jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xiaoyu Wu | Xianyu Song | Yifei Yue | Rui Zheng | Jianwen Jiang
Source: The Journal of Physical Chemistry Letters (article, 2025-02-27)
Field: Materials Science — Materials Chemistry

MOF-KAN: Kolmogorov–Arnold Networks for Digital Discovery of Metal–Organic Frameworks Digital discovery of functional materials, such as metal-organic frameworks (MOFs), entails accurate and data-efficient approaches to navigate complex chemical and structural space. Based on an innovative deep learning approach, namely, Kolmogorov-Arnold Networks (KANs), we introduce MOF-KAN, a state-of-the-art architecture as the first application of KANs to digital discovery of MOFs. Through meticulous fine-tuning of network architecture, we demonstrate that MOF-KAN outperforms standard multilayer perceptrons (MLPs) in predicting diverse properties for MOFs, including gas separation, electronic band gap, and thermal expansion. Furthermore, MOF-KAN excels in low-data regimes, facilitating robust performance in challenging prediction scenarios. Feature importance analysis reveals that MOF-KAN accurately captures critical features of MOFs relevant to targeted properties. MOF-KAN not only serves as a transformative tool for the rational design of functional materials but also holds broad applicability across various domains in physical sciences. Computer science Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; X-ray Diffraction in Crystallography
Authors: Jie Sun | Shaojun Liu | G. R. Liao | Shuai Wang | Yingda Lu | Cheng Wu | Yijing He | Nana Sun | Weidong Li
Source: Annals of the New York Academy of Sciences (review, 2025-09-10)
Field: Chemistry — Analytical Chemistry

Influencing Factors and Predictive Models of Oil–Water Emulsions: A Comprehensive Review and Future Outlook Oil-water emulsions are prevalent in petroleum, chemical, and materials industries, where their rheological properties significantly impact processing efficiency. This review systematically examines the key factors influencing the apparent viscosity of oil-water emulsions, including oil composition, water characteristics, temperature, shear conditions, and emulsifier properties. It traces the evolution of viscosity prediction methodologies, encompassing conventional, complex, and Pickering emulsions, and assesses modeling approaches ranging from early theoretical frameworks to contemporary machine learning techniques. The reliability and applicability of these models are critically evaluated across various industrial contexts. Furthermore, the review identifies key challenges, research gaps, and prospective directions, emphasizing potential advancements in experimental strategies and modeling methodologies. While focusing on petrochemical emulsions, the insights and analytical approaches discussed are applicable to biological, medical, and other industrial systems, offering guidance for future research and practical implementation. Reliability (semiconductor) ; Key (lock) ; Viscosity ; Rheology ; Predictive modelling Petroleum Processing and Analysis ; Enhanced Oil Recovery Techniques ; Surfactants and Colloidal Systems
Authors: Hamidreza Rohani Raftar | Mahdieh Safyari | Masoud Moshtaghi
Source: Procedia Structural Integrity (article, 2025-01-01)
Field: Materials Science — Metals and Alloys

Hydrogen-assisted fatigue crack growth in pipeline steels: a machine learning approach This study presents a machine learning methodology aimed at predicting the fatigue crack growth rate and examining the impact of alloying elements on hydrogen environment embrittlement in pipeline steels. The research marks the inaugural exploration of such predictive techniques in this specific domain. A predictive model was constructed using the bagged trees method, chosen for its ability to minimize metric errors like Mean Squared Error and Root Mean Squared Error. The accuracy of the model was confirmed, demonstrating its reliability in predicting outcomes effectively. Feature importance scores reveal that hydrogen pressure is a significant environmental factor, aligning with expectations. Moreover, the analysis identifies Mn and S as primary features that exert a strong negative influence on hydrogen environment embrittlement in pipeline steels. The machine learning approach is anticipated to be valuable for comprehending the behavior of pipeline steel under hydrogenous conditions and can be applied to enhance fatigue resistance in hydrogen applications. Pipeline (software) ; Paris' law ; Materials science ; Metallurgy ; Crack closure ; Composite material ; Engineering ; Mechanical engineering ; Fracture mechanics Hydrogen embrittlement and corrosion behaviors in metals ; Non-Destructive Testing Techniques ; Material Properties and Failure Mechanisms
Authors: Zacharie Waysenson | Arthur France‐Lanord | Alessandra Serva | Patrice Simon | Mathieu Salanne | A. Marco Saitta
Source: ACS Nano (article, 2025-08-08)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Electrode Flexibility Enhances Electrolyte Dynamics during Supercapacitor Charging Supercapacitors are energy storage devices with high power density and long cycle life. Combined with spectroscopy and electrochemistry, molecular simulations and theory have allowed to characterize their charging mechanisms, and we now have an excellent understanding of the effect of properties such as nanopore size or structural disorder on the supercapacitor performances. However, the influence of electrode flexibility remains to be addressed as state-of-the-art models focused on the polarization effects of the electrode, but enforced its structural rigidity, as an approximation. Here we overcome this limitation by integrating a constant-potential molecular dynamics scheme with a state-of-the-art machine-learning potential for carbon, while controlling the applied potential. Using nanoporous sp<sup>2</sup>/sp<sup>3</sup> carbon electrodes filled with an ionic liquid electrolyte, we compare the behavior of the rigidified and flexible frameworks; the latter allowing for local atomic relaxation, breathing modes, etc. We demonstrate that flexibility significantly enhances in-pore ionic diffusivity, thus shortening the characteristic charging time by a factor of 3 relative to the rigid analogue, while the specific capacitance remains in the experimental range (≈140 F·g<sup>-1</sup>) for both cases. More specifically, our analyses demonstrate that flexibility accelerates co-ion expulsion, mitigates pore overcrowding, and promotes a homogeneous induced-charge profile that penetrates deeply from the very start of the applied voltage onset, explaining the improved kinetics. Supercapacitor ; Materials science ; Electrolyte ; Chemical physics ; Ionic liquid ; Molecular dynamics ; Nanotechnology ; Nanoporous ; Electrode ; Capacitance ; Chemistry ; Computational chemistry ; Physical chemistry Supercapacitor Materials and Fabrication ; Electrocatalysts for Energy Conversion ; Conducting polymers and applications
Authors: Sreenivas Raguraman | Adam J. Griebel | Maitreyee Sharma Priyadharshini | Paulette Clancy | Timothy P. Weihs
Source: Nature Reviews Materials (review, 2025-10-06)
Field: Materials Science — Materials Chemistry

A call to elevate the role of processing in AI-driven materials design Pillar ; Bridge (graph theory) ; Transformative learning ; Hinge ; Bridging (networking) Machine Learning in Materials Science ; Titanium Alloys Microstructure and Properties ; Advanced ceramic materials synthesis
Authors: David van der Spoel | A. Hosseini
Source: Physical Chemistry Chemical Physics (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Point + Gaussian charge model for electrostatic interactions derived by machine learning At short distances between atoms, point charges are a poor approximation of the electrostatic interaction. Due to overlapping electron clouds, charges are effectively shielded and the electrostatic interaction energy is modified. In molecular simulations, two main approaches have surfaced to deal with this. First, the Thole-screening [B. T. Thole, <i>Chem. Phys.</i>, 1981, <b>59</b>, 341-350], which introduces a mathematical modification of the Coulomb interaction at short range, and second, the use of Gaussian-distributed charges [C. M. Smith and G. G. Hall, <i>Theor. Chim. Acta</i> 1986, <b>69</b>, 63-69]. Here, we show that these approaches are practically equivalent, that is the screening functions are numerically very similar and their parameters related by a simple expression. A quantitative comparison between electrostatic interactions in alkali-halide ion pairs, computed using high level symmetry-adapted perturbation theory (SAPT), and in point charge models shows that the electrostatic interactions are not always weaker than those predicted by point charge approximations, highlighting that more complex atomic models may be needed. We then proceed to use machine learning with the Alexandria Chemistry Toolkit to train models for alkali-halides based on a positive core and a virtual site with a negative Gaussian-distributed charge and show that this model yields energies very close to SAPT. Gaussian ; Charge (physics) ; Point (geometry) ; Statistical physics ; Electrostatics ; Electrostatic interaction ; Point particle ; Physics ; Chemical physics ; Computer science ; Artificial intelligence ; Chemistry ; Computational chemistry ; Quantum mechanics ; Mathematics ; Geometry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Van-Thanh Nguyen | Vu‐Hieu Nguyen | S. Naili
Source: International Journal of Mechanical Sciences (article, 2025-07-29)
Field: Chemistry — Spectroscopy

Deep learning-based multiscale prediction of composite aerogels’ effective thermal conductivity Thermal conductivity ; Composite number ; Materials science ; Composite material ; Aerogel ; Thermal ; Thermodynamics ; Physics Aerogels and thermal insulation ; Thermal properties of materials ; Building Energy and Comfort Optimization
Authors: Trofim Polikovskiy | Victoria E. Gontcharenko | Anna Ivanova | Daria A. Metlina | Mikhail A. Kiskin | Ilya V. Taydakov | Yury A. Belousov
Source: New Journal of Chemistry (article, 2025-01-01)
Field: Chemistry — Organic Chemistry

Real world luminescence thermometry through a novel mixed-metal Eu/Tb complex with furan-containing 1,3-diketone The study explores minor chemical modifications effects on the thermometric performance of a novel complex [Tb 0.94 Eu 0.06 L 3 (TPPO) 2 ]. A convolutional neural network was employed to analyze temperature-dependent luminescence under real-world conditions. Chemistry ; Furan ; Luminescence ; Metal ; Diketone ; Photochemistry ; Nuclear chemistry ; Medicinal chemistry ; Organic chemistry ; Optics Chemical Thermodynamics and Molecular Structure ; Lanthanide and Transition Metal Complexes ; Luminescence Properties of Advanced Materials
Authors: Mohammed Faraj Saeid | Bashir Abubakar Abdulkadir | Muhammad Ashraf Fauzi | Herma Dina Setiabudi
Source: Environmental Quality Management (article, 2025-06-01)
Field: Materials Science — Materials Chemistry

Carbon Materials for Hydrogen Storage: A Bibliometric Analysis on Current Trends and Future Prospects ABSTRACT This article offers an overview of bibliometric analysis of carbon materials for hydrogen storage (CMHS) research over the past 33 years, from January 1991 to April 2024. A total of 3544 research papers centered on carbon‐based hydrogen storage materials were analyzed. The analysis encompassed various aspects, including the countries where publications originated, authors' affiliations, prominent journals, research areas, and key terms driving discussions in the field. The research findings reveal a rising significance of carbon‐based hydrogen storage materials studies. China leads the top 10 countries in the number of research papers, contributing 1124 publications. When considering the Total Link Strength metric, which reflects international collaborative efforts, China, the United States, and India ranked first, second, and third, respectively, demonstrating their substantial engagement in global collaborations. The International Journal of Hydrogen Energy is prominent for its significant publication volume, totaling 679 articles. Research trends have progressed, initially concentrating on carbon nanotubes and graphene, shifting to activated carbons, and ultimately focusing on confining MgH 2 within carbon‐based materials for hydrogen storage applications. Current challenges include low hydrogen volumetric density and the need for improved desorption kinetics at ambient conditions. Future research should focus on developing novel carbon composites, optimizing synthesis methods, and leveraging machine learning for material discovery. Hydrogen storage ; Carbon fibers ; Hydrogen ; China ; Carbon nanotube ; Tonne ; Environmental science ; Nanotechnology ; Computer science ; Materials science ; Political science ; Chemistry ; Engineering ; Waste management Hydrogen Storage and Materials ; Ammonia Synthesis and Nitrogen Reduction ; Hybrid Renewable Energy Systems
Authors: Brigitte Enriquez | Miriam Rangel Ayala | Yogesh Kumar | J. Escorcia−García | J. F. Gómez‐Aguilar | Sanghamitra Khandual | Vivechana Agarwal
Source: Journal of environmental chemical engineering (article, 2025-07-03)
Field: Materials Science — Materials Chemistry

Multifunctional Arthrospira platensis biomass derived carbon dots: Sensing/removal of heavy metal ions, high-power light-emitting devices, and some machine learning assisted approaches for solid state sensor Materials science ; Carbon fibers ; Biomass (ecology) ; Metal ions in aqueous solution ; Metal ; Solid-state ; Ion ; Heavy metals ; Optoelectronics ; Nanotechnology ; Chemical engineering ; Chemistry ; Environmental chemistry ; Composite number ; Metallurgy ; Composite material Carbon and Quantum Dots Applications ; Mercury impact and mitigation studies ; Advanced Nanomaterials in Catalysis
Authors: Dianming Chu | Chenyu Gao | Zongchao Ji | Yan Li | Jin Qi | Yan He | Wenjuan Bai
Source: Materials Today Chemistry (article, 2025-03-04)
Field: Materials Science — Materials Chemistry

Advancements in CNT research: Integrating machine learning with microscopic simulations, macroscopic techniques, and application of performance prediction and functional optimization Computer science ; Artificial intelligence ; Materials science ; Machine learning ; Nanotechnology Machine Learning in Materials Science ; Carbon Nanotubes in Composites ; Thermal properties of materials
Authors: Tomasz Urbańczyk | Jakub Bożek | Szymon Mirczak | J. Koperski | Marek Krośnicki
Source: Chemometrics and Intelligent Laboratory Systems (article, 2025-05-17)
Field: Chemistry — Analytical Chemistry

Kolmogorov–Arnold neural network for identification of functional groups from FTIR spectra Identification (biology) ; Fourier transform infrared spectroscopy ; Artificial neural network ; Pattern recognition (psychology) ; Biological system ; Artificial intelligence ; Computer science ; Biology ; Engineering ; Chemical engineering ; Botany Spectroscopy and Chemometric Analyses ; Molecular spectroscopy and chirality ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Newton A. Ihoeghian | Qing Shao
Source: Supramolecular Materials (article, 2025-04-07)
Field: Materials Science — Biomaterials

Fundamental and Application of Co-assembly of Peptides and Proteins: Experiment and Computation Peptides and proteins can co-assemble into various nanostructures based on complementary non-covalent interactions such as electrostatic forces, hydrogen bonding, and hydrophobic associations. These co-assemblies create a design space of functional materials for a wide spectrum of energy, environmental, and biomedical applications. This review focuses on the fundamentals and applications of three co-assembling systems: ultra-short peptides, peptides, and proteins. We will present and discuss experimental studies demonstrating applications in drug delivery, tissue engineering, and biomaterials development. We will also discuss the contribution of computational research, including molecular dynamics simulations and machine learning, to enhance our understanding of assembly mechanisms. Integrating experimental and computational methods would provide crucial insights for understanding and designing robust functional co-assemblies with precisely controlled compositions and properties. These advances would support the development of sophisticated biomaterials for applications ranging from targeted therapeutics to regenerative medicine. Computation ; Chemistry ; Computer science ; Computational biology ; Biological system ; Biology ; Algorithm Supramolecular Self-Assembly in Materials ; Polydiacetylene-based materials and applications ; Lipid Membrane Structure and Behavior
Authors: David Valiente | Fernando Rodríguez‐Mas | Juan V. Alegre‐Requena | David Dalmau | María Flores | Juan Carlos Ferrer
Source: Expert Systems with Applications (article, 2025-07-16)
Field: Materials Science — Materials Chemistry

General machine learning models for interpreting and predicting efficiency degradation in organic solar cells • Optimal ML models predict PCE behavior over time for polymer-based OSCs. • Benchmarking shows ML models outperform classical regression in OSC prediction. • Best ML model accurately predicts PCE behavior of unseen OSC devices. • Feature analysis reveals key factors influencing OSC performance. • Standardized framework ensures reproducibility and transparency of ML models. Photovoltaic (PV) energy plays a key role in addressing the growing global energy demand. Organic solar cells (OSCs) represent a promising alternative to silicon-based PVs due to their low cost, lightweight, and sustainable production. Despite achieving power conversion efficiencies (PCEs) over 20 %, OSCs still face challenges in stability and efficiency. Recent advances in manufacturing, artificial intelligence and machine learning (ML) achieve optimized and screened OSCs for greater sustainability and commercial viability, thus potentially reducing costs while ensuring stable and long term performance. This work presents optimal ML models to represent the temporal degradation on the PCE of polymeric OSCs with structure ITO/PEDOT:PSS/P3HT:PCBM/Al. First, we generated a database with 166 entries with measurements of 5 OSCs, and up to 7 variables regarding the manufacturing and environmental conditions for more than 180 days. Then, we relied on a software framework that provides a conglomeration of automated ML protocols that execute sequentially against our database by simply command-line interface. This easily permits hyper-optimizing the ML models through exhaustive benchmarking so that optimal models are obtained. The accuracy for predicting PCE over time reaches values of the coefficient determination widely exceeding 0.90, whereas the root mean squared error, sum of squared error, and mean absolute error are significantly low. Additionally, we assessed the predictive ability of the models using an unseen OSC as an external set. For comparative purposes, classical Bayesian regression fitting are also presented, which only perform sufficiently for univariate cases of single OSCs. Degradation (telecommunications) ; Computer science ; Organic solar cell ; Artificial intelligence ; Machine learning ; Photovoltaic system ; Engineering ; Telecommunications ; Electrical engineering Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Silicon and Solar Cell Technologies
Authors: Abhishek Chandra | Taniya Kapoor | Bram Daniels | Mitrofan Curti | Koen Tiels | Daniel M. Tartakovsky | E.A. Lomonova
Source: Computer Physics Communications (article, 2025-05-05)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Generalizable models of magnetic hysteresis via physics-aware recurrent neural networks Hysteresis is a ubiquitous phenomenon in magnetic materials; its modeling and identification are crucial for understanding and optimizing the behavior of electrical machines. Such machines often operate under uncertain conditions, necessitating modeling methods that can generalize across unobserved scenarios. Traditional recurrent neural architectures struggle to generalize hysteresis patterns beyond their training domains. This paper mitigates the generalization challenge by introducing a physics-aware recurrent neural network approach to model and generalize the hysteresis manifesting in sequentiality and history-dependence. The proposed method leverages ordinary differential equations (ODEs) governing the phenomenological hysteresis models to update hidden recurrent states. The effectiveness of the proposed method is evaluated by predicting generalized scenarios, including first-order reversal curves and minor loops. The results demonstrate robust generalization to previously untrained regions, even with noisy data, an essential feature that hysteresis models must have. The results highlight the advantages of integrating physics-based ODEs into recurrent architectures, including superior performance over traditional methods in capturing the complex, nonlinear hysteresis behaviors in magnetic materials. The codes and data related to the paper are at github.com/chandratue/HystRNN . Hysteresis ; Artificial neural network ; Statistical physics ; Physics ; Computer science ; Condensed matter physics ; Artificial intelligence Magnetic Properties and Applications ; Non-Destructive Testing Techniques ; Neural Networks and Applications
Authors: Mohammad Hossein Nargesi | Kamran Kheiralipour
Source: LWT (article, 2025-10-01)
Field: Chemistry — Analytical Chemistry

Estimating ash content in wheat flour using visible-near infrared hyperspectral imaging and machine learning methods The quality of baked products is highly depending on the quality of wheat flour. Ash content is a key indicator of the flour extraction rate and plays a crucial role in evaluating flour quality and classification. Traditional methods for determining flour ash have destructive nature, time-consuming processes, and require skilled persons. In the present research, visible-near infrared hyperspectral imaging was employed as a non-destructive approach to estimate the ash content of wheat flour. The samples were prepared from the flour of Tufton and Berberd breads and their ash levels were determined through standard chemical tests. After acquiring hypercubes, they were processed by developing an algorithm in MATLAB software. The selected effective wavelengths using principal component analysis for Taftoon flour were 493.84, 652.59, 725.35, 826.23, 872.53, 894.85, and 944.46 nm, and for and Barbari flour were 422.73, 601.33, 746.85, 789.84, 803.07, 896.51, and 941.16 nm. Predictive models were then developed using artificial neural networks and partial least squares regression. The performance results of the two models showed prediction accuracies of 98.96 and 97.05% for Tafton flour, and 99.55 and 92.27% for Barbari flour, respectively. The findings also demonstrated that combining hyperspectral imaging with machine learning models can be applied for the accurate, rapid, and non-destructive estimation of flour ash content. This integrated approach presents a promising alternative to traditional quality control methods in food processing industries. • Hyperspectral imaging was applied to estimate the ash content of wheat flour. • The effective wavelengths and channels were selected using PCA method. • Efficient features were extracted from the hypercubes’ effective channels. • ANN model achieved higher prediction accuracy in Barbari and Tafton flour. Hyperspectral imaging ; Wheat flour ; Partial least squares regression ; Principal component analysis ; Artificial neural network ; Whole wheat ; Predictive modelling Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Smart Agriculture and AI