jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: M. Swetha | R.J. Gadha | R.D. Eithiraj
Source: Computational Condensed Matter (article, 2025-12-12)
Field: Materials Science — Materials Chemistry

Investigating a novel antiperovskite K3HTe via first principles study supported by machine learning Antiperovskite ; Seebeck coefficient ; Thermoelectric effect ; Effective mass (spring–mass system) ; Thermoelectric materials ; Conductivity ; Charge carrier ; Band gap Thermal Expansion and Ionic Conductivity ; Advanced Battery Materials and Technologies ; Perovskite Materials and Applications
Authors: Yash Dixit | Yang Liu | Siew Ling Ong | Sujay Prabakar | Marlon M. Reis
Source: Talanta (article, 2025-09-06)
Field: Chemistry — Analytical Chemistry

Spectroscopic detection of chemical markers for traceability in complex biological matrices: A case study on wet-blue leather This study investigated whether unique spectral features from tracer compounds could be reliably detected and discriminated from the complex spectral background of biomaterial matrix. Spectroscopy combined with chemical markers present a promising solution for traceability in complex biological matrices, such as leather and other biomaterials involving intricate processing. This study explores the potential of Near InfraRed Spectroscopy (NIRS), both benchtop (350-2500 nm) and miniaturized (900-1700 nm), as a rapid, non-destructive tool for detecting molecular tracers embedded within wet-blue leather. Three candidate tracers-collagen hydrolysate, Cloisite Na<sup>+</sup> (CNa), and 2-Thiocyanomethylthiobenzothiazole (TCMTB)-were tested across two experimental phases under both industrial tannery and controlled laboratory conditions. Initial analyses using Principal Component Analysis (PCA) indicated TCMTB as the most promising tracer based on spectral distinctiveness. Subsequently, advanced machine learning approaches, including Support Vector Machines (SVM) and feature selection via Genetic Algorithm (GA) coupled with Random Forest (RF), were employed to optimize classification performance and identify key wavelengths contributing to tracer detection. The results confirmed that molecular tracers exhibit identifiable and reproducible spectral signatures, though their detectability is significantly influenced by the inherent variability of the leather matrix and the side of sample scanning. The integration of chemometrics and intelligent feature selection enhances detection sensitivity, highlighting the critical role of spectral range and algorithmic robustness. This study underscores the viability of spectroscopy and machine learning as a platform technology for traceability in complex bio-derived materials, with leather serving as a representative use case. Traceability ; Chemometrics ; Feature selection ; Principal component analysis ; Support vector machine ; Pattern recognition (psychology) ; TRACER ; Spectroscopy ; Hyperspectral imaging Spectroscopy and Chemometric Analyses ; Collagen: Extraction and Characterization ; Identification and Quantification in Food
Authors: Dayton G. Kizzire | M. V. Eremenko | Matthew G. Tucker | Yuanpeng Zhang
Source: Structural Dynamics (article, 2025-03-01)
Field: Materials Science — Materials Chemistry

Combined Machine Learning and Exhaustive Symmetry Approach for Phase Transition Studies Revealing the symmetry change across a phase transition is fundamentally important to understanding and controlling properties such as polarization (ferroelectric transition), conductivity (metal-insulator transition) and other unconventional properties including piezoelectricity, multiferroics, and superconductivity. The recently developed exhaustive symmetry search (ESS) technique has been proven to be an effective tool for systematically studying complex phase transitions. Here we present a combined machine learning and ESS approach that has been developed to reveal the subgroup symmetry of a material from neutron scattering powder diffraction. XsymNet is a convolutional neural network trained with different representations of simulated neutron diffraction data from perturbed members of the subgroup tree. In this work we discuss the general applicability of the technique, how it lowers the barrier to entry for complex phase transition studies, and we present a specific case where XsymNet is applied to study the long-standing morphotropic phase boundary (MPB) problem in the complex temperature-composition phase diagram of PbZr1-xTixO3 (PZT). Computer science ; Symmetry (geometry) ; Phase transition ; Phase (matter) ; Artificial intelligence ; Condensed matter physics ; Physics ; Mathematics ; Quantum mechanics ; Geometry Machine Learning in Materials Science
Authors: Chengyang Tu (20637852) | Xin Li (51274) | Junmin Chen (625300) | Bo Sun (177052) | Kuang Yu (1494592)
Source: Figshare (article, 2025-08-12)
Field: Materials Science — Materials Chemistry

Enhancing ThermalConductivity Computation of Polymersvia Machine Learning Techniques Accurate prediction of the thermal conductivity (κ) of polymers is generally challenging due to their complex structures. Currently available ab initio methods (e.g., DFT-BTE) are prohibitively expensive, and the classical force fields used in molecular dynamics lack accuracy. In this study, we combine ab initio hybrid machine learning (ML)/multipolar polarizable potential (i.e., PhyNEO) with ML-facilitated heat flux calculation. This approach provides reliable heat flux trajectories, which are then used to predict polymer κ quantitatively. Using poly(ethylene oxide) as an example, we compare our calculation results with reliable experimental reference obtained from time-domain thermoreflectance measurement, reaching excellent agreement. This work enables the quantitative prediction of bulk polymer κ starting from only small cluster quantum data, warranting broad applications in future. Thermal conductivity ; Ab initio ; Computation ; Polarizability ; Polymer ; Work (physics) ; Thermal ; Cluster (spacecraft) Machine Learning in Materials Science ; Thermal properties of materials ; Phase Equilibria and Thermodynamics
Authors: Zongfan Wei | Yong Xie | Waseem Akhtar | Nan Qu | Yong Liu | Yuchen Xiao | L. Sun | Baoan Wu | Jingchuan Zhu
Source: Physica Scripta (article, 2025-09-09)
Field: Materials Science — Materials Chemistry

Elucidating the key drivers of twin formation in copper alloys via a small-sample AutoML framework Abstract The twin structure in materials has become a current research hotspot. To clarify the intrinsic effect of alloying elements on twin formation in copper, this study constructed descriptors based on 31 sets of first-principles calculation data and performed a three-level screening on the 132 generated descriptors, including constant elimination, correlation filtering, and comprehensive importance ranking. Six key features were identified through forward accumulation cross-validation. Automated machine learning(AtuoML) was used for modeling, tuning, and validation, successfully distinguishing the tendency of elements to form twins and mapping the results onto the entire periodic table. SHAP interpretative analysis shows that the minimum covalent radius and maximum GSbandgap play a dominant role in twin formation. This method not only explains the mechanism of twin formation at the atomic scale but also provides a basis for the rapid screening of alloy elements. Key (lock) ; Atomic radius ; Copper ; Scale (ratio) ; Atomic units ; Alloy ; Structure formation Corrosion Behavior and Inhibition ; Machine Learning in Materials Science ; Nanoporous metals and alloys
Authors: Xueqin Ran | C. Wang
Source: Symmetry (article, 2025-10-21)
Field: Materials Science — Metals and Alloys

Capturing the Asymmetry of Pitting Corrosion: An Interpretable Prediction Model Based on Attention-CNN Fossil fuels are crucial to the global energy supply, with pipelines being a vital transportation method. However, these vital assets are highly susceptible to pitting corrosion, an insidious form of degradation that can lead to catastrophic failures. Unlike uniform corrosion, which represents a symmetric form of material loss, pitting corrosion is a highly asymmetric and localized phenomenon. The inherent complexity and asymmetry of this process make its prediction a significant challenge. To address this, this study presents SSA-CNN-Attention, a deep learning model specifically designed to analyze the complex, nonlinear interactions among environmental factors. The model employs a Convolutional Neural Network (CNN) to extract local features, while a crucial attention mechanism allows it to asymmetrically weight the importance of these features, enhancing its ability to recognize intricate interactions. Additionally, the Sparrow Search Algorithm (SSA) optimizes the model’s hyperparameters for improved accuracy and stability. Furthermore, a post hoc interpretability analysis using the LIME framework validates that the model’s learned feature relationships are consistent with established corrosion science, revealing how the model accounts for the asymmetric influence of key variables. The experimental results demonstrate that the proposed model reduces mean squared error (MSE) by 61.3% and mean absolute error (MAE) by 26.6%, while improving the coefficient of determination (R2) by 28.2% compared to traditional CNNs. These findings highlight the model’s superior performance in predicting a fundamentally asymmetric process and provide valuable insights into the underlying corrosion mechanisms. Interpretability ; Hyperparameter ; Feature (linguistics) ; Nonlinear system ; Process (computing) ; Classification of discontinuities ; Asymmetry ; Mean squared error Hydrogen embrittlement and corrosion behaviors in metals ; Non-Destructive Testing Techniques ; Machine Learning in Materials Science
Authors: Bhargava Ganti | Karthi Balasubramanian | Sandipan Pati
Source: IEEE Access (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

A Taxonomy-Driven Survey of AI for Seizure Detection: Thalamic Signals, Phase Dynamics, and Translational Gaps Seizure prediction and postictal recovery remain critical challenges in epilepsy care, particularly in real-world, resource-constrained settings. This survey presents a taxonomy-driven synthesis of 150+ peer-reviewed studies spanning seizure phase modeling, thalamic EEG biomarkers, edge inference, and clinical AI integration.We introduce an eight-axis framework covering neurophysiological foundations, machine learning advances, wearable inference pipelines, large language model (LLM) assistants, and privacy-preserving architectures. A key focus is the emerging role of thalamic stereo-EEG (sEEG) as a high-fidelity substrate for modeling seizure transitions and informing closed-loop interventions. Unlike prior reviews, this work explicitly unifies preictal and postictal phase dynamics with modern AI tools—such as federated learning, explainable deep learning, and agentic reasoning. We highlight gaps in dataset diversity, clinical interpretability, and cross-center generalization, while proposing a translational roadmap toward ethical, explainable, and deployment-ready seizure care. Epilepsy ; Neurophysiology ; Electroencephalography ; Focus (optics) ; Epileptic seizure ; Deep learning ; Inference ; Wearable computer Machine Learning in Materials Science ; Neural Networks and Applications ; EEG and Brain-Computer Interfaces
Authors: Zhihang Chen
Source: Science and Technology of Engineering Chemistry and Environmental Protection (article, 2025-10-23)
Field: Materials Science — Materials Chemistry

Advances in Hydrogen Storage Materials: Mechanistic Insights and AI Applications Hydrogen energy is a clean and efficient form of energy, and it is getting more and more attention for its wide use in transportation, energy systems, and industry. But because hydrogen has low density and spreads easily, storing it safely, efficiently, and at low cost is still a big challenge. Many materials like metal–organic frameworks (MOFs), metal hydrides, and carbon-based nanomaterials have been studied for storing hydrogen. However, most of them still have problems, such as low storage ability, needing strict conditions, and not working well after repeated use. These issues make them hard to use in real life. In recent years, artificial intelligence (AI) has brought big changes to hydrogen storage research. Machine learning and deep learning help predict material performance, understand how things work, and design new materials. Together with lab experiments, AI forms a full cycle of “design, test, and improve,” which speeds up the development of better materials. This paper presents a comprehensive review of mainstream hydrogen storage mechanisms and materials, evaluates their practical limitations, and highlights how AI techniques are being applied to address these challenges. The study aims to support the rational design of next-generation hydrogen storage systems and promote deeper integration between AI and materials science to advance the scalable adoption of hydrogen energy. Hydrogen storage ; Scalability ; Energy storage ; Big data ; Mainstream ; Scope (computer science) ; Scale (ratio) Hydrogen Storage and Materials ; Hybrid Renewable Energy Systems ; Spacecraft and Cryogenic Technologies
Authors: Xiaokai Yang | Lin Guo | Christopher W. Tessum
Source: Journal of Geophysical Research Machine Learning and Computation (article, 2025-11-27)
Field: Materials Science — Materials Chemistry

Atmospheric Chemistry Surrogate Modeling With Sparse Identification of Mass Action Dynamics Abstract Modeling gas‐phase atmospheric chemistry is computationally challenging because the underlying dynamics are high‐dimensional (requiring many state variables) and stiff (characterized by Jacobian eigenvalues that span many orders of magnitude). Previous work has demonstrated the promise of machine learning (ML) to accelerate air quality model simulations, but such models commonly suffer from dynamical instability or runaway error propagation. Here, to address this limitation, we introduce a physics‐informed parsimonious machine‐learning framework for atmospheric chemistry. This Sparse Identification of Mass Action Dynamics (SIMADy) framework operates by (a) using a linear autoencoder to create an interpretably‐compressed low‐dimensional state representation, and (b) representing the underlying dynamics of that state constrained by the law of mass action and therefore ensuring boundedness. Using the full Master Chemical Mechanism—one of the most detailed atmospheric chemical mechanisms—as the reference model, the root mean square (RMS) error of the ML model prediction for ozone () concentration over 10 days is 25% of the RMS reference model concentration across all simulations in testing data set when using six state variables. As expected, the ML model predictions remain bounded and exhibit no numerical divergence in all tested simulations. The surrogate model is 5,300 times faster on a CPU (for one simulation) and approximately 1.1 million times faster on a GPU (for many simulations in parallel) compared to the reference model. In future work, the fast, stable, and relatively accurate surrogate models learned by SIMADy can be used to accelerate 3D models of air quality and climate. Identification (biology) ; Jacobian matrix and determinant ; Action (physics) ; Atmospheric chemistry ; Atmospheric dispersion modeling ; Atmospheric model ; Stability (learning theory) ; Divergence (linguistics) ; Mathematical model ; State (computer science) Machine Learning in Materials Science ; Atmospheric chemistry and aerosols ; Air Quality Monitoring and Forecasting
Authors: Yanhao Deng | T. Jiang | Zeyu Deng | Yanming Wang
Source: Research Square (preprint, 2025-05-15)
Field: Chemistry — Physical and Theoretical Chemistry

luMOD: A lightweight universal Mid-range atomic Orientational Descriptor Range (aeronautics) ; Computer science ; Physics ; Theoretical physics ; Materials science ; Statistical physics ; Composite material Various Chemistry Research Topics ; Computational Drug Discovery Methods ; History and advancements in chemistry
Authors: Jian Guan (288307) | Zhenhua Dai (190310) | Hang Zhou (230977) | Zejiang Huang (18161315) | Xuelu Wang (6883574) | Wenlong Zhang (283123) | Xiaohong Guan (351976) | Mengshuai Liu (1685530) | Haitao Lv (1310865) | Shifa Zhong (1685533)
Source: Figshare (article, 2025-10-13)
Field: Materials Science — Materials Chemistry

Design, Synthesis,and Screening of COFs for CO<sub>2</sub> Adsorption by Gaussian Process Effective CO<sub>2</sub> capture requires careful design of Covalent Organic Frameworks (COFs). Current computational approaches often rely solely on simulated properties, neglecting critical chemical and synthetic factors that determine real-world COF performance. We present an integrated computational-experimental framework combining machine learning with experimental validation. Our study analyzes 240 unique COFs (617 samples) with experimentally measured CO<sub>2</sub> adsorption capacities across varied synthesis conditions. Gaussian Process and CatBoost models were developed to predict CO<sub>2</sub> adsorption by simultaneously considering chemical structures, synthesis parameters, and measurement protocols. The GP model demonstrates improved generalization and uncertainty quantification compared to CatBoost. SHAP analysis reveals the model’s focus on COF type and synthesis conditions. Using a database of 181 building blocks, we generated 5557 COF structures with synthesis condition recommendations based on experimental similarity. Experimental validation confirmed the predictions for three synthesized COFs, demonstrating the framework’s practical utility for COF design and optimization. Adsorption ; Generalization ; Gaussian process ; Process (computing) ; Gaussian ; Focus (optics) ; Chemical process ; Experimental data Covalent Organic Framework Applications ; Carbon Dioxide Capture Technologies ; Carbon dioxide utilization in catalysis
Authors: Domenico Bonanni | Yuedong Zhang | Davide Gadioli | Gianluca Scarpellini | Pietro Morerio | Alessio Del Bue | Andrea R. Beccari | Gianluca Palermo
Source: Journal of Chemical Information and Modeling (article, 2025-11-21)
Field: Materials Science — Materials Chemistry

Condensation of Force Field Parameters from Machine Learning Predicted Distributions for High-Throughput Virtual Screening Applications Transferable biomolecular force fields are developed by fitting either ab initio or experimental data related to representative molecules and can then be used to model chemical entities that are similar to the ones they were developed for. However, once parametrized on a given dataset, they are difficult to refit once new chemical entities, sensing schemes, or functional forms are introduced. On the other hand, Machine Learning Force Fields (MLFF) have recently gained attention for their accuracy and ease of expanding their Applicability Domain (AD). Nonetheless, their prediction times make them incompatible with High-Throughput Virtual Screening (HTVS) requirements. In this work, we follow the inverse of the widely adopted approach with transferable force fields and propose a new condensation approach that takes advantage of machine learning algorithms to massively predict force field parameters. The generated numerical distributions are then condensed in a single value that captures in a statistical way the chemical variability of the underlying molecules sharing that specific force field parameter and giving rise to the distribution itself, improving 30× computational efficiency with limited reduction in predicted molecular geometries accuracy. When tested on the public release of the OpenFF Industry Benchmark Season 1 v1.1 dataset, the molecular structures optimized by minimizing the Potential Energy Surface built with condensed FF parameters only show a minor decrease in Root Mean Squared Deviation (RMSD) and Torsion Fingerprint Deviations (TFD) performances compared to those obtained using molecule-specific FF parameters predicted at runtime. To give more context, the original MLFF and its condensed version are evaluated with respect to several well-known transferable force fields widely used for biomolecular simulations. Force field (fiction) ; Ab initio ; Field (mathematics) ; Inverse ; Inverse problem ; Probability distribution ; Condensation ; Benchmark (surveying) ; Experimental data Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Luhao Qiu | Weifeng Peng | Bingyu Zou | Bingxi He | Feng Bao | Huanyu Lei | Mingjun Huang
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Polymers and Plastics

Comprehensive Advantages of Colorless, Transparent, and Thermally Stable Aromatic Polyimides Via Fluorinated Terphenyl Dianhydride Terphenyl ; Materials science ; Chemical engineering ; Optoelectronics ; Polymer chemistry ; Organic chemistry ; Chemistry ; Engineering Synthesis and properties of polymers ; Epoxy Resin Curing Processes
Authors: Takaaki Fujita
Source: ChemRxiv (preprint, 2025-09-18)
Field: Chemistry — Physical and Theoretical Chemistry

Three-Dimensional or Multi-Dimensional Molecular Hyperstructure and Molecular HyperGraph This paper develops a unified, geometry-aware framework for representing molecules and their higher-order organization. We formalize 𝑑-dimensional molecular structures, hyperstructures, and superhyperstructures by combining labeled (hyper)graphs with Euclidean embeddings and rank-aware operations on iterated power sets. Our definitions render geometric observables—bond lengths, bond angles, and dihedral torsions—intrinsic, and we specify configuration spaces modulo rigid motions. A canonical lifting scheme propagates coordinates from atoms to sets-of-sets, enabling consistent embeddings of hyperedges and superedges via convex carriers or simplex parametrizations. We prove reduction theorems: hyperstructures collapse to embedded molecular graphs when all hyperedges are binary, and superhyperstructures reduce to hyperstructures at rank zero. Worked chemical examples (staggered ethane in 3D, planar benzene in 2D, and a hydrogen-bonded water dimer as a hyper/superhyper instance) verify the axioms with explicit numerical coordinates. The framework supports coarse-grained modeling, constraint-based geometry, and machine-learning representations, while remaining compatible with classical cheminformatics notions of atoms, bonds, functional groups, and molecular assemblies. Iterated function ; Modulo ; Dihedral angle ; Axiom ; Simplex ; Hypergraph ; Ramachandran plot ; Scheme (mathematics) ; Regular polygon Various Chemistry Research Topics