jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kingsley Yeboah Gyabaah | Bernard Mahoney | Anthony Kwasi Martey | Cheng Yan | Patrick Mensah | Guoqiang Li
Source: AI Materials (article, 2026-01-17)
Field: Materials Science — Materials Chemistry

Machine Learning-Assisted Polymer and Polymer Composite Design for Additive Manufacturing Additive manufacturing (AM) of polymers and polymer composites is changing how customized, lightweight, and complex parts are produced across various industries. However, predicting the final properties of printed parts remains challenging due to variations in material compositions, processing conditions, and microstructural characteristics. This review explores how machine learning (ML) is being used to address these challenges. It examines the application of various ML approaches in polymer and polymer composite design for AM, including supervised, unsupervised, semi-supervised, self-supervised, and reinforcement learning, for predicting key properties such as mechanical strength, thermal stability, and electrical performance. The review also highlights hybrid techniques that combine ML with physics-informed modeling, including the use of digital twins, to enhance AM process control. Challenges and future perspectives, such as data scarcity, model interpretability, and computational demands, are discussed. In summary, ML is showing strong potential to support faster, more reliable, and more sustainable development of advanced polymers and polymer composites for AM. Polymer ; Composite number ; Process (computing) ; Thermal ; Key (lock) Machine Learning in Materials Science ; Additive Manufacturing and 3D Printing Technologies ; Smart Materials for Construction
Authors: Pranay Singh | Himanshu Sekhar Panda
Source: Journal of Materials Science Materials in Electronics (article, 2026-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Biomass-derived activated carbon: a review on process parameters, material properties, and machine learning approaches for supercapacitor Carbon-based electrode materials are widely recognized for their outstanding electrical conductivity, large specific surface area, and robust chemical stability, thereby rendering them highly suitable for electric double-layer capacitors (EDLCs) that rely on electrostatic charge storage. Among these materials, biomass-derived activated carbon (AC)—especially from agroforestry waste—has gained prominence as a potential candidate due to its cost-effectiveness, abundant availability, and simple synthesis processes. Numerous experimental investigations have explored the synthesis of AC from diverse biomass sources. Also, the research is now increasingly shifting toward predictive modeling to understand and optimize AC properties for supercapacitor applications. Recent efforts focus on forecasting key performance metrics such as yield, heteroatom content, and specific capacitance based on structural and compositional parameters, including pore architecture, chemical constituents, graphitization level, testing conditions, and electrolyte choice. This review presents a detailed analysis of AC fabrication techniques, the influence of processing variables on material performance, and the growing role of machine learning (ML) in predicting electrochemical behavior. Particular emphasis is placed on linking synthesis conditions with structural characteristics and their impact on specific capacitance. Supercapacitor ; Capacitance ; Capacitor ; Process (computing) ; Heteroatom ; Fabrication Supercapacitor Materials and Fabrication ; Catalysis for Biomass Conversion ; Microbial Fuel Cells and Bioremediation
Authors: Seza DINIBUTUN | Y. Alshammari | L. Bolzoni
Source: Metals (article, 2026-02-19)
Field: Materials Science — Materials Chemistry

Machine Learning-Based Prediction of Young’s Modulus in Ti-Alloys This study explores the use of machine learning to predict the experimental Young’s modulus of titanium alloys based on their mechanical and microstructural properties. Several regression models were developed and compared, including Random Forest, XGBoost, CatBoost, Multi-Layer Perceptron, and a Stacking Regressor. Among these, Random Forest, XGBoost and CatBoost achieved the most accurate results with R2 values above 0.85. To improve interpretability, SHapley Additive exPlanations were applied to examine which input features most strongly influenced the predictions. The results showed that yield strength, hardness, and the molybdenum equivalent parameter (moe) were among the most influential descriptors. While yield strength and hardness were positively associated with the predicted values, higher moe values corresponded to lower predicted Young’s modulus. This study focuses on the prediction of Young’s modulus, a comparatively less explored elastic property in Ti-alloy machine learning studies and combines systematic model comparison with SHAP-based interpretability to provide physically consistent insights into feature–property relationships. Interpretability ; Yield (engineering) ; Stacking ; Modulus ; Elastic modulus ; Regression analysis ; Linear regression ; Titanium alloy Titanium Alloys Microstructure and Properties ; Machine Learning in Materials Science ; Metallurgy and Material Forming
Authors: Jinrong Shi | Yantao Gao | Zan Lu | Wenfeng Hu
Source: Applied Composite Materials (article, 2026-01-08)
Field: Materials Science — Materials Chemistry

Advances in the Application of Machine Learning in Composite Materials Interpretability ; Property (philosophy) ; Process (computing) ; Composite number ; Reinforcement learning ; Manufacturing process Machine Learning in Materials Science ; Epoxy Resin Curing Processes ; Fiber-reinforced polymer composites
Authors: F. Adams | Daiwei Zhu | David Steuerman | A. Gilad Kusne | Ichiro Takeuchi
Source: arXiv (Cornell University) (preprint, 2026-01-16)
Field: Materials Science — Materials Chemistry

Quantum Kernel Machine Learning for Autonomous Materials Science Autonomous materials science, where active learning is used to navigate large compositional phase space, has emerged as a powerful vehicle to rapidly explore new materials. A crucial aspect of autonomous materials science is exploring new materials using as little data as possible. Gaussian process-based active learning allows effective charting of multi-dimensional parameter space with a limited number of training data, and thus is a common algorithmic choice for autonomous materials science. An integral part of the autonomous workflow is the application of kernel functions for quantifying similarities among measured data points. A recent theoretical breakthrough has shown that quantum kernel models can achieve similar performance with less training data than classical models. This signals the possible advantage of applying quantum kernel machine learning to autonomous materials discovery. In this work, we compare quantum and classical kernels for their utility in sequential phase space navigation for autonomous materials science. Specifically, we compute a quantum kernel and several classical kernels for x-ray diffraction patterns taken from an Fe-Ga-Pd ternary composition spread library. We conduct our study on both IonQ's Aria trapped ion quantum computer hardware and the corresponding classical noisy simulator. We experimentally verify that a quantum kernel model can outperform some classical kernel models. The results highlight the potential of quantum kernel machine learning methods for accelerating materials discovery and suggest complex x-ray diffraction data is a candidate for robust quantum kernel model advantage. Kernel (algebra) ; Kernel method ; Quantum ; Quantum machine learning ; Kernel embedding of distributions ; Phase space ; Quantum computer ; Radial basis function kernel Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Quantum many-body systems
Authors: Simeng Qi | Ran Fang | Lizi Yang
Source: Journal of Catalysis (article, 2026-02-16)
Field: Chemistry — Organic Chemistry

Integrating DFT and machine learning for a dual-descriptor strategy in Gold(I) catalyst design: π-bond activation and orbital interactions Ligand (biochemistry) ; Density functional theory ; Set (abstract data type) ; Function (biology) ; Regioselectivity ; Phosphine Catalytic Alkyne Reactions ; Machine Learning in Materials Science ; Organometallic Complex Synthesis and Catalysis
Authors: Sandeep Olhan | Bindu Antil | P. Maimí
Source: Applied Composite Materials (article, 2026-02-05)
Field: Materials Science — Materials Chemistry

Machine Learning in Next-Generation Polymer Composites: Recent Advances and Perspectives Key (lock) ; Structural health monitoring ; Polymer ; Composite number ; Emerging technologies Machine Learning in Materials Science ; Mechanical Behavior of Composites ; Model Reduction and Neural Networks
Authors: Sayed Ehsan Saghaian | Milad Hemmati | Syed Mahedi Hasan | Othmane Benafan
Source: The International Journal of Advanced Manufacturing Technology (article, 2026-02-01)
Field: Materials Science — Materials Chemistry

Machine-learning framework for predicting process-property relationships in additively manufactured NiTi shape memory alloys Nickel titanium ; Shape-memory alloy ; Artificial neural network ; Transformation (genetics) ; Support vector machine ; Approximation error ; Generalization ; Property (philosophy) ; Process (computing) ; Random forest Shape Memory Alloy Transformations ; Additive Manufacturing Materials and Processes ; High Entropy Alloys Studies
Authors: Erfan Mohammadipour | Faezeh Adib | Mohammad Ghorbani
Source: Results in Engineering (article, 2026-02-04)
Field: Materials Science — Biomaterials

Machine learning–guided optimization of PEO coating parameters using support vector regression for enhanced corrosion resistance of AZ31 magnesium alloy • Machine learning optimizes PEO coatings on 31AZ magnesium alloy. • Support Vector Regression models predict corrosion behavior from process data. • Multi-objective optimization identifies a novel high-performance process window. • Optimized coating shows dramatic improvement in corrosion resistance. • Prospective validation confirms model accuracy and process robustness. Plasma Electrolytic Oxidation (PEO) is a highly effective surface treatment for enhancing the corrosion resistance of lightweight magnesium alloys, but its performance is critically dependent on a complex interplay of process parameters. Traditional optimization methods are often resource-intensive and struggle to capture non-linear process–property relationships under experimentally conditions. In this study, we demonstrate a local, proof-of-concept machine learning-guided optimization framework to systematically explore a restricted PEO process window for enhanced corrosion protection of AZ31 magnesium alloy. Support Vector Regression (SVR) was employed to build surrogate models mapping current density and treatment time to corrosion potential (E corr ) and corrosion current density (I corr ). A comparative benchmark against Artificial Neural Networks (ANNs) confirmed numerical stability of SVR in this small-data regime. The SVR-based Icorr model demonstrated statistically meaningful predictive capability (R² = 0.58), while the Ecorr model was retained as a qualitative directional constraint rather than a quantitatively predictive surrogate. These surrogates were coupled with a Differential Evolution algorithm to solve a constrained multi-objective local optimization problem. The framework identified a previously untested optimal condition of 2309 mA/cm² and 218 s within the experimentally explored domain. A prospective validation experiment confirmed the practical effectiveness of the optimization outcome, yielding a coating with an exceptionally low mean corrosion current density of 0.65 ± 0.26 µA/cm²—representing a substantial improvement relative to all initial experimental conditions. Despite the limited dataset, this work demonstrates that carefully constrained surrogate-based optimization, when coupled with prospective validation, can provide actionable guidance for process improvement in data-scarce PEO systems. Corrosion ; Support vector machine ; Magnesium alloy ; Coating ; Process optimization ; Process (computing) ; Benchmark (surveying) ; Plasma electrolytic oxidation ; Magnesium Magnesium Alloys: Properties and Applications ; Corrosion Behavior and Inhibition ; High-Temperature Coating Behaviors
Authors: Dongyi Xiao | Sheng-Rui Wang | X L Liu | Wei-Hai FANG | Ganglong Cui
Source: The Journal of Physical Chemistry Letters (article, 2026-01-03)
Field: Materials Science — Materials Chemistry

Accurate Prediction of Excited-State Energies from Molecular Orbital Energies Based on Graph Neural Network with Transfer Learning In the high-throughput screening of molecules with desired excited-state properties, machine learning offers an efficient alternative to quantum chemical calculations but suffers from limited excited-state data. Here, we introduce a transfer learning framework that leverages the ground-state energy gap between the highest occupied and the lowest unoccupied molecular orbital (HL gap) to enhance prediction of lowest singlet (S<sub>1</sub>) and triplet (T<sub>1</sub>) energies in low-data regimes. We train a graph neural network with a graph isomorphism network with edge features (GINE) and a set transformer pooling scheme to predict the HL gap and then evaluate three transfer strategies for excited-state energy prediction tasks. Among them, a hybrid strategy that concatenates the HL gap to the graph-level readout and transfers parameters from the GINE layers of the HL gap pretrained model consistently yields the best performance across training sets of 10k, 50k, and 100k molecules. At 10k training samples, this strategy reduces the test mean absolute error for both S<sub>1</sub> and T<sub>1</sub> by 41% relative to baseline models trained directly. These results establish the HL gap as a chemically interpretable and effective source domain for improving predictions of the lowest-lying excited-state energies. Artificial neural network ; Pooling ; Graph ; Transfer of learning ; Test set ; Graph isomorphism ; Band gap ; Curse of dimensionality Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: César Gabriel Vera de la Garza | Serguei Fomine
Source: Next Materials (article, 2026-02-17)
Field: Materials Science — Materials Chemistry

Machine-learning-accelerated band gap prediction from chemical composition with near-experimental accuracy Accurate prediction of electronic band gaps from chemical composition alone remains a formidable challenge in materials informatics, with existing approaches often limited by reliance on internal validation or computational targets that inherit density functional theory's systematic errors. Here, we present a machine learning framework that achieves high accuracy in predicting experimental band gaps using only compositional features. Through rigorous Bayesian optimization and zero-overlap external validation, our XGBoost model attains a mean absolute error of 0.424 eV on 1885 completely unseen compounds, demonstrating robust generalization beyond its training distribution. Comprehensive learning curve analysis reveals remarkable data efficiency, with the model achieving useful performance (MAE < 0.5 eV) with only 1381 training samples and showing near-linear computational scaling. SHAP interpretability analysis confirms the model captures physically meaningful relationships, with valence electron configuration emerging as the dominant predictor, aligning with established band structure principles. This work establishes composition-based machine learning as a powerful tool for high-throughput materials screening, accelerating the discovery of functional materials for energy conversion and electronic applications. • Achieves 0.42 eV MAE for experimental band gap prediction from composition alone. • Demonstrates robust generalization via rigorous zero-overlap external validation. • Model shows high data efficiency, reaching useful accuracy with only ∼1400 samples. • SHAP analysis confirms physically meaningful, interpretable model decisions. • Enables millisecond, experimental-quality screening for novel materials discovery. Interpretability ; Generalization ; Bayesian optimization ; Band gap ; Experimental data ; Valence (chemistry) ; Valence band ; Bayesian probability Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; X-ray Diffraction in Crystallography
Authors: Andreas Abildskov Thomsen | Maj Rabjerg | Niels Marcussen | Lars Lund | Ole N Jensen
Source: medRxiv (article, 2026-01-15)
Field: Chemistry — Spectroscopy

Mass spectrometry and machine learning for classification and molecular phenotyping of renal cell carcinoma and benign tumors Abstract Pathology classification of cancer tumor subtypes and benign tumors can be difficult due to cellular heterogeneity and similar morphological features. Renal Cell Cancer (RCC) poses a major challenge for uropathologists as advanced RCC is often incidentally diagnosed with complex histological characteristics. We hypothesised that molecular profiling of RCC and benign tumor specimen by mass spectrometry with machine learning methods enable accurate tumor classification and identification of candidate RCC biomarkers. Mass spectrometry imaging and quantitative proteomics provided peptide and protein profiles of RCC neoplasms and benign tumors presented in tissue microarray (TMA) format of 541 tissue cores from 128 patients. Machine learning models classified RCC subtypes: Clear cell RCC (ccRCC), Chromophobe RCC (chRCC), papillary RCC (pRCC), and oncocytoma (OC). Known and novel candidate protein biomarker panels for discrimination of RCC subtypes were identified. RCC peptide profiles and machine learning classified ccRCC, chRCC, pRCC, OC and healthy tissue with 95.6% - 100% accuracy at the patient level. Quantitative proteomics identified over 5000 proteins and machine learning classified ccRCC, chRCC, pRCC, OC and healthy tissue with 98.6% accuracy. We report new candidate protein biomarkers for ccRCC and pRCC and distinguish histologically similar subtypes OC (benign) and chRCC (malignant), as demonstrated by mass spectrometry-based immunohistochemistry. Candidate prognostic biomarkers for ccRCC contribute to risk of relapse (CDK6, CDK18) and risk of metastasis (DDB2, MK03). Digital histo-molecular differentiation of cancer tumor subtypes and benign tumors by mass spectrometry and macbine learning enables retrospective and prospective studies. Identified candidate protein biomarkers complement existing protocols in tumor pathology. Chromophobe cell ; Renal cell carcinoma ; Biomarker ; Biomarker discovery ; Proteomics ; Clear cell renal cell carcinoma ; Clear cell ; Metastasis ; Oncocytoma Advanced Proteomics Techniques and Applications ; Renal cell carcinoma treatment ; Advanced Biosensing Techniques and Applications
Authors: Yucheng Ji | Xiaoqian Fu | Poulumi Dey | Chaofang Dong
Source: Materials Letters (article, 2026-01-29)
Field: Materials Science — Metals and Alloys

Revealing hydrogen dynamics and embrittlement resistance in Cu-modified Al-Sc alloys using machine learning potential Hydrogen embrittlement ; Embrittlement ; Hydrogen ; Stress (linguistics) ; Fracture mechanics ; Degradation (telecommunications) Hydrogen embrittlement and corrosion behaviors in metals ; Machine Learning in Materials Science ; Hydrogen Storage and Materials
Authors: Abrar Faiyad | Ashlie Martini
Source: Journal of Chemical Theory and Computation (article, 2026-02-24)
Field: Materials Science — Materials Chemistry

Machine Learning Interatomic Potentials Enable Molecular Dynamics Simulations of Doped MoS <sub>2</sub> Dopants can tune the performance of MoS<sub>2</sub> in various applications, but the use of molecular dynamics simulations for doped MoS<sub>2</sub> materials discovery is limited by the lack of multidopant interatomic potentials. Universal machine learning interatomic potentials (MLIPs) could be a solution, but the accuracy of these potentials must first be evaluated. Here, we evaluate the accuracy of a recently developed MLIP, META's Universal Model for Atoms, for 25 different MoS<sub>2</sub> dopants spanning metals, nonmetals, and transition metals in Mo-substitution, S-substitution, and intercalated positions by benchmarking the MLIP-predicted formation energy and the dopant-induced structural change against density functional theory (DFT) calculations. The computational framework for MLIP validation and simulations is described in detail, and the source code is made available online. The MLIP is then demonstrated by performing heating-cooling simulations of MoS<sub>2</sub> supercells with all 25 dopants. These simulations capture complex phenomena including dopant clustering, MoS<sub>2</sub> layer fracturing, interlayer diffusion, and chemical compound formation at orders-of-magnitude reduced computational cost compared to DFT. This work provides a computational workflow for the application-oriented design of doped-MoS<sub>2</sub>, enabling high-throughput screening of dopant candidates and optimization of compositions for targeted tribological, electronic, and optoelectronic performance. Dopant ; Molecular dynamics ; Interatomic potential ; Density functional theory ; Workflow ; Doping ; Work (physics) ; Benchmarking Machine Learning in Materials Science
Authors: Waqas Akhtar | Nan Qu | Danni Yang | Ahmed Ishfaq | Zongfan Wei | Wei Zhang | Jingteng Xue | Haining Yu | Yuan Tao | Yong Liu | Jingchuan Zhu
Source: Physica B Condensed Matter (article, 2026-02-05)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Data-driven discovery of Mn-based full Heusler Alloys: Machine learning and DFT insights into elastic, thermal, and mechanical properties Bayesian optimization ; Workflow ; Feature (linguistics) ; Bayesian probability ; Plot (graphics) ; Thermal ; Naive Bayes classifier Heusler alloys: electronic and magnetic properties ; Machine Learning in Materials Science ; Shape Memory Alloy Transformations
Authors: Osman Yigit | Muhammed Acikgoz
Source: Journal of Materials Chemistry C (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Unveiling rare earth dopant configurations and crystal field analysis in tetragonal BaTiO <sub>3</sub> <i>via</i> machine learning and superposition modeling Machine-learning structural optimization combined with crystal-field modeling reveals systematic site preference, lattice distortion, and stabilization trends across the full rare-earth series in BaTiO 3 . Tetragonal crystal system ; Dopant ; Superposition principle ; Rare earth ; Lattice (music) ; Crystal (programming language) Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Model Reduction and Neural Networks
Authors: Jun Cong | Shaohua Luo
Source: Journal of Energy Chemistry (article, 2026-01-18)
Field: Materials Science — Materials Chemistry

Artificial intelligence empowering innovation in key materials for sodium-ion batteries: Machine learning driven design and optimization of cathode and anode materials Key (lock) ; Anode ; Cathode ; Applications of artificial intelligence Machine Learning in Materials Science ; Advancements in Battery Materials ; Advanced Battery Technologies Research