jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tugba Haciefendioglu (21436199) | Erol Yildirim (1532215)
Source: Figshare (article, 2025-05-28)
Field: Materials Science — Materials Chemistry

Band Gap and ReorganizationEnergy Prediction of ConductingPolymers by the Integration of Machine Learning and Density FunctionalTheory The performance and reliability of machine learning (ML)-quantitative structure–property relationship (QSPR) models depend on the quality, size, and diversity of the data set used for model training. In this study, we manually curated a large-scale data set containing 3120 donor–acceptor (D–A) conjugated polymers (CPs) by selecting the most utilized 60 donors and 52 acceptors. This data set serves as a valuable resource for ML-based prediction of key electronic properties such as band gap energy (<i>E</i><sub>g</sub>) and hole reorganization energy (λ<sub>h</sub>), calculated using density functional theory (DFT) to advance organic photovoltaics (OPV). Beyond data set construction, we systematically investigated how different descriptor and fingerprint types impact performance of the ML model. Recognizing that not all features contributed equally to the model performance, we conducted an in-depth analysis to identify the most informative descriptors for the fundamental optoelectronic properties. Our findings show that kernel partial least-squares (KPLS) regression utilizing radial and molprint2D fingerprints achieved the highest accuracy in predicting <i>E</i><sub>g</sub>, with <i>R</i><sup>2</sup> values of 0.899 and 0.897, respectively. For λ<sub>h</sub> prediction, models integrating electronic descriptors such as frontier orbital energy levels significantly improved performance, achieving an <i>R</i><sup>2</sup> value of 0.830. This study provides a comprehensive investigation of how different descriptors influence model performance in OPV research. By analyzing why certain models succeed while others fail, our findings offer insight into feature selection and data set optimization for accurate target property prediction in organic electronics. The developed ML models provide a predictive framework for high-performance OPV materials design, significantly reducing the reliance on labor-intensive experimental procedures and computationally expensive first-principle calculations. Feature selection ; Set (abstract data type) ; Organic solar cell ; Energy (signal processing) ; Data set ; Predictive modelling ; Kernel (algebra) ; Feature (linguistics) ; Experimental data ; Band gap Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Conducting polymers and applications
Authors: Maryame Lakrade | Zakaria Mighouar | Laidi Zahiri | Khalifa Mansouri
Source: Springer Link (Chiba Institute of Technology) (article, 2025-12-19)
Field: Materials Science — Materials Chemistry

An Integrated FEM-AI Methodology for Predictive Modelling and Optimization of Filled Bio-Composites This paper proposes a unified approach merging finite element modelling (FEM) and artificial intelligence (AI) to forecast the mechanical behavior of filled bio-composites, which are growing in importance for sustainable material applications. The proposed method applies Digimat software for multi-scale simulations to account for anisotropic behavior and interfacial effects, with AI algorithms, such as neural networks and nonlinear regression, creating predictive correlations between microstructural parameters and mechanical performance. The FEM simulations produce high-fidelity data on stress-strain distributions, which are subsequently employed to train the AI models, establishing a synergistic feedback loop that improves predictive accuracy. In contrast, differences between AI forecasts and FEM findings direct adjustments to the simulation framework, which leads to reliable modelling results. This cyclical merging resolves the constraints of isolated methods, where in finite element analysis by itself may be inadequate for investigating extensive parameter domains, and artificial intelligence frameworks may be compromised by limited or noisy numerical data. The hybrid FEM-AI model achieves high accuracy across mechanical properties, outperforming standalone FEM and AI approaches. Overall, the integrated approach offers a powerful and generalizable framework for efficient exploration of complex material behaviors. Finite element method ; Artificial neural network ; Nonlinear system ; Software ; Model predictive control Machine Learning in Materials Science ; Composite Material Mechanics ; Model Reduction and Neural Networks
Authors: Rachel Gorelik (21373968) | Tara M. Boland (16412295) | Arunima K. Singh (2000641)
Source: Figshare (article, 2025-05-16)
Field: Materials Science — Materials Chemistry

FundamentalFactors Governing Stabilization of Janus2D–Bulk Heterostructures with Machine Learning The more than 6000 2D materials predicted thus far provide a huge combinatorial space for forming functional heterostructures with bulk materials, with potential applications in nanoelectronics, sensing, and energy conversion. In this work, we investigate nearly 1000 heterostructures, the largest number of heterostructures thus far, of 2D Janus and bulk materials’ surfaces using <i>ab initio</i> simulations and machine learning (ML) to deduce the structure–property relationships of the complex interfaces in such heterostructures. We first perform van der Waals-corrected density functional theory simulations using a high-throughput computational framework on 51 Janus 2D materials and 19 metallic, cubic phase, elemental bulk materials that exhibit low lattice mismatches and low coincident site lattices. The formation energies of the resultant 1147 Janus 2D–bulk heterostructures were analyzed, and 828 were found to be thermodynamically stable. ML models were trained on the computed data, and we found that they could predict the binding energy and <i>z</i>-separation of 2D–bulk heterostructures with root mean squared errors (RMSEs) of 0.05 eV/atom and 0.14 Å, respectively. The feature importance of the models reveals that the properties of the bulk materials dominate the heterostructures’ energies and interfacial structures heavily. These findings are in line with experimentally observed behavior of several well-known 2D materials–bulk systems. The data used within this paper are freely available in the <i>Ab Initio</i> 2D-Bulk Heterostructure Database (aiHD). The fundamental insights into 2D–bulk heterostructures and the predictive ML models developed in this work could accelerate the application of thousands of 2D–bulk heterostructures, thus stimulating research within a wide range of electronic, quantum computing, sensing, and energy applications. Heterojunction ; Janus ; Density functional theory ; Lattice (music) ; Work (physics) ; k-nearest neighbors algorithm ; Parameter space 2D Materials and Applications ; Machine Learning in Materials Science ; Boron and Carbon Nanomaterials Research
Authors: Matthew D. Witman (1949530) | Brendan C. Davis (21685923) | Vitalie Stavila (589917) | Terry Johnson (14841759)
Source: Figshare (article, 2025-07-09)
Field: Materials Science — Materials Chemistry

System and MachineLearning-Guided Materials Designfor High-Pressure Hydrogen Compression Cost-effective and reliable hydrogen compression remains a challenging barrier in the widespread adoption of hydrogen as an energy carrier. The prevailing technology of mechanical compression suffers from several drawbacks, some of which can be addressed by nonmechanical compression strategies (e.g., electrochemical or metal hydride-based thermal compression). Thermally driven metal hydride compression strategies typically rely on multistage metal hydride-based compressors; however, discovering or optimizing low-stability metal hydrides that can pressurize hydrogen upward of 1000 bar is difficult, both with respect to computational predictions and experimental validation. Here, we (1) demonstrate that simple machine learning-derived design rules can inform the rational design of alloying strategies yielding low-stability hydrides, (2) validate their experimental pressure–composition–temperature (PCT) isotherms up to 875 bar, and (3) utilize a dynamic system-level model of a metal hydride compressor design to evaluate their performance under realistic operating conditions. Importantly, this analysis yields predicted operational efficiencies of both 2-stage (90–875 bar) and 3-stage (20–875 bar) metal hydride compressors to enable further evaluation of this technology and its techno-economic outlook. Gas compressor ; Compression (physics) ; Hydride ; Hydrogen ; Hydrogen storage ; Bar (unit) ; Thermal Hydrogen Storage and Materials ; Carbon dioxide utilization in catalysis ; Machine Learning in Materials Science
Authors: Yong Ju Lee | H. Kim | Seo Young Won | Geon‐Woo Kim | Hyoung-Jin Kim
Source: Journal of Natural Fibers (article, 2025-01-01)
Field: Materials Science — Biomaterials

One-Dimensional Convolutional Neural Networks for Classification of Korean Traditional Paper Using Raman Spectroscopy Traditional Korean handmade paper (Hanji) varies in fiber composition and manufacturing conditions; however, these differences are typically subtle and difficult to distinguish visually. In this study, Raman spectroscopy combined with one-dimensional convolutional neural network (1D CNN) modeling was used to classify Hanji at three levels: paper mulberry type, cooking agent, and product identity. Using second-derivative Raman spectra in the 200–1650-cm−1 region, the 1D CNN model achieved an F1-score of 0.880 for product-level classification. Feature attribution using Grad-CAM demonstrated that the model primarily relied on the Raman bands associated with cellulose crystallinity and the residual lignin composition, providing a chemical basis for the classification. The model was further applied to two unknown Hanji samples, enabling the inference of their product groups and manufacturing conditions. These results demonstrate that the proposed Raman–CNN approach provides a nondestructive and interpretable method for identifying and characterizing traditional handmade papers. Convolutional neural network ; Pattern recognition (psychology) ; Residual ; Raman spectroscopy ; Artificial neural network ; Segmentation ; Product (mathematics) ; Feature (linguistics) Advanced Cellulose Research Studies ; Material Properties and Processing ; Spectroscopy and Chemometric Analyses
Authors: Arpan Sur | Sudipta Saha | Chih‐Yu Lee | Ichiro Takeuchi
Source: ArXiv.org (article, 2025-12-22)
Field: Materials Science — Materials Chemistry

Multilevel Photonic Switching in GST-467 for Deep Neural Network Inference Phase-change materials (PCMs) have emerged as key enablers of non-volatile, ultra-compact photonic switches for energy-efficient deep neural network (DNN) applications. In this work, we investigate the recently discovered $\mathrm{Ge_{4}Sb_{6}Te_{7}}$ (GST-467) as a high-contrast optical PCM and demonstrate its suitability for multi-level photonic computing. The complex refractive indices of amorphous and crystalline GST-467 were experimentally extracted and used to propose a segmented silicon-on-insulator photonic switch optimized at 1550 nm. Three-dimensional FDTD simulations reveal that segmentation significantly enhances the extinction ratio while maintaining low insertion loss, resulting in a more than seven times higher design figure of merit than an unsegmented design. Laser-induced thermo-optical simulations further establish efficient, reversible switching with sub-nJ energy requirements for crystallization and amorphization. Compared with established GST, GSST, and GSS compositions, GST-467 provides the largest transmission contrast and supports up to 48 resolvable optical states. When deployed as multi-level weights in photonic DNN architectures, the GST-467 switch achieves superior classification accuracy on EMNIST and Fashion-MNIST benchmarks. These results position GST-467 as a highly promising PCM for scalable, low-energy photonic computing and neuromorphic hardware. Photonics ; Artificial neural network ; Neuromorphic engineering ; Optical switch ; Transmission (telecommunications) ; Photonic crystal ; Position (finance) ; Figure of merit ; Segmentation Phase-change materials and chalcogenides ; Neural Networks and Reservoir Computing ; 2D Materials and Applications
Authors: Rachel Gorelik | Tara M. Boland | Arunima K. Singh
Source: ACS Applied Materials & Interfaces (article, 2025-05-16)
Field: Materials Science — Materials Chemistry

Fundamental Factors Governing Stabilization of Janus 2D–Bulk Heterostructures with Machine Learning The more than 6000 2D materials predicted thus far provide a huge combinatorial space for forming functional heterostructures with bulk materials, with potential applications in nanoelectronics, sensing, and energy conversion. In this work, we investigate nearly 1000 heterostructures, the largest number of heterostructures thus far, of 2D Janus and bulk materials' surfaces using <i>ab initio</i> simulations and machine learning (ML) to deduce the structure-property relationships of the complex interfaces in such heterostructures. We first perform van der Waals-corrected density functional theory simulations using a high-throughput computational framework on 51 Janus 2D materials and 19 metallic, cubic phase, elemental bulk materials that exhibit low lattice mismatches and low coincident site lattices. The formation energies of the resultant 1147 Janus 2D-bulk heterostructures were analyzed, and 828 were found to be thermodynamically stable. ML models were trained on the computed data, and we found that they could predict the binding energy and <i>z</i>-separation of 2D-bulk heterostructures with root mean squared errors (RMSEs) of 0.05 eV/atom and 0.14 Å, respectively. The feature importance of the models reveals that the properties of the bulk materials dominate the heterostructures' energies and interfacial structures heavily. These findings are in line with experimentally observed behavior of several well-known 2D materials-bulk systems. The data used within this paper are freely available in the <i>Ab Initio</i> 2D-Bulk Heterostructure Database (aiHD). The fundamental insights into 2D-bulk heterostructures and the predictive ML models developed in this work could accelerate the application of thousands of 2D-bulk heterostructures, thus stimulating research within a wide range of electronic, quantum computing, sensing, and energy applications. Janus ; Materials science ; Heterojunction ; Nanotechnology ; Engineering physics ; Optoelectronics ; Engineering Machine Learning in Materials Science ; 2D Materials and Applications ; Nanowire Synthesis and Applications
Authors: Bideault, Marthe
Source: theses.fr (ABES) (article, 2025-09-19)
Field: Materials Science — Materials Chemistry

Modélisation de la synergie entre nanoparticules et promoteurs lors de processus catalytiques : cas de la synthèse de l'ammoniac Ammonia is a promising vector for green hydrogen storage, yet its current synthesis via the Haber-Bosch process remains unsuitable for sustainable applications. An alternative catalytic route involving cobalt nanoparticles encapsulated in barium oxide has attracted growing interest. This thesis investigates, through atomistic modeling, the interactions between cobalt nanoparticles and potential promoters (Ba, Ca), by developing machine learning potentials (MLPs) and robust analytical methodologies. A high-performance Spectral Neighbor Analysis Potential (SNAP) was developed for pure cobalt, accurately capturing its static and dynamic properties from the bulk phase to the nanoscale. After numerous unsuccessful attempts, a stable Co-Ba potential was finally obtained at the end of the thesis. In parallel, an advanced method was designed to characterize atomic environments in complex systems, leading to quantitative insights into surface activation and melting phenomena in pure cobalt nanoparticles. These findings, along with the developed methodology, open new avenues for the dynamic analysis of nanoparticles under catalytically relevant temperatures. Cobalt ; Nanoparticle ; Catalysis ; Cobalt oxide ; Oxide Machine Learning in Materials Science ; Ammonia Synthesis and Nitrogen Reduction ; Electrocatalysts for Energy Conversion
Authors: Grier M. Jones | Konstantinos D. Vogiatzis
Source: ChemRxiv (article, 2025-08-18)
Field: Materials Science — Surfaces, Coatings and Films

Capturing Electron Correlation with Machine Learning through a Data-Driven CASPT2 Framework Multireference perturbation theory methods, such as complete active space second-order perturbation theory (CASPT2), are often employed to recover the missing electron correlation from multiconfigurational zeroth-order wavefunctions. Here, we introduce the data-driven CASPT2 (DDCASPT2) method to capture dynamical electron correlation using features generated from lower-level electronic structure methods such as Hartree-Fock and complete active space self-consistent field (CASSCF) theory. We examine the effects of system size, basis set size, and number of two-electron excitations using a small, but diverse, set of molecules. We also provide insights into our physics-based feature set using SHapley Additive exPlanation (SHAP) analysis, a feature analysis method based on cooperative game theory. In this paper, we utilize these insights to introduce a DDCASPT2 method, which provides a machine learning-based alternative to traditional single- and multi-state CASPT2 for capturing dynamical electron correlation with near-CASPT2 quality accuracy. Computer science ; Correlation ; Electron ; Artificial intelligence ; Machine learning ; Pattern recognition (psychology) ; Physics ; Mathematics ; Geometry ; Nuclear physics Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science ; Nuclear Physics and Applications
Authors: Yiming Chen | Shyue Ping Ong | Maria F. Chan
Source: ECS Meeting Abstracts (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

Robust Machine Learning Inference from X-Ray Absorption Near Edge Spectra through Featurization X-ray absorption spectroscopy (XAS) is a powerful tool for probing local structures, oxidation states, and electronic properties of functional materials. Based on energy proximity to the absorption edge, spectra are categorized into XANES (near-edge) and EXAFS (extended) regions. However, interpreting XANES typically requires reference spectra, which are often limited in quality and availability. We first addressed this gap by generating a large-scale computational database of L-edge XANES spectra, using the FEFF9 code. Over 130,000 spectra for transition metal compounds were produced and made publicly accessible via the Materials Project, laying a foundation for machine learning (ML) applications in XAS. Then we explored how different spectral representations affect ML performance. We featurized the spectra and benchmarked the ML algorithms on oxidation state classification and bond length prediction tasks. The cumulative distribution function feature offered the best accuracy and robustness while remained explainable from physics. Experimental validation further confirmed the model's predictive ability on unseen data. Together, these projects establish a robust data resource and demonstrate how ML can accelerate and enhance XAS data interpretation. XANES ; Robustness (evolution) ; Extended X-ray absorption fine structure ; Spectral line ; Inference ; Spectroscopy ; Pattern recognition (psychology) ; Absorption spectroscopy Machine Learning in Materials Science ; X-ray Spectroscopy and Fluorescence Analysis ; CO2 Reduction Techniques and Catalysts
Authors: Huanyu Xu | Jincang Zhang | Baoyu Xu | Dongbo Dai | Huiran Zhang | Shengzhou Li
Source:  (article, 2025-12-19)
Field: Materials Science — Materials Chemistry

Machine Learning Prediction of Properties across U Values and Phases: A Case Study on TiO <sub>2</sub> Machine learning (ML) has become an effective tool for predicting material properties. In density functional theory (DFT), the Hubbard U correction mitigates localized-electron errors; however, selecting an appropriate U is challenging because it varies across materials, phases, and targeted properties, and conventional trial-and-error scanning over many U values is computationally costly. ML provides an efficient alternative by learning U-dependent trends and enabling rapid property prediction without extensive calculations. Here, we trained regression models to reproduce the U-dependent energy, bandgap, and relative energy of TiO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> with high accuracy and stable. Transfer-learning tests further show that adding only a few samples from a new phase, typically around three, is sufficient to recover high predictive accuracy, demonstrating that U-dependent trends can be effectively transferred across TiO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> phases. Property (philosophy) ; Regression ; Predictive modelling ; Energy (signal processing) ; Regression analysis ; Linear regression Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Catalysis and Oxidation Reactions
Authors: Harpriya Minhas (13220737) | Rahul Kumar Sharma (3861805) | Biswarup Pathak (1491820)
Source: Figshare (article, 2025-10-25)
Field: Materials Science — Materials Chemistry

Antibonding StatesDrive Anharmonicity and Low ThermalConductivity in Edge-Sharing Metal Chalcogenides Stereochemically active lone pairs (SCALPs) and orbital hybridization in edge-sharing polyhedra play a crucial role in suppressing lattice thermal conductivity (κ<sub>L</sub>) in thermoelectric materials. Strong mixing between pnictogen <i>s</i>- and chalcogen <i>p</i>-orbitals generates active lone pairs, which enhance lattice anharmonicity and lead to ultralow κ<sub>L</sub>. In this study, we leverage machine learning interatomic potential to systematically probe bonding-driven mechanisms and their influence on thermal transport in noncentrosymmetric pnictogen chalcogens. We show that the combined effects of SCALPs, Pn–Pn bonding, and edge-sharing polyhedra enable the formation of antibonding states near the valence band maxima, intensifying phonon scattering. To quantify the underlying anharmonicity, we introduce a set of bonding descriptorslone pair angle, lone pair distance, ionicity, and hybridizationthat capture the influence of local structural motifs and antibonding features near the valence band edge. This bonding-centric framework not only elucidates the origins of ultralow κ<sub>L</sub> but also offers a rational design strategy for accelerating the discovery of high-performance thermoelectric materials. Antibonding molecular orbital ; Pnictogen ; Lone pair ; Anharmonicity ; Valence (chemistry) ; Chalcogen ; Thermoelectric materials Advanced Thermoelectric Materials and Devices ; 2D Materials and Applications ; Machine Learning in Materials Science