jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ann Bragg | Fengdeng Liu | Zhifei Yang | Dong‐Hwan Kim | Nitzan Hirshberg | M. Garber | Brayden Lukaskawcez | L. F. Thompson | Scott Macdonald | Hayden Binger | Devon Uram | Ashley Bucsek | Bharat Jalan | Alexander McLeod
Source: Nano Letters (article, 2025-12-18)
Field: Materials Science — Materials Chemistry

Machine-Learning-Enabled Discovery of Coexisting Phases through Nanospectroscopy of a Wide-Bandgap Semiconductor Wide bandgap semiconductors with high room temperature mobilities are promising materials for high-power electronics. Stannate films provide wide bandgaps and optical transparency, although electron-phonon scattering can limit mobilities. In SrSnO<sub>3</sub>, epitaxial strain engineering stabilizes a high-mobility tetragonal phase at room temperature, resulting in a 3-fold increase in electron mobility among doped films. However, strain relaxation in thicker films leads to nanotextured coexistence of tetragonal and orthorhombic phases with unclear implications for optoelectronic performance. The observed nanoscale phase coexistence demands nanospectroscopy to supply spatial resolution beyond conventional, diffraction-limited microscopy. With nanoinfrared spectroscopy, we provide a comprehensive analysis of phase coexistence in SrSnO<sub>3</sub> over a broad energy range, distinguishing inhomogeneous phonon and plasma responses arising from structural and electronic domains. We establish Nanoscale Imaging and Spectroscopy with Machine-learning Assistance (NISMA) to map nanotextured phases and quantify their distinct optical responses through a robust quantitative analysis, which can be applied to a broad array of complex oxide materials. Tetragonal crystal system ; Semiconductor ; Nanoscopic scale ; Band gap ; Phonon ; Phase (matter) ; Wide-bandgap semiconductor ; Relaxation (psychology) ; Strain engineering Electronic and Structural Properties of Oxides ; Machine Learning in Materials Science ; Copper-based nanomaterials and applications
Authors: Ying Li | Yi Lin | Zhipeng Mai | Qian Deng | Zhenyu Ju | Bing Cao | Weiguo Li
Source: Materials Today Communications (article, 2025-08-06)
Field: Materials Science — Materials Chemistry

Theory-guided machine learning for strength prediction of polymers at different temperatures and strain rates with physical information Materials science ; Strain (injury) ; Polymer ; Machine learning ; Composite material ; Artificial intelligence ; Computer science Machine Learning in Materials Science ; Advanced machining processes and optimization ; Injection Molding Process and Properties
Authors: Abhishek S. Bhesania | Timothy W. Sirk | B.C. Rinderspacher | M. Laviola U. Campora G. Benvenuto | Munetaka Kubota | Sanjib C. Chowdhury | John W. Gillespie
Source: Molecular Simulation (article, 2025-03-04)
Field: Materials Science — Materials Chemistry

Optimisation of silane chemistry with machine learning and molecular dynamics Silane ; Molecular dynamics ; Chemistry ; Nanotechnology ; Computational chemistry ; Computer science ; Materials science ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Jing Luo | Yejun Gu | Yanfei Wang | Xiaolong Ma | Jaafar A. El‐Awady
Source: Acta Materialia (article, 2025-10-10)
Field: Materials Science — Materials Chemistry

Uncertainty-aware machine learning framework for predicting dislocation plasticity and stress–strain response in metallic alloys, Part I : FCC systems Dislocation ; Plasticity ; Homogenization (climate) ; Crystallite ; Probability density function ; Phenomenological model ; Uncertainty quantification ; Crystal plasticity Microstructure and mechanical properties ; Microstructure and Mechanical Properties of Steels ; Advanced Materials Characterization Techniques
Authors: Xuemin Zhang | Wenjun Xiao | Xun Zhou | Gang Wang | Degui Wang | Mingqiang Liu | Yan Wu | Zhen Wang | Abuduwayiti Aierken | Xuan Chen | Changsong Gao | Jinshun Bi | Wentao Liang | Wenting Li | Yao Chen | Yanghua Luo | Jiaxing Liu | Xuefei Liu
Source: International Journal of Hydrogen Energy (article, 2025-12-08)
Field: Materials Science — Materials Chemistry

Dual main-group metal doping in covalent organic polymers boosts hydrogen evolution via interlayer coupling engineering: A machine learning aided density functional theory (DFT) investigation Density functional theory ; Doping ; Catalysis ; Covalent bond ; Hydrogen ; Metal ; Redistribution (election) ; Polymer ; Heterojunction Covalent Organic Framework Applications ; Electrocatalysts for Energy Conversion ; CO2 Reduction Techniques and Catalysts
Authors: Katia Santos | Humberto Gracher Riella | Cíntia Soares | Natan Padoin
Source: ChemRxiv (preprint, 2025-03-10)
Field: Materials Science — Polymers and Plastics

OptiDots_v1.0: A Comprehensive Database for Machine Learning-Driven Optimization of Green Synthesis Carbon Dots Understanding the relationships between the physicochemical properties of Carbon Dots (CDs) and synthesis parameters is crucial for optimizing their use and accelerating the development of CDs. However, this task is complex due to the diversity of materials, heterogeneity of published data, and limited sampling in individual studies. This work addresses this gap by introducing OptiDots_v1.0, a comprehensive database designed to support the scientific community in optimizing the synthesis of CDs thought machine learning (ML) or by integrating other analytical techniques. From a comprehensive set of 157 publications on the synthesis of CDs derived from green precursors and hydrothermal synthesis, we meticulously obtained data samples related to characteristics such as particle size, quantum yield, synthesis yield, maximum emission and excitation, fluorescence, elemental composition, and applications of 199 CDs, as well as experimental conditions of time, temperature, and precursor type. As a case study, we applied exploratory data analysis and ML techniques to OptiDots_v1.0 to demonstrate its potential in predictive modeling and experimental design. We show the relationships between continuous variables such as synthesis yield and nitrogen contente, as well as particle size and photolumiscence, and with machine learning methods, it was possible to make inferences about emission wavelenght. This approach, integrating quantitative and qualitative data, provides a roadmap to investigate the data on the properties of CDs in the literature and suggests that meta-analysis can help develop methods to predict and optimize the applications of these nanomaterials. Computer science ; Carbon fibers ; Artificial intelligence ; Computer architecture ; Database ; Algorithm Conducting polymers and applications ; Electrochemical sensors and biosensors
Authors: Yijun Wang | Gao Shu-qing | Junxian Wu | Xiang Li | Yaqiu Zhao | Zhou Li | Yutong Hua | Liping Kang | Zi‐Dong Qiu
Source: npj Science of Food (article, 2025-11-29)
Field: Chemistry — Spectroscopy

Non-destructive food authentication by vapor-assisted desorption chemical ionization mass spectrometry paired with machine learning The in-situ, rapid and non-destructive identification of complex food samples is a long-standing core technical challenge in food chemistry. Herein, vapor assisted desorption chemical ionization mass spectrometry (VADCI-MS) device was independently constructed, which facilitates rapid and non-destructive analysis of complex foods with diverse morphologies. Additionally, an integrated strategy that combines MS fingerprinting, feature selection, and machine learning classification models was established and is particularly effective for complex foods that are challenging to differentiate using conventional methods. As a paradigm, VADCI-MS was employed to rapidly (<1.0 min/sample) and non-destructively obtain the MS fingerprints of 101 batches of Zanthoxylum bungeanum (ZB) samples and 415 feature peaks were selected. Further, using machine learning classification models, a high accuracy differentiation of ZB from different origins was achieved (accuracy rate = 96.88%) and was verified to be substantially improved over traditional GC-MS. Conclusively, VADCI-MS coupled with machine learning holds significant potential for accurate and non-destructive identification of foods. Mass spectrometry ; Pattern recognition (psychology) ; Identification (biology) ; Feature (linguistics) ; Desorption ; Authentication (law) Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Traditional Chinese Medicine Analysis
Authors: Ling Wu | Ludovic Noels
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Convolutional Neural Network-Based Mapping of Material Micro-Structures to Deep Material Networks for Non-Linear Mechanical Response Prediction Convolutional neural network ; Computer science ; Artificial neural network ; Artificial intelligence Machine Learning in Materials Science ; Industrial Vision Systems and Defect Detection ; Ultrasonics and Acoustic Wave Propagation
Authors: Abdel-baset H. Mekky
Source: Proceedings of the Institution of Mechanical Engineers Part E Journal of Process Mechanical Engineering (article, 2025-02-05)
Field: Materials Science — Materials Chemistry

Machine learning insights into the strain-stress behavior of nitinol alloys at low temperatures: Utilizing the orange data mining tool This study entails a new technique, based on machine learning algorithms, for predicting the strain-stress behavior of Nitinol alloys. The study utilizes Orange data mining software to determine if algorithms like Linear Regression, Random Forest, k-nearest neighbors, and decision tree are related to the effectiveness. The nitinol-based alloy, which has both shape memory and superelasticity, is used in a wide range of biomedical devices, aerospace constructions, and automated apparatus. Temperature variation is a main factor affecting the Nitinol behavior. It is therefore required to carry out an in-depth analysis of strain-stress patterns. Employing machine learning algorithms along with data mining tools allows the exact prediction of the Nitinol alloy performance under any given condition. The research is purposed to establish the relationship between temperature and mechanical strength of Nitinol by analyzing strain-stress curves that were obtained from tensile tests carried out at different temperatures. Looking at overall results, kNN has the highest accuracy of the models upon comparing MSE and RMSE, and has higher R² and, therefore stands as the model of high reliability. Results show that machine learning algorithms successfully forecast the mechanical responses of Nitinol under temperature variations, which assists in determining their formability properties and hence, advancing Nitinol applications in various fields. This research demonstrates machine learning uses in developing materials science and engineering knowledge by showing the ability to predict Nitinol alloy behavior, which includes strain-stress characteristics at low temperatures. Furthermore, the research's significance lies in addressing ethical considerations and practical issues, such as the necessity of implementing an organized approach to achieve environmental benefits. Orange (colour) ; Materials science ; Stress–strain curve ; Strain (injury) ; Stress (linguistics) ; Metallurgy ; Composite material ; Chemistry ; Medicine ; Food science ; Deformation (meteorology) Shape Memory Alloy Transformations ; Titanium Alloys Microstructure and Properties ; Machine Learning in Materials Science
Authors: Mohammad Javad Hooshmand | Mohammad Abu Hasan Khondoker
Source: 3D Printing and Additive Manufacturing (article, 2025-02-27)
Field: Materials Science — Polymers and Plastics

Designing UV-Curable Resin-Made Polymeric Foams with Lattice Structures for Desired Stiffness via Machine Learning Several industries extensively utilize polymeric foams due to their exceptional characteristics. The mechanical properties of a foam structure play a significant role in compression, so it is necessary to optimize foam deformation to achieve the desired outcome. The cell structures of foams are created randomly, but the issue has been resolved by lattice structures. Compared with traditional foams, lattice structures can enhance mechanical properties and facilitate the development of novel applications. Despite extensive research on lattice structures in both rigid and soft materials, there is a notable lack of predictive modeling specifically for soft thermoset Ultraviolet (UV)-curable lattice structures. This study employs additive manufacturing (AM) and machine learning (ML) to address this discrepancy. In this work, 93 lattice designs were produced using AM and evaluated for their geometric structure through compression tests utilizing ML techniques, specifically artificial neural network (ANN) and random forest (RF). The process involves the preparation of data, training of ML models, and evaluation. The RF model surpasses ANN model and is the most effective at predicting lattice geometries using force, strain, and lattice-type inputs in a graphical user interface. Hence, this study improves ML comprehension and utilization in the design of lattice structures to optimize the performance of soft materials across diverse applications. Materials science ; Composite material ; Stiffness ; Lattice (music) ; Acoustics ; Physics Polymer composites and self-healing ; Cellular and Composite Structures ; Polymer Foaming and Composites
Authors: Fu‐Ming Pan | Guangyao Li | Jiaqiu Xu | Zuhao Wang | Zepeng Fan | Zhang Ai | Dawei Wang
Source: Chemical Engineering Journal (article, 2025-11-20)
Field: Materials Science — Materials Chemistry

CeNEP: A general-purpose machine learning interatomic potential for cementitious materials Cementitious ; Benchmark (surveying) ; Molecular dynamics ; Interatomic potential ; Graphics ; Tetrahedron ; Principal component analysis ; Generalization Machine Learning in Materials Science ; Concrete and Cement Materials Research ; Electrocatalysts for Energy Conversion
Authors: Svenja Both | Andrey D. Poletayev | Timo Danner | Arnulf Latz | M. Saiful Islam
Source: ACS Applied Materials & Interfaces (article, 2025-09-25)
Field: Materials Science — Materials Chemistry

Probing Surface Degradation Pathways of Charged Nickel-Oxide Cathode Materials Using Machine-Learning Interatomic Potentials While nickel-based layered oxide cathodes offer promising energy and power densities in lithium-ion batteries, they suffer from instability when fully delithiated upon charge. Ex situ studies often report a structural degradation of the charged cathode materials, but the precise mechanism is still poorly understood on the atomic scale. In this work, we combine high-level ab initio calculations with molecular dynamics using machine-learning interatomic potentials to study structural degradation of fully delithiated LiNiO<sub>2</sub> surfaces at the top of charge. We find a previously unreported, stable reconstruction of the (012) facet with more facile oxygen loss compared to the pristine surfaces. The oxygen vacancy formation energy closely corresponds to the experimental decomposition temperatures of charged cathodes. Furthermore, we use molecular dynamics simulations to sample Ni ion migration into alkali-layer sites that is a kinetically plausible initiation step for surface degradation toward thermodynamically stable products. Molecular dynamics ; Cathode ; Degradation (telecommunications) ; Oxide ; Decomposition ; Ab initio quantum chemistry methods ; Vacancy defect ; Ab initio ; Oxygen ; Ion Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Semiconductor materials and devices
Authors: Shuo Tao | Li Zhu
Source: Computational Materials Today (article, 2025-11-19)
Field: Materials Science — Materials Chemistry

Machine-learning-guided discovery of direct-gap Si-Ge alloys in hexagonal and orthorhombic silicon allotropes The pursuit of direct band gap silicon allotropes with enhanced photon absorption for photovoltaic applications has remained a longstanding challenge. While crystal structure prediction offers a theoretical route to discover such materials, it is often computationally expensive—even when aided by evolutionary algorithms like genetic algorithms or swarm intelligence. A more cost-effective alternative involves substituting silicon atoms in known stable or metastable Si phases with group IV or III-V elements such as Ge or AlP, potentially yielding direct-gap materials. In this work, we systematically investigate Si 1 − x Ge x alloys derived from 4H-Si and C m c m -Si 24 prototypes to identify promising candidates with direct band gaps. Leveraging our recently developed machine learning model, EOSnet, which incorporates Gaussian Overlap Matrix fingerprints to embed many-body interactions, we prescreen the candidates to eliminate structures with undesirable electronic properties. This screening step significantly reduces computational cost prior to hybrid-functional density functional theory calculations. Our study demonstrates the potential of Ge substitution to enhance light absorption in Si allotropes, laying the groundwork for future design of efficient Si-based photovoltaic materials. Silicon ; Orthorhombic crystal system ; Metastability ; Photovoltaic system ; Crystal structure prediction ; Density functional theory ; Band gap ; Absorption (acoustics) Machine Learning in Materials Science ; Nanowire Synthesis and Applications ; 2D Materials and Applications
Authors: Ying‐Ying Liu | Liang Gao
Source: Research Square (preprint, 2025-06-26)
Field: Materials Science — Materials Chemistry

Parameter Optimization and Performance Evaluation in Polyvinyl Butyral Synthesis via Integrated Response Surface Methodology and Machine Learning Polyvinyl butyral ; Response surface methodology ; Surface (topology) ; Computer science ; Mathematics ; Materials science ; Machine learning ; Composite material Machine Learning in Materials Science ; Manufacturing Process and Optimization ; Industrial Vision Systems and Defect Detection
Authors: Yue Zhu | Fengyu Gao | Yaxin Niu | Tingkai Xiong | Xinyu Xu | Honghong Yi | Shunzheng Zhao | Yuansong Zhou | Ya Wang | Xiaolong Tang
Source: Journal of Hazardous Materials (article, 2025-12-23)
Field: Materials Science — Materials Chemistry

Based on machine learning prediction and experimental verification: The optimization of NH3-SCR performance on Ce/Co-modified MnNi catalysts Catalysis ; Ternary operation ; Adsorption ; NOx ; Ionic radius ; Ionic bonding ; Ternary complex ; Ternary numeral system ; MATLAB Catalytic Processes in Materials Science ; Industrial Gas Emission Control ; Electrocatalysts for Energy Conversion
Authors: Ling Wu | Ludovic Noels
Source: Computer Methods in Applied Mechanics and Engineering (article, 2025-11-20)
Field: Materials Science — Materials Chemistry

Convolutional neural network-based mapping of material micro-structures to deep material networks for non-linear mechanical response prediction Realization (probability) ; Consistency (knowledge bases) ; Feature (linguistics) ; Encoder ; Cluster analysis ; Artificial neural network ; Convolutional neural network ; Material properties Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Composite Material Mechanics
Authors: Siya Zhu | ‪Doğuhan Sarıtürk | Raymundo Arróyave
Source: Research Square (preprint, 2025-07-30)
Field: Materials Science — Materials Chemistry

Machine Learning Potentials for Alloys: A Detailed Workflow to Predict Phase Diagrams and Benchmark Accuracy Benchmark (surveying) ; Workflow ; Computer science ; Phase (matter) ; Phase diagram ; Artificial intelligence ; Machine learning ; Geology ; Database ; Physics ; Geodesy Machine Learning in Materials Science ; Aluminum Alloy Microstructure Properties
Authors: Fanghui Mi | Ying Ou | Rui Luo | Shixian Wang | Zhijun Zhang | Chunwen Sun | Zhaoxiang Wang | Yurui Gao
Source: Journal of Materials Chemistry A (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Microscopic insights into the ring-opening reaction of ethylene carbonate on LiCoO <sub>2</sub> by on-the-fly machine learning molecular dynamics On-the-fly machine learning molecular dynamics simulations reveal spontaneous EC ring-opening and self-healing dynamics at the LiCoO 2 /EC interface at 300 K as well as the substantial role of surface Li-deficiency in promoting EC ring-opening. Ethylene carbonate ; Ethylene ; Carbonate ; Ring (chemistry) ; On the fly ; Molecular dynamics ; Materials science ; Chemistry ; Chemical engineering ; Computer science ; Computational chemistry ; Organic chemistry ; Engineering ; Physical chemistry ; Operating system Machine Learning in Materials Science
Authors: Xingyu Zhou | Ziang Liu | Chao Yu | Ziyi Wang
Source: Acta Mechanica Sinica (article, 2025-05-16)
Field: Materials Science — Materials Chemistry

An artificial neural network-based data-driven constitutive model of shape memory alloys Artificial neural network ; Constitutive equation ; Computer science ; Shape-memory alloy ; Artificial intelligence ; Structural engineering ; Finite element method ; Engineering Shape Memory Alloy Transformations ; Topology Optimization in Engineering
Authors: D.E. Zenkhri | M.I. Sayyed
Source: Radiation Physics and Chemistry (article, 2025-10-15)
Field: Materials Science — Ceramics and Composites

Prediction of Half-Value Layer of glass-based materials using machine learning algorithms Boosting (machine learning) ; Gradient boosting ; Random forest ; Bayesian optimization ; Ensemble learning ; Electromagnetic shielding ; Mean squared prediction error ; Bayesian probability Glass properties and applications ; Advanced X-ray and CT Imaging ; Machine Learning in Materials Science
Authors: Xiaoliang Zhang | Yanjun Xie | Tao Feng | Chenxi Sun | Dawei Tang
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Thermal Transport in Mosi 2 N 4 Monolayer: A Molecular Dynamics Study Based on Machine Learning Monolayer ; Dynamics (music) ; Molecular dynamics ; Thermal ; Materials science ; Chemical physics ; Computer science ; Chemistry ; Nanotechnology ; Physics ; Thermodynamics ; Computational chemistry Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Thermal properties of materials
Authors: Ke Wang | Ming Lei | Xinggui Zhou | De Chen | Yi‐An Zhu
Source: Industrial & Engineering Chemistry Research (article, 2025-05-16)
Field: Materials Science — Materials Chemistry

Rational Design of Perovskite Anode Materials in Solid Oxide Electrolysis Cells: Machine Learning-Assisted Prediction of Thermal Expansion Coefficients Perovskite oxides have been recognized as promising electrode materials for solid oxide electrolysis cells. In this work, the phonon dispersion and thermal expansion coefficients (TECs) of LaBO3 (B = Sc–Cu), LaFe0.5B0.5O3 (B = Cr, Mn, Co, Ni), and LaCrO3−δ and LaFeO3−δ (δ = 0, 0.25, 0.5) have been studied by performing density functional perturbation theory calculations under the quasi-harmonic approximation. The calculated TECs agree well with experimental data, and LaFe0.5Co0.5O3 and LaFe0.5Ni0.5O3 have TECs close to those of the electrolyte yttria-stabilized zirconia (YSZ). A machine learning model is then developed to enable high-throughput screening of potential perovskite anode materials, where the data set is established based on our calculated TECs and experimentally reported values. The SHAP analysis indicates dominant factors governing the TEC include the cation radius and Mulliken electronegativity of the B-site element and the crystal gamma angle. Finally, 507 candidates compatible with YSZ are identified from 13,095 perovskite oxides. Thermal expansion ; Perovskite (structure) ; Materials science ; Electrolysis ; Oxide ; Anode ; Chemical engineering ; Thermal ; Rational design ; Thermodynamics ; Composite material ; Nanotechnology ; Chemistry ; Metallurgy ; Physical chemistry ; Physics ; Electrode ; Engineering ; Electrolyte Advancements in Solid Oxide Fuel Cells ; Machine Learning in Materials Science ; Molten salt chemistry and electrochemical processes