jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Chunyu Yang (269294) | Bing Yan (170259)
Source: Figshare (article, 2025-05-28)
Field: Materials Science — Materials Chemistry

MultifunctionalHydrogen-Bonded Organic Frameworksfor Intelligent Anti-Counterfeiting and Food Safety Monitoring In an era of increasing digital threats and product counterfeiting, this study introduces MA-IPA@NPA, a groundbreaking hydrogen-bonded organic framework (HOF) material designed for advanced anticounterfeiting applications. This innovative material showcases distinctive optical properties, including dual fluorescence-phosphorescence emission and an ultralong phosphorescence lifetime, making it exceptionally difficult to replicate. Additionally, MA-IPA@NPA demonstrates efficacy as a fluorescence sensor for detecting tryptamine and gallic acid in food products, addressing both security and food safety concerns. Through electrospinning technology, we successfully developed a multifunctional composite nanofiber membrane by integrating MA-IPA@NPA with poly­(vinyl alcohol) nanofibers, thereby combining the superior optical properties of the HOF with the flexibility and durability of polymer nanofibers. This advanced composite forms the foundation of our Multiple-Responsive Intelligent Anticounterfeiting label, which exhibits dynamic responses to diverse stimuli and incorporates multilevel verification through sophisticated machine learning methods, including binary image recognition and a Back-Propagation Neural Network. Our comprehensive research not only presents a novel strategy for developing advanced, multifunctional anticounterfeiting systems but also offers a timely solution to the escalating demand for enhanced security and authenticity verification in our increasingly digital world, paving the way for more robust and reliable anticounterfeiting measures across various industries. Flexibility (engineering) ; Electrospinning ; Sustainability ; Product (mathematics) ; Food sector Carbon and Quantum Dots Applications ; Advanced Chemical Sensor Technologies ; Biosensors and Analytical Detection
Authors: Jia Cheng | Wenqiang Sun | Haifan Li | Tao Chen | Xiyuan Chen | Yuan Li
Source:  (article, 2025-04-25)
Field: Chemistry — Analytical Chemistry

NIR Spectroscopy Analysis on the Insulating Paper DP by Data Augmentation-Enhanced CNN Near-infrared (NIR) spectroscopy has been widely applied to assess the degree of polymerization (DP) of insulating paper. However, traditional chemometric methods relying on dimensionality reduction struggle with nonlinear spectral data and generalization issues, limiting their practical applicability. To address these challenges, this paper proposes a novel deep learning framework that integrates multifactor data augmentation with convolutional neural networks (DA-CNN). The data augmentation strategy simulates real-world variations in spectra, enhancing the model’s robustness against noise and measurement inconsistencies. Meanwhile, CNNs effectively capture deep spectral features, improving the model’s ability to learn complex nonlinear relationships between NIR spectra and DP values. Experimental results demonstrate that DA-CNN achieves a minimum root mean square error of 45.8 and a relative error rate below 10%, significantly outperforming conventional methods. This study provides a more accurate and generalizable approach for NIRS-based DP evaluation, offering new insights into deep learning applications in insulation diagnostics. Spectroscopy ; Computer science ; Materials science ; Optoelectronics ; Physics Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Industrial Vision Systems and Defect Detection
Authors: Michele Assante | Sara Pahlén | Brenda Pijper | Ole Specht | Nadja A. Simeth | Alexei A. Lapkin | Clive Green | Giulia Bergonzini | Mikhail Kabeshov
Source: ChemRxiv (preprint, 2025-10-08)
Field: Materials Science — Materials Chemistry

End-to-end integration of Density Functional Theory simulations and Machine Learning affords broad feasibility model for Nickel metallaphotoredox Csp2-Csp3 cross-coupling reactions Cheminformatics ; Scope (computer science) ; Applicability domain ; Density functional theory ; Training set ; Transfer of learning Machine Learning in Materials Science ; Optical Imaging and Spectroscopy Techniques ; Digital Radiography and Breast Imaging
Authors: Lukas Bischof | Rudolf Marcel Füchslin | Kurt Stockinger | Pavel Sulimov
Source: ArXiv.org (preprint, 2025-12-15)
Field: Materials Science — Materials Chemistry

Quanvolutional Neural Networks for Spectrum Peak-Finding The analysis of spectra, such as Nuclear Magnetic Resonance (NMR) spectra, for the comprehensive characterization of peaks is a challenging task for both experts and machines, especially with complex molecules. This process, also known as deconvolution, involves identifying and quantifying the peaks in the spectrum. Machine learning techniques have shown promising results in automating this process. With the advent of quantum computing, there is potential to further enhance these techniques. In this work, inspired by the success of classical Convolutional Neural Networks (CNNs), we explore the use of Quanvolutional Neural Networks (QuanvNNs) for the multi-task peak finding problem, involving both peak counting and position estimation. We implement a simple and interpretable QuanvNN architecture that can be directly compared to its classical CNN counterpart, and evaluate its performance on a synthetic NMR-inspired dataset. Our results demonstrate that QuanvNNs outperform classical CNNs on challenging spectra, achieving an 11\% improvement in F1 score and a 30\% reduction in mean absolute error for peak position estimation. Additionally, QuanvNNs appear to exhibit better convergence stability for harder problems. Convergence (economics) ; Convolutional neural network ; Artificial neural network ; Stability (learning theory) ; Position (finance) ; Reduction (mathematics) ; Task (project management) Machine Learning in Materials Science ; Advanced NMR Techniques and Applications ; Molecular spectroscopy and chirality
Authors: Zhipeng Luo | Peng Cai | Wenhui Chen | Shilong Cai | H. Huang | Zixin Liu | Hefeng Zhang | Yifu Huang
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Polymers and Plastics

Enhanced Photoluminescence in Frozen/Thawed Polyvinyl Alcohol Ionogels Through Ultra-Low Concentration Doping with Benzimidazole-Linked 2d Polymer Nanosheets Polyvinyl alcohol ; Benzimidazole ; Photoluminescence ; Doping ; Materials science ; Polymer ; Chemical engineering ; Chemistry ; Optoelectronics ; Composite material ; Organic chemistry Conducting polymers and applications ; Advanced Sensor and Energy Harvesting Materials ; Aerogels and thermal insulation
Authors: Betala, Siddharth | Gleason, Samuel P. | Ramlaoui, Ali | Xu, Andy | Channing, Georgia | Levy, Daniel | Fourrier, Clémentine | Kazeev, Nikita | Joshi, Chaitanya K. | Kaba, Sékou-Oumar | Therrien, Félix | Hernandez-Garcia, Alex | Mercado, Rocío | Krishnan, N. M. Anoop | Duval, Alexandre
Source: SPIRE - Sciences Po Institutional REpository (preprint, 2025-12-04)
Field: Materials Science — Materials Chemistry

LeMat-GenBench: A Unified Evaluation Framework for Crystal Generative Models Generative machine learning (ML) models hold great promise for accelerating materials discovery through the inverse design of inorganic crystals, enabling an unprecedented exploration of chemical space. Yet, the lack of standardized evaluation frameworks makes it challenging to evaluate, compare, and further develop these ML models meaningfully. In this work, we introduce LeMat-GenBench, a unified benchmark for generative models of crystalline materials, supported by a set of evaluation metrics designed to better inform model development and downstream applications. We release both an open-source evaluation suite and a public leaderboard on Hugging Face, and benchmark 12 recent generative models. Results reveal that an increase in stability leads to a decrease in novelty and diversity on average, with no model excelling across all dimensions. Altogether, LeMat-GenBench establishes a reproducible and extensible foundation for fair model comparison and aims to guide the development of more reliable, discovery-oriented generative models for crystalline materials. Generative grammar ; Benchmark (surveying) ; Suite ; Set (abstract data type) ; Generative model ; Novelty Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Block Copolymer Self-Assembly
Authors: Vivin Vinod | Dongyu Lyu | Marcel Ruth | Ulrich Kleinekathöfer | Peter R. Schreiner | Peter Zaspel
Source: ChemRxiv (preprint, 2025-01-23)
Field: Materials Science — Materials Chemistry

Predicting Molecular Energies of Small Organic Molecules with Multifidelity Methods Multifidelity methods in machine learning (ML) have seen an increasing usage for the prediction of quantum chemical properties. These methods, such as ∆-ML and multifidelity ML, have been shown to significantly reduce the computational cost of generating training data. This work implements and analyzes several multifidelity methods including ∆-ML and multifidelity ML for the prediction of electronic molecular energies at DLPNO-CCSD(T) level, i.e., at the level of coupled cluster theory including single and double excitations and perturbative triples corrections. The models for small organic molecules are evaluated not only on the basis of accuracy of prediction, but also on efficiency in terms of the time-cost of generating training data. In addition, the models are evaluated for the prediction of energies for molecules sampled from a public dataset, in particular for atmospherically relevant molecules, isomeric compounds, and highly conjugated complex molecules. Organic molecules ; Small molecule ; Molecule ; Chemical physics ; Computational chemistry ; Materials science ; Chemistry ; Organic chemistry Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions
Authors: Maninder Kaur | Nicolas T. Sandino | Jason P. Terry | Mahdi Ghafariasl | Yohannes Abate
Source: ArXiv.org (preprint, 2025-11-13)
Field: Materials Science — Materials Chemistry

Excitonic Landscapes in Monolayer Lateral Heterostructures Revealed by Unsupervised Machine Learning Two-dimensional (2D) in-plane heterostructures including compositionally graded alloys and lateral heterostructures with defined interfaces display rich optoelectronic properties and offer versatile platforms to explore one-dimensional interface physics and many-body interaction effects. Graded \(\mathrm{Mo}_x\mathrm{W}_{1-x}\mathrm{S}_2\) alloys show smooth spatial variations in composition and strain that continuously tune excitonic emission, while \(\mathrm{MoS}_2\)--\(\mathrm{WS}_2\) lateral heterostructures contain atomically sharp interfaces supporting one-dimensional excitonic phenomena. These single-layer systems combine tunable optical and electronic properties with potential for stable, high-performance optoelectronic devices. Hyperspectral and nano-resolved photoluminescence (PL) imaging enable spatial mapping of optical features along with local variations in composition, strain, and defects, but manual interpretation of such large datasets is slow and subjective. Here, we introduce a fast and scalable unsupervised machine-learning (ML) framework to extract quantitative and interpretable information from hyperspectral PL datasets of graded \(\mathrm{Mo}_x\mathrm{W}_{1-x}\mathrm{S}_2\) alloys and \(\mathrm{MoS}_2\)--\(\mathrm{WS}_2\) heterostructures. Combining principal-component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE), and density-based spatial clustering (DBSCAN), we uncover spectrally distinct domains associated with composition, strain, and defect variations. Decomposition of representative spectra reveals multiple emission species, including band-edge excitons and defect-related transitions, demonstrating that ML-driven analysis provides a robust and automated route to interpret rich optical properties of 2D materials. Heterojunction ; Exciton ; Cluster analysis ; Photoluminescence ; Hyperspectral imaging ; Interface (matter) ; Monolayer ; Plasmon 2D Materials and Applications ; Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides
Authors: Zhengwei Chen | Mingjin Tang | Xiahe Chen | Debo Ding | Juehan Gao | Yuanbin She | Yun‐Fang Yang
Source: Organic & Biomolecular Chemistry (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Using machine learning methods to predict the diabatic bond dissociation energy of non-heme iron complexes Bond dissociation energy (BDE) is an important property in chemical research. In the process of non-heme iron complex catalytic reactions, diabatic BDE has a significant impact on the selectivity of halogenation and hydroxylation reactions. Measuring or calculating BDE by using traditional experimental or theoretical methods is often expensive and complex, so we propose the first application of machine learning on non-heme iron complexes to predict and rationalize the diabatic BDEs of Fe-X and Fe-OH bonds in order to assist in the study of selectivity in non-heme iron complex catalytic reactions. We built a reliable and representative dataset containing over 600 types of non-heme iron complexes and used density functional theory (DFT) to calculate nearly 900 diabatic BDE for machine learning. In terms of model training, we used 2D molecular fingerprints and 3D descriptors as inputs to train the regression model. The results indicate that the ensemble algorithm combined with Morgan fingerprints can effectively predict the diabatic BDEs of non-heme iron complexes. Using the Gradient Boosting Regressor (GBR) model and Morgan fingerprints can achieve an accurate prediction of <i>R</i><sup>2</sup> = 0.791 and the mean absolute error (MAE) = 10.23 kcal mol<sup>-1</sup>. The incorporation of 3D descriptors significantly improves the predictive performance of molecular fingerprints other than Morgan fingerprints. Notably, the SOAP descriptor effectively captures key 3D molecular information, making it particularly advantageous for predicting isomers with large ΔBDE. However, when the ΔBDE of isomers in the dataset is small, Morgan fingerprints remain the more efficient choice. Diabatic ; Dissociation (chemistry) ; Heme ; Catalysis ; Bond-dissociation energy ; Chemistry ; Selectivity ; Bond energy ; Chemical bond ; Property (philosophy) ; Bond ; Computational chemistry ; Photochemistry ; Physical chemistry ; Molecule ; Thermodynamics ; Organic chemistry ; Physics ; Business ; Philosophy Machine Learning in Materials Science
Authors: Benedykt R. Jany
Source: IOP Conference Series Materials Science and Engineering (article, 2025-02-01)
Field: Materials Science — Materials Chemistry

Machine Learning techniques in microscopic characterisation of nanomaterials Abstract The characterisation of nanomaterials presents significant challenges due to their unique properties and size-dependent behaviours. Recent breakthroughs in Machine Learning (ML) techniques have enabled the development of innovative methods for analysing microscopic data, thereby facilitating the extraction of reliable information on nanomaterials’ chemical composition, structure, and other properties at the nanoscale level. This study provides an overview of selected ML-based approaches applied to microscopic characterisation of nanomaterials, including chemical composition quantification, structure analysis via imaging, and nano-electrical characterisation. The results demonstrate that ML techniques can significantly enhance and simplify the task of nanomaterials characterisation, while minimising data analysis bias and uncertainty. This approach has the potential to derive a comprehensive understanding of a given material’s properties by integrating information obtained from diverse characterisation techniques. The synergistic coupling between microscopic imaging and Machine Learning techniques provides new quality, perspectives, and opportunities for future materials characterisation. Nanomaterials ; Nanotechnology ; Materials science ; Computer science Machine Learning in Materials Science ; Force Microscopy Techniques and Applications ; Mineral Processing and Grinding
Authors: Zhixin Liang | Yunlong Wang | Chi Ding | Junjie Wang | Hui‐Tian Wang | Dingyu Xing | Jian Sun
Source: ArXiv.org (preprint, 2025-09-05)
Field: Materials Science — Materials Chemistry

Hot-Ham: an accurate and efficient E(3)-equivariant machine-learning electronic structures calculation framework The combinations of machine learning with ab initio methods have attracted much attention for their potential to resolve the accuracy-efficiency dilemma and facilitate calculations for large-scale systems. Recently, equivariant message passing neural networks (MPNNs) that explicitly incorporate symmetry constraints have demonstrated promise for interatomic potential and density functional theory (DFT) Hamiltonian predictions. However, the high-order tensors used to represent node and edge information are coupled through the Clebsch-Gordan tensor product (CGTP), leading to steep increases in computational complexity and seriously hindering the performance of equivariant MPNNs. Here, we develop High-order Tensor machine-learning Hamiltonian (Hot-Ham), an E(3) equivariant MPNN framework that combines two advanced technologies local coordinate transformation and Gaunt tensor product (GTP) to efficiently model DFT Hamiltonians. These two innovations significantly reduce the complexity of tensor products from O(L^6) to O(L^3) or O(L^2 log^2 L) for the max tensor order L, and enhance the performance of MPNNs. Benchmarks on several public datasets demonstrate its state-of-the-art accuracy with relatively few parameters, and the applications to multilayer twisted moiré systems, heterostructures and allotropes showcase its generalization ability and high efficiency. Our Hot-Ham method provides a new perspective for developing efficient equivariant neural networks and would be a promising approach for investigating the electronic properties of large-scale materials systems. Equivariant map ; Tensor (intrinsic definition) ; Tensor product ; Hamiltonian (control theory) ; Artificial neural network ; Topology (electrical circuits) ; Generalization Machine Learning in Materials Science ; Advanced Chemical Physics Studies
Authors: Weijie Wu (610600) | Yu Wang (12152) | Xiaoting Chen (4113034) | Qingduan Li (4387726) | Yue-Peng Cai (701205) | Songyang Yuan (9575825) | Shengjian Liu (1318404)
Source: Figshare (article, 2025-09-24)
Field: Materials Science — Materials Chemistry

Feature-GuidedInverse Design of Organic A‑SiteCations for Perovskites Dimensional Engineering Organic spacer cations critically influence the dimensionality and stability of low-dimensional perovskites (LDPs), yet current A-site candidate selection remains largely empirical. Herein, we present a machine-learning-driven molecular generation framework based on a Long Short-Term Memory Network model guided by key molecular descriptors, incorporating a Double-Fit strategy to improve dimensional property alignment and structural rationality of LDPs. Our model inversely generates organic cations targeting specific structural dimensions. Subsequent density functional theory calculations identify candidates with favorable thermodynamic stability and configurational features. Experimental synthesis and structural characterization of the resulting perovskites confirm the model’s predictive accuracy. This approach provides a rational design paradigm for A-site cations in LDPs and establishes a general platform to accelerate discovery of new organic–inorganic hybrid perovskite materials. Curse of dimensionality ; Perovskite (structure) ; Rational design ; Density functional theory ; Stability (learning theory) ; Inverse ; Structural stability ; Characterization (materials science) ; Chemical stability Machine Learning in Materials Science ; Perovskite Materials and Applications ; Inorganic Chemistry and Materials
Authors: Valentina Sierra-Jiménez | Farid Chejne | Manuel Garcı̀a-Pèrez
Source: FlatChem (article, 2025-10-19)
Field: Chemistry — Analytical Chemistry

First principles and neural network-driven biochar spectral database: Raman, XPS, IR, and NMR Raman spectroscopy ; Biochar ; Spectral line ; X-ray photoelectron spectroscopy ; Density functional theory ; NMR spectra database ; Char ; Boron Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Geochemistry and Geologic Mapping
Authors: Hirokatsu Miyata (1858258) | Kosuke Manabe (22144676) | Yoshiyuki Sugahara (1851664) | Yusuke Asakura (1787985) | Jie Wang (16762) | Watcharop Chaikittisilp (1235253) | Yusuke Yamauchi (1410556)
Source: Figshare (article, 2025-08-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine-Learning-GuidedMultiscale Design of Nitrogen-DopedCarbon for Enhanced Supercapacitor Performance Hierarchical structural control of nanomaterials across multilength scales is critical for optimizing mass and charge transport in energy-related applications. In this study, we combine machine learning analysis and rational synthesis to design nitrogen-doped carbonaceous hybrid materials with multiscale architectures tailored for supercapacitor electrodes. Data-driven insights from the literature reveal that, in addition to surface area, the presence of pyrrolic nitrogen strongly correlates with enhanced capacitance. Guided by this finding, we synthesized nanohybrids through the heterogeneous self-assembly of dopamine and block copolymers on graphene oxide (GO) nanosheets, followed by polymerization and thermal carbonization. Dopamine serves as both the carbon and nitrogen sources and facilitates the formation of mesoporous structures. The resulting hybrids feature atomic-scale nitrogen doping (10<sup>–10</sup> m), mesoporous carbon networks (10<sup>–8</sup> m), and macroscopic sheet-like morphology (10<sup>–6</sup> m) with tunable thickness. Uniform coating of mesoporous N-doped carbon on both sides of the reduced GO increases the accessible surface area while maintaining ion mobility. This work demonstrates a data-informed strategy for designing hierarchical carbon-based materials and highlights the synergy between machine learning and multiscale structural engineering for high-performance energy storage applications. Supercapacitor ; Graphene ; Carbon fibers ; Mesoporous material ; Coating ; Nanomaterials ; Energy storage ; Oxide Supercapacitor Materials and Fabrication ; Machine Learning in Materials Science ; Catalysis for Biomass Conversion
Authors: Nuttapon Pisitpipathsin | Krittapong Deshsorn | Varisara Deerattrakul | Pawin Iamprasertkun
Source: Chemistry - An Asian Journal (article, 2025-07-01)
Field: Materials Science — Materials Chemistry

Cover Feature: Machine Learning Assisted for Preparation of Graphene Supported Cu‐Zn Catalyst for CO<sub>2</sub> Hydrogenation to Methanol (Chem. Asian J. 13/2025) Cover (algebra) ; Methanol ; Graphene ; Catalysis ; Feature (linguistics) ; Materials science ; Nanotechnology ; Chemistry ; Organic chemistry ; Engineering ; Mechanical engineering Machine Learning in Materials Science