jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Saba Bashir (12046291) | Issraa Beniani (14675526) | Keisean Stevenson (21224160) | Juan Manuel Montiel-Leon (21224163) | Simon Forest (1405246) | Malama Chisanga (1357056) | Jean-François Masson (1979476)
Source: Figshare (article, 2025-05-01)
Field: Chemistry — Organic Chemistry

UntargetedProfiling and Classification of Maple SyrupQuality from the Chromatographic and Mass Spectrometric Analysis ofVolatile and Nonvolatile Chemical Fractions Maple syrup production faces challenges from the “buddy flavor” defect (√R5), a sensory issue compromising syrup quality. This study employed a multimodal approach combining headspace solid-phase microextraction-gas chromatography–mass spectrometry (HS-SPME-GC/MS) and ultraperformance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS/MS) combined with chemometrics to profile volatile and nonvolatile compounds associated with √R5 and regular (REG) maple syrup. HS-SPME-GC/MS identified key volatile markers, including pyrazines and sulfur-containing compounds such as dimethyl disulfide, many of which were previously unreported. UPLC-QTOF-MS/MS investigated nonpolar compounds and provided insights into nonvolatile chemical signatures underlying maple syrup quality. Chemometric and machine learning analyses classified syrup based on metabolite variations, with principal component analysis (PCA) models showing distinct clustering and PCA-support vector machine (PCA-SVM) achieving 93.8% classification accuracy. This study contributes to a comprehensive understanding of the √R5-associated chemical profiles and underscores the potential of our proposed analytical tools to improve quality assurance frameworks and identification of flavor-altering chemical changes. Chemometrics ; Mass spectrometry ; Principal component analysis ; Retention time ; Chemical constituents ; Sugar ; Metabolomics Plant-Derived Bioactive Compounds ; Isotope Analysis in Ecology ; Fermentation and Sensory Analysis
Authors: Yanyan Lu (3709831) | Yu Qiao (462684) | Haoming Bao (4040648) | Kang Chen (285159) | Yi Wei (434518) | Qian Zhao (217607) | Guo Kang Leon (21077069) | Hongwen Zhang (829826) | Xing Yi Ling (1476664) | Weiping Cai (299830)
Source: Figshare (article, 2025-04-11)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine Learning-DrivenSurface Plasmon-Enhanced DualSpectroscopies Improve Recognition and Real-Time Monitoring of HazardousChemicals To address the challenges of precise identification and real-time monitoring of hazardous chemicals, this work proposes and develops surface-plasmon-enhanced dual spectroscopies (SPEDS). This technique combines highly recognizable surface-enhanced Raman spectroscopy (SERS) with real-time plasmon-mediated differential ultraviolet–visible spectroscopy (P-DUS). The feasibility of this technique is demonstrated by successfully acquiring SPEDS signals of thiourea with a plasmonic gold colloidal system. By combining SPEDS with machine learning algorithms, we achieve accurate identification and precise quantification of chemicals, with accuracies of 98.2 and 98.6%, respectively, significantly outperforming single P-DUS (63.2 and 95.1%) and SERS (80.3 and 86.5%). Additionally, we demonstrate the universality and expandability of SPEDS through other plasmonic nanostructures of various shapes and surface modifications. Using a CuS-coated Au nanoarray, we demonstrate multiple 8-h monitoring sessions of Hg<sup>2+</sup> with good anti-interference and robust quantification, thereby highlighting the practical potential of SPEDS in real-world applications. These results position SPEDS as a cutting-edge and multifunctional chemical sensing platform, unlocking transformative possibilities for advancing environmental monitoring, industrial safety, and public health. Hazardous waste ; Surface-enhanced Raman spectroscopy ; Plasmon ; Raman spectroscopy ; Spectroscopy ; Identification (biology) ; Nanostructure ; Dual (grammatical number) ; Leverage (statistics) Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Nanomaterials in Catalysis
Authors: Xiangle Meng | Shaohong Liu | Liang Dong | Jinsheng Li | Chen Chen | Pengfei Li | Li-Min Wang | Xiwei Qi
Source: Journal of Alloys and Compounds (article, 2025-11-20)
Field: Materials Science — Materials Chemistry

Recent progress in narrow-band gap tellurium-based semiconducting glasses of thermoelectric materials Thermoelectric materials ; Thermoelectric effect ; Fabrication ; Amorphous solid ; Thermal conductivity ; Field (mathematics) ; Amorphous semiconductors ; Electrical resistivity and conductivity Advanced Thermoelectric Materials and Devices ; Phase-change materials and chalcogenides ; Thermal properties of materials
Authors: Matthew C. Kuner | Aaron D. Kaplan | Kristin A. Persson | Mark Asta | D. C. Chrzan
Source: ArXiv.org (preprint, 2025-07-08)
Field: Materials Science — Materials Chemistry

MP-ALOE: An r2SCAN dataset for universal machine learning interatomic potentials We present MP-ALOE, a dataset of nearly 1 million DFT calculations using the accurate r2SCAN meta-generalized gradient approximation. Covering 89 elements, MP-ALOE was created using active learning and primarily consists of off-equilibrium structures. We benchmark a machine learning interatomic potential trained on MP-ALOE, and evaluate its performance on a series of benchmarks, including predicting the thermochemical properties of equilibrium structures; predicting forces of far-from-equilibrium structures; maintaining physical soundness under static extreme deformations; and molecular dynamic stability under extreme temperatures and pressures. MP-ALOE shows strong performance on all of these benchmarks, and is made public for the broader community to utilize. Stability (learning theory) ; Soundness ; Benchmark (surveying) ; Interatomic potential ; Series (stratigraphy) ; Extreme learning machine Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Topic Modeling
Authors: Yanhui Sun | Zhangbin Yu | Jiaxue Zhang | Ying Xiao
Source: Surfaces and Interfaces (article, 2025-05-24)
Field: Materials Science — Materials Chemistry

Machine learning accelerated screening of zeolite structures for selective ammonia adsorption in mixed gas environments for automotive exhaust applications Materials science ; Zeolite ; Automotive industry ; Adsorption ; Exhaust gas ; Ammonia ; Chemical engineering ; Automotive engineering ; Waste management ; Catalysis ; Organic chemistry ; Engineering ; Aerospace engineering Catalytic Processes in Materials Science ; Ammonia Synthesis and Nitrogen Reduction ; Zeolite Catalysis and Synthesis
Authors: Fan Wu | J. S. Mullen | Yangchao Deng | Shashidhara Marathe | Saranarayanan Ramachandran | Mert Çelikin | A.G. Murphy | W.H. Sillekens | Wajira Mirihanage | David J. Browne
Source: Acta Materialia (article, 2025-10-22)
Field: Materials Science — Materials Chemistry

Probing dendrite growth under microgravity via machine learning-aided multi-scale characterisation Growth kinetics and orientation selection play a significant role in microstructure evolution during metal solidification, while gravity-induced convection adds significant complexity to the process. In-situ, time-resolved X-ray imaging of solidifying grain-refined Al–20 wt.% Cu alloy onboard the MASER-13 sounding rocket enabled the study of equiaxed dendrite growth under diffusion-controlled conditions, eliminating the influence of gravity. A machine learning-enabled analytical pipeline was developed to extract and evaluate the spatiotemporal behaviour of a large number of individual dendrites, including their growth characteristics, rotations and interactions. Post-flight synchrotron X-ray computed tomography and electron backscatter diffraction were used to reconstruct the three-dimensional dendrite structure with embedded details of crystallographic orientations. Correlated data analysis confirmed that most dendrites grew along directions parallel to the {100} plane under highly isothermal, diffusion-controlled conditions. However, growth along atypical directions was also observed, even in this simplified regime. The benchmark data revealed variation in dendrite arm evolution, influenced by local grain interactions and crystallographic orientation selection. It is shown that the equiaxed grains have random crystallographic orientations and evidence suggests that these survive from shortly after nucleation in the bulk liquid under microgravity conditions. The data processing protocols demonstrated here highlight the potential of integrating advanced experimental techniques with modern data science approaches to analyse solidification microstructure formation in metallic alloys under terrestrial and microgravity conditions. Equiaxed crystals ; Dendrite (mathematics) ; Nucleation ; Electron backscatter diffraction ; Microstructure ; Synchrotron ; Alloy ; Grain growth ; Diffraction Solidification and crystal growth phenomena ; Crystallization and Solubility Studies ; Metallurgical Processes and Thermodynamics
Authors: Catarina Manuelito | João de Deus | Miguel Damásio | André Leitão | Luís Alcino Conceição | Rocío Arias‐Calderón | Carla Inês | A.M. Cordeiro | Eduardo de Oliveira Fernandes | Luís Fernando Teixeira Albino | Miguel Barbosa | Filipe Rhodes da Fonseca | José Silvestre
Source: Applied Biosciences (article, 2025-07-02)
Field: Chemistry — Analytical Chemistry

Multi-Sensor Comparison for Nutritional Diagnosis in Olive Plants: A Machine Learning Approach The intensification of olive growing has raised environmental concerns, particularly regarding nutrient loss from excessive fertiliser use. In line with the European Union’s Farm to Fork strategy, which aims to halve the soil nutrient losses by 2030, this study evaluates the effectiveness of two sensor-based approaches—proximal sensing with a FLAME spectrometer and remote sensing via UAV-mounted multispectral imaging—compared with foliar chemical analyses as the reference standard, for diagnosing the nutritional status of olive trees. The research was conducted in Elvas, Portugal, between 2022 and 2023, across three olive cultivars (‘Azeiteira’, ‘Arbequina’, and ‘Koroneiki’) subjected to different fertilisation regimes. Machine learning (ML) models showed strong correlations between sensor data and nutrient levels: the multispectral sensor performed best for phosphorus (P) (determination coefficient [R2] = 0.75) and potassium (K) (R2 = 0.73), while the FLAME spectrometer was more accurate for nitrogen (N) (R2 = 0.64). These findings underscore the potential of sensor-based technologies for non-destructive, real-time nutrient monitoring, with each sensor offering specific strengths depending on the target nutrient. This work contributes to more sustainable and data-driven fertilisation strategies in precision agriculture. Computer science ; Artificial intelligence ; Machine learning Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Advanced Chemical Sensor Technologies
Authors: Surya Adi Putra | Rista Putri Nur Ifa | Darkon Kudakwashe Chemugarira | Surya Panangian Munthe
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Prediction of Gamma Radiation Shielding Thickness Using Machine Learning with Model Random Forest Regression and Phits Simulation for Epdm-Based Rubber Composites Natural rubber ; Electromagnetic shielding ; Materials science ; Composite material ; Random forest ; Monte Carlo method ; Radiation ; Nuclear engineering ; Computer science ; Nuclear physics ; Artificial intelligence ; Mathematics ; Engineering ; Statistics ; Physics Graphite, nuclear technology, radiation studies ; Radiation Shielding Materials Analysis ; Nuclear and radioactivity studies
Authors: Ndenga Lumbu Barack
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

AI-Driven Light-Spectrum Optimization for Photonic Drug Discovery Pipeline (software) ; Photonics ; Reinforcement learning ; Drug discovery ; Reduction (mathematics) ; Efficient energy use ; Molecular binding ; Energy (signal processing) Photochromic and Fluorescence Chemistry ; Photoreceptor and optogenetics research ; Machine Learning in Materials Science
Authors: Varughese, Bilvin | Loeffler, Troy D. | Banik, Suvo | Koneru, Aditya | Manna, Sukriti | Balasubramanian, Karthik | Batra, Rohit | Cherukara, Mathew J. | Yildiz, Orcun | Peterka, Tom | Sumpter, Bobby G. | Sankaranarayanan, Subramanian K. R. S.
Source: arXiv (Cornell University) (preprint, 2025-11-25)
Field: Materials Science — Materials Chemistry

Physically Interpretable Interatomic Potentials via Symbolic Regression and Reinforcement Learning The development of next-generation molecular simulation models requires moving beyond pre-defined functional forms toward machine learning (ML) techniques that directly capture multiscale physics. Here, we demonstrate such an approach using symbolic regression (SR) with equation learner networks and a reinforcement learning search engine to derive interpretable equations for interatomic interactions. Training data were generated through nested ensemble sampling with density functional theory (DFT) energetics, spanning crystalline to highly disordered states. The optimization of the learner network employed continuous-action Monte Carlo Tree Search (MCTS) combined with gradient descent, enabling efficient exploration of function space. For copper as a representative transition metal, an unconstrained search produced models that outperformed fixed-form Sutton-Chen EAM potentials. The SR-derived models (SR1 and SR2) reproduced key material properties - lattice constants, cohesive energies, equations of state, elastic constants, phonon dispersion, defect formation energies, surface/bulk energetics, and phase transformation with significantly improved accuracy. Furthermore, stringent melting simulations using two-phase solid-amorphous interfaces confirmed that SR models accurately capture the interplay of vibrational entropy, cohesive energy, and structural dynamics, surpassing SC-EAM in both qualitative and quantitative predictions. This highlights the potential of SR to deliver fast, accurate, flexible, and physically meaningful potentials, advancing predictive modeling across scales. Symbolic regression ; Artificial neural network ; Monte Carlo method ; Interatomic potential ; Transformation (genetics) ; Lattice (music) ; Regression ; Function (biology) Machine Learning in Materials Science ; Quantum many-body systems ; Block Copolymer Self-Assembly
Authors: Liqin Tan | Pin Chen | Menghan Liu | Xiean Wang | Jianhuan Cen | Qingsong Zou
Source: ArXiv.org (preprint, 2025-11-12)
Field: Materials Science — Materials Chemistry

Benchmarking GNNs for OOD Materials Property Prediction with Uncertainty Quantification We present MatUQ, a benchmark framework for evaluating graph neural networks (GNNs) on out-of-distribution (OOD) materials property prediction with uncertainty quantification (UQ). MatUQ comprises 1,375 OOD prediction tasks constructed from six materials datasets using five OFM-based and a newly proposed structure-aware splitting strategy, SOAP-LOCO, which captures local atomic environments more effectively. We evaluate 12 representative GNN models under a unified uncertainty-aware training protocol that combines Monte Carlo Dropout and Deep Evidential Regression (DER), and introduce a novel uncertainty metric, D-EviU, which shows the strongest correlation with prediction errors in most tasks. Our experiments yield two key findings. First, the uncertainty-aware training approach significantly improves model prediction accuracy, reducing errors by an average of 70.6\% across challenging OOD scenarios. Second, the benchmark reveals that no single model dominates universally: earlier models such as SchNet and ALIGNN remain competitive, while newer models like CrystalFramer and SODNet demonstrate superior performance on specific material properties. These results provide practical insights for selecting reliable models under distribution shifts in materials discovery. Uncertainty quantification ; Benchmark (surveying) ; Benchmarking ; Monte Carlo method ; Property (philosophy) ; Key (lock) ; Predictive modelling ; Regression ; Artificial neural network Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Artificial Intelligence in Healthcare and Education
Authors: Dharani Shakthivel
Source: DigitalCommons - Kennesaw State University (Kennesaw State University) (article, 2025-11-25)
Field: Chemistry — Analytical Chemistry

UR-0225 Carbonyl Detection in IR Using Deep Learning The goal of this project is to train a Convolutional Neural Network (CNN) to recognize carbonyl groups in infrared (IR) spectra. A carbonyl group is defined by a characteristic C=O double bond, which produces a strong, easily recognizable absorption peak near 1700 cm⁻¹. To develop and evaluate the model, I am using spectra prepared through three common techniques: KBr disc, nujol mull, and liquid film. Among these, liquid-film spectra provide the cleanest signal and most closely resemble what a chemist visually relies on when identifying carbonyls. In contrast, both the KBr disc and nujol mull methods require mixing the target compound with additional materials, which can introduce interference and make automated detection more challenging. Convolutional neural network ; Deep learning ; Pattern recognition (psychology) ; Artificial neural network ; Absorption spectroscopy ; Infrared spectroscopy ; Mixing (physics) Spectroscopy and Chemometric Analyses ; Machine Learning in Materials Science ; Advanced Chemical Sensor Technologies
Authors: Luoqian Yang | Chen Wang | Li Su | Zhen Tian | Cuicui Zhuang | Yong Zhao
Source:  (article, 2025-05-16)
Field: Materials Science — Polymers and Plastics

Photonic Film Based on Triboelectrificationinduced Electroluminescence for Self-Powered Visualization and Handwriting Recognition Luminescent materials play an important role in human-machine interaction and information security. However, practical applications of conventional luminescent materials are hampered due to the requirement for additional power supply, high-stress threshold, and insufficient performance. In this paper, a photonic film composed of ZnS: Cu phosphors embedded into polyvinylidene-fluoride-co-hexafluoropropylene (PVDF-HFP) is developed by using solvent casting method based on triboelectrification-induced electroluminescence (TIEL). The photonic film can visualize the contact trajectory of the external sliding object with a stress threshold value as low as 0.6 N and an excellent repeatability of more than 2500 cycles without any external power supply. In addition, combined with the machine learning technology, this self-powered, high-brightness, excellent stability and durability photonic film is successfully used for handwriting recognition with an accuracy of 90.28%. This work demonstrates the potential of photonic film based on TIEL for information security and visualized interactions with widespread applications in electronic skin, human-machine interaction, soft robots, etc. Electroluminescence ; Handwriting ; Visualization ; Computer science ; Photonics ; Intelligent character recognition ; Materials science ; Artificial intelligence ; Optoelectronics ; Image (mathematics) ; Character recognition ; Nanotechnology Conducting polymers and applications ; Advanced Computing and Algorithms ; Polydiacetylene-based materials and applications
Authors: Akram Alomainy | Stephen Henthorn | Qammer H. Abbasi | Fraser Burton | Aaron Walker | Yangyishi Zhang | Jalil ur Rehman Kazim | Farooq A. Tahir | Imran Muhammad | Milo Baraclough | Euan Humphreys | Miguel Navarro‐Cía | Mustafa K. Taher Al‐Nuaimi | William G. Whittow | Rupam Das | Anikó Német | Syeda Fizzah Jilani | Muhammad Aslam | Alexander W. Powell
Source: Journal of Physics D Applied Physics (article, 2025-12-11)
Field: Materials Science — Electronic, Optical and Magnetic Materials

The 2026 roadmap on wireless and microwave metasurfaces Abstract Microwave and wireless metasurfaces are transitioning from specialist electromagnetic structures to enabling technologies with growing impact across telecommunications, sensing, healthcare, and defence. This roadmap provides a comprehensive overview of the current state of research on microwave and wireless metasurfaces in the UK, highlighting recent advances and the key challenges that must be addressed to enable widespread deployment. Contributions from academia, industry, and government laboratories are organised around three cross-cutting themes: application-driven developments, emerging fundamental science, and practical considerations related to manufacture, integration, and scalability. The roadmap reviews progress in areas including reconfigurable intelligent surfaces for 5G and beyond-5G communications, enhanced radio coverage and sensing in wireless and healthcare settings, terahertz metasurfaces for beam manipulation, radar cross-section control, bioelectronic applications, and flexible and conformal metasurface platforms. Across these domains, recurring challenges are identified, such as bandwidth and angular limitations, reconfigurability with low loss and low power consumption, robustness to manufacturing tolerances, system-level integration, and long-term reliability. Emerging solutions are discussed, including machine learning-assisted design, advanced and additive manufacturing techniques, new functional materials, and hybrid electronic, optical, and mechanical control strategies. By synthesising perspectives across multiple sectors, this roadmap provides a clear snapshot of the current research landscape while outlining priority directions for future work, with the aim of supporting researchers, industry stakeholders, and policymakers in accelerating the translation of microwave and wireless metasurfaces from laboratory demonstrations to reliable, scalable technologies with real-world impact. Metamaterial ; Microwave ; Electromagnetic radiation ; Wireless ; Metamaterial antenna ; Microwave engineering ; Microwave transmission ; Dielectric Metamaterials and Metasurfaces Applications ; Antenna Design and Analysis ; Advanced Antenna and Metasurface Technologies
Authors: Ezekiel Oyeniyi | Omololu Akin‐Ojo
Source: ArXiv.org (preprint, 2025-12-11)
Field: Materials Science — Materials Chemistry

Excitation energies and UV-Vis absorption spectra from INDO/s+ML The semi-empirical INDO/s method is popular for studies of excitation energies and absorption of molecules due to its low computational requirement, making it possible to make predictions for large systems. However, its accuracy is generally low, particularly, when compared with the typical accuracy of other methods such as time-dependent density functional theory (TDDFT). Here, we present machine learning (ML) models that correct the INDO/s results with negligible increases in the amount of computing resources needed. While INDO/s excitations energies have an average error of about 1.1 eV relative to TDDFT energies, the added ML corrections reduce the error to 0.2 eV. Furthermore, this combination of INDO/s and ML produces UV-Vis absorption spectra that are in good agreement with the TDDFT predictions. Time-dependent density functional theory ; Excitation ; Spectral line ; Absorption (acoustics) ; Density functional theory ; Absorption spectroscopy Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Advanced Physical and Chemical Molecular Interactions