jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: ShiDi Xie | Lijuan Peng | J Zhang | Yun-Sun Kang | Hanying Zhou | Zhifeng Ren
Source: Analytical Methods (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

LEC-former: enhancing functional group identification in FTIR spectra by improving weak peak perception Fourier transform infrared (FTIR) spectroscopy is widely applied in chemical structure analysis and functional group identification, serving as an essential tool for the investigation of unknown compounds. Traditional rule-based or machine learning approaches in spectral analysis often rely on prominent characteristic peaks, while failing to effectively exploit potential weak features within spectral signals, thereby exhibiting limitations in functional group identification tasks. In this study, we propose a functional group recognition model, LEC-former, which enhances the representation of weak spectral peaks. The model employs a self-attention-based spectral peak coupling encoder to capture long-range dependencies among infrared spectral peaks, while an innovatively designed LEC module enhances fine-grained perception of local features. This enables reinforced representation of weak peaks and effective integration of discrete peak positions, thereby improving the identification of key spectral regions associated with target functional groups. In this study, 23 337 FTIR spectra from the NIST Chemistry WebBook were selected to construct a multi-label functional group recognition task. The proposed model was compared against mainstream machine learning models across multiple evaluation metrics. The results demonstrate that LEC-former achieves significant improvements in functional group recognition accuracy and exhibits outstanding performance in molecular exact match rate. Pattern recognition (psychology) ; Functional group ; Representation (politics) ; Fourier transform infrared spectroscopy ; Fourier transform ; Identification (biology) ; Encoder ; Density functional theory ; Infrared Spectroscopy and Chemometric Analyses ; Remote-Sensing Image Classification ; Molecular spectroscopy and chirality
Authors: Julie M. Schoenung | Oladele A. Ogunseitan | Haoyang He
Source: Journal of Sustainable Metallurgy (article, 2025-09-09)
Field: Materials Science — Materials Chemistry

Human Health and Ecotoxicity Considerations in Metallic Alloy Design The increasingly prevalent use of metallic alloys in consumer products, medical devices, and sophisticated industrial applications warrants consideration of their potential adverse impacts on both human health and ecosystems, early in product and process development. We review recent advances in understanding metallic toxicity and the criteria used by national and international databases to categorize metals in terms of potential hazards and precautional regulatory responses. We note that the existing databases are populated with scientific information on individual metallic elements, and these databases contain gaps and occasional incompatible data, all of which may not apply neatly to the attributes of metallic elements in alloys. We present three case studies, two on conventional metallic alloys (Al 3003 vs Al 7075, Monel vs Inconel 718), and one on high entropy alloys (Cantor vs a recent refractory composition) to explore the potential contributions of individual elements to alloy composition and predictable characteristics associated with human health effects and ecotoxicity. Finally, we note that in addition to direct toxicity characteristics, the production of metals and metallic alloys can be energy intensive, and the process may generate additional toxic compounds worth considering in their overall environmental footprint and potential contributions to the burden of disease in human populations. New tools including machine learning and generative artificial intelligence may support innovative approaches for developing more precise appraisal of human health and ecotoxicity considerations in metallic alloy design. Human health ; Monel ; Process (computing) ; Risk assessment ; Ecotoxicity ; Inconel Nanoparticles: synthesis and applications
Authors: Md Nahiduzzaman | Xiulin Ji
Source: Journal of Bio- and Tribo-Corrosion (article, 2025-11-12)
Field: Materials Science — Materials Chemistry

Tribocorrosion of Titanium Alloys in Seawater: Recent Advances and Strategies Tribocorrosion ; Corrosion ; Titanium alloy ; Crevice corrosion ; Artificial seawater ; Titanium Titanium Alloys Microstructure and Properties ; Orthopaedic implants and arthroplasty ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Yongcheng Zhu | Zhaoxin Du | Tianhao Gong | Zhiyong Yue | Shuzhi Zhang | Jun Cheng | Xudong Kang | Qiang Zeng | Jingshun Liu
Source: Progress in Natural Science Materials International (article, 2025-05-07)
Field: Materials Science — Materials Chemistry

Accurate reconstruction and prediction of T55511 titanium alloy microstructure using DDPM model and quantitative evaluation Materials science ; Microstructure ; Titanium alloy ; Alloy ; Metallurgy ; Titanium Titanium Alloys Microstructure and Properties ; Additive Manufacturing Materials and Processes ; Aluminum Alloy Microstructure Properties
Authors: Lin Wang | Yifan Wu | Hao Luo | Minglong Liang | Yihang Zhou | Cheng Chen | Chris Liu | Jing Zhang | Yang Zhang
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-03-19)
Field: Materials Science — Materials Chemistry

Directed Chemical Evolution via Navigating Molecular Encoding Space Abstract Deep-learning techniques have significantly advanced small-molecule drug discovery. However, a critical gap remains between representation learning and small molecule generations, limiting their effectiveness in developing new drugs. We introduce Ouroboros, a unified framework that integrates molecular representation learning with generative modeling, enabling efficient chemical space exploration using pre-trained molecular encodings. By reframing molecular generation as a process of encoding space compression and decompression, Ouroboros resolves the challenges associated with iterative molecular optimization and facilitates directed chemical evolution within the encoding space. Comprehensive experimental tests demonstrate that Ouroboros significantly outperforms conventional approaches across multiple drug discovery tasks, including ligand-based virtual screening, chemical property prediction, and multi-target inhibitor design and optimization. Unlike task-specific models in traditional approaches, Ouroboros leverages a unified framework to achieve superior performance across diverse applications. Ouroboros offers a novel and highly scalable protocol for rapid chemical space exploration, fostering a potential paradigm shift in AI-driven drug discovery. Encoding (memory) ; Chemical space ; Space (punctuation) ; Computational biology ; Molecular evolution ; Computer science ; Biology ; Genetics ; Gene ; Artificial intelligence ; Bioinformatics ; Phylogenetics ; Drug discovery Machine Learning in Materials Science ; Genetics, Bioinformatics, and Biomedical Research ; Fractal and DNA sequence analysis
Authors: Alejandro Castillo | Elizabeth Contreras‐López | Jesús Guadalupe Pérez‐Flores | Laura García‐Curiel | Emmanuel Pérez-Escalante | Karla Soto-Vega | Carlos Angel‐Jijón | Alicia Cervantes‐Elizarrarás
Source: TECNOCIENCIA CHIHUAHUA (article, 2025-06-11)
Field: Chemistry — Analytical Chemistry

Improving kinetic model fitting for total titratable acidity in bananas using genetic algorithms This research aimed to automate the fitting of kinetic models using genetic algorithms (GAs) to optimize the estimation of kinetic parameters—the reaction rate constant (k) and the initial value of total titratable acidity (TTA, C₀)—and enhance predictive accuracy. Experimental data were collected from bananas (Musa paradisiaca L.) at ripening stage 2 on Von Loesecke scale, measuring TTA at specific intervals, and three kinetic models—zero-order, first-order, and second-order—were fitted. For optimization, Python-based GA was used to minimize the mean squared error (MSE) and evaluate performance based on R2, AIC, and BIC. Results indicated that while the zero-order model provided the best statistical fit (R2 = 0.918, lowest AIC and BIC), validation on unseen data favored the second-order model, which exhibited superior predictive accuracy (test R2 = 0.9349, lowest test MSE). The integration of GAs enhanced the robustness of kinetic fitting by effectively navigating parameter space and avoiding local minima, outperforming traditional optimization methods, demonstrating their ability to refine parameter estimation and improve model generalization. Future research should explore hybrid models that combine mechanistic kinetic equations with machine learning techniques (e.g., neural networks, or support vector regression), to better capture the biochemical processes underlying acidity changes in bananas. DOI: https://doi.org/10.54167/tch.v19iEspecial.1847 Titratable acid ; Kinetic energy ; Genetic algorithm ; Algorithm ; Computer science ; Chemistry ; Biological system ; Mathematics ; Mathematical optimization ; Physics ; Biology ; Food science Spectroscopy and Chemometric Analyses ; GABA and Rice Research ; Banana Cultivation and Research
Authors: Cameron S. Movassaghi | Anne M. Andrews
Source: ACS Chemical Neuroscience (review, 2025-12-10)
Field: Chemistry — Electrochemistry

Machine Learning for Neurotransmitter Monitoring by Fast Voltammetry: Current and Future Prospects Chemical neuroscience wields tools to uncover the molecular mysteries of the brain. Sensors can be fabricated with properties tailored to the scales needed to decode neurochemical information. Current instrumentation is capable of measurement rates that exceed neurochemical release rates. Modern machine learning models are approaching parameterization near the number of brain synapses. Fast voltammetry has remained a neuroanalytical workhorse technique for nearly half a century and has undergone significant transformations in many aspects due to advances in hardware and computation. Here, we review current and future uses of machine learning coupled with fast voltammetry to quantify neurochemical dynamics in the brains of behaving animal and human subjects. We focus on the advances that machine learning offers to pervasive problems in fast voltammetry. We identify current challenges and limitations for in vivo studies and delineate several routes for future development. Neurochemical ; Current (fluid) ; Instrumentation (computer programming) ; Neurotransmitter ; Focus (optics) ; Deep learning Electrochemical Analysis and Applications ; Molecular Sensors and Ion Detection ; Electrochemical sensors and biosensors
Authors: Yu Li | Honglin Li | Jialu Li | Wan‐Lu Li
Source: Advanced Intelligent Discovery (article, 2025-07-24)
Field: Chemistry — Inorganic Chemistry

Topology‐Aware Machine Learning for High‐Throughput Screening of MOFs in C8 Aromatic Separation This study applies machine learning (ML) and high‐throughput screening to evaluate 15,335 metal–organic frameworks (MOFs) from the CoRE‐MOFs database for the separation of C8 aromatic isomers. Four categories of descriptors—basic structural, chemical, pore size distribution bins (PSD_bins), and topological—are employed to construct predictive models. The Light Gradient Boosting Machine (LGBM) algorithm achieves the best performance, with topological descriptors derived from persistent homology contributing approximately 30% to overall model importance. These topological features, especially those associated with ring connectivity and cavity morphology, serve as robust indicators of separation efficiency. In high‐performing MOFs, chemical and PSD_bins descriptors show increased significance, indicating the influence of aromatic functional groups, pore size compatibility, and open metal sites (OMS). Approximately 60% of MOFs possess at least one OMS, with transition and lanthanide metals dominating among top candidates. A distinct 2.43 Å signal in two‐dimensional topological features aligns with the benzene ring radius, suggesting the presence of “aromatic traps” that promote π–π and C–H···π interactions. The candidate LUYBIS displays favorable pore geometry and water stability, as confirmed by ab initio molecular dynamics simulations. This work establishes a topology‐aware, data‐driven framework for identifying critical design features governing MOF performance in C8 aromatic separations. Separation (statistics) ; Throughput ; High-throughput screening ; Topology (electrical circuits) ; Computer science ; Machine learning ; Artificial intelligence ; Chemistry ; Engineering ; Electrical engineering ; Telecommunications ; Biochemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Xiangyu Wen | Xiao Geng | Jing Fang | Rushi Liu | Lifen Liu
Source: Journal of Environmental Sciences (article, 2025-12-01)
Field: Materials Science — Materials Chemistry

AI-DFT-guided discovery of synergistic catalysts for photo-electrocatalytic CO2 reduction Bimetallic strip ; Catalysis ; Bespoke ; Olefin fiber ; Electrochemistry ; Nanoparticle Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Advanced Photocatalysis Techniques
Authors: Haoran Meng | Danheng Gao | Qihan Zhang | Meng Luo | Kun He | Zhaonan Huang | Jiahao Liu | Tao Geng | Weimin Sun
Source: Food Chemistry (article, 2025-11-15)
Field: Chemistry — Analytical Chemistry

A rapid wine brand identification method based on the joint application of SERS and machine learning techniques Wine ; Convolutional neural network ; Software portability ; Identification (biology) ; Feature extraction ; Pattern recognition (psychology) ; Feature (linguistics) ; Principal component analysis Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Fermentation and Sensory Analysis
Authors: Nataliya L. Gulay | Dongsheng Wen | J. Griffiths | Judith Clymo | Luke M. Daniels | Jonathan Alaria | Matthew S. Dyer | John B. Claridge | Matthew J. Rosseinsky
Source: Advanced Science (article, 2025-07-04)
Field: Materials Science — Materials Chemistry

Topology Augmented with Geometry in the Assembly of Structural Databases: Kagome Intermetallics Creation of well-curated databases tailored to specific structural motifs can underpin and drive materials discovery, as the properties of materials are governed by composition and structure. The role of such motifs in directing the intricate interplay between structure and properties is exemplified by intermetallic compounds with structures that contain kagome layers that exhibit a variety of exotic physical states. Two prevailing approaches have previously been applied to identify such materials: evaluation of structural topology or geometry assessment, however, both present limitations if deployed individually. We augment topological screening with geometrical filtering to allow versatile control over the identification of kagome layers. Applying this approach with minimal further constraints labels over 9000 kagome-containing intermetallics which are assigned to four structural classes, revealing connections between symmetry, composition, direct space structure, and flatband electronic structures in reciprocal space. A machine learning model is used to predict new element combinations that favour the formation of kagome layers. Several highly-ranked phase fields correspond to known kagome-containing materials that were absent from the training dataset, demonstrating that the workflow can identify chemistries affording kagome layers. This motivates the extension of the approach beyond kagome to other property-conferring motifs, such as honeycomb, square planar or triangular plane nets. Intermetallic ; Topology (electrical circuits) ; Workflow ; Computer science ; Materials science ; Geometry ; Database ; Mathematics ; Combinatorics Machine Learning in Materials Science ; Advanced Condensed Matter Physics ; Geochemistry and Geologic Mapping
Authors: Anupma Kumari | Jaswinder Singh
Source: Scientific Reports (article, 2025-09-30)
Field: Chemistry — Analytical Chemistry

Designing of guava quality classification model based on ANOVA and machine learning The precise maturity quality classification of guava is crucial at farm level, retail, storage, and supply chain. The manual classification causes substantial postharvest losses, which increases the demand for material and resources. Therefore, the present study proposed an automated, precise, and accurate model for quality classification according to the maturity stages (Green, Mature Green, Ripe) of three varieties (Local Sindhi, Riyali, Thadhrami) of guava. The study aimed to develop a precise, accurate and automated model for the classification of guava according to their maturity stages. The guava images were used to extract color, shape, and texture features. Analysis of Variance (ANOVA) was used for the selection of important features. The six different machine learning (ML) classifiers; Artificial Neural Network (ANN), k-Nearest Neighbor (KNN), Support Vector Machine (SVM), Cubic SVM, Quadratic SVM, and Random Forest (RF) were used to find out the best classifier for maturity classification. Among the proposed classifiers, the RF classifier was found to be the best classifier for all three varieties of guava. The Quadratic SVM classifier showed the lowest classification accuracy. The study concluded that RF classifier was found to be a robust model for the maturity classification of guava. Quadratic classifier ; Support vector machine ; Classifier (UML) ; Random forest ; Artificial neural network ; Pattern recognition (psychology) ; Decision tree Spectroscopy and Chemometric Analyses ; Date Palm Research Studies ; Psidium guajava Extracts and Applications
Authors: Daohui Zhao | Mao Wang | Zhiming Zhang | Jianwen Jiang
Source: Molecular Systems Design & Engineering (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Discovery of metal–organic frameworks for inverse CO<sub>2</sub>/C<sub>2</sub>H<sub>2</sub> separation by synergizing molecular simulation and machine learning Top-performing metal–organic frameworks are discovered for inverse CO 2 /C 2 H 2 separation. Materials science ; Metal-organic framework ; Inverse ; Separation (statistics) ; Metal ; Analytical Chemistry (journal) ; Chemical engineering ; Computer science ; Physical chemistry ; Chemistry ; Environmental chemistry ; Metallurgy ; Engineering ; Machine learning ; Mathematics Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Catalysis and Oxidation Reactions
Authors: Pungponhavoan Tep | Marc Bernacki‫
Source: Acta Materialia (article, 2025-09-10)
Field: Materials Science — Materials Chemistry

High-fidelity grain growth modeling: Leveraging deep learning for fast computations Grain growth simulation is crucial for predicting metallic material microstructure evolution during annealing and resulting final mechanical properties, but traditional partial differential equation-based methods are computationally expensive, creating bottlenecks in materials design and manufacturing. In this work, we introduce a machine learning framework that combines a Convolutional Long Short-Term Memory networks with an Autoencoder to efficiently predict grain growth evolution. Our approach captures both spatial and temporal aspects of grain evolution while encoding high-dimensional grain structure data into a compact latent space for pattern learning, enhanced by a novel composite loss function combining Mean Squared Error, Structural Similarity Index Measurement, and Boundary Preservation to maintain structural integrity of grain boundary topology of the prediction. Results demonstrated that our machine learning approach accelerates grain growth prediction by up to 89 × faster, reducing computation time from 10 min to approximately 10 s while maintaining high-fidelity predictions. The best model (S-30-30) achieving a structural similarity score of 86.71 % and mean grain size error of just 0.07 %. All models accurately captured grain boundary topology, morphology, and size distributions. This approach enables rapid microstructural prediction for applications where conventional simulations are prohibitively time-consuming, potentially accelerating innovation in materials science and manufacturing. • This work modeled grain growth evolution as a spatio-temporal task in machine learning framework and developed a hybrid neural networks that combine an Autoencoder and a Convolutional Long Short-Term Memory for predicting grain growth evolution. • A custom adaptive weight loss function combining Mean Squared Error, Structural Similarity Index Measurement, and Boundary Preservation is introduced to maintain structural integrity of grain boundary topology. • The developed machine learning framework successfully accelerates the prediction of grain growth evolution up to 89 × faster compared to conventional simulation that based on partial-differential equations, while maintaining high-fidelity. • Computation time reduced from approximately 10 min to around 10 s. • Grain boundary topology, morphology and size distributions are accurately captured across all models. • Best model achieved 86.71 % structural similarity score with only 0.07 % error in mean grain size. Autoencoder ; Deep learning ; Grain growth ; Grain boundary ; Mean squared error ; Convolutional neural network ; Computation ; Pattern recognition (psychology) Machine Learning in Materials Science ; Hydrocarbon exploration and reservoir analysis ; Reservoir Engineering and Simulation Methods