jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tanumoy Banerjee | Kevin Ji | Wei Xia | Gaoyuan Ouyang | Tyler Del Rose | Ihor Z. Hlova | B. G. Ueland | D. D. Johnson | Cai‐Zhuang Wang | Ganesh Balasubramanian | Prashant Singh
Source: International Journal of Hydrogen Energy (article, 2025-07-15)
Field: Materials Science — Materials Chemistry

Machine-learning and first-principles investigation of lightweight medium-entropy alloys for hydrogen-storage applications Hydrogen storage ; Computer science ; Materials science ; Thermodynamics ; Process engineering ; Metallurgy ; Alloy ; Physics ; Engineering Hydrogen Storage and Materials ; High Entropy Alloys Studies ; Machine Learning in Materials Science
Authors: Xiaolin Jiang | Guojun Liu | Lifu Zhang | Zhenpeng Hu
Source: Catalysts (article, 2025-03-25)
Field: Materials Science — Materials Chemistry

Integrating Density Functional Theory Calculations and Machine Learning to Identify Conduction Band Minimum as a Descriptor for High-Efficiency Hydrogen Evolution Reaction Catalysts in Transition Metal Dichalcogenides Identifying efficient and physically meaningful descriptors is crucial for the rational design of hydrogen evolution reaction (HER) catalysts. In this study, we systematically investigate the HER activity of transition metal dichalcogenide (TMD) monolayers by combining density functional theory (DFT) calculations and machine learning techniques. By exploring the relationship between key electronic properties, including the conduction band minimum (CBM), pz band center, and hydrogen adsorption free energy (ΔG*H), we establish a strong linear correlation between the CBM and ΔG*H, identifying the CBM as a reliable and physically meaningful descriptor for HER activity. Furthermore, this correlation is validated in vacancy-defected TMD systems, demonstrating that the CBM remains an effective descriptor even in the presence of structural defects. To enable the rapid and accurate prediction of the CBM, we develop an interpretable three-dimensional model using the Sure Independence Screening and Sparsifying Operator (SISSO) algorithm. The SISSO model achieves a high predictive accuracy, with correlation coefficients (r) and coefficients of determination (R2) reaching 0.98 and 0.97 in the training and 0.99 and 0.99 in the validation tests, respectively. This study provides an efficient computational framework that combines first-principles calculations and machine learning to accelerate the screening and design of high-performance TMD-based HER catalysts. Density functional theory ; Materials science ; Transition metal ; Conduction band ; Catalysis ; Chemical physics ; Nanotechnology ; Computational chemistry ; Condensed matter physics ; Chemistry ; Quantum mechanics ; Physics ; Electron Machine Learning in Materials Science ; 2D Materials and Applications ; Chalcogenide Semiconductor Thin Films
Authors: Mojtaba Khakpour Komarsofla | Kavian Khosravinia | Amirkianoosh Kiani
Source: Batteries (article, 2025-07-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Integrated Machine Learning Framework Combining Electrical Cycling and Material Features for Supercapacitor Health Forecasting The ability to predict capacity retention is critical for ensuring the long-term reliability of supercapacitors in energy storage systems. This study presents a comprehensive machine learning framework that integrates both electrical cycling data and experimentally derived material and structural features to forecast the degradation behavior of commercial supercapacitors. A total of seven supercapacitor samples were tested under various current and voltage conditions, resulting in over 70,000 charge–discharge cycles across three case studies. In addition to electrical measurements, detailed physical and material characterizations were performed, including electrode dimension analysis, Scanning Electron Microscopy (SEM), Energy Dispersive X-ray Spectroscopy (EDS), and Thermogravimetric Analysis (TGA). Three machine learning models, Linear Regression (LR), Random Forest (RF), and Multi-Layer Perceptron (MLP), were trained using both cycler-only and combined cycler + material features. Results show that incorporating material features consistently improved prediction accuracy across all models. The MLP model exhibited the highest performance, achieving an R2 of 0.976 on the training set and 0.941 on unseen data. Feature importance analysis confirmed that material descriptors such as porosity, thermal stability, and electrode thickness significantly contributed to model performance. This study demonstrates that combining electrical and material data offers a more holistic and physically informed approach to supercapacitor health prediction. The framework developed here provides a practical foundation for accurate and robust lifetime forecasting of commercial energy storage devices, highlighting the critical role of material-level insights in enhancing model generalization and reliability. Supercapacitor ; Cycling ; Computer science ; Process engineering ; Machine learning ; Artificial intelligence ; Environmental science ; Electrode ; Capacitance ; Engineering ; Chemistry Supercapacitor Materials and Fabrication ; Advanced Battery Technologies Research ; Conducting polymers and applications
Authors: Behzad Vaferi | Mohsen Dehbashi | Reza Yousefzadeh | Ali Hosin Alibak
Source: Scientific Reports (article, 2025-03-06)
Field: Chemistry — Analytical Chemistry

Prediction of the packaging chemical migration into food and water by cutting-edge machine learning techniques Chemicals transfer from the packaging materials and their dissolution in food and water can create health risks. Due to the costly and time-intensive nature of experimental measurements, employing artificial intelligence (AI) methodologies is beneficial. This research uses five renowned AI-based techniques (namely, long short-term memory, gradient boosting regressor, multi-layer perceptron, Random Forest, and convolutional neural networks) to anticipate chemical migration from packaging materials to the food/water structure, considering variables such as temperature, chemical characteristics, and packaging/food types. The relevance analysis has been employed for monitoring the way that these explanatory variables impact the chemical migration from packaging materials into foods and water. Optimizing the hyperparameters, evaluating the prediction accuracy, and comparing the performance of these AI models reveal that the gradient boosting regressor (GBR) is the superior method for this simulation. The proposed GBR model accurately predicts 1847 experimental datasets, showcasing mean squared error, mean absolute error, root mean squared error, relative absolute error percent, and regressing coefficient, of 0.06, 0.15, 0.24, 6.46%, and 0.9961 respectively. Additionally, implementing a leverage algorithm for outlier detection further affirms the reliability of this modeling study. Gradient boosting ; Computer science ; Outlier ; Mean squared error ; Random forest ; Boosting (machine learning) ; Machine learning ; Leverage (statistics) ; Artificial intelligence ; Artificial neural network ; Convolutional neural network ; Statistics ; Mathematics Dye analysis and toxicity ; Advanced Chemical Sensor Technologies ; Cultural Heritage Materials Analysis
Authors: Nannapat Sopittakamol | Sirikarn Sattawitchayapit | Natchayaporn Sindhurattavej | Kanokkorn Pimcharoen | Allan Abraham B. Padama | Kōji Shimizu | Tongjai Chookajorn
Source: Acta Materialia (article, 2025-03-18)
Field: Materials Science — Materials Chemistry

Data-driven design of multicomponent alloy electrodeposits: A machine learning-based analysis of Ni–Co anomalous co-deposition Materials science ; Alloy ; Deposition (geology) ; Metallurgy ; Chemical engineering ; Engineering Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Electrodeposition and Electroless Coatings
Authors: Dingkai Hu | Dezhi Cao | Shijian Lu | Qiang Wang | Bohak Yoon
Source: ACS Sustainable Chemistry & Engineering (article, 2025-06-17)
Field: Materials Science — Materials Chemistry

Quantum Chemistry-Guided Machine Learning for Accelerated Design of CO<sub>2</sub>-Solubilizing Deep Eutectic Solvents Deep eutectic solvents (DESs) offer promise for CO2 capture due to their tunability and low cost, yet their development is constrained by limited predictive accuracy for CO2 solubility. In this work, we present the first machine learning framework that integrates quantum chemical descriptors for solubility prediction. We compiled 2287 experimental measurements from 119 DESs over wide temperature (293.15–353.15 K) and pressure (26.3–7620 kPa) ranges. Through density functional theory (DFT) calculations, 48 electronic structure parameters for hydrogen bond donors (HBDs) and acceptors (HBAs) were extracted and combined with operational variables to build predictive models. Among various algorithms, CatBoost achieved superior performance (test R2 = 0.9921; RMSE = 0.1152), reducing residual deviation by 72% compared to conventional σ-profile methods. SHAP interpretability analysis revealed that CO2 solubility is primarily governed by the electrostatic potential distribution of HBAs and the electron delocalization of HBDs. Leveraging this model, we screened 2176 potential DES combinations and identified several high-performance candidates. This quantum chemistry-informed machine learning framework offers an interpretable and efficient computational tool to guide molecular design of DESs, significantly reducing experimental efforts and establishing a new paradigm for rational solvent development in carbon capture technologies. Eutectic system ; Quantum chemistry ; Deep eutectic solvent ; Chemistry ; Quantum ; Quantum chemical ; Nanotechnology ; Green chemistry ; Chemical engineering ; Organic chemistry ; Materials science ; Reaction mechanism ; Engineering ; Catalysis ; Physics ; Molecule Machine Learning in Materials Science ; Ionic liquids properties and applications ; Carbon Dioxide Capture Technologies
Authors: Mosab Kaseem | Talitha Tara Thanaa | Krishna Kumar Yadav | Hagar H. Hassan | Arash Fattah‐alhosseini
Source: Journal of Materials Research and Technology (article, 2025-07-01)
Field: Materials Science — Biomaterials

Divalent cation engineering of PEO/LDH coatings for corrosion protection of AZ31 magnesium alloy supported by machine learning analysis This research explores the anticorrosive performance of AZ31 magnesium alloy treated with plasma electrolytic oxidation (PEO), further enhanced by layered double hydroxides (LDHs) containing Co, Ni, or Zn. The PEO layer serves as a baseline protective coating, while adding CoFeLDH, NiFeLDH, and ZnFeLDH films addresses its inherent porosity and enhances corrosion resistance. Potentiodynamic polarization (PDP) and electrochemical impedance spectroscopy (EIS) tests, conducted in a 3.5 wt% NaCl solution, reveal that all LDH coatings significantly improve corrosion protection over the PEO layer alone. Among them, CoFeLDH demonstrates the highest corrosion resistance, with a corrosion current density ( i corr ) of 8.4 × 10 −9 A/cm 2 and a corrosion potential ( E corr ) of −0.251 V, outperforming the PEO coating ( i corr of 2.7 × 10 −6 A/cm 2 and E corr of −0.545 V). This superior performance is attributed to the densely packed nanosheet structure of CoFeLDH, which creates an effective physical barrier against corrosive species while promoting ion exchange that may help minimize chloride ion penetration. A machine learning model was employed alongside the experimental findings to identify key electrochemical parameters influencing corrosion behavior and to support data-driven interpretation. The results emphasize the critical importance of cation selection and illustrate the potential of integrating experimental design with machine learning for optimizing LDH-based protective coatings on Mg alloys. Materials science ; Corrosion ; Magnesium alloy ; Magnesium ; Alloy ; Metallurgy ; Divalent Magnesium Alloys: Properties and Applications ; Corrosion Behavior and Inhibition ; Hydrogen Storage and Materials
Authors: Junyun Pan | Renhai Shi | Zhihao Zhang | Hongtao Zhang | Yuhong Zhao | Weidong Li | Huadong Fu | Jianxin Xie
Source: International Journal of Plasticity (article, 2025-08-30)
Field: Materials Science — Materials Chemistry

Composition-deformation mechanism-property machine learning model for strength-ductility improvement of β-type titanium alloys Materials science ; Titanium alloy ; Ductility (Earth science) ; Deformation (meteorology) ; Mechanism (biology) ; Deformation mechanism ; Titanium ; Property (philosophy) ; Metallurgy ; Composite material ; Mechanical engineering ; Alloy ; Microstructure ; Engineering ; Creep Titanium Alloys Microstructure and Properties ; Machine Learning in Materials Science ; Advanced materials and composites
Authors: Mohammad Javad Shojaei | Abeiram Sivarajah | Tayeba Safdar | Oxana V. Magdysyuke | Chu Lun Alex Leung | Chun Huang
Source: Solid State Ionics (article, 2025-03-05)
Field: Materials Science — Surfaces, Coatings and Films

Advanced battery cathode microstructure analysis through operando synchrotron X-ray tomography and super-resolution deep learning Lithium ion batteries are pivotal for clean energy storage and mitigating climate change. In this study, we employ operando synchrotron X-ray computed tomography to investigate the dynamic evolution of battery cathode microstructure. We focus on tracking changes in porosity and pore size distribution at the microscale and cathode thickness at the macroscale during the lithiation and delithiation processes within a commercially configured battery. Image quality was enhanced using both conventional image processing methods and a Super-Resolution Convolutional Neural Network (SRCNN) model. Our findings revealed a slight increase in the cathode solid volume fraction and specific surface area as the battery transitioned from its pristine state to fully lithiated, followed by a reduction during delithiation. This behavior was attributed to the expansion of the cathode material and phase transitions during lithiation, which split larger pores into smaller ones, as evidenced by the increase in surface area. Cathode thickness also exhibited expansion during lithiation and contraction during delithiation. These results offer valuable insights into the structural changes that contribute to battery aging, helping researchers better understand how these different parameters change over time. This understanding is crucial for designing more durable and sustainable batteries in the future, both in terms of specific design and material selection, to enhance resistance during charge and discharge cycles to improve performance and longevity. • Operando synchrotron XCT reveals cathode microstructure evolution during cycling. • SRCNN enhances XCT image quality for improved pore size distribution analysis. • Cathode expands during lithiation, dividing large pores into smaller ones. • Framework developed to study commercial batteries with synchrotron imaging. Synchrotron ; Microstructure ; Cathode ; Battery (electricity) ; Materials science ; X-ray ; Tomography ; Resolution (logic) ; Synchrotron radiation ; Optics ; Metallurgy ; Chemistry ; Physics ; Computer science ; Artificial intelligence ; Physical chemistry Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Advancements in Battery Materials
Authors: K. Loke | Xuan Han Lim | M.A. Osman | Siew Chun Low | Wen‐Da Oh
Source: Journal of Analytical and Applied Pyrolysis (review, 2025-01-30)
Field: Materials Science — Materials Chemistry

Enhancing plastic pyrolysis for carbon nanotubes synthesis through machine learning integration: A review Pyrolysis ; Carbon nanotube ; Materials science ; Carbon fibers ; Nanotechnology ; Chemical engineering ; Engineering ; Composite material ; Composite number Carbon Nanotubes in Composites ; Fiber-reinforced polymer composites ; Graphene research and applications
Authors: Shan Zhong | Hangyu Shi | Lishan Zhang | Guoguan Liu | Qian Zhang | Xuan Ru | Yifu Li
Source: Energy (article, 2025-07-31)
Field: Materials Science — Materials Chemistry

Machine learning-driven screening of g-C3N4 supported single-atom-catalysts for CO2 conversion and potential syngas production Syngas ; Catalysis ; Atom (system on chip) ; Chemical engineering ; Production (economics) ; Chemistry ; Process engineering ; Materials science ; Computer science ; Engineering ; Organic chemistry Catalytic Processes in Materials Science ; Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts
Authors: Alec Lamens | Jürgen Bajorath
Source: Molecular Informatics (article, 2025-03-01)
Field: Materials Science — Materials Chemistry

Comparing Explanations of Molecular Machine Learning Models Generated with Different Methods for the Calculation of Shapley Values Feature attribution methods from explainable artificial intelligence (XAI) provide explanations of machine learning models by quantifying feature importance for predictions of test instances. While features determining individual predictions have frequently been identified in machine learning applications, the consistency of feature importance-based explanations of machine learning models using different attribution methods has not been thoroughly investigated. We have systematically compared model explanations in molecular machine learning. Therefore, a test system of highly accurate compound activity predictions for different targets using different machine learning methods was generated. For these predictions, explanations were computed using methodological variants of the Shapley value formalism, a popular feature attribution approach in machine learning adapted from game theory. Predictions of each model were assessed using a model-agnostic and model-specific Shapley value-based method. The resulting feature importance distributions were characterized and compared by a global statistical analysis using diverse measures. Unexpectedly, methodological variants for Shapley value calculations yielded distinct feature importance distributions for highly accurate predictions. There was only little agreement between alternative model explanations. Our findings suggest that feature importance-based explanations of machine learning predictions should include an assessment of consistency using alternative methods. Shapley value ; Machine learning ; Artificial intelligence ; Computer science ; Feature (linguistics) ; Consistency (knowledge bases) ; Mathematics ; Game theory Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Explainable Artificial Intelligence (XAI)
Authors: Qin Xu | Qinghang Wang | Xinbing Zhao | Shouxin Xia | Li Wang | Jiabao Long | Yuhui Zhang | Bin Jiang
Source: Journal of Materials Informatics (article, 2025-02-22)
Field: Materials Science — Biomaterials

A property-oriented self-decision design strategy of low-alloyed rare earth-free magnesium alloys with a good strength-ductility synergy based on machine learning Machine learning (ML) is revolutionizing alloy design, yet traditional models face challenges with limited data and complex nonlinearities. Our study presents a self-decision design strategy that integrates target property determination, reverse and forward modeling, and feature importance analysis to optimize low-alloyed rare earth (RE)-free magnesium alloys for strength-ductility synergy. The strategy was validated with experimental data, leading to the development of a new Mg-2Al-1Zn-0.6Ca-0.4Mn (wt%) alloy processed at specific conditions, achieving a tensile strength of 344 MPa and an elongation-to-failure (EL) of 21.3% at room temperature. The discrepancies between experimental and predicted results were less than 5%, underscoring the accuracy of this approach. This streamlined design strategy not only promises to accelerate the development of low-cost, high-performance alloys but also minimizes the need for human intervention, thereby enhancing the efficiency and precision of alloy design. Ductility (Earth science) ; Magnesium ; Materials science ; Property (philosophy) ; Rare earth ; Earth (classical element) ; Metallurgy ; Mathematics Magnesium Alloys: Properties and Applications ; Aluminum Alloy Microstructure Properties ; Aluminum Alloys Composites Properties
Authors: Salvatore Romano | Pablo Montero de Hijes | Matthias Meier | Georg Kresse | Cesare Franchini | Christoph Dellago
Source: Journal of Chemical Theory and Computation (article, 2025-02-13)
Field: Materials Science — Materials Chemistry

Structure and Dynamics of the Magnetite(001)/Water Interface from Molecular Dynamics Simulations Based on a Neural Network Potential The magnetite/water interface is commonly found in nature and plays a crucial role in various technological applications. However, our understanding of its structural and dynamical properties at the molecular scale remains still limited. In this study, we developed an efficient Behler-Parrinello neural network potential (NNP) for the magnetite/water system, paying particular attention to the accurate generation of reference data with density functional theory. Using this NNP, we performed extensive molecular dynamics simulations of the magnetite (001) surface across a wide range of water coverages, from single molecules to bulk water. Our simulations revealed several new ground states of low coverage water on the Subsurface Cation Vacancy (SCV) model and yielded a density profile of water at the surface that exhibits marked layering. By calculating mean square displacements, we obtained quantitative information on the diffusion of water molecules on the SCV for different coverages, revealing significant anisotropy. Additionally, our simulations provided qualitative insights into the dissociation mechanisms of water molecules at the surface. Molecular dynamics ; Magnetite ; Dynamics (music) ; Interface (matter) ; Computer science ; Artificial neural network ; Chemical physics ; Nanotechnology ; Materials science ; Artificial intelligence ; Chemistry ; Physics ; Computational chemistry ; Metallurgy Machine Learning in Materials Science ; Geophysical and Geoelectrical Methods ; Hydrocarbon exploration and reservoir analysis
Authors: Y. Nissi Talluri | Subramanian K. R. S. Sankaranarayanan | H. Christopher Fry | Rohit Batra
Source: Science Advances (article, 2025-06-11)
Field: Materials Science — Biomaterials

Discovery of unconventional and nonintuitive self-assembling peptide materials using experiment-driven machine learning Prediction of peptide secondary structure is challenging because of complex molecular interactions, sequence-specific behavior, and environmental factors. Traditional design strategies, based on hydrophobicity and structural propensity, can be biased and could indeed prevent discovery of interesting, diverse, and unconventional peptides with desired nanostructure assembly. Using β sheet formation in pentapeptides as a case study, we used an integrated high-throughput experimental workflow and an artificial intelligence-driven active learning framework to improve prediction accuracy of self-assembly. By focusing on sequences where machine learning (ML) predictions deviate from conventional design strategies, we synthesized and tested 268 pentapeptides, successfully finding 96 forming β sheet assemblies, including unconventional sequences (e.g., ILFSM, LMISI, MITIY, MISIW, and WKIYI) not predicted by traditional methods. Our ML models outperformed conventional β sheet propensity tables, revealing useful chemical design rules. A web interface is provided to facilitate community access to these models. This work highlights the value of ML-driven approaches in overcoming the limitations of current peptide design strategies. Computer science ; Peptide ; Nanotechnology ; Chemistry ; Materials science ; Biochemistry Supramolecular Self-Assembly in Materials ; Chemical Synthesis and Analysis ; Lipid Membrane Structure and Behavior