage and Materials
Authors: Muzammil M. N. Ahmed | Parth Ganeriwala | Anthi Savvidou | Nicholas Breen | Siddhartha Bhattacharyya | Pavithra Pathirathna
Source: Journal of Sensor and Actuator Networks (article, 2025-07-09)
Field: Chemistry — Electrochemistry

AI-Driven Differentiation and Quantification of Metal Ions Using ITIES Electrochemical Sensors Electrochemical sensors, particularly those based on ion transfer at the interface between two immiscible electrolyte solutions (ITIES), offer significant advantages such as high selectivity, ease of fabrication, and cost effectiveness for toxic metal ion detection. However, distinguishing between cyclic voltammograms (CVs) of analytes with closely spaced half-wave potentials, such as Cd2+ and Cu2+, remains a challenge, especially for non-expert users. In this work, we present a novel methodology that integrates advanced artificial intelligence (AI) models with ITIES-based sensing to automate and enhance metal ion detection. Our approach first employed a convolutional neural network to classify CVs as either ideal or faulty with an accuracy exceeding 95 percent. Ideal CVs were then further analyzed for metal ion identification, achieving a classification accuracy of 99.15 percent between Cd2+ and Cu2+ responses. Following classification, an artificial neural network was used to quantitatively predict metal ion concentrations, yielding low mean absolute errors of 0.0158 for Cd2+ and 0.0127 for Cu2+. This integrated AI–ITIES system not only provides a scientific methodology for differentiating analyte responses based on electrochemical signatures but also substantially lowers the expertise barrier for sensor signal interpretation. To our knowledge, this is the first report of the AI-assisted differentiation and quantification of metal ions from ITIES-based CVs, establishing a robust framework for the future development of user-friendly, automated electrochemical sensing platforms for environmental and biological applications. ITIES ; Electrochemistry ; Computer science ; Metal ions in aqueous solution ; Metal ; Ion ; Biological system ; Materials science ; Chemistry ; Electrode ; Metallurgy ; Cyclic voltammetry ; Physical chemistry ; Organic chemistry Electrochemical Analysis and Applications ; Analytical Chemistry and Sensors ; Advanced Chemical Sensor Technologies
Authors: Harsh Mishra (14365983) | Prathamesh Deepak Ladhe (22487168) | Shikhar Misra (3096849)
Source: Figshare (article, 2025-10-23)
Field: Materials Science — Materials Chemistry

Machine Learning-EnabledOptical Property Predictionof Thin Films Using Spectral Data Extraction from Scientific Literature The integration of machine learning (ML) into materials science has demonstrated significant potential in accurately and efficiently predicting material properties. This study presents a data-driven approach for extracting refractive index spectra from line plots in scientific literature, transforming previously underutilized graphical data into a structured data set for predictive modeling. Advanced data extraction tools, including ChartOCR and Plot2Spectra, were employed alongside rigorous feature engineering to curate a high-quality data set comprising 1,770 samples. The preprocessing pipeline encompassed spectral data extraction, normalization, and the incorporation of key features, such as elemental composition, synthesis parameters, and wavelength-dependent properties. An XGBoost model was trained and evaluated, achieving a mean absolute percentage error (MAPE) of 0.149, highlighting the effectiveness of this approach. These findings underscore the potential of ML-driven methodologies for enhancing optical property predictions and automating data extraction, thereby facilitating broader applications in materials research. Preprocessor ; Pipeline (software) ; Property (philosophy) ; Data set ; Data pre-processing ; Set (abstract data type) ; Feature extraction ; Term (time) ; Data integration ; Data extraction Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; X-ray Diffraction in Crystallography
Authors: Miroslava Nedyalkova | Diana Heredia | Joaquı́n Barroso-Flores | Marco Lattuada
Source: ACS Omega (article, 2025-01-16)
Field: Chemistry — Organic Chemistry

Comparative Analysis of p<i>K</i><sub>a</sub> Predictions for Arsonic Acids Using Density Functional Theory-Based and Machine Learning Approaches Arsonic acids (RAsO(OH)<sub>2</sub>), prevalent in contaminated food, water, air, and soil, pose significant environmental and health risks due to their variable ionization states, which influence key properties such as lipophilicity, solubility, and membrane permeability. Accurate p<i>K</i> <sub>a</sub> prediction for these compounds is critical yet challenging, as existing models often exhibit limitations across diverse chemical spaces. This study presents a comparative analysis of p<i>K</i> <sub>a</sub> predictions for arsonic acids using a support vector machine-based machine learning (ML) approach and three density functional theory (DFT)-based models. The DFT models evaluated include correlations to the maximum surface electrostatic potential (<i>V</i> <sub>S,max</sub>), atomic charges derived from a solvation model (solvation model based on density), and a scaled solvent-accessible surface method. Results indicate that the scaled solvent-accessible surface approach yielded high mean unsigned errors, rendering it less effective. In contrast, the atomic charge-based method on the conjugated arsonate base provided the most accurate predictions. The ML-based approach demonstrated strong predictive performance, suggesting its potential utility in broader chemical spaces. The obtained values for p<i>K</i> <sub>a</sub> from <i>V</i> <sub>S,max</sub> show a weak prediction level, because the way of predicting p<i>K</i> <sub>a</sub> is related only to the electrostatic character of the molecule. However, p<i>K</i> <sub>a</sub> is influenced by many factors, including the molecular structure, solvation, resonance, inductive effects, and local atomic environments. <i>V</i> <sub>S,max</sub> cannot fully capture these different interactions, as it gives a simplistic view of the overall molecular potential field. Density functional theory ; Computer science ; Mathematics ; Artificial intelligence ; Chemistry ; Computational chemistry Free Radicals and Antioxidants ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Sapna Kushwah | M. Chidhvilas Tanay | Avadh Kishor | Pramod Kumar Singh
Source:  (article, 2025-12-19)
Field: Materials Science — Materials Chemistry

Optimized ADMET-Driven Molecular Design Using GRU-GCN Architecture with Multi-Objective Optimization High failure rates due to inadequate ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) profiles are often a challenging factor in drug discovery, affecting the clinical success of potential drugs. In this paper, we introduce an improved ADMET-driven reconstruction methodology employing a GRU/GCN-based encoder and GRU-based decoder architecture, optimized through multi-objective optimization (MOO) methods. Our proposed model maps SMILES (Simplified Molecular Input Line Entry System) representations of compounds into latent vectors and reconstructs valid chemical compounds from there. The innovation is the combination of multi-objective optimization, while balancing four key components, namely, reconstruction precision, structural confirmation, chemical validity, and improved ADMET properties. Experimental analysis shows the model's better performance in predicting drug-like molecules with better ADMET properties compared with traditional approaches. This paper presents a novel methodology for the drug discovery process, with the combination of deep learning (DL) and MOO ensuring chemical feasibility and pharmacological interest. Architecture ; Process (computing) ; Systems architecture ; Key (lock) ; Component (thermodynamics) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Quantum-Dot Cellular Automata
Authors: Abdullahi Maina | jisieke Chiazor Faustina | Chukwu Onwuka
Source: Open MIND (article, 2025-09-01)
Field: Materials Science — Materials Chemistry

Exploring Machine Learning Models for Predicting Efficiency in Carbazole-Based DSSC Sensitizers This study explores the application of machine learning models in predicting the efficiency of carbazole-based dye-sensitized solar cell (DSSC) sensitizers. The research evaluates multiple machine learning algorithms to model structure–property relationships and optimize photovoltaic performance parameters. The results demonstrate the potential of data-driven approaches in accelerating material discovery and improving predictive accuracy in DSSC efficiency modeling. This work contributes to the growing integration of artificial intelligence in materials science and renewable energy research. Renewable energy ; Photovoltaic system ; Efficient energy use ; Predictive modelling ; Work (physics) ; Energy (signal processing) ; Solar energy ; Support vector machine Machine Learning in Materials Science ; TiO2 Photocatalysis and Solar Cells ; Chemical and Physical Properties of Materials
Authors: Daisuke Takaya
Source: Nihon Kessho Gakkaishi (article, 2025-08-31)
Field: Materials Science — Materials Chemistry

A Dataset of Interaction Energies from FMO Calculations on Representative Protein Structures The biological functions of macromolecules are closely associated with their three-dimensional structures and corresponding electronic states. Quantum chemical calculations are an approach that can determine electronic states from first principles without relying on empirical parameters. In this study, we present a quantum mechanics-based dataset derived from representative structures curated in SCOP2. The calculations were performed using the fragment molecular orbital（FMO）method, which is applicable to large biomolecules such as proteins. The dataset comprises total energies for approximately 5,000 structures and over 200 million inter-fragment interaction energies, including electrostatic and dispersion components. This resource is expected to support functional analyses based on electronic structure and to facilitate machine learning studies in biomolecular interaction studies. Electronic structure ; Biomolecule ; Macromolecule ; Biomolecular structure ; Quantum ; Quantum chemical ; Density functional theory ; Protein–protein interaction Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Diptarka Hait (2835971) | Jan D. Estrada Pabón (22565813) | Martin Stöhr (1397935) | Todd J. Martínez (1278021)
Source: Figshare (article, 2025-11-07)
Field: Materials Science — Materials Chemistry

Locating Ab InitioTransition States via GeodesicConstruction on Machine-Learned Potential Energy Surfaces Efficient and reliable identification and optimization of transition state structures is a longstanding challenge in computational chemistry. Popular chain-of-states methods require hundreds if not thousands of <i>ab initio</i> calculations to generate initial guesses for local quasi-Newton optimizers, with persistent risk of collapse to an alternative stationary point on the potential energy surface (PES). Here, we show that high-quality guess structures for transition state optimization can be obtained by constructing the geodesic path between reactant and product structures on the PES generated by machine learning potentials (MLPs). We present an algorithm for optimization of such geodesic paths, as well as the associated codebase. We demonstrate effectiveness of this approach using the recent eSEN-sm-cons MLP. On average, the highest-energy point along these MLP geodesics requires 30% fewer quasi-Newton optimization steps to converge to the transition state compared to guesses from the fully <i>ab initio</i> freezing string method. Our approach therefore completely eliminates the need for <i>ab initio</i> calculations for the generation of transition state guesses and considerably speeds up subsequent structural optimization. Geodesic construction on ML PES thus promises to be a useful approach for efficient computational elucidation of complex chemical reaction networks. Geodesic ; Potential energy surface ; Ab initio ; State (computer science) ; Point (geometry) ; Potential energy ; Transition state ; Optimization problem Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics