jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Pengfei Jia | Qingbin Zeng | Mingxiang Wang | Yiyi Zhang | Jiefeng Liu | Min Xu | Dachang Chen
Source: Materials Today Communications (article, 2025-04-20)
Field: Materials Science — Materials Chemistry

Machine learning-assisted screening of transition metal-doped TMDs for binding energy and charge transfer prediction Materials science ; Doping ; Charge (physics) ; Binding energy ; Transition metal ; Metal ; Chemical physics ; Nanotechnology ; Atomic physics ; Optoelectronics ; Metallurgy Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Gas Sensing Nanomaterials and Sensors
Authors: Chengcheng Peng | Guosheng Chen | Cailiu Yin | Xinjiang Zhang | Chunqiang Yi | Ziyan Shu | Yongping Lu | Xinghao Fan
Source: Materials Today Communications (article, 2025-03-01)
Field: Materials Science — Polymers and Plastics

Application of machine learning to identify key features affecting conductivity in CNT/ PEDOT:PSS nanocomposites Materials science ; PEDOT:PSS ; Nanocomposite ; Key (lock) ; Conductivity ; Conductive polymer ; Composite material ; Nanotechnology ; Polymer ; Computer science Conducting polymers and applications ; Advanced Sensor and Energy Harvesting Materials ; Polymer Nanocomposite Synthesis and Irradiation
Authors: Rick Oerder | Christian Hölzer | Jan Hamaekers
Source: Journal of Chemical Information and Modeling (article, 2025-08-07)
Field: Materials Science — Materials Chemistry

ConfRank+: Extending Conformer Ranking to Charged Molecules We present a machine learning model for high-throughput energetic ranking of charged molecular conformers. Based on the ConfRank (Hölzer et al. <i>J. Chem. Inf. Model.</i> <b>2024</b> <i>64</i>, 8909-8925) approach, the model is trained in a pairwise fashion to predict energy differences for pairs of conformers. By conditioning the model on data set embedding vectors, we are able to train our model on two different reference levels simultaneously, allowing for a larger training data set and to emulate multiple reference methods. In particular, we train our model on a large subset of the SPICE 2.0.1 data set with ωB97M-D3(BJ)/def2-TZVPPD range-separated hybrid meta-GGA DFT reference computations and a self-developed conformer data set based on the GEOM data set including r2SCAN-3c references. The result is a single multifidelity model that can reproduce both reference levels up to ML-typical model errors for small- and medium-sized molecules including the following elements: H, Li, B, C, N, O, F, Na, Mg, Si, P, S, Cl, K, Ca, Br, and I. By including partial atomic charges obtained from the electronegativity equilibration charge model, our model incorporates information about the charge distribution in a molecule, allowing the treatment of charged closed-shell species and explicit treatment of electrostatic interactions. We test the ranking capability of the model on various data sets, paying special attention to molecular charges of -1, 0, 1. Throughout all tests, we find our model to be as accurate as current AIMNet2 and MACE-OFF23(L) models, while requiring an order of magnitude fewer parameters and matching the robustness of the state-of-the-art semiempirical quantum method GFN2-xTB. Conformational isomerism ; Embedding ; Set (abstract data type) ; Molecule ; Charge (physics) ; Range (aeronautics) ; Test set ; Computer science ; Pairwise comparison ; Ranking (information retrieval) ; Chemistry ; Statistical physics ; Computational chemistry ; Physics ; Artificial intelligence ; Materials science ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Mukesh Kumar | Che‐Hua Yang | Khaja Moiduddin | V. Kavimani | Adeolu Adesoji Adediran | Syed Hammad Mian
Source: Scientific Reports (article, 2025-09-30)
Field: Materials Science — Polymers and Plastics

Wettability-Driven void suppression and enhanced mechanical performance in Si₃N₄-Reinforced bamboo Fiber epoxy composites using COCOSO and ANN modeling The incorporation of ceramic particles as fillers and natural fibers as reinforcements in polymer composites has gained significant attention in recent years. However, defects such as particle agglomeration, poor wettability, non-homogeneous distribution, and void formation often arise due to unoptimized process parameters. This study aims to enhance wettability between fillers, reinforcement, and the matrix while improving uniform dispersion by optimizing filler percentage, stirring time, and stirring speed. To achieve this, Silicon Nitride (Si₃N₄) fillers were dispersed in bamboo fiber-reinforced epoxy composites using an L₂₇ orthogonal array, with process parameters varied across Si₃N₄ filler content (3%, 6%, and 9%), stirring time (6, 12, and 18 min), and stirring speed (250, 350, and 450 rpm). The response parameters such as void formation, contact angle, and tensile strength were analyzed, and the Combined Compromise Solution (COCOSO) method, integrated with an Artificial Neural Network (ANN), was used to determine the optimal parameter settings and compare experimental results with predictions. The findings reveal that using 6% Si₃N₄, a stirring time of 12 min, and a stirring speed of 350 rpm improves wettability by 25.1% while reducing void formation by 79.3%. These results highlight the importance of optimizing wettability through precise control of filler content and processing parameters to achieve high-performance composites with minimal defects and enhanced mechanical properties. Void (composites) ; Wetting ; Ultimate tensile strength ; Epoxy ; Ceramic ; Silicon nitride ; Composite number ; Filler (materials) Natural Fiber Reinforced Composites ; Tribology and Wear Analysis ; Mechanical Behavior of Composites
Authors: Zijian Li | Zunhong Xu | Ruichu Cai | Zhenhui Yang | Yuguang Yan | Zhifeng Hao | Guangyi Chen | Kun Zhang
Source: IEEE Transactions on Pattern Analysis and Machine Intelligence (article, 2025-08-13)
Field: Materials Science — Materials Chemistry

Identifying Semantic Component for Robust Molecular Property Prediction Although graph neural networks have achieved great success in the task of molecular property prediction in recent years, their generalization ability under out-of-distribution (OOD) settings is still under-explored. Most of the existing methods rely on learning discriminative representations for prediction, often assuming that the underlying semantic components are correctly identified. However, this assumption does not always hold, leading to potential misidentifications that affect model robustness. Different from these discriminative-based methods, we propose a generative model to ensure the Semantic-Components Identifiability, named SCI. We demonstrate that the latent variables in this generative model can be explicitly identified into semantic-relevant (SR) and semantic-irrelevant (SI) components, which contributes to better OOD generalization by involving minimal change properties of causal mechanisms. Specifically, we first formulate the data generation process from the atom level to the molecular level, where the latent space is split into SI substructures, SR substructures, and SR atom variables. Sequentially, to reduce misidentification, we restrict the minimal changes of the SR atom variables and add a semantic latent substructure regularization to mitigate the variance of the SR substructure under augmented domain changes. Under mild assumptions, we prove the block-wise identifiability of the SR substructure and the comment-wise identifiability of SR atom variables. Experimental studies achieve state-of-the-art performance and show general improvement on 21 datasets in 3 mainstream benchmarks. Moreover, the visualization results of the proposed SCI method provide insightful case studies and explanations for the prediction results. Identifiability ; Discriminative model ; Computer science ; Robustness (evolution) ; Latent variable ; Machine learning ; Artificial intelligence ; Generalization ; Theoretical computer science ; Data mining ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Thanasee Thanasarnsurapong | S. Jana | Panyalak Detrattanawichai | Waraporn Namunmong | Wisit Hirunpinyopas | Pawin Iamprasertkun | Adisak Boonchun
Source: ACS Materials Au (article, 2025-06-26)
Field: Materials Science — Materials Chemistry

Accelerating Lattice Thermal Conductivity Calculations in MXenes: A Machine Learning Force Field Approach Traditionally, lattice thermal conductivity is evaluated using the phonon Boltzmann transport equation (PBTE) in combination with density functional theory (DFT) calculations. However, this approach is computationally intensive. In this study, we predicted lattice thermal conductivity of Ti<sub>2</sub>C and Ti<sub>3</sub>C<sub>2</sub> MXenes, along with their functionalized variants featuring surface terminations (O, F, OH), by using active DFT-based on-the-fly machine learning force fields (MLFF). The predicted thermal conductivities of Ti<sub>2</sub>C and Ti<sub>3</sub>C<sub>2</sub> are 73.10 and 101.15 W m<sup>-1</sup> K<sup>-1</sup>, respectively, close to previously calculated DFT values. The introduction of surface functional groups significantly reduces the lattice thermal conductivity. Furthermore, the MLFF-based predictions of lattice thermal conductivity are tens to thousands of times faster than conventional DFT calculations, dramatically accelerating the study of thermal transport in MXenes. This efficiency highlights the potential of MLFF as a powerful tool for exploring and optimizing the thermal properties of two-dimensional materials. MXenes ; Thermal conductivity ; Lattice (music) ; Materials science ; Condensed matter physics ; Force field (fiction) ; Field (mathematics) ; Statistical physics ; Computer science ; Physics ; Artificial intelligence ; Mathematics ; Nanotechnology ; Composite material MXene and MAX Phase Materials ; Thermal properties of materials ; 2D Materials and Applications
Authors: Duc Tien Nguyen | Nguyen Duc Manh | Nguyen Manh Dzung | Dinh Gia Ninh
Source: European Journal of Mechanics - A/Solids (article, 2025-09-23)
Field: Materials Science — Materials Chemistry

Highly efficient prediction of beating phenomena in laminated nanocomposite plates using a hybrid neural–numerical–analytical framework Natural frequency ; Vibration ; Sensitivity (control systems) ; Displacement (psychology) ; Nonlinear system ; Artificial neural network ; Harmonic ; Categorical variable ; Range (aeronautics) High voltage insulation and dielectric phenomena ; Structural Health Monitoring Techniques ; Infrastructure Maintenance and Monitoring
Authors: Bernhard Kretz | Ivor Lončarić
Source: Inorganic Chemistry (article, 2025-05-29)
Field: Materials Science — Materials Chemistry

Tuning Electronic Properties of Nanoporous Graphene Different nanoporous graphene structures have shown great promise for a wide variety of applications. However, due to limitations in experimental or computational throughput, nanoporous graphenes have not been investigated systematically. In this work, we combine density functional theory and machine learning to study 460 structures of nanoporous graphene made from four different templates. We shed light on structure-band gap relations and perform molecular dynamics simulations and phonon calculations in order to determine the role of electron-phonon coupling on the renormalization of temperature-dependent band gaps. Our results uncover that certain subsets of nanoporous graphene exhibit a similar trend in the band gap as a function of a structural parameter as has been observed for armchair graphene nanoribbons. Furthermore, we find that electron-phonon coupling varies over a large range in the investigated nanoporous graphenes and that it drives the closing of the band gap with larger temperatures. Finally, we suggest nanoporous graphene structures for different applications, such as field-effect transistors. Thus, our work can help guide the development and improvement of nanoporous graphene-based devices. Chemistry ; Nanoporous ; Graphene ; Nanotechnology ; Organic chemistry Graphene research and applications ; Supercapacitor Materials and Fabrication ; Advancements in Battery Materials
Authors: Morteza Khomami Abadi | Mohammad Zaman Kabir | Kamran Nikbin
Source: Procedia Structural Integrity (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

An innovative solution for determining stress intensity factors (SIF) using the GMDH neural network The Stress Intensity Factor, SIF, is a conventional index to assess the elastic stress state at the tip of a mathematically sharp crack in fracture mechanics and also is an appropriate quantity for crack opening and fracture development in brittle materials. This paper presents a statistical solution based on the Group Method of Data Handling, GMDH, to determine the stress intensity fac-tors (KI). The results of this method are compared and validated with the previous analytical and numerical methods. In order to extract the SIFs, a certain number of tests were performed by changing the geometrical parameters, boundary and loading conditions using DOF (Design of Experiments) method. Each experiment is independently simulated in the Abaqus software and the values of stress intensity factors are calculated. The inputs of each test, including geometric dimensions, crack location, crack depth, boundary conditions and loading situation, along with the test output, are introduced to the GMDH algorithm. The best and most optimal connection between the inputs and outputs by considering the lowest error was set after processing the data in the algorithm. In this study, the stress intensity factors are calculated for beams under bending moment and transverse loading and plates containing two types of crack; single-edge crack and central crack. The accuracy of the presented statistical method is validated by J-integral method in Abaqus software. The comparison of the results from the current study with those obtained using existing the analytical and numerical methods are satisfied with less than 8.4% differences. It is known that there are limitations in the analytical method for crack depth (a/L>0.7). However, the statistical approach is strong enough to provide solutions for deep cracks. The main advantage of this method, based on the neural network, is its flexibility and versatility for any types of geometric, material property, loading and boundary conditions. Artificial neural network ; Intensity (physics) ; Stress intensity factor ; Materials science ; Stress (linguistics) ; Composite material ; Computer science ; Artificial intelligence ; Optics ; Fracture mechanics ; Physics Material Properties and Failure Mechanisms ; Engineering Diagnostics and Reliability ; Statistical and Computational Modeling
Authors: William E. Gilbraith | J. Chance Carter | Kristl L. Adams | Isio Sota‐Uba | Barry K. Lavine | Karl S. Booksh | J. M. Ottaway
Source: Applied Spectroscopy Practica (article, 2025-06-01)
Field: Chemistry — Analytical Chemistry

Classification and Peroxide Value Prediction of Naturally Aged Edible Oils Using Raman and Infrared Spectral Analysis Developing global classification and calibration models for identifying edible oil type and predicting peroxide value, a measure of the degree of oxidation directly related to oil freshness is predicated on capturing variance differences within and between growing seasons across a large set of 19 naturally aged (5–7 years) oil types. Our approach utilizes a larger sample set and longer aging period than reported studies (typically 2–5 years), includes multiple growing seasons, and is based on natural rather than accelerated thermal aging of oil samples. We provide spectroscopic data (Raman scattering, near- and mid-infrared absorption) for 19 edible oil types, which represent the most diverse and only naturally aged sample set reported to date. The data, which consist of three data collection campaigns acquired using different analytical methods at different times during the aging process, provide opportunities to investigate and validate new machine learning approaches for classification, calibration, and data fusion. In the supplemental material, vibrational spectroscopy data from all samples are included along with the R programming language code for preprocessing data and building basic classification and calibration models. Peroxide value ; Infrared ; Edible oil ; Spectral analysis ; Raman spectroscopy ; Value (mathematics) ; Chemistry ; Food science ; Mathematics ; Statistics ; Physics ; Optics ; Spectroscopy Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Essential Oils and Antimicrobial Activity
Authors: Zhenhao Chen | Zhuangwei Shi | Jianchen Zi | Chenhui Wang | Hai Bi | Yunlong Zhu
Source: ACS Omega (article, 2025-04-03)
Field: Chemistry — Analytical Chemistry

Ethanol Concentration Determination in Baijiu by Graph-Regularized PCA and Random Forest-Based Raman Spectroscopy Baijiu is a type of traditional Chinese alcoholic beverage with significant economic and cultural value. Ethanol concentration determination through machine learning-based Raman spectroscopy offers the advantages of being contact-free and rapid, and the technique holds considerable potential for baijiu quality control in the industrial manufacturing process. However, current applications of Raman spectroscopy for the quantitative analysis of biochemical materials are restricted by measurement accuracy, as well as the flexibility and robustness of chemometric tools. To address these issues, we propose a method that combines graph-regularized principal component analysis (graph-regularized PCA) and an ensemble learning framework, random forest, to capture effective low-dimensional representations from high-dimensional Raman spectra data while reducing spectra data instability. Furthermore, we propose a protocol that adopts ethanol solutions with various concentrations as the training set for fitting a single regression model to determine the ethanol concentrations of different types of baijiu. In ethanol concentration detection across all three types of baijiu, our proposed method achieves a mean average percentage error (MAPE) of 0.415% on ethanol concentration determination of all three types of baijiu, outperforming all other methods. The results validate the accuracy and robustness of our proposed method. Raman spectroscopy ; Graph ; Random forest ; Ethanol ; Spectroscopy ; Chemistry ; Analytical Chemistry (journal) ; Mathematics ; Environmental chemistry ; Physics ; Computer science ; Artificial intelligence ; Combinatorics ; Biochemistry ; Optics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Metabolomics and Mass Spectrometry Studies
Authors: Tomaso Vairo | Davide Cademartori | Davide Clematis | Antonio Maria Asensio | Antonio Barbucci | Maria Paola Carpanese
Source: International Journal of Hydrogen Energy (article, 2025-09-17)
Field: Materials Science — Materials Chemistry

A physics-informed neural network (PINN) predicting the performance of air electrodes for solid oxide cells: a pilot-scale demonstration on four microstructures Electrode ; Mean squared error ; Artificial neural network ; Electrochemistry ; Solid oxide fuel cell ; Oxide ; Diffusion ; Microstructure ; Kinetic energy Advancements in Solid Oxide Fuel Cells ; Electronic and Structural Properties of Oxides ; Machine Learning in Materials Science
Authors: Shuqi Xie | Kun Ni | Yanwu Zhu
Source: Precision Chemistry (article, 2025-05-15)
Field: Materials Science — Materials Chemistry

Nanotube Derived Ordered Carbons Predicted by Neural Network Potential Searching for novel carbon allotropes with excellent mechanical and interesting electronic properties is valuable, but such a large structural search remains a challenge if purely based on the traditional density functional theory (DFT) combined with Monte-Carlo (MC) methods. Herein, the neural network potential is utilized to accelerate the sampling of the stochastic surface walking algorithm for a global structural search of ordered carbons from carbon nanotubes (CNTs) under pressure. A variety of unreported ordered carbons are obtained, among which CNTs with diameters smaller than 0.7 nm are more sensitive to pressure than bigger tubes. Most ordered carbons obtained show great thermodynamical and kinetic stability, verified by <i>ab initio</i> molecular dynamics simulations and phonon spectra. The ordered carbons demonstrate direct or indirect band gaps in the range of 0 to 4.4 eV, including 13 superhard (H<sub>v</sub> > 40 GPa) structures and 1 ductile (Pugh's Ratio G/B < 0.57) structure, in which the modulus of ordered carbons exhibits a linear correlation with the density. Our study provides a pathway to create new carbons from nanotubes and a deeper understanding of the structural evolution of CNTs under pressure. Nanotube ; Carbon nanotube ; Artificial neural network ; Nanotechnology ; Materials science ; Computer science ; Artificial intelligence Machine Learning in Materials Science ; Carbon Nanotubes in Composites ; Graphene research and applications
Authors: Xin Li | Bing Yan
Source: Analytical Chemistry (article, 2025-11-10)
Field: Chemistry — Inorganic Chemistry

Tb(III)-Functionalized Hydrogen-Bonded Organic Framework with Dual-Emission for Liver Health Biomarker Detection and a Smartphone-Integrated Bionic Visual Diagnostic Platform Developing a sensitive analytical platform for monitoring tiopronin (MPG), its metabolite 2-mercaptopropionic acid (MPA), and the key liver biomarker glutathione (GSH) is crucial for liver health assessment. Here, an artificial intelligence-assisted bionic vision platform based on a dual-emission Tb<sup>3</sup><sup>+</sup>-functionalized hydrogen-bonded organic framework (Tb@HOF-dobpdc) is constructed. The ratiometric fluorescent sensor exhibits a distinct ″turn-on″ response, enabling highly sensitive detection of MPG, MPA, and GSH with low limits of detection (0.20, 0.68, and 0.31 μM, respectively). For practical application, Tb@HOF-dobpdc can achieve rapid detection of target analytes in real serum and urine samples by combining with the hydrogel. To overcome the human eye's limitation in discerning subtle color changes, RGB channel processing is used to generate visually distinguishable pseudocolor signals. Furthermore, a backpropagation neural network (BPNN) is applied for accurately distinguishing analyte concentrations by identifying fluorescence images. Leveraging the strong correlation between these analytes and liver health, an AND logic gate diagnostic system is established, outputting ″healthy″ only when all biomarker levels are within safe ranges. This intelligent platform combines advanced material design and artificial intelligence, providing a powerful tool for point-of-care liver health diagnosis. Analyte ; Biomarker ; Fluorescence ; Artificial neural network ; Pattern recognition (psychology) ; Metabolite Metal-Organic Frameworks: Synthesis and Applications ; Sulfur Compounds in Biology ; Molecular Sensors and Ion Detection