jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hameed Ghalib Hussian Al-Surraiwy | Margarita A. Goncharova | Vladimir T. Erofeev | Valentina V. Dergunova | Alexander I. Rodin | Elena S. Dergunova
Source:  (article, 2025-11-12)
Field: Materials Science — Materials Chemistry

Mathematical Methods of Planning Cement Composites Based on Man-made Ferrous Metallurgy Waste The present work is devoted to the study of mathematical methods used to optimize the composition of cement mixtures. The quality and properties of cement composites depend on many factors, including the chemical and mineralogical composition of the initial components, mixing proportions, and manufacturing technology. One of the most important tasks facing the construction industry is to obtain high-strength, durable and cost-effective mixtures. Solving this problem requires an integrated approach, including the development of rational production technologies and the selection of optimal formulations using blast furnace slag. The article examines the theoretical foundations, mathematical tools used, and practical techniques currently used to optimize the chemical composition and proportions of cement mixtures using metallurgical waste. Special attention is paid to such popular techniques as linear programming, the least squares method and the simplex method based on artificial intelligence and neural networks. Cement ; Mixing (physics) ; Blast furnace ; Mathematical model ; Quality (philosophy) ; Work (physics) ; Basic oxygen steelmaking Structural mechanics and materials ; Ergonomics and Human Factors ; Advanced Computational Techniques in Science and Engineering
Authors: Fan Zhu | Xianming Deng | Tiancai Zhang | Lin Wang | Yixiang Zhang | Xinrui Wu
Source:  (article, 2025-03-03)
Field: Materials Science — General Materials Science

A method for detecting the environmental adaptability of spectral temperature brightness characteristics of polyurethane functional materials A method for detecting the environmental adaptability of spectral temperature brightness characteristics of polyurethane functional materials is proposed. This method fully combines the advantages of local preserving projection algorithm, morphological dual window constrained detection model, feature fusion method of BEMD and W transform, deep fusion neural network model, multi evidence combination D-S inference model, multi-dimensional ROC fusion evaluation model, weighted joint regression network model, and random forest model based on forward distribution and Adaboost correction. It also constructs a degradation law model of the image fusion degree between polyurethane functional materials and natural environment, and a model of the influence of natural environmental factors in typical regions on the fusion characteristics of polyurethane functional materials. The classification index of environmental adaptability level of fusion features of polyurethane functional materials is formulated. The experimental results indicate that the environmental adaptability levels of polyurethane functional materials in natural environments in Chongqing and Lhasa are first level adaptation and second level adaptation, respectively. Adaptability ; Polyurethane ; Brightness ; Materials science ; Biological system ; Environmental science ; Computer science ; Composite material ; Optics ; Physics ; Biology ; Ecology Material Properties and Applications
Authors: Kevin Sawade | Tobias Lemke | Christine Peter
Source: Journal of Chemical Information and Modeling (article, 2025-08-20)
Field: Materials Science — Materials Chemistry

EncoderMap III: A Dimensionality Reduction Package for Feature Exploration in Molecular Simulations EncoderMap is a dimensionality reduction method that is tailored for the analysis of molecular simulation data. It relies on a neural network autoencoder architecture augmented with an additional multidimensional scaling (MDS)-like loss term. Due to this additional cost function between the high-dimensional input and the latent space, EncoderMap emerges as a method that has advantages over other dimensionality reduction methods and straightforward autoencoders alike. In particular, the low-dimensional projections created by EncoderMap are guided by the MDS-like cost function adopted from the sketch-map algorithm, which produces projections with a better correlation between high- and low-dimensional similarities and dissimilarities. EncoderMap has been successfully applied to a wide range of molecular dynamics data. Here, we present a new version of the EncoderMap package. Porting EncoderMap to version 2 of the TensorFlow library not only ensures that it can be used on modern computers but most importantly allowed for the introduction of new features and a wide range of customization options (e.g., user-defined custom loss functions). We introduce new visualization capabilities and encoding/decoding features and make EncoderMap modular so that researchers can better understand the training process. Most of these additions can be used on general high-dimensional data, while some specifically aid in working with biomolecular simulation data. Furthermore, we added the possibility to provide sparse inputs to EncoderMap's autoencoder, which can be used to train the same network on topologically different proteins. We demonstrate these new features with the help of three different molecular dynamics data sets in the context of the ubiquitin system. Dimensionality reduction ; Feature (linguistics) ; R package ; Reduction (mathematics) ; Computer science ; Curse of dimensionality ; Multifactor dimensionality reduction ; Chemistry ; Pattern recognition (psychology) ; Artificial intelligence ; Mathematics ; Computational science Machine Learning in Materials Science ; Protein Structure and Dynamics ; Enzyme Structure and Function
Authors: Yuwei Pan | Ling Zhang | Lei Sun | Dong Zhai | Wenliang Li | Zhen Li | Tie Yu | Weiqiao Deng | Li Yang
Source: JACS Au (article, 2025-11-26)
Field: Chemistry — Inorganic Chemistry

Accelerated Screening of Zeolites for Methanol-to-Propylene Conversion Using Machine Learning with Interpretable Selectivity Function Methanol-to-propylene (MTP) is one of the important propylene production technologies that is commonly achieved by the use of zeolite catalysts. However, effectively regulating the selectivity toward propylene remains challenging due to the insufficient understanding of the principles behind selectivity control. In this study, we proposed an interpretable function to assess the propylene/ethylene (P/E) selectivity based on a thorough investigation of the MTP selectivity control mechanism and implemented a machine learning (ML) method for high-throughput screening of zeolite frameworks for MTP. Density functional theory and grand canonical Monte Carlo simulations have been used to build the ML model. The P/E selectivity predictions are supported by the reported and our own experiments. We chose Mobil Five (MFI) (rank 374) and Deca-dodecasil 3R (DDR) (rank 5869) for experimental validation. Experimental results showed that the Ga-MFI catalyst achieved a P/E ratio of 12.3 and a remarkably longer operational lifespan of 121.3 h in the MTP process, surpassing previously reported values. Additionally, the marked superiority of Ga-MFI over Ga-DDR, especially in terms of P/E ratios and lifespans, further underscores the effectiveness of our zeolite screening strategy. Selectivity ; Function (biology) ; Zeolite ; Monte Carlo method ; Density functional theory ; Probability density function Zeolite Catalysis and Synthesis ; Catalysis and Oxidation Reactions ; Machine Learning in Materials Science
Authors: A. K. Misra
Source: MRS Bulletin (article, 2025-10-01)
Field: Materials Science — Materials Chemistry

The materials research ecosystem in the age of AI Research Society (MRS), is completing 50 years of publishing in the year 2025.Through perspectives and commentaries, our editorial team has written about the impact of MRS Bulletin since the mid-1970s, 1 a guide to the authors on the current structure of the journal, 2 and about how artificial intelligence (AI) and machine learning (ML) approaches are impacting materials research. 3,4The October, November, and December 2025 special issues feature a collection of 20 invited articles celebrating a cross section of high impact topical areas such as AI/ML, nanotechnologies, soft/biomaterials, electronics and energy materials, sustainability, high-entropy alloys, and nanomechanics.These articles are authored by researchers from many different countries (and continents) and collectively highlight collaborations in the global community in support of fundamental research in the science of engineering materials.I am thankful to the teamwork by Gopal R. Rao (past Editor who initiated the planning for the 50-year anniversary special issues) and the MRS Bulletin editorial team, and all the authors and peer reviewers.MRS as a professional society and MRS Bulletin as a peerreviewed publication champion the materials research community through presentations and publications of latest research in hot topical areas.As we look into the future, 4 materials research laboratories are likely to become more autonomous by adopting new approaches in AI, ML, and robotics.Likewise, scientific publishing is being impacted by the digital age.On one hand, the time for online publication and the barriers for co-authors to write/edit manuscripts have been reduced, and it is easier to access electronic copies of articles with high-resolution images, data, and metadata.On the other hand, there are now a plethora of open access "commercial" journals and concerns raised about plagiarism and fraudulent scientific articles. 5,6Using data science and statistical analysis, Richardson et al. 6 have argued that going beyond individual cases of unethical behavior, there are a growing number of coordinated scientific fraud activities involving entities such as research paper mills, brokers, and predatory journals.A recent news article in Science on research integrity 7 indicates that AI tools such as ChatGPT and freely available public data may have contributed to the growing number of fake articles in "research paper mills," particularly in for-profit-driven journals. Champion ; Publishing ; Electronic publishing ; Associate editor ; Special section ; Digital library ; Teamwork ; The Internet Machine Learning in Materials Science
Authors: Min Li | Haihui Liu | Zhijie Li | Ziqing Yang | Gang Yin | Huan Liu | Zhimin Wang | Jingjing Zhu
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Authentication of Gynostemma pentaphyllum tea by hyperspectral imaging with deep learning Gynostemma pentaphyllum ; Hyperspectral imaging ; Deep learning ; Pattern recognition (psychology) ; Feature extraction ; Authentication (law) Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Smart Agriculture and AI
Authors: Klaus Roppert | Lukas Domenig | A Reinbacher-Köstinger | Manfred Kaltenbacher | Laurent Daniel
Source:  (preprint, 2025-09-10)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Physics-Informed Neural Anhysteresis Surrogate for Magneto-Elastic Vector Hysteresis in Device Simulations In many applications, accurately capturing the magneto-mechanical coupling and dissipative effects at the material level is essential for realistic simulations. Embedding the Simplified Multiscale Model (SMSM) inside an energy-based hysteresis framework yields high fidelity but is computationally intensive for three dimensional finite element (FE) analyses. This paper introduces NNSMSM, a physics-informed multi-task deep neural network that emulates the expensive SMSM operator. A hybrid Latin-hypercube/Sobol sampling strategy efficiently explores the magneto-mechanical loading space. The network is trained with a composite loss that simultaneously fits magnetisation and magnetostrictive strain while enforcing reciprocity and positive definiteness of the susceptibility tensor. The traced TorchScript model is linked to the open source FE software openCFS, replacing the SMSM inside the Vector Play Model (VPM) hysteresis model with zero code changes. The benchmark of a PMSM device simulation shows a speed-up of wall clock time by a factor of 11 while preserving global accuracy of hysteresis losses. Hysteresis ; Dissipative system ; Artificial neural network ; Benchmark (surveying) ; Embedding ; Control theory (sociology) ; Magnetostriction ; Finite element method ; Reciprocity (cultural anthropology) Magnetic Properties and Applications ; Piezoelectric Actuators and Control ; Electric Motor Design and Analysis
Authors: Huiting Lv | Jiawei Yin | Dayong Wu | Ziyuan Rao | Chunhua Su | Jie Kang | Qian Wang | Haikun Ma | Huicong Dong | Yandong Wang | Ru Su
Source: Advanced Science (article, 2025-11-07)
Field: Materials Science — Materials Chemistry

Data‐Driven Fatigue Prediction of Superalloys: A Novel Strategy Integrating Transfer Learning and Partial Label Learning for Addressing Ambiguous Data Machine learning has emerged as a powerful tool for predicting material properties due to its efficiency and accuracy. However, challenges related to data integrity, particularly the presence of ambiguous data, have limited its broad application. In this work, a novel strategy is proposed that integrates partial label learning and transfer learning to accurately address ambiguous compositional data in predicting the fatigue performance of superalloys. Subsequently, key microstructural features are enriched through thermodynamic calculations based on the composition data, enhancing model interpretability by revealing composition-microstructure-property relationships. This approach not only achieves superior predictive accuracy but also exhibits robust generalization across experimental validation. Given the widespread presence of ambiguous data, this framework holds significant potential for broader applications in materials science. Interpretability ; Transfer of learning ; Generalization ; Key (lock) ; Knowledge transfer ; Transfer (computing) Machine Learning in Materials Science ; High Temperature Alloys and Creep ; Fatigue and fracture mechanics
Authors: Raphael M. Tromer
Source: Journal of Chemical Theory and Computation (article, 2025-08-12)
Field: Materials Science — Materials Chemistry

Dynamic Collision Fingerprints (DCF): Introducing a New Descriptor Linking Lattice Interactions to 2D Structural Data Signatures We introduce a novel method for structural characterization of two-dimensional (2D) materials based on classical particle trajectories and elastic collisions with atomic lattices. This approach, called the dynamic collision fingerprint (DCF), encodes structural features through statistical patterns of collisions, capturing symmetry, porosity, and disorder using purely geometric information. Applied to systems such as graphene, phagraphene, CEY-graphene, and h-BN, the method extracts descriptors, including mean free path, diffusivity, angular entropy, and Fourier-based symmetry metrics. These form compact interpretable vectors suitable for classification and machine learning. All calculations were performed on a personal computer, underscoring the method's efficiency and accessibility. DCF provides a robust and general tool for 2D materials analysis, particularly well-suited for carbon-based and atomically thin systems. Computer science ; Collision ; Fourier transform ; Path (computing) ; Fingerprint (computing) ; Entropy (arrow of time) ; Statistical physics ; Algorithm ; Physics ; Artificial intelligence Graphene research and applications ; Machine Learning in Materials Science ; 2D Materials and Applications
Authors: Keerthana B. Chigateri | Ajit M. Hebbale | Evan Varun Dsouza | Mladen Saldanha | Pavankumar S Lali | P Santhosh Rayalu
Source:  (article, 2025-06-17)
Field: Materials Science — Materials Chemistry

An Approach to Predicting the Oxidation of Coated Metals Through Machine Learning The aim of the research is to predict weight gain of metal T11 during cyclic heating and cooling processes and by comparing their impact on different changes in additives. With the information obtained from which the base metal T11 was heated up at 800°C for an hour, and then left at room temperature for 20 minutes, with this process repeated for 50 cycles. Besides the base metal, three additive compositions are introduced in this work: NiCrAlY, NiCrAlY + Al₂O₃ and NiCrAlY + YSZ, with the same procedure applied for each composition. As such, it aims to build a machine learning model capable of predicting the weight change over future cycles from recorded data. Among the machine learning algorithms included, there are: Random Forest Regressor, Linear Regression, Gradient Boost, K-Nearest Neighbors (KNN) and Polynomial Regression. Metrics like Mean Squared Error (MSE) and R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> score show that the model with a Random Forest Regressor is the best predictor for all the metal compositions. This ability can then provide an interesting source to look at the applications at high temperatures through the long-term effects of thermal cycling on these materials. Computer science ; Artificial intelligence ; Machine learning Machine Learning in Materials Science ; Corrosion Behavior and Inhibition ; Catalytic Processes in Materials Science
Authors: Hui null Gao | Chang null Liu | Lin null Mou | Mengtong null Zhao
Source: Archiv für Lebensmittelhygiene (article, 2025-12-29)
Field: Chemistry — Analytical Chemistry

Meat Freshness Evaluation Methods Based on Spectral Technology: From Traditional to Machine Learning-Enhanced Approaches: A Review Meat freshness is a critical indicator of meat quality and safety. The deterioration in the meat freshness results in both resource waste and poses potential risks to human health. Given the growing public concern about food safety, the development of rapid, non-destructive, and efficient freshness detection technologies is crucial. Recently, spectral techniques, known for their non-invasive nature, fast response, and ability to analyze multiple components, have been widely used in meat freshness detection. Key strategies include near-infrared spectroscopy (NIRS), Raman spectroscopy, and hyperspectral imaging (HSI), which can assess meat freshness by analyzing changes in intrinsic chemical components, such as Total Volatile Basic Nitrogen (TVB-N), water content, and protein content. However, the practical application of spectral techniques faces challenges, including limited model generalizability, complex data processing, and relatively high equipment costs. Thus, the future development of spectral techniques should focus on higher precision, intelligence, portability, and integration with other technologies, such as nanotechnology and machine learning. These advancements will facilitate real-time monitoring and automated detection of meat freshness, thereby providing robust technical support for quality control and food safety assurance in the meat industry. Hyperspectral imaging ; Food safety ; Quality (philosophy) ; Food quality ; Meat packing industry ; Quality assurance ; Resource (disambiguation) Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Listeria monocytogenes in Food Safety
Authors: João Morado (10345050) | Kirill Zinovjev (1464025) | Lester O. Hedges (22590374) | Daniel J. Cole (213778) | Julien Michel (151377)
Source: Figshare (article, 2025-11-10)
Field: Materials Science — Materials Chemistry

Enhancing ElectrostaticEmbedding for ML/MM Free EnergyCalculations Hybrid ML/MM approaches that combine machine learning (ML) potentials with molecular mechanics (MM) potentials offer a promising balance between computational cost and accuracy. Most ML/MM simulations reported to date employ mechanical embedding schemes, and rely on Lennard-Jones and Coulomb potentials to model intermolecular interactions between the ML and MM regions. A promising approach to improving ML/MM schemes is to use electrostatic embedding, where polarization effects on the ML region by the MM region are explicitly incorporated. The electrostatic machine learning embedding (EMLE) method has been developed for this purpose. Here, we compute absolute hydration free energies for a set of small organic molecules to derive robust methodologies for training EMLE models using quantum mechanical data. We establish protocols for fine-tuning the static and induced components of electrostatic interactions and evaluate the accuracy limits of fitting these components to first-principles calculations. We also introduce an empirical adjustment to enhance agreement with experimental results, strengthening the competitiveness of ML/MM simulations relative to state-of-the-art methods. Overall, our findings provide valuable insights into the challenges and opportunities of electrostatic embedding ML/MM simulations, and offer strategies for achieving robust modeling of classes of drug-like molecules where the accuracy of conventional MM force fields fall short. Embedding ; Electrostatics ; Coulomb ; Electric potential energy ; Intermolecular force ; Quantum ; Set (abstract data type) Machine Learning in Materials Science ; Protein Structure and Dynamics ; Advanced Chemical Physics Studies
Authors: Antonio Cánovas-Saura | Javier Padilla
Source: Solar Energy Materials and Solar Cells (article, 2025-11-04)
Field: Materials Science — Polymers and Plastics

Spatially resolved optical characterization of large-area electrochromic devices using digital image-based spectral reconstruction The optical characterization of large-area electrochromic (EC) devices remains a significant challenge, particularly when aiming to evaluate spatial uniformity, switching kinetics, and long-term stability with high precision. Traditional methods based on UV–Vis spectrophotometry provide accurate spectral data but are inherently limited to small sampling areas and cannot capture spatial variations across full devices. In this study, we validate a digital image-based system capable of reconstructing visible-range transmittance spectra from photographs and videos using calibrated color correction and neural network-based spectral reconstruction. The system has been used to quantify four core electrochromic performance indicators for devices of aprox. 40 cm 2 area, namely color homogeneity, optical contrast at any wavelength within the visible range, switching speed and cycling stability. Characterization is simultaneously obtained for the whole device surface, delivering quantitative color-coded maps at high spatial resolution. Validation was performed for a set of polymer electrochromic devices covering a complete visible color gamut. The resulting maps successfully revealed non-uniformities in contrast, kinetics, and degradation across the devices—patterns that would not be detectable using conventional single-point measurements. The method demonstrated strong correlation between visual and quantitative degradation indicators. While this work focuses on electrochromic systems, the proposed approach is broadly applicable to any large-area device which performance is linked to visible-range transmittance. This tool provides a fast, non-invasive, and scalable alternative to spectrophotometry, opening new possibilities for the optical evaluation of smart coatings, thin films, and other functional materials. • Image-based system quantifies optical response over entire EC device surfaces. • Real-time maps of contrast, switching speed, and stability are generated. • Validated with electrochromic polymers covering full visible color range. • Enables scalable, non-invasive optical evaluation beyond single spot measurements. Electrochromism ; Digital micromirror device ; Transmittance ; Electrochromic devices ; Characterization (materials science) ; Near-infrared spectroscopy ; Spectral imaging ; Optical filter ; Sampling (signal processing) Transition Metal Oxide Nanomaterials ; Conducting polymers and applications ; Advanced Sensor and Energy Harvesting Materials