jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Fengling Li | Lei Wen | Juan Li | Xiaoting Wang | J. J. Su | Tangnuer Sahati | Xiahenazi Aierkenjiang | Runxi Tian | Weihong Zhou | Jixiong Zhang | Jingjing Xia
Source: Foods (article, 2025-05-28)
Field: Chemistry — Analytical Chemistry

A Dual-Technology Approach: Handheld NIR Spectrometer and CNN for Fritillaria spp. Quality Control <i>Fritillaria</i> spp. has an extremely high edible and medicinal value. Different parts of it exhibit significant variations in medicinal efficacy. To rapidly and accurately identify the origin and adulteration of <i>Fritillaria</i> spp., a handheld near-infrared spectrometer was combined with a convolutional neural network (CNN) to establish an efficient and convenient quality assessment method. First, for the origin of <i>Fritillaria</i> spp., the CNN could achieve high accuracy, with 100 ± 0%. The features contributing to the origin of <i>Fritillaria</i> spp. were visualized using gradient-weighted class activation mapping (Grad-CAM). For the adulteration of <i>Fritillaria</i> spp., compared with partial least squares regression (PLSR), the CNN yielded the best performance, with the R<sup>2</sup> of the test set being 0.9897. Additionally, to improve the interpretability of the adulteration model, a CNN model was established using data whose dimensions had been reduced by PCA (PCA-CNN), which also achieved an R<sup>2</sup> of 0.9876. The features extracted by PCA focused on 1400-1500 nm, which was consistent with Grad-CAM. The visualization of Grad-CAM and the adulteration detection model achieved mutual validation, showing the effectiveness of both methods in analyzing the samples. The experimental results demonstrated that the integration of a handheld near-infrared spectrometer with a CNN enabled both reliable authentication of <i>Fritillaria</i> spp. geographical origins and quantitative determination of adulteration levels, establishing a novel analytical framework for rapid quality evaluation of <i>Fritillaria</i> spp. materials. Mobile device ; Quality (philosophy) ; Dual (grammatical number) ; Spectrometer ; Computer science ; Physics ; Optics ; World Wide Web ; Art Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Bowen Hou | Xian Xu | Wen-Jong Wu | Dong Qiu
Source: ArXiv.org (preprint, 2025-07-07)
Field: Materials Science — Materials Chemistry

MBFormer: A General Transformer-based Learning Paradigm for Many-body Interactions in Real Materials Recently, radical progress in machine learning (ML) has revolutionized computational materials science, enabling unprecedentedly rapid materials discovery and property prediction, but the quantum many-body problem -- which is the key to understanding excited-state properties, ranging from transport to optics -- remains challenging due to the complexity of the nonlocal and energy-dependent interactions. Here, we propose a symmetry-aware, grid-free, transformer-based model, MBFormer, that is designed to learn the entire many-body hierarchy directly from mean-field inputs, exploiting the attention mechanism to accurately capture many-body correlations between mean-field states. As proof of principle, we demonstrate the capability of MBFormer in predicting results based on the GW plus Bethe Salpeter equation (GW-BSE) formalism, including quasiparticle energies, exciton energies, exciton oscillator strengths, and exciton wavefunction distribution. Our model is trained on a dataset of 721 two-dimensional materials from the C2DB database, achieving state-of-the-art performance with a low prediction mean absolute error (MAE) on the order of 0.1-0.2 eV for state-level quasiparticle and exciton energies across different materials. Moreover, we show explicitly that the attention mechanism plays a crucial role in capturing many-body correlations. Our framework provides an end-to-end platform from ground states to general many-body prediction in real materials, which could serve as a foundation model for computational materials science. Exciton ; Quasiparticle ; Wave function ; Hierarchy ; Key (lock) ; Function (biology) ; Mechanism (biology) Machine Learning in Materials Science ; Quantum many-body systems ; 2D Materials and Applications
Authors: Giuseppe Scelfo | Vassilis M. Charitopoulos | Fabrizio Vicari | Alessandro Tamburini | I. David L. Bogle | Giorgio Micale | Andrea Cipollina
Source: Desalination (article, 2025-11-25)
Field: Materials Science — Materials Chemistry

Surrogate-based optimization analysis and scale-up of an ultra-concentrated brine pilot treatment chain for sustainable mineral recovery Process optimization ; Operating cost ; Brine ; Energy consumption ; Process design ; Exergy ; Valorisation ; Production (economics) ; Exergy efficiency ; Industrial production Magnesium Oxide Properties and Applications ; Phosphorus and nutrient management ; Bauxite Residue and Utilization
Authors: Jittiwat Nithikarnjanatharn | Araya Smuthkochorn | Jetnipat Pimollukanakul | Wannisa Nutkhum | Ukrit Thanasuptawee | Kitti Wirotrattanaphaphisan | Panuput Satuwong
Source: Research Square (preprint, 2025-05-16)
Field: Materials Science — Polymers and Plastics

Prediction of mechanical properties of polypropylene blends with recycled polypropylene using ANN methods Polypropylene ; Materials science ; Composite material ; Polymer science Natural Fiber Reinforced Composites ; Recycling and Waste Management Techniques ; Tribology and Wear Analysis
Authors: Yue Hao | Jishun Zhang | Xi Sun | Zihan Qu | Zuyu Xu | Yunlai Zhu | Yuehua Dai
Source: Journal of Electroceramics (article, 2025-09-03)
Field: Materials Science — Materials Chemistry

Classification of magnetic ground States and prediction of magnetic moments for ABX3 perovskites utilizing machine learning techniques Magnetic moment ; Condensed matter physics ; Materials science ; Nuclear magnetic resonance ; Artificial intelligence ; Computer science ; Physics Machine Learning in Materials Science ; Magnetic and transport properties of perovskites and related materials ; Electronic and Structural Properties of Oxides
Authors: Hephaes Chuen Chau | Julia Y.H. Liu | John A. Rudd
Source: Scientific Reports (article, 2025-04-16)
Field: Chemistry — Analytical Chemistry

An application of deep learning model InceptionTime to predict nausea, vomiting, diarrhoea, and constipation using the gastro-intestinal pacemaker activity drug database (GIPADD) The accurate preclinical prediction of adverse drug reactions (ADRs), such as nausea and vomiting, remains a challenge. The Gastro-Intestinal Pacemaker Activity Drug Database (GIPADD) ( http://www.gutrhythm.com/public_database ) is a new source of electrophysiological big data for drug research. Over the past 2 years, the database has doubled in size, and now contains the electrophysiological profiles of 172 drugs across 11,943 datasets. This study used a state-of-the-art deep-learning model with time-series classification to explore the feasibility of using raw electrophysiological recordings from tissues to predict ADRs. The GIPADD contains the recordings of the electrical activity of various gastrointestinal tissues (stomach, duodenum, ileum, and colon) exposed to a drug at three or more different concentrations, representing the effects of the drug on gastrointestinal pacemaker activity. Each drug in the database is associated with at least 60 recordings. The datasets are divided in a ratio of 8:2 for training and validation. A modified InceptionTime classifier (ICT) was used to predict whether a drug induces ADRs, using data from the SIDER database as the target. Concentrations and tissues were added as covariates and added to the input of the model during forward propagation. We also established a negative control with shuffled target labels, and external validation was conducted using time-shifted recording predictions. The best model for predicting nausea, vomiting, diarrhoea, and constipation achieved by-drug accuracies of 0.87, 0.89, 0.85, and 0.91, respectively; by-drug precision (class 1) of 0.88, 0.90, 0.99, and 0.89, respectively; and area under the receiver operating characteristic curve (AUROC) values of 0.84, 0.87, 0.94, and 0.96, respectively. The best model was an ensemble of five independent ICT classifiers trained on the same dataset. Models trained using shuffled labels (negative controls) exhibited significantly lower accuracy, precision, and AUROC values than models trained using correctly labelled datasets, indicating that ICT classifiers successfully identified latent features in the raw recordings associated with ADRs. The combined benefits of the GIPADD and deep learning may accelerate drug safety testing and drug development by enabling the reliable analysis of electrophysiological drug profiles during the preclinical stage. Vomiting ; Database ; Nausea ; Constipation ; Medicine ; Drug ; Computer science ; Internal medicine ; Pharmacology Analytical Methods in Pharmaceuticals ; Cardiac electrophysiology and arrhythmias ; Gastrointestinal motility and disorders
Authors: Raj Kumar Gupta | S. Mayakannan | Sivakumar Vijayaraghavalu | M. Poornima | S. Nanthakumar | N. A. Bhaskaran
Source: Journal of Environmental Nanotechnology (article, 2025-01-14)
Field: Materials Science — Polymers and Plastics

Multi-objective Optimization of Bamboo-madar Fiber Reinforced Composites for Lightweight Automotive Applications using Machine Learning and Genetic Algorithms The accelerated growth of the automobile industry intensifies sustainability issues, primarily because of its significant carbon emission and the resulting impacts on global environmental systems. These emissions are directly correlated with fuel usage, which is subsequently affected by the material weight utilized in automobile systems. This research utilized machine learning (ML) techniques to optimize the production parameters of natural fiber (NF)-reinforced materials for airplane body applications. A study was conducted using the Taguchi optimization approach to investigate the effect of different fiber lengths, concentrations of sodium hydroxide treatment, Nano SiO2 and hybrid fiber (bamboo fibers, and madar fibers (BMF)) on the performance of the material. In order to find the best combination of the factors that were considered for automobile structural applications with low fuel consumption (low carbon emissions) and high reliability, multi-objective optimization (MOO) methods like additive ratio assessment (ARAS) and genetic algorithms (GA) were utilized within the MATLAB programming environment. With R2 values above 80%, the regression analysis-derived models demonstrated good predictive accuracy. The optimization of ARAS for the developed composite by the GA identified the optimal process parameters for achieving lightweight materials suitable for automobile applications as 35% BMF, 10% Nano SiO2 at a fiber length of 24mm, and a 9 % concentration of sodium hydroxide, with ARAS reaching its maximum level of unity. Automotive industry ; Bamboo ; Composite material ; Computer science ; Fiber ; Genetic algorithm ; Materials science ; Algorithm ; Engineering ; Machine learning ; Aerospace engineering Natural Fiber Reinforced Composites ; Additive Manufacturing and 3D Printing Technologies ; Innovations in Concrete and Construction Materials
Authors: Congnan Zhang | Xi Luo | Min Jiang | Zhongyu Zuo | Wei Xue | Ting Li | Li Wang
Source: Journal of Food Composition and Analysis (article, 2025-08-22)
Field: Chemistry — Analytical Chemistry

Rapid prediction model of amylose content in rice based on RVA fingerprints and machine learning: Insights from pasting characteristics Amylose ; Content (measure theory) ; Computer science ; Chemistry ; Food science ; Mathematics ; Starch Spectroscopy and Chemometric Analyses ; GABA and Rice Research ; Rice Cultivation and Yield Improvement
Authors: Peng Liu | Bo Dong | Donghai Mei
Source: ACS Catalysis (article, 2025-09-16)
Field: Materials Science — Materials Chemistry

Methylnaphthalene Isomerization and Diffusion Behavior over H-BEA Zeolites Using Molecular Dynamics Simulations with Machine Learning Potentials The monomolecular isomerization of methylnaphthalene, which is crucial for understanding alkyl aromatic ring reactions in the petrochemical industry, has been studied using ab initio and machine learning-based molecular dynamics simulations. The diffusional free energy landscapes suggest that the free energy barriers for methylnaphthalene translocation between straight channels are comparable when either both channels share a Brønsted acid site or neither possesses one. Furthermore, ab initio molecular dynamics simulations coupled with metadynamics provided detailed free energy landscapes for the monomolecular isomerization reaction network. The methyl transfer process is identified as the most feasible pathway, representing the rate-limiting step with a maximum free energy barrier of 124 kJ/mol. Interestingly, the more stable Wheland intermediate does not participate in the predominant reaction pathway. Utilizing eXtreme Gradient Boosting and Shapley Additive exPlanations, a strong correlation between the charges of molecular moieties and free energy barriers has been established. The present study provides significant insights into the mechanisms underlying the isomerization reactions of polyaromatics, which are pivotal in the development of efficient zeolite catalysts for petrochemical processes. Isomerization ; Metadynamics ; Molecular dynamics ; Ab initio ; Petrochemical ; Energy landscape ; Catalysis ; Ab initio quantum chemistry methods ; Energy profile Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis ; Catalysis and Hydrodesulfurization Studies
Authors: Mengjie Qiu | Wen Zhu | Xiaoxu Zheng | Zhong Chen | Yanqin Lin
Source: ChemBioChem (review, 2025-04-23)
Field: Chemistry — Spectroscopy

NMR Pure Shift Spectroscopy and Its Potential Applications in the Pharmaceutical Industry <sup>1</sup>H nuclear magnetic resonance (NMR) spectroscopy plays an important role in the pharmaceutical industry, but for complex substances, spectral analysis is challenging due to the narrow chemical shift range and signal splitting caused by scalar coupling. Pure shift techniques can suppress scalar coupling, improving spectral resolution. This article provides a review of pure shift techniques, including the main homonuclear broadband decoupling experiments and the methods for obtaining optimal pure shift spectra with the assistance of deep learning. Furthermore, it explores the potential application directions of pure shift techniques in the pharmaceutical industry, supported by relevant scientific examples. By summarizing recent advances and application opportunities, this article aims to promote the development and practical implementation of pure shift NMR techniques in the pharmaceutical industry. Homonuclear molecule ; Paradigm shift ; Pharmaceutical industry ; Nuclear magnetic resonance spectroscopy ; Computer science ; Spectroscopy ; Biochemical engineering ; Chemistry ; Nuclear magnetic resonance ; Physics ; Engineering ; Molecule ; Organic chemistry Advanced NMR Techniques and Applications ; NMR spectroscopy and applications ; Advanced MRI Techniques and Applications
Authors: Sergei Zorkaltsev | Rafał Topolnicki | Tal-El Carmon | Santhosh Mathesan | Paweł Dłotko | Dan Mordehai | Maciej Harańczyk
Source: Materials & Design (article, 2025-10-27)
Field: Materials Science — Materials Chemistry

Transferable 3D convolutional neural networks for elastic constants prediction in nanoporous metals The topology of nanoporous metals is crucial for determining their mechanical response. In this work, we generated 6,000 gold and 422 silver nanoporous structures and calculated three components of elastic modulus with Molecular Dynamics simulations, resulting in 19,263 data points. This study compared two distinct approaches of predicting elastic modulus: a Fully-Connected neural network trained on precomputed topological descriptors, and several 3D Convolutional neural network architectures adapted from computer vision. The 3D CNNs outperformed the descriptor-based baseline model ( ), with top-performing DenseNet-201 architecture achieving . Additionally, the effects of training grid resolution, dataset size, and descriptor integration into a model were investigated. We further demonstrated model robustness through Transfer learning: a pretrained model was fine-tuned on a much smaller dataset of denser gold structures and the dataset of denser silver structures. Finally, the trained model was employed to evaluate the mechanical properties of 100,000 stochastic nanoporous gold structures and identify the Pareto optimal designs. • 3D Convolutional neural nets directly learn nanoporous metals topology, surpassing descriptor baseline. • DenseNet-201 retains 92 % of score going from to training data resolution. • Training on 50 % data yields 4 % drop in score, demonstrating data-efficient learning. • Transfer learning adapts NPG-trained model to a new dataset with a few additional samples. • Screening 100,000 stochastic designs identifies Pareto-optimal nanoporous gold structures. Nanoporous ; Convolutional neural network ; Robustness (evolution) ; Artificial neural network ; Transfer of learning ; Elastic modulus ; Pattern recognition (psychology) Nanoporous metals and alloys ; Machine Learning in Materials Science ; Anodic Oxide Films and Nanostructures
Authors: Junhui Kou | Tianle Liu | Guosheng Jiang | Zhengwen Zhou | Guokun Yang | Yuanhai Pan | Xiaoyang Ni | Hao Liu
Source: Journal of environmental chemical engineering (article, 2025-11-13)
Field: Chemistry — Inorganic Chemistry

Interpretable machine learning assisted screening of metal-organic frameworks for methane adsorption mechanism exploration under low and high pressures Methane ; Adsorption ; Curse of dimensionality ; Work (physics) ; Support vector machine ; Hyperparameter Metal-Organic Frameworks: Synthesis and Applications ; Hydrocarbon exploration and reservoir analysis ; Carbon Dioxide Capture Technologies
Authors: Mosayeb Naseri | Maicon Pierre Lourenço | Viki Kumar Prasad | Rishabh Shukla | Yasna Naseri | Gabriela Díaz | Daya Ram Gaur | Sergey Gusarov | Dennis R. Salahub
Source: Computational Materials Science (article, 2025-08-08)
Field: Materials Science — Materials Chemistry

ABO3 materials clustering with quantum swap-test algorithm Swap (finance) ; Cluster analysis ; Quantum ; Computer science ; Algorithm ; Materials science ; Physics ; Artificial intelligence ; Quantum mechanics ; Business Machine Learning in Materials Science ; Machine Learning and ELM ; Electronic and Structural Properties of Oxides
Authors: Andre Niyongabo Rubungo | Fernando Fajardo-Rojas | Diego A. Gómez‐Gualdrón | Adji Bousso Dieng
Source: Journal of the American Chemical Society (article, 2025-12-16)
Field: Chemistry — Inorganic 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 data set of nearly 1 million MOFs (MOFMinE) and their properties, including simulated strain energies, as well as simulated 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) pretrained 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<sub>MOFatom</sub> on free energy prediction. Remarkably, this same model, without retraining, demonstrated exceptional versatility by predicting whether MOFs were above or below an empirical free energy-based synthesizability threshold with a 97% <i>F</i>1 score. Furthermore, it correctly selected the lowest free energy 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<sub>MOFatom</sub> and remaining above 60% even when having to sort out free energy differences as low as 0.16 kJ/mol<sub>MOFatom</sub>. Bottleneck ; Energy (signal processing) ; sort ; Representation (politics) ; Set (abstract data type) ; Free energy perturbation ; Efficient energy use ; Strain energy Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Supramolecular Chemistry and Complexes
Authors: Liping Zhou | Jin Sun | Rong Ma | Xiaoming Lyu | Shimei Wang | Jing Wu | Xuanyang Ding | Zibo Gao | Siqi Gong | Wang Ying-ji | Yang Li
Source: Analytical Chemistry (article, 2025-07-17)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Overcoming Fluorescence Interference: “Planar Hotspot” Substrates for Vitamin B Detection with SERS Surface-enhanced Raman scattering (SERS) has demonstrated remarkable potential for molecular detection due to its specificity. However, detecting fluorescent vitamin B molecules remains challenging due to fluorescence interference and weak Raman cross-sections. To address this, we developed an innovative large-area silver nanodisk substrate with "planar hotspots" featuring an extensive effective hotspot surface area. This design enables the adsorption of target molecules into the planar hotspot region, effectively quenching fluorescence through direct interaction with the silver substrate while amplifying Raman signals. For the first time, we detected high-fluorescence vitamin B molecules using SERS. This method successfully detected vitamin B molecules in simulated biofluid environments by integrating advanced machine-learning algorithms. As a precise, rapid, reproducible, noninvasive, and multifunctional detection platform, it holds significant promise for intelligent trace-level analysis of vitamin B molecules in food, pharmaceuticals, and human samples. Raman scattering ; Fluorescence ; Chemistry ; Raman spectroscopy ; Molecule ; Planar ; Nanotechnology ; Materials science ; Optics ; Computer science Gold and Silver Nanoparticles Synthesis and Applications ; Biosensors and Analytical Detection ; Spectroscopy Techniques in Biomedical and Chemical Research