jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jiapeng Huang (7343783) | Chao Wu (199897)
Source: Figshare (article, 2025-10-28)
Field: Materials Science — Materials Chemistry

An Accurate Two-BodyInteraction Model for Describingthe Structure–Energy Relationship of Binary Alloys Extraordinary properties of alloys rely on their atomistic level structures; however, a systematic study of their rich and complex structures often remains missing due to the lack of efficient and accurate tools. Herein, a simple two-body interaction model is developed to quantitatively describe the structure–energy relationship for 57 body-centered cubic (bcc) and face-centered cubic (fcc) binary transition metal alloys. The interaction parameters are extracted by the energy differences of a few configurations of an alloy (AB) with A (or B) as the bulk element and two substitutive B (or A) atoms as the <i>k</i>th nearest neighbors (<i>k</i>NN) in fcc/bcc supercells, whose configuration energies are computed using first-principles density functional theory (DFT) calculations. This simple model with two metal atoms at <i>k</i>NN (named the 2M-<i>k</i>NN model) then can be used to both qualitatively and quantitatively predict and explain the relative stability of configurations of an alloy. The parameters of the 2M-<i>k</i>NN model have clear physical meaning and the corresponding energy prediction has similar or higher accuracy for the <i>binary</i> alloys than the machine learning potential and cluster expansion methods. In combination with Monte Carlo simulations, the 2M-<i>k</i>NN model can afford short-range order patterns for the alloys at various temperatures. The structure–energy relationship is further wrapped in a self-defined order parameter that can convert the structural information on a configuration to its energy. An energy extrapolation mechanism is later designed to accurately predict the total energy of any configuration of any sized supercell, with the DFT-level accuracy, only using its order parameter and the DFT energy of a small configuration of the same atom ratio. This 2M-<i>k</i>NN model, in principle, may be extended to treat the structure–energy relationship of alloys of multiple components. Extrapolation ; Interaction model ; Binary number ; Interaction energy ; Monte Carlo method ; Energy (signal processing) ; Cluster (spacecraft) ; Stability (learning theory) ; Simple (philosophy) Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Advanced Physical and Chemical Molecular Interactions
Authors: Bastion Jader Rana | Noor Ageng Setiyanto | Muhamad Akrom
Source: Journal of Applied Informatics and Computing (article, 2025-03-15)
Field: Chemistry — Organic Chemistry

Prediction of Corrosion Inhibitor Efficiency Based on Quinoxaline Compounds Using Polynomial Regression Corrosion is a natural process that leads to material degradation due to environmental factors. It significantly impacts financial and safety aspects, including structural weakening and economic losses in various industries such as oil, gas, and nuclear. Corrosion inhibitors, especially organic compounds like quinoxaline, are widely used to reduce corrosion by forming protective layers on metal surfaces. Quinoxaline compounds, characterized by their heterocyclic structure with nitrogen atoms, demonstrate promising inhibition efficiency in corrosive environments. In this study, machine learning (ML) approaches are utilized to predict the corrosion inhibition efficiency of quinoxaline compounds. Algorithms such as Gradient Boosting Regressor (GBR), Extreme Gradient Boosting Regressor (XGBR), and Automatic Relevance Determination (ARD) regression are compared. The implementation of polynomial functions significantly improves the prediction accuracy of these models. Among them, GBR achieved the best value with MSE, RMSE, MAE, MAPE, and R2 values of 0.0000001, 0.0003229, 0.0000029, 0.0002294, and 0.999999998, respectively. These findings highlight the potential of polynomial-enhanced ML models in accurately predicting corrosion inhibition efficiency. Moreover, the study demonstrates the viability of GBR as a reliable tool for analyzing and optimizing corrosion inhibitors for industrial applications. Quinoxaline ; Regression ; Corrosion ; Polynomial ; Regression analysis ; Polynomial regression ; Quantitative structure–activity relationship ; Corrosion inhibitor ; Mathematics ; Chemistry ; Biological system ; Statistics ; Organic chemistry ; Stereochemistry ; Biology ; Mathematical analysis Synthesis and Biological Evaluation
Authors: L. Thirion | Yorick L. A. Schmerwitz | Max Kroesbergen | Gianluca Levi | Elvar Ö. Jónsson | Pavlo Bilous | Hannes Jónsson | P. Hansmann
Source: ArXiv.org (preprint, 2025-10-31)
Field: Materials Science — Materials Chemistry

Natural-Orbital-Based Neural Network Configuration Interaction Selective configuration interaction methods approximate correlated molecular ground- and excited states by considering only the most relevant Slater determinants in the expansion. While a recently proposed neural-network-assisted approach efficiently identifies such determinants, the procedure typically relies on canonical Hartree-Fock orbitals, which are optimized only at the mean-field level. Here we assess approximate natural orbitals - eigenfunctions of the one-particle density matrix computed from intermediate many-body eigenstates - as an alternative. Across our benchmarks for H$_2$O, NH$_3$, CO, and C$_3$H$_8$ we see a consistent reduction in the required determinants for a given accuracy of the computed correlation energy compared to full configuration interaction calculations. Our results confirm that even approximate natural orbitals constitute a simple yet powerful strategy to enhance the efficiency of neural-network-assisted configuration interaction calculations. Configuration interaction ; Eigenvalues and eigenvectors ; Full configuration interaction ; Eigenfunction ; Slater determinant ; Atomic orbital ; Simple (philosophy) ; Reduction (mathematics) Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Advanced Chemical Physics Studies
Authors: Kazuaki Tokuyama | Souta Miyamoto | Taichi Masuda | Katsuaki Tanabe
Source: arXiv (Cornell University) (preprint, 2025-12-23)
Field: Materials Science — Materials Chemistry

Composition-Based Machine Learning for Screening Superconducting Ternary Hydrides from a Curated Dataset We present an ensemble machine-learning approach for composition-based, structure-agnostic screening of candidate superconductors among ternary hydrides under high pressure. Hydrogen-rich hydrides are known to exhibit high superconducting transition temperatures, and ternary or multinary hydrides can stabilize superconducting phases at reduced pressures through chemical compression. To systematically explore this vast compositional space, we construct an ensemble of 30 XGBoost regression models trained on a curated dataset of approximately 2000 binary and ternary hydride entries. The model ensemble is used to screen a broad set of A-B-H compositions at pressures of 100, 200, and 300 GPa, with screening outcomes evaluated statistically based on prediction consistency across ensemble members. This analysis highlights several high-scoring compositional systems, including Ca-Ti-H, Li-K-H, and Na-Mg-H, which were not explicitly included in the training dataset. In addition, feature-importance analysis indicates that elemental properties such as ionization energy and atomic radius contribute significantly to the learned composition-level trends in superconducting transition temperature. Overall, these results demonstrate the utility of ensemble-based machine learning as a primary screening tool for identifying promising regions of chemical space in superconducting hydrides. Ternary operation ; Superconductivity ; Consistency (knowledge bases) ; Binary number ; Set (abstract data type) ; Ensemble learning ; Construct (python library) Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Hydrogen Storage and Materials
Authors: Seyed Oveis Mirabootalebi | Allan D. Mackie | Gideon Vos | Mostafa Rahimi Azghadi | Yang Liu
Source: ChemElectroChem (article, 2025-03-17)
Field: Chemistry — Electrochemistry

Machine Learning‐Assisted Pulse Electrodeposition of Copper for Enhanced Nitrate Sensing Abstract Overexposure to nitrate, the most stable and prevalent form of dissolved inorganic nitrogen, harms the environment, causing soil acidification, eutrophication, and water contamination. Among various methods for nitrate detection, electrochemical sensors have attracted considerable attention due to their inherent simplicity, high sensitivity, and low cost. However, several challenges remain, including the overpotential for nitrate reduction reaction, which leads to poor selectivity, repeatability and stability. In this work, copper modified electrodes fabricated by pulse electrodeposition method were developed for the selective detection of nitrate . The electrode modification process that determines the sensing performance was investigated by machine learning approaches to understand the relationship between the sensors’ output and the copper deposition parameters. The developed networks successfully predicted the peak current, peak potential, and current stability for electrochemical reduction of nitrate based on the pulse electrodeposition parameters. Furthermore, the most important parameter that influenced the nitrate reduction peak current was revealed by the sensitivity analysis of the designed networks. The experimental results indicate that the proposed sensor achieved a sensitivity of 9.928 μA/mM and a linear range of 0.1 to 20 mM, along with satisfactory recoveries in real sample analysis. Copper ; Nitrate ; Pulse (music) ; Materials science ; Computer science ; Nanotechnology ; Optoelectronics ; Metallurgy ; Chemistry ; Telecommunications ; Detector Electrochemical Analysis and Applications ; Electrochemical sensors and biosensors ; Analytical Chemistry and Sensors
Authors: Gijs Bellaard | Bart Smets | Remco Duits
Source: ArXiv.org (preprint, 2025-04-04)
Field: Materials Science — Materials Chemistry

Universal Collection of Euclidean Invariants between Pairs of Position-Orientations Euclidean E(3) equivariant neural networks that employ scalar fields on position-orientation space M(3) have been effectively applied to tasks such as predicting molecular dynamics and properties. To perform equivariant convolutional-like operations in these architectures one needs Euclidean invariant kernels on M(3) x M(3). In practice, a handcrafted collection of invariants is selected, and this collection is then fed into multilayer perceptrons to parametrize the kernels. We rigorously describe an optimal collection of 4 smooth scalar invariants on the whole of M(3) x M(3). With optimal we mean that the collection is independent and universal, meaning that all invariants are pertinent, and any invariant kernel is a function of them. We evaluate two collections of invariants, one universal and one not, using the PONITA neural network architecture. Our experiments show that using a collection of invariants that is universal positively impacts the accuracy of PONITA significantly. Equivariant map ; Euclidean geometry ; Invariant (physics) ; Euclidean space ; Scalar (mathematics) ; Artificial neural network ; Euclidean group ; Kernel (algebra) Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Advanced Graph Neural Networks
Authors: Udo Seiffert | Ashish Jadhav | Andreas Backhaus
Source:  (article, 2025-01-01)
Field: Materials Science — Biomaterials

Machine Learning on Smartphone-Captured Diffraction Data This study presents a novel approach for classifying oily or cream-like substances using diffraction data captured on a smartphone camera, applied specifically to assessing engine oil quality.Utilising the COMPOLYTICS TapCorder approach, optical diffraction patterns were analysed with a tailored feature extraction method.The performance of three machine learning paradigms -Multilayer Perceptrons (MLP), Learning Vector Quantization (LVQ), and Radial Basis Function Networks (RBFN) -was analysed in classifying new and used oil samples.MLP achieved the highest accuracy, while LVQ required the least computation time, highlighting trade-offs relevant for consumer-focused applications.This work clearly demonstrates the feasibility of accessible, low-cost chemical substance analysis via smartphone-based systems. Computer science ; Diffraction ; Artificial intelligence ; Human–computer interaction ; Optics ; Physics Diatoms and Algae Research ; Geographic Information Systems Studies ; Environmental Monitoring and Data Management
Authors: Qinyi Zhang | Duanyu Feng | R.J. Han | Yangshuai Wang | Hao Wang
Source: ArXiv.org (preprint, 2025-11-12)
Field: Materials Science — Materials Chemistry

MicroEvoEval: A Systematic Evaluation Framework for Image-Based Microstructure Evolution Prediction Simulating microstructure evolution (MicroEvo) is vital for materials design but demands high numerical accuracy, efficiency, and physical fidelity. Although recent studies on deep learning (DL) offer a promising alternative to traditional solvers, the field lacks standardized benchmarks. Existing studies are flawed due to a lack of comparing specialized MicroEvo DL models with state-of-the-art spatio-temporal architectures, an overemphasis on numerical accuracy over physical fidelity, and a failure to analyze error propagation over time. To address these gaps, we introduce MicroEvoEval, the first comprehensive benchmark for image-based microstructure evolution prediction. We evaluate 14 models, encompassing both domain-specific and general-purpose architectures, across four representative MicroEvo tasks with datasets specifically structured for both short- and long-term assessment. Our multi-faceted evaluation framework goes beyond numerical accuracy and computational cost, incorporating a curated set of structure-preserving metrics to assess physical fidelity. Our extensive evaluations yield several key insights. Notably, we find that modern architectures (e.g., VMamba), not only achieve superior long-term stability and physical fidelity but also operate with an order-of-magnitude greater computational efficiency. The results highlight the necessity of holistic evaluation and identify these modern architectures as a highly promising direction for developing efficient and reliable surrogate models in data-driven materials science. Benchmark (surveying) ; Key (lock) ; Field (mathematics) ; Stability (learning theory) ; Fidelity ; Set (abstract data type) ; Computational model Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Generative Adversarial Networks and Image Synthesis
Authors: Luyang Jia (14038585) | Zhan-Yun Zhang (11664132) | Lin Shen (507259) | Wei-Hai Fang (1268982)
Source: Figshare (article, 2025-07-08)
Field: Materials Science — Materials Chemistry

Machine Learningwith Physically Inspired Descriptorsto Predict Solvation Free Energies of Neutral and Ionic Solutes inAqueous and Nonaqueous Solvents Solvation free energy is a key property for understanding various chemical processes such as ion solvation and phase transfer. The former corresponds to ionic solutes, while the latter is relevant to nonaqueous solvents. However, more attention has been paid to the prediction of the solvation free energies of neutral solutes in aqueous solvents. In the present work, we start from our published research (<i>J. Phys. Chem. Lett.</i> <b>2023</b>, 14, 1877–1884), which was developed for predicting experimental hydration free energies of neutral solutes, and propose extensive machine learning models to predict solvation free energies of neutral and ionic solutes in aqueous and nonaqueous solvents. Two types of descriptors have been developed for solvents and ionic solutes. The former accounts for fundamental physical and chemical properties of solvents, and the latter is rationally designed based on thermodynamic cycles for the ion solvation process. Combined with our previously developed physically inspired descriptors, three machine learning predictors are built, achieving mean absolute errors of 0.44, 1.72, and 1.60 kcal/mol for neutral, anionic, and cationic solutes, respectively. Further analysis of the prediction performance and feature importance suggests the potential to improve prediction accuracy, especially for ionic solutes. Solvation ; Ionic bonding ; Aqueous solution ; Ion ; Ionic liquid ; Activity coefficient Machine Learning in Materials Science ; Free Radicals and Antioxidants ; Computational Drug Discovery Methods
Authors: Xiao Yu (139738) | Li-ping Feng (4576996) | Lingfeng Jia (13579278) | Xin Zhang (35492) | Yunjia Huang (21638909) | Pengfei Liu (136115) | Xitong Wang (9145336) | Yulong Yang (14229374) | Dongming Sun (206061) | Ting Yu (156230)
Source: Figshare (article, 2025-07-02)
Field: Materials Science — Materials Chemistry

High-Performance <i>p</i>‑Type Field-EffectTransistor Based on WS<sub>2</sub> Monolayer Prepared by Liquid-Phase-AssistedSpace-Confined CVD Although some progress has been made in the synthesis of <i>p</i>-type two-dimensional (2D) WS<sub>2</sub>, the hole mobility of WS<sub>2</sub> field-effect transistors (FETs) is still very low, significantly impeding its application in logic complementary circuits. In this study, <i>p</i>-type 2D WS<sub>2</sub> has been successfully prepared by liquid-phase assisted space-confined chemical vapor deposition (LASCVD) method and machine learning (ML) has been employed to optimize the process parameters of the growth of WS<sub>2</sub> nanosheets. Results show that, under the optimal growth conditions, the maximum domain size of WS<sub>2</sub> monolayer reaches 475 μm. Additionally, W vacancies in WS<sub>2</sub> nanosheets have been observed through STEM-HAADF, Raman and PL analysis, contributing to the <i>p</i>-type characteristic of 2D WS<sub>2</sub>. Moreover, <i>p</i>-type high-performance FETs based on the WS<sub>2</sub> monolayer have been fabricated, exhibiting a high hole mobility of 46.87 cm<sup>2</sup> V<sup>–1</sup> s<sup>–1</sup> and an on/off ratio of 10<sup>5</sup>. This study provides a promising direct synthesis method for <i>p</i>-type 2D TMDCs. Monolayer ; Chemical vapor deposition ; Transistor ; Electron mobility ; Field-effect transistor ; Raman spectroscopy 2D Materials and Applications ; Solar-Powered Water Purification Methods ; Nanowire Synthesis and Applications
Authors: Shuangnan Wang | Shuang Du | Xingyue Liu | Lei Wang | Ya‐Guang Sun | Ying Zhang | Mingchang Zhu | Shuangyan Wu
Source: Inorganic Chemistry Communications (article, 2025-11-29)
Field: Chemistry — Inorganic Chemistry

Luminescent MOFs with defect-induced abundant recognition sites and macropores for highly sensitive detection of carcinoid biomarker Luminescence ; Detection limit ; Analyte ; Macropore ; Biomarker ; Porosity Metal-Organic Frameworks: Synthesis and Applications ; Carbon and Quantum Dots Applications ; Nanoplatforms for cancer theranostics
Authors: Wei Wang | Yao Zhang | Hongyang Zheng | Shuju Fang | Xuewen Tan | Jianjun Wang | Yi Tang
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Synergistic Random Forest-Genetic Algorithm: A New Strategy for Preparation of Europium-Functionalized Zr-Mof Fluorescent Ratio Probe Europium ; Fluorescence ; Genetic algorithm ; Computer science ; Nanotechnology ; Algorithm ; Chemistry ; Materials science ; Optoelectronics ; Machine learning ; Luminescence ; Physics ; Optics Lanthanide and Transition Metal Complexes ; Medical Imaging Techniques and Applications ; Radioactive element chemistry and processing
Authors: Ting Wang | Shushan Mo | Zi‐Yi Du | Shujun Cai | Chao Deng | Feng Wang | Q. Peng | Hongxiang Li | Jianhua Chen
Source: Chemical Engineering Journal (article, 2025-08-28)
Field: Materials Science — Polymers and Plastics

Crystallinity and energy level modulation in organic mixed ion-electron conductors: facilitating high-performance OECTs and diverse applications Crystallinity ; Modulation (music) ; Ion ; Electrical conductor ; Electron ; Materials science ; Energy (signal processing) ; Optoelectronics ; High energy ; Chemical engineering ; Engineering physics ; Nanotechnology ; Chemistry ; Physics ; Engineering ; Composite material ; Organic chemistry ; Acoustics ; Quantum mechanics Conducting polymers and applications ; Organic Electronics and Photovoltaics ; Analytical Chemistry and Sensors
Authors: Ying Cao (170160) | Yuchen Zhou (1601536) | Xiaodan Liu (849235) | Dazhou Li (12075431)
Source: Figshare (article, 2025-10-31)
Field: Materials Science — Materials Chemistry

Geometry-DrivenAttention Model with 3D MolecularFeatures for Multi-Property Prediction of OLED Materials As Organic Light-Emitting Diode (OLED) technology advances in applications such as high-end displays, medical devices, and VR/AR systems, the development of high-performance materials that improve energy efficiency and support environmental sustainability has become increasingly important. Traditional methods like Density Functional Theory (DFT) are accurate but computationally expensive, motivating the need for efficient prediction tools. This paper presents 3D-SAT-OLED, a deep learning model based on three-dimensional self-attention to predict key OLED performance parameters. The model integrates 3D molecular structures with a Gaussian kernel-based feature extractor (GPE) and spatial position encoding, while Dynamic Tanh (DyT) and Swish-Gated Linear Unit (SwiGLU) activations enhance stability and nonlinear modeling. Experiments show that 3D-SAT-OLED achieves state-of-the-art performance on QM9 and OLED QM properties data sets, significantly outperforming existing models across <i>R</i><sup>2</sup>, MAE, and other metrics, while demonstrating strong generalization and predictive accuracy. OLED ; Nonlinear system ; Generalization ; Stability (learning theory) ; Feature (linguistics) ; Key (lock) ; Scalability ; Position (finance) Machine Learning in Materials Science ; Green IT and Sustainability ; Organic Light-Emitting Diodes Research
Authors: Le Thi Ngoc Trang | Nguyen Huynh Duy Khang | Nguyễn Thị Trúc Linh | Nguyễn Thành Đạt | Huỳnh Đình Chương | Hoàng Đức Tâm
Source: Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment (article, 2025-04-21)
Field: Materials Science — Materials Chemistry

Determination of the mass attenuation coefficient over a broad energy range based on the Rayleigh to Compton scattering ratio: Curve-fitting and machine learning methods Attenuation ; Rayleigh scattering ; Mass attenuation coefficient ; Compton scattering ; Range (aeronautics) ; Attenuation coefficient ; Physics ; Computational physics ; Curve fitting ; Energy (signal processing) ; Scattering ; Optics ; Materials science ; Mathematics ; Statistics Radiation Shielding Materials Analysis ; Advanced X-ray and CT Imaging ; Radioactivity and Radon Measurements
Authors: Weiyou Yang | Pengcheng Li | Nengrong Tu | Yang Yang | Junxiang Li | Huilin Hou | Dongjiang Yang
Source: Research Square (preprint, 2025-11-21)
Field: Materials Science — Materials Chemistry

Plasmon Polarization Induced Multi-Field Coupling in Molybdenum-Oxide Based Heterojunctions to Efficiently Hydrolyze Ammonia Borane Delocalized electron ; Ammonia borane ; Plasmon ; Electric field ; Electron ; Antibonding molecular orbital ; Polarization (electrochemistry) ; Atomic orbital Hydrogen Storage and Materials ; Ammonia Synthesis and Nitrogen Reduction ; Porphyrin Metabolism and Disorders