jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Екатерина Марченко | Maria G. Khrenova | Vadim Korolev | Eugene A. Goodilin | А. В. Тарасов
Source: Mendeleev Communications (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Topological representation of layered hybrid lead halides for machine learning using universal clusters Prediction of band gaps in layered hybrid halide compounds promising for photovoltaic and optoelectronic applications was performed using a machine learning approach. In order to facilitate the discovery and design of new hybrid halide materials with tailored electronic properties, machine learning models were enhanced with invariant topological representations of these materials using the atom-specific persistent homology method. Chemistry ; Halide ; Representation (politics) ; Lead (geology) ; Topology (electrical circuits) ; Organic chemistry ; Combinatorics Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Advanced Memory and Neural Computing
Authors: Mohamed Barhoumi | N. Zeiri | Mohamed E. El Sayed | Ahmed Samir | M. Saïd
Source: Materials Science in Semiconductor Processing (article, 2025-10-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine learning-driven exploration of optoelectronic properties in 2D SrFBr: First-principles insights and precise absorption modeling Density functional theory ; Absorption (acoustics) ; Artificial neural network ; Band gap ; Ionic bonding ; Work (physics) ; Feature selection ; Lattice (music) Ga2O3 and related materials ; 2D Materials and Applications ; Electronic and Structural Properties of Oxides
Authors: Yong Niu | Bing-tao Wang | Yaoqi Wang | Yanchun Zhu
Source: International Journal of Hydrogen Energy (article, 2025-06-12)
Field: Materials Science — Materials Chemistry

Effect of hydrogen on time-temperature-transformation behavior in TC17 titanium alloy Titanium alloy ; Materials science ; Alloy ; Transformation (genetics) ; Hydrogen ; Metallurgy ; Titanium ; Thermodynamics ; Chemistry ; Physics Titanium Alloys Microstructure and Properties ; Hydrogen embrittlement and corrosion behaviors in metals ; Microstructure and Mechanical Properties of Steels
Authors: Kexin Wang | Haishan Yu | Xiangwei Lu | Kai Chen
Source: The Journal of Physical Chemistry A (article, 2025-07-30)
Field: Materials Science — Materials Chemistry

Machine Learning-Driven Deconvolution of Mixed X-ray Absorption Spectra X-ray absorption spectroscopy (XAS) plays a crucial role in determining the oxidation states, local atomic structures, and electronic configurations of materials. However, the interpretation of complex spectra with multiple ion sites and varying local chemical environments can be labor-intensive and requires specialized expertise, presenting challenges for researchers. To address these issues, this study employs machine learning (ML) techniques to analyze mixed XAS data, with a particular focus on the cobalt (Co) element. We constructed a simulated data set of L-edge spectra, incorporating various combinations and energy shifts produced from multiplet calculations. Through a systematic evaluation of dimensionality reduction and ML methodologies, we identified a combination that achieved both high accuracy and efficiency in classification tasks. Our approach involved isolating individual components from the mixed spectra and implementing an automatic fitting algorithm to extract critical parameters, such as component proportions and Gaussian broadening. The fitting results closely aligned with the input spectra, thereby validating our methodology. This innovative process facilitates the determination of valence states, local crystal field parameters, and composition ratios, significantly enhancing the efficiency of spectral analysis. Deconvolution ; Absorption (acoustics) ; Spectral line ; Absorption spectroscopy ; Computer science ; Materials science ; Artificial intelligence ; Physics ; Optics ; Astronomy Machine Learning in Materials Science ; Advanced X-ray and CT Imaging ; X-ray Diffraction in Crystallography
Authors: Yingying Wei | Xingguo Mo | Shanshan Yu | Saisai Wu | He Chen | Yuanyuan Qin | Zhikang Zeng
Source: Agriculture (article, 2025-06-30)
Field: Chemistry — Analytical Chemistry

An Optimized Multi-Stage Framework for Soil Organic Carbon Estimation in Citrus Orchards Based on FTIR Spectroscopy and Hybrid Machine Learning Integration Soil organic carbon (SOC) is a critical indicator of soil health and carbon sequestration potential. Accurate, efficient, and scalable SOC estimation is essential for sustainable orchard management and climate-resilient agriculture. However, traditional visible–near-infrared (Vis–NIR) spectroscopy often suffers from limited chemical specificity and weak adaptability in heterogeneous soil environments. To overcome these limitations, this study develops a five-stage modeling framework that systematically integrates Fourier Transform Infrared (FTIR) spectroscopy with hybrid machine learning techniques for non-destructive SOC prediction in citrus orchard soils. The proposed framework includes (1) FTIR spectral acquisition; (2) a comparative evaluation of nine spectral preprocessing techniques; (3) dimensionality reduction via three representative feature selection algorithms, namely the Successive Projections Algorithm (SPA), Competitive Adaptive Reweighted Sampling (CARS), and Principal Component Analysis (PCA); (4) regression modeling using six machine learning algorithms, namely the Random Forest (RF), Support Vector Regression (SVR), Gray Wolf Optimized SVR (SVR-GWO), Partial Least Squares Regression (PLSR), Principal Component Regression (PCR), and the Back-propagation Neural Network (BPNN); and (5) comprehensive performance assessments and the identification of the optimal modeling pathway. The results showed that second-derivative (SD) preprocessing significantly enhanced the spectral signal-to-noise ratio. Among feature selection methods, the SPA reduced over 300 spectral bands to 10 informative wavelengths, enabling efficient modeling with minimal information loss. The SD + SPA + RF pipeline achieved the highest prediction performance (R2 = 0.84, RMSE = 4.67 g/kg, and RPD = 2.51), outperforming the PLSR and BPNN models. This study presents a reproducible and scalable FTIR-based modeling strategy for SOC estimation in orchard soils. Its adaptive preprocessing, effective variable selection, and ensemble learning integration offer a robust solution for real-time, cost-effective, and transferable carbon monitoring, advancing precision soil sensing in orchard ecosystems. Fourier transform infrared spectroscopy ; Spectroscopy ; Carbon fibers ; Soil carbon ; Environmental science ; Stage (stratigraphy) ; Agricultural engineering ; Materials science ; Biological system ; Agronomy ; Agroforestry ; Chemistry ; Computer science ; Soil science ; Soil water ; Biology ; Physics ; Engineering ; Chemical engineering ; Algorithm ; Composite number Spectroscopy and Chemometric Analyses
Authors: Souvik Mitra | Clara Zens | Stephan Kupfer | Andreas Heuer | Diddo Diddens
Source: The Journal of Physical Chemistry C (article, 2025-08-06)
Field: Materials Science — Materials Chemistry

Machine Learning Models for Predicting Electronic Coupling in TEMPO/TEMPO <sup>+</sup> Systems Organic radical batteries (ORBs) based on the TEMPO (2,2,6,6-tetramethylpiperidin-1-yl oxyl) radical have drawn significant attention, owing to their unique redox properties. A key factor influencing ORB’s redox properties, i.e., the kinetics of the electron transfer between the TEMPO–TEMPO+ pairs, is the communication between the underlying redox-active states as given by the electronic coupling. However, due to the complex structure, predicting accurate electronic couplings for these pairs is computationally expensive and challenging. In this study, we introduce a machine learning (ML) workflow to predict the electronic coupling for TEMPO–TEMPO+ pairs simply by their specific geometric orientations. For the ML models, a data set was generated through time-dependent density functional theory calculations coupled with the Generalized Mulliken Hush method to assess energies, (transition-)dipole moment, and couplings for specific TEMPO–TEMPO+ configurations obtained from classical molecular dynamics simulations that mimic a realistic electrolyte environment. Our results demonstrate that, among the three ML models─linear regression, kernel ridge regression (KRR), and random forest─the KRR model, with its kernel-based approach, most effectively handles the correlated orientation-based descriptors. Moreover, our SHapley Additive exPlanations (SHAP)-based feature importance analysis indicates that multiple orientation factors jointly influence electronic coupling, rather than any single distance or angle dominating, with each parameter’s impact strongly contingent on the values of the others which is in agreement with previous studies computational by the consortium. Coupling (piping) ; Computer science ; Artificial intelligence ; Engineering Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Catalysis and Oxidation Reactions
Authors: Yaoping Lu | Xing Yang | Ben Niu | Titao Li | Zhenjie Zheng | Lemin Jia | Duanyang Chen | Hongji Qi | Kelvin H. L. Zhang | Minmin Zhu | Haizhong Zhang | Xiaoqiang Lu
Source: Advanced Functional Materials (article, 2025-10-21)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Achievement of High‐Quality Gallium Oxide Epitaxial Growth via Machine Learning Abstract The development of gallium oxide (Ga 2 O 3 )‐based electronics has been hampered by the persistent challenge of obtaining high‐quality epilayers with device‐grade performance metrics. While metal‐organic chemical vapor deposition has proven effective in producing device‐grade films, the realization of ideal step‐flow epitaxial growth with high deposition rates in Ga 2 O 3 remains a significant scientific challenge. Here, a machine learning (ML)‐guided approach is presented to overcome conventional epitaxial limitations, which are historically constrained by narrow process windows and empirical growth paradigms. Specifically, our ML‐guided approach successfully achieves perfect step‐flow epitaxy at 1.2 µm h −1 on universal β‐Ga 2 O 3 substrates, eliminating traditional requirements for large mis‐cut angle substrates and low growth rates. This breakthrough resolves the long‐standing trade‐off between crystal quality and growth efficiency. The resultant epilayers exhibit atomically smooth surfaces with record‐low roughness (0.121 nm) featuring 6 Å‐high single‐atomic‐layer steps, coupled with exceptional electrical properties. The technological viability is further validated through Schottky barrier photodiodes with excellent solar‐blind detection performance: ultra‐fast decay time (3.28 µs), ultra‐high photo‐to‐dark current ratio (PDCR &gt; 10 5 ), and ultralow dark current density (6.2 × 10 −9 A cm −2 ). This work not only establishes ML as a revolutionary accelerator for β‐Ga 2 O 3 development but also provides a transformative methodology for next‐generation semiconductor manufacturing. Epitaxy ; Chemical vapor deposition ; Schottky barrier ; Crystal (programming language) ; Graphene ; Surface roughness ; Semiconductor ; Schottky diode ; Sapphire Ga2O3 and related materials ; Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides
Authors: Shangjun Zhang | William N. Robinson | Kristen A. Fichthorn
Source: The Journal of Chemical Physics (article, 2025-09-03)
Field: Materials Science — Materials Chemistry

Predicting the shapes of Au55 and Au147: Force fields vs density-functional theory Gold nanocrystals have been widely used in sensing and medicine, where nanocrystal shape can profoundly influence properties. To describe and predict the structure and properties of Au nanomaterials, first-principles studies are the most accurate. Force fields can provide effective surrogates for first-principles calculations, and in the case of Au, many such force fields exist. To clarify the current state of Au structure prediction using force fields, we used parallel tempering molecular dynamics simulations to explore the temperature-dependent shape distributions of Au147 and Au55 from three different Embedded-Atom Method (EAM) force fields. We used machine learning to classify nanoparticle shapes and observe a wide variation in the temperature-dependent shape distributions among the various force fields, accompanied by a wide range of melting temperatures. We also compared the EAM structures to those from literature studies employing an Artificial Neural Network (ANN), a Gaussian process regression machine-learning force field, density functional tight binding, the Gupta potential, and Density Functional Theory (DFT). We re-optimized the lowest-energy structures from each force field/study using DFT and found that the lowest-energy structures for both Au147 and Au55 are amorphous. These structures were predicted by the ANN and DFT. Most of the 30 structures with minimum energy for each size are either hollow or partially to totally disordered and not icosahedral, as the sizes of these nanocrystals might imply. Force field (fiction) ; Density functional theory ; Icosahedral symmetry ; Materials science ; Statistical physics ; Molecular dynamics ; Atom (system on chip) ; Parallel tempering ; Potential energy ; Nanocrystal ; Nanotechnology ; Physics ; Computer science ; Classical mechanics ; Chemistry ; Artificial intelligence ; Quantum mechanics ; Crystallography Machine Learning in Materials Science ; Nanocluster Synthesis and Applications ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Yoshiyasu Takefuji
Source: Materials Today Communications (article, 2025-11-23)
Field: Materials Science — Materials Chemistry

Beyond prediction accuracy: Assessing SHAP-driven interpretability for refractory high entropy alloys hardness This study critically examines feature selection methods in materials informatics, addressing concerns about reliability in supervised machine learning applications. Using an alloy oxidation temperature dataset with 886 instances, we compare supervised models (XGBoost, Random Forest), unsupervised techniques (Feature Agglomeration, Highly Variable Gene Selection), and Spearman correlation for predictive accuracy and feature importance stability. Results reveal that Feature Agglomeration achieves superior performance (R²=0.6345) with perfect feature ranking stability, while supervised models show concerning instability despite reasonable accuracy. XGBoost particularly underperforms (R²=0.3588) with dramatic feature ranking inconsistency. Notably, experimental parameters were consistently identified as crucial by better-performing methods, aligning with metallurgical principles. This work demonstrates that high prediction accuracy doesn't guarantee reliable feature importance identification and recommends using multiple complementary approaches with stability testing for robust materials property-structure relationship discovery, challenging current practices in materials informatics. Interpretability ; Feature selection ; Feature (linguistics) ; Pattern recognition (psychology) ; Stability (learning theory) ; Ranking (information retrieval) ; Entropy (arrow of time) ; Reliability (semiconductor) Machine Learning in Materials Science ; High Entropy Alloys Studies ; Additive Manufacturing Materials and Processes
Authors: Tingyu Liu
Source: Manufacturing Review (article, 2025-01-01)
Field: Materials Science — Ceramics and Composites

Process optimization of SiC/(Mo, W)Si<sub>2</sub> nanocomposites preparation based on BOA-SSA Silicon carbide/(molybdenum, tungsten) disilicide nanocomposites have important application value in aerospace, high-temperature structural materials and other fields, but the current preparation process still needs to be optimized. In order to prepare high-performance silicon carbide/(molybdenum, tungsten) disilicide nanocomposites, a three-layer structure backpropagation neural network model is constructed, and the specific construction process and training structure are analyzed. Sparrow search algorithm is used for model optimization. At the same time, in response to the problem easily getting stuck in local optima, the butterfly optimization algorithm is proposed to optimize it and solve the optimal preparation process. The results indicated that the error between the predicted and experimental values of the bending strength of the prepared material using the backpropagation neural network model was less than 2%, and the minimum relative error was only 0.15%. Meanwhile, the best process combination obtained by the Butterfly Optimization Algorithm-Sparrow Search achieved a bending strength of up to 752 MPa, while the traditional Sparrow Search only achieved a bending strength of 662 MPa. The material preparation process optimization method has significant advantages and can effectively improve the bending strength of the prepared silicon carbide/(molybdenum, tungsten) disilicide nanocomposites, providing an effective solution for solving multidimensional nonlinear optimization problems. Materials science ; Nanocomposite ; Process (computing) ; Process optimization ; Process engineering ; Nanotechnology ; Chemical engineering ; Computer science ; Engineering ; Operating system Advanced ceramic materials synthesis ; Advanced materials and composites ; Aluminum Alloys Composites Properties
Authors: Chunyan Zhou | Xiaorui Li | X. Zhang | Jiandong Mao | Hu Zhao | Bai Zhang
Source: International Journal of Optics (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Hyperspectral and 1D‐CNN‐Based Early Monitoring Study of Mite Infestation on <i>Lycium barbarum</i> Sprouts Lycium barbarum sprouts, as a green food with significant development potential, are vulnerable to pests and diseases, particularly mite infestation that can cause leaf distortion and deformation, severely impacting yield and quality. Hyperspectral technology, with its exceptional spectral and spatial resolution, has demonstrated great potential in the monitoring of crop pests and diseases. However, its high data dimensionality and feature similarity pose challenges for analysis. In this study, hyperspectral data from Lycium barbarum sprout leaves were collected to establish a dataset representing four levels of pest severity. KNN, RF, SVM, and 1D‐CNN classification models were then employed for modeling and predicting pest levels. Experimental results indicated that the 1D‐CNN model in deep learning achieved the highest classification accuracy, reaching 96.08%. Traditional machine learning algorithms, such as random forest (RF), also exhibited high accuracy, with 94.12%. In particular, when classifying severe and moderate pest infestations, the 1D‐CNN demonstrates superior precision, recall, and F1‐score. The study further revealed that hyperspectral technology can effectively detect changes in leaf physiological structure, offering a theoretical foundation for early pest detection. This study presents an efficient, nondestructive technical method for the early detection of pests and diseases in Lycium barbarum sprouts. Additionally, it allows for the determination of varying levels of pest infestation. This will guide farmers in adopting effective control measures, reducing the reliance on chemical pesticides, and enhancing the yield and quality of Lycium barbarum sprouts. The research methodology of this paper provides a scientific foundation for precision agriculture management. Lycium ; Hyperspectral imaging ; Mite ; Infestation ; Biology ; Horticulture ; Computer science ; Botany ; Artificial intelligence ; Medicine ; Pathology Spectroscopy and Chemometric Analyses ; Date Palm Research Studies ; Smart Agriculture and AI
Authors: Chunling Xie | Jinmei Chen | Xiufeng Xiao
Source: Sensors and Actuators B Chemical (article, 2025-10-11)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Application of a TNTs/Ag nanoparticle-based SERS platform for rapid recognition of breast cancer cells Breast cancer ; Linear discriminant analysis ; Substrate (aquarium) ; Human breast ; Principal component analysis ; Raman spectroscopy ; Cell ; Nanoparticle Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Photoacoustic and Ultrasonic Imaging
Authors: Stephen G. Bierschenk | Michael F. Becker | Desiderio Kovar
Source: Journal of Thermal Spray Technology (article, 2025-04-11)
Field: Materials Science — Materials Chemistry

Simulating the Effects of the Native Oxide Layer During Kinetic Spraying of Tantalum Particles Using a Deep Learning Interatomic Potential Tantalum ; Materials science ; Kinetic energy ; Layer (electronics) ; Oxide ; Interatomic potential ; Metallurgy ; Gas dynamic cold spray ; Chemical physics ; Composite material ; Chemical engineering ; Nanotechnology ; Molecular dynamics ; Coating ; Computational chemistry ; Classical mechanics ; Chemistry Nuclear Materials and Properties ; Catalytic Processes in Materials Science ; Machine Learning in Materials Science
Authors: Anuj Kumar Ray | Sandeep Nagar | Girish Varma | U. Deva Priyakumar | Ankan Paul
Source: Physical Chemistry Chemical Physics (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Leveraging high-spin DFT features for prediction of spin state gaps in 3d transition metal complexes Determining spin state energy gaps (SSE) of 3d transition metal complexes (TMCs) is a major challenge in theoretical chemistry, as high-level quantum methods, though reliable, are computationally impractical for large-scale studies. This work explores a machine learning (ML)-based approach to predict DFT adiabatic SSE gaps using descriptors derived from a single high-spin DFT calculation. This approach is adopted to eliminate the differential treatment of electronic correlation between high-spin and low-spin structures. Our descriptors aim to incorporate the knowledge of crystal field theory into the ML model. They include atomic energy levels of bare metal ions, natural charges of ligating atoms, d-orbital molecular orbital eigenvalues derived from an high spin calculation, HOMO-LUMO gaps of free ligands, and simple identity-based features. We train ML models on 1434 SSE values spanning 934 complexes and demonstrate their transferability to more challenging complexes having bidentate π-bonding ligands despite being trained on simpler Werner-type monodentate complexes. We achieved a minimum MAE of 4.0 kcal mol<sup>-1</sup> on the monodentate test set, and maintained a comparable MAE of 6.6 kcal mol<sup>-1</sup> in the transferability assessment. This approach bypasses the need for multi-reference low-spin optimizations while retaining predictive accuracy, offering a cost-effective strategy for SSE estimation in transition metal chemistry. We hope the insights covered in this study will contribute to the development of additional electronic structure-based descriptors for SSE predictions. Transferability ; Denticity ; Spin (aerodynamics) ; Transition metal ; Eigenvalues and eigenvectors ; Density functional theory ; Force field (fiction) ; Perturbation theory (quantum mechanics) ; Ground state Machine Learning in Materials Science ; Advanced NMR Techniques and Applications ; Magnetism in coordination complexes