jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shih-Hung Cheng | Er-Feng Hsieh | Ting-I Kuo | Wen–Jeng Hsueh
Source: Journal of Alloys and Compounds (article, 2025-05-01)
Field: Materials Science — Materials Chemistry

Electrically controlled edge-contact spin valves based on two-dimensional transition metal dichalcogenides Transition metal ; Materials science ; Enhanced Data Rates for GSM Evolution ; Condensed matter physics ; Spin (aerodynamics) ; Nanotechnology ; Chemistry ; Physics ; Engineering ; Thermodynamics ; Telecommunications 2D Materials and Applications ; Perovskite Materials and Applications ; Advanced Memory and Neural Computing
Authors: Chenxi Peng | Pu Wang | Yong Pei
Source: Nanoscale (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Prediction of UV-vis absorption spectra of thiolate-protected gold nanoclusters based on graph convolutional neural networks Accurate and efficient prediction of the absorption spectra of gold nanoclusters (Au<sub><i>n</i></sub>(SR)<sub><i>m</i></sub>) is essential for elucidating the structure-activity relationship between the cluster structure and optical properties, as well as guiding the synthesis of new clusters. In this study, we develop a graph convolutional neural network (GCNN) method that directly predicts the absorption spectrum and properties of Au<sub><i>n</i></sub>(SR)<sub><i>m</i></sub> clusters based on their atomic-level structural characteristics. In this method, Au<sub><i>n</i></sub>(SR)<sub><i>m</i></sub> clusters are represented as topological graphs and various features of the atomic surrounding environment are generated, including tensors such as the bond length and bond angle. The model framework can accurately predict the properties of nanoclusters with limited training data. Based on this neural network framework, we propose four extension methodologies for the precise prediction of the UV-vis absorption spectral curves of gold nanoclusters: prediction through key points, oscillator intensity, the discretized spectral curve, and encoder dimensionality reduction representation. We demonstrate the graph-based characterization method for gold nanoclusters and the ability of GCNN to predict complex targets, such as absorption spectra. Nanoclusters ; Convolutional neural network ; Materials science ; Graph ; Absorption (acoustics) ; Photochemistry ; Absorption spectroscopy ; Computer science ; Nanotechnology ; Chemistry ; Artificial intelligence ; Optics ; Physics ; Theoretical computer science ; Composite material Nanocluster Synthesis and Applications ; Gold and Silver Nanoparticles Synthesis and Applications ; Advanced Nanomaterials in Catalysis
Authors: Muneeb Ur Rahman | Habiba Akram | M. Saeed | Erwin Adams
Source: Electrophoresis (article, 2025-12-06)
Field: Chemistry — Spectroscopy

Green Analytical Techniques for Impurity Determination in Pharmaceuticals Green analytical chemistry (GAC) aims to achieve faster, safer, accurate, and sustainable analysis by minimizing the hazardous impact of organic solvents on the environment. Here, the focus will be on impurity profiling of pharmaceuticals. This can be done by employing green chromatographic techniques using eco-friendly solvents, advanced spectroscopic approaches, microextraction, and miniaturization techniques. Pharmaceutical analysis's greenness can be evaluated using various assessment tools and the advanced analytical greenness metric (AGREE). This review comparatively discusses all these tools by highlighting their eco-friendly nature, methodology, adherence to the 12 major principles of green chemistry, and validation of their results. Furthermore, state-of-the-art case studies utilizing green chemistry principles for impurity profiling of pharmaceuticals have also been discussed, along with their employed techniques and observations, thus offering insights into successful implementation. Still, cost, affordability, scalability, and reproducibility vary across techniques, with, for example, supercritical fluid chromatography (SFC) showing more substantial potential than nuclear magnetic resonance (NMR). Greenness metrics also lack global harmonization, limiting comparability. Challenges like stringent regulatory frameworks and a lack of standardized global policies should be overcome by integrating artificial intelligence (AI) and machine learning (ML), as well as more interdisciplinary collaboration among associated professionals, for the widespread adoption of efficient, sustainable, and eco-friendlier pharmaceutical analysis techniques. For AI/ML, trustworthiness will require validation and regulatory alignment to ensure they complement rather than replace existing protocols. Limiting ; Profiling (computer programming) ; Trustworthiness ; Analytical technique ; Miniaturization ; Supercritical fluid Analytical Chemistry and Chromatography ; Pharmaceutical and Antibiotic Environmental Impacts ; Microfluidic and Capillary Electrophoresis Applications
Authors: Yuxinxin Chen | Pavlo O. Dral
Source: Journal of Chemical Theory and Computation (article, 2025-09-12)
Field: Materials Science — Materials Chemistry

One to Rule Them All: A Universal Interatomic Potential Learning across Quantum Chemical Levels With the development of universal machine learning interatomic potentials, a rapidly growing number of chemical space data sets have appeared. One of the biggest challenges is that these data sets are mostly generated at different quantum chemical (QC) levels. However, a general framework that is scalable to learning across both the chemical space and quantum chemical levels remains unmet. In this work, we propose an all-in-one approach that enables simultaneous learning on an arbitrary number of QC levels from various data sets, presenting a more general and easier-to-use alternative to transfer learning. We showcase the superiority of our all-in-one strategy by creating OMNI-P1─the first-ever universal interatomic potential capable of simultaneously learning and making predictions at different QC levels. The generalization capability of the universal model OMNI-P1 for organic molecules is comparable to semiempirical GFN2-xTB and common density functional theory (DFT) methods with a double-ζ basis set, while its speed is orders of magnitude faster. Due to its unique ability to make predictions at different levels, a single model trained with our approach provides a straightforward way to also generate correction terms. This can be used in Δ-learning models without the need to train a dedicated correction model. We utilized this capability of OMNI-P1 to correct the DFT ωB97X-D4 level to obtain the Ω-ωB97X-D4 method with superior accuracy. Quantum chemical ; Generalization ; Scalability ; Chemical space ; Space (punctuation) ; Basis (linear algebra) ; Quantum ; Development (topology) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Huixin Liu | Qiao Liu | Danni Chen | Miaomiao Wang | Guang-Xiong Peng | Siyao Zhang | Yaqian Zhou | Yang Liu | Fangzhou Yin
Source: Microchemical Journal (article, 2025-08-08)
Field: Chemistry — Analytical Chemistry

Traceability of Mume fructus pulp origin, processing method, authenticity based on flash GC e-nose and FT-NIRS technology combined with machine learning and data fusion strategy Traceability ; Chromatography ; Chemistry ; Electronic nose ; Pulp (tooth) ; Flash (photography) ; Computer science ; Artificial intelligence ; Medicine ; Physics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: Limei Xu | Yanli Wang | Wentao Li
Source: Chemical Physics (article, 2025-07-15)
Field: Chemistry — Spectroscopy

Global potential energy surface for the ground state of H2O− and dynamics studies of the O− + H2 → H + OH− reaction Chemistry ; Potential energy surface ; Ground state ; Dynamics (music) ; Computational chemistry ; Reaction dynamics ; Energy (signal processing) ; Potential energy ; Surface (topology) ; Chemical physics ; Physical chemistry ; Atomic physics ; Organic chemistry ; Molecule ; Quantum mechanics ; Geometry ; Physics Spectroscopy and Laser Applications ; Spectroscopy and Quantum Chemical Studies ; Quantum, superfluid, helium dynamics
Authors: Wenbo Zhao | Ying Sun | Jiaxiang Li | Peng Yuan | Toshiaki Iitaka | Xin Zhong | Hefei Li | Yue‐Wen Fang | Hanyu Liu | Ion Errea | Yu Xie
Source: ArXiv.org (preprint, 2025-01-21)
Field: Materials Science — Ceramics and Composites

The Impact of Ionic Anharmonicity on Superconductivity in Metal-Stuffed B-C Clathrates Metal-stuffed B$-$C compounds with sodalite clathrate structure have captured increasing attention due to their predicted exceptional superconductivity above liquid nitrogen temperature at ambient pressure. However, by neglecting the quantum lattice anharmonicity, the existing studies may result in an incomplete understanding of such a lightweight system. Here, using state-of-the-art ab initio methods incorporating quantum effects and machine learning potentials, we revisit the properties of a series of $XY$$\text{B}_{6}\text{C}_{6}$ clathrates where $X$ and $Y$ are metals. Our findings show that ionic quantum and anharmonic effects can harden the $E_g$ and $E_u$ vibrational modes, enabling the dynamical stability of 15 materials previously considered unstable in the harmonic approximation, including materials with previously unreported ($XY$)$^{1+}$ state, which is demonstrated here to be crucial to reach high critical temperatures. Further calculations based on the anisotropic Migdal-Eliashberg equation demonstrate that the $T_\text{c}$ values for KRb$\text{B}_{6}\text{C}_{6}$ and Rb$\text{B}_{3}\text{C}_{3}$ among these stabilized compounds are 102 and 115 K at 0 and 15 GPa, respectively, both being higher than $T_\text{c}$ of 92 K of KPb$\text{B}_{6}\text{C}_{6}$ at the anharmonic level. These record-high $T_\text{c}$ values, surpassing liquid nitrogen temperatures, emphasize the importance of anharmonic effects in stabilizing B-C clathrates with large electron-phonon coupling strength and advancing the search for high-$T_\text{c}$ superconductivity at (near) ambient pressure. Anharmonicity ; Superconductivity ; Ionic bonding ; Condensed matter physics ; Lattice (music) ; Metal ; Materials science ; Physics ; Crystallography ; Chemistry ; Ion ; Quantum mechanics ; Metallurgy Advanced ceramic materials synthesis ; MXene and MAX Phase Materials ; Boron and Carbon Nanomaterials Research
Authors: Mariya L. Ivanova | Mike Nicholls | Nicola Russo | Gueorgui Mihaylov | Konstantin Nikolic
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Toward Predictable Nanomedicine: Current Forecasting Frameworks for Nanoparticle-Biology Interactions Bridging (networking) ; Scarcity ; Computational model ; Boosting (machine learning) ; Replicate ; Nanomedicine ; Prioritization Nanoparticles: synthesis and applications ; Nanoparticle-Based Drug Delivery ; Computational Drug Discovery Methods
Authors: Eduardo José‐Trujillo | Carlos Rubio‐González | Julio Alejandro Rodríguez‐González | Rafael Batres
Source: Applied Composite Materials (article, 2025-03-13)
Field: Materials Science — Polymers and Plastics

Mechanical Behavior Prediction of GFRP Composites Aged at Different Immersion Times Using Artificial Neural Network Materials science ; Composite material ; Immersion (mathematics) ; Artificial neural network ; Fibre-reinforced plastic ; Machine learning ; Computer science Natural Fiber Reinforced Composites ; Mechanical Behavior of Composites ; Textile materials and evaluations
Authors: Diego Vergara | Antonio del Bosque | Pablo Fernández‐Arias
Source: Computers, materials & continua/Computers, materials & continua (Print) (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

A Decade of Digital Twins in Materials Science and Engineering Digital twins (DTs) are rapidly emerging as transformative tools in materials science and engineering, enabling real-time data integration, predictive modeling, and virtual testing. This study presents a systematic bibliometric review of 1106 peer-reviewed articles published in the last decade in Scopus and Web of Science. Using a five-stage methodology, the review examines publication trends, thematic areas, citation metrics, and keyword patterns. The results reveal exponential growth in scientific output, with Materials Theory, Computation, and Data Science as the most represented area. A thematic analysis of the most cited documents identifies four major research streams: foundational frameworks, DTs in additive manufacturing, sector-specific applications, and intelligent production systems. Keyword co-occurrence and strategic mapping show a strong foundation in modeling, simulation, and optimization, with growing links to machine learning and sustainability. The review highlights current challenges and proposes future research directions for advancing DTs in materials science. Engineering ethics ; Engineering physics ; Engineering Machine Learning in Materials Science ; Technology Assessment and Management ; Digital Transformation in Industry
Authors: Santhy Wyantuti | Anisa Amalia Citra | Natasha Fransisca | Ari Hardianto | Irkham Irkham | Uji Pratomo | Fajriana Shafira Nurrusyda | Nova Rachmadona | Rudiawan Edwin | Jacob Yan Mulyana
Source: International Journal of Electrochemical Science (article, 2025-09-23)
Field: Chemistry — Electrochemistry

Simultaneous electrochemical detection of Gd, Dy, and Eu using boron-doped diamond electrode combined with machine learning Rare earth elements (REE) are currently high-demand mineral commodities for various countries. The electrochemical technique plays a crucial role in determining REE, offering high sensitivity compared to X-ray fluorescence spectrometry (XRF). This study aimed to detect the content of Gd, Dy, and Eu in a mixture without passing through a chemical separation, using the Differential Pulse Voltammetry (DPV) method and Boron Doped Diamond (BDD) working electrode combined with machine learning. A total of 125 variations of Gd, Dy, and Eu mixture solutions were prepared as the training set and measured using the DPV method. By employing the BDD working electrode, the current peak of Eu appeared separately from that of Gd and Dy, at a potential of -0.6 V. Meanwhile, Gd and Dy appeared in a single current peak at a potential of -1.4 V. Eu exhibited a Limit of Detection (LoD) and Limit of Quantification (LoQ) at 3.040 ppm and 9.211 ppm, Gd at 17.201 ppm and 7.475 ppm, as well as Dy at 22.652 ppm and 5.676 ppm, respectively. After algorithm selection and preprocessing in machine learning, the best model obtained was GLMNET for Eu with an R 2 of 0.853, Dy at 0.376, and SVM for Gd with 0.557. These algorithms correctly predicted the closeness of the percentage recovery of the Gd, Dy, and Eu combination to the actual percentage recovery. • Boron-Doped Diamond (BDD) working electrodes enable the distinct detection of Europium (Eu) at -0.6 V, separate from Gadolinium (Gd) and Dysprosium (Dy) which share a peak at -1.4 V when using Differential Pulse Voltammetry (DPV). • The Limit of Detection (LoD) and Limit of Quantification (LoQ) for Eu were 3.040 ppm and 9.211 ppm, respectively. For Gd, the LoD and LoQ were 7.475 ppm and 17.201 ppm, and for Dy, they were 22.652 ppm and 5.676 ppm. • Machine learning algorithms were successfully applied to analyze rare earth element (REE) mixtures, with the selected machine learning algorithms (GLMNET and SVM) improved the R² values, leading to more accurate predictions of %Recovery for the REE mixture. Detection limit ; Analytical Chemistry (journal) ; Diamond ; Europium ; Differential pulse voltammetry ; Dysprosium ; Electrode ; Electrochemistry Electrochemical Analysis and Applications ; Analytical chemistry methods development ; Extraction and Separation Processes
Authors: Emad Sharifi | Mohammad Razaghi | Keyhan Hosseini
Source: Scientific Reports (article, 2025-08-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

ANN assisted design of broadband near perfect metasurface absorbers exploiting InAs nanorods for visible light harvesting This research focuses on the design of broadband, polarization-insensitive absorber layers based on all-dielectric metasurfaces for use across the entire visible light spectrum, i.e., 400-800 nm, for applications in solar cells and photovoltaic devices. The patterned metasurfaces utilize InAs nanorods with two different radii and heights, whose size diversity generates optical resonances at diverse wavelengths and increases the absorption bandwidth. The nanorods are based on a substrate of the same material, which is, in turn, backed by a SiO2 layer. Two absorbers are designed for different purposes, utilizing an accurate and efficient artificial neural network (ANN) model. The first absorber exhibits an extremely high average absorption of 99.5% under normal incidence. The second structure provides the highest angular stability by maintaining high absorption above 90% up to an angle of 70<sup>o</sup> for both transverse electric (TE) and transverse magnetic (TM) polarizations. Parametric analysis shows that the performance of the designed absorbers is robust to fabrication tolerances. The ANN-predicted results are verified by analytical approaches based on the electric polarizabilities of the nanorods. Compared to previous plasmonic and dielectric-based counterparts, the proposed absorbers exhibit superior performance. Nanorod ; Broadband ; Optoelectronics ; Materials science ; Nanotechnology ; Computer science ; Telecommunications Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Energy Harvesting in Wireless Networks
Authors: Leandro Benatto | Marlus Koehler | Rodrigo B. Capaz | Graziâni Candiotto
Source: Physical Chemistry Chemical Physics (article, 2025-01-01)
Field: Chemistry — Organic Chemistry

Near zero singlet–triplet gap through nonfullerene core modification with phenalene derivative building blocks Recent advances in data-driven machine learning have highlighted the critical importance of the singlet-triplet gap (Δ<i>E</i><sub>ST</sub> = <i>E</i><sub>S<sub>1</sub></sub> - <i>E</i><sub>T<sub>1</sub></sub>) in non-fullerene acceptor (NFA) molecules as a useful figure of merit to predict the efficiency of organic photovoltaic devices. By reducing Δ<i>E</i><sub>ST</sub>, the photovoltaic performance can be improved through the suppression of triplet state channels for non-geminate charge recombination. Encouraged by this strategy, we propose and theoretically explore the properties (particularly relative to Δ<i>E</i><sub>ST</sub>) of a new class of NFAs derived from modifications of the central core of the Y6 molecule (C<sub>82</sub>H<sub>86</sub>F<sub>4</sub>N<sub>8</sub>O<sub>2</sub>S<sub>5</sub>). The idea is to replace the benzothiadiazole chemical group by building blocks of phenalene derivatives, recognized for their unique inverted Δ<i>E</i><sub>ST</sub>. Using computational analysis that incorporates a double-hybrid exchange-correlation functional as a benchmark method, we anticipate a remarkable reduction of Δ<i>E</i><sub>ST</sub> upon phenalene derivative substitution, with some molecules achieving a near zero singlet-triplet gap. This is the first report that calls attention to new chemical strategies to synthesize NFA molecules with very low (eventually zero) Δ<i>E</i><sub>ST</sub>. Moreover, some modified molecules exhibited higher <i>E</i><sub>T<sub>1</sub></sub> compared to Y6, which is interesting to mitigate non-geminate recombination. The molecular modifications also lead to a decrease in intramolecular reorganization energy, thereby lowering the energy barrier for electron transfer. Additionally, a significant increase in the quadrupole moment component along the π-π stacking direction was observed-an essential property for strengthening quadrupole-quadrupole intermolecular interactions, which play a crucial role in molecular packing and charge transport. Overall, our research yields valuable insights into optimizing NFAs, opening the possibility of alternative molecular architectures to further the development of high-efficiency organic photovoltaic devices. Singlet state ; Core (optical fiber) ; Chemistry ; Materials science ; Physics ; Quantum mechanics ; Composite material Fullerene Chemistry and Applications ; Carbon Nanotubes in Composites ; Synthesis and Properties of Aromatic Compounds
Authors: Yu.V. Pustovit | Ye.P. Lytveniuk | Ye.D. Limarev
Source: Ukrainian Journal of Physics (article, 2025-08-11)
Field: Materials Science — Materials Chemistry

U-Net Based Method for ARPES Spectra Processing Angle-resolved photoemission spectroscopy (ARPES) is a powerful tool for investigating the electronic structure of materials. However, resolving the electronic dispersion from ARPES spectra can be challenging due to the broadening effects, presence of different types of noise, resolution limitations, etc. This paper proposes a new approach for determining dispersion from ARPES spectra based on the U-Net neural network. The energy band extraction problem is regarded as the semantic segmentation task. We will show that the U-Net trained only with generated data can determine band structure from the experimentally obtained spectra, without prior denoising. Angle-resolved photoemission spectroscopy ; Spectral line ; Dispersion (optics) ; Electronic structure ; Artificial neural network ; Noise (video) ; Resolution (logic) ; Materials science ; Computational physics ; Physics ; Computer science ; Condensed matter physics ; Artificial intelligence ; Optics ; Quantum mechanics ; Image (mathematics) Machine Learning in Materials Science ; Transition Metal Oxide Nanomaterials ; Electron and X-Ray Spectroscopy Techniques
Authors: Andre Niyongabo Rubungo | Fernando Fajardo-Rojas | Diego A. Gómez‐Gualdrón | Adji Bousso Dieng
Source: ChemRxiv (preprint, 2025-07-14)
Field: Materials Science — Materials Chemistry

Highly Accurate and Fast Prediction of MOF Free Energy Via Machine Learning A major bottleneck in translating computational screening of metal-organic frameworks (MOFs) to laboratory synthesis is the uncertain synthetic accessibility of computer-generated MOF designs. While MOF free energy calculations are emerging as a way to anticipate synthetic accessibility, the computational cost of traditional molecular simulation methods remains incompatible with the immense MOF design space, which spans trillions of structures. Here we propose and demonstrate the efficient prediction of this important quantity using machine learning. Our approach is based on three ingredients: (1) a newly curated dataset of nearly 1 million MOFs and their properties, including measured free energies for a subset of these structures; (2) a novel sequence representation of MOFs, called MOFSeq, that incorporates both local and global MOF features; and (3) a large language model (LLM-Prop) pre-trained on strain energy prediction and then fine-tuned on free energy prediction using MOFSeq. Our trained model achieves a mean absolute error (MAE) of 0.789 kJ/mol on free energy prediction. Remarkably, this same model, without retraining, demonstrated exceptional versatility by predicting MOF synthesizability with a 97% F1 score. Furthermore, it correctly selected the right MOF polymorph with an average accuracy of 78.1%, with this accuracy increasing to 100% for free energy differences greater than 4.05 kJ/mol and remaining above 60% even when the free energy difference was as low as 0.16 kJ/mol. Bottleneck ; Computer science ; Energy (signal processing) ; Representation (politics) ; Artificial intelligence ; Mean absolute error ; Machine learning ; Algorithm ; Mathematics ; Mean squared error ; Statistics Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; X-ray Diffraction in Crystallography