jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sri Sowmya Sirigineedi | Sangeeta Meena | Nidhi Verma | Ashok Kumar
Source: ACS Omega (article, 2026-01-23)
Field: Materials Science — Materials Chemistry

Machine Learning-Guided Ab Initio Study of Na Super Ionic Conductor-Type Na <sub>4</sub> Hf <sub>2</sub> (SiO <sub>4</sub> ) <sub>3</sub> Solid-State Electrolyte for Sodium-Ion Batteries The development of solid-state electrolytes (SSEs), which are essential for enabling safe and effective sodium-ion batteries, is being accelerated by machine learning (ML). This study evaluated the potential of a NASICON-type Na<sub>4</sub>Hf<sub>2</sub>(SiO<sub>4</sub>)<sub>3</sub>compound as a promising SSE by using ML techniques in conjunction with density functional theory (DFT). A predictive machine learning model was developed based on ionic conductivity data from the literature reported experimentally. The model was trained on log-transformed conductivity and temperature, and features were selected using statistical correlation techniques. SHAP analysis was used to identify key characteristics influencing ionic transport. Because materials databases lack specific filters for NASICON-type compounds, hypothetical structures were developed for model validation. Na<sub>4</sub>Hf<sub>2</sub>(SiO<sub>4</sub>)<sub>3</sub>was chosen due to its high expected conductivity and structural stability. DFT validation included assessments of electronic band gap and dynamical, thermal, and mechanical stability in addition to ionic conductivity, which was computed using molecular dynamics simulations. This study demonstrates the usefulness of a hybrid ML-DFT strategy in accelerating the search for solid-state electrolytes. Ionic conductivity ; Electrolyte ; Ionic bonding ; Ab initio ; Conductivity ; Stability (learning theory) ; Density functional theory ; Key (lock) Thermal Expansion and Ionic Conductivity ; Machine Learning in Materials Science ; Advanced Battery Materials and Technologies
Authors: Vishnu Vijay Kumar | T. M. B. SANTOS | Caroliny M. Santos | Bahati Shabani Nzeyimana | Khaled Shahin | H. Jeevan Rao | J. H. O. Nascimento
Source: Polymer Composites (article, 2026-03-25)
Field: Materials Science — Materials Chemistry

Artificial Intelligence and Machine Learning Approaches for Composite Materials: Recent Developments and Applications ABSTRACT This review presents a thorough examination of the current trends in Artificial Intelligence (AI) and Machine Learning (ML) as applied to the field of composite materials science and engineering. It delves into the utilization of diverse AI and ML methodologies, such as Support Vector Machines, Artificial Neural Networks, Convolutional Networks, Recurrent Networks, genetic algorithms, and generative adversarial networks, for tasks including the prediction of mechanical properties, microstructure analysis, defect detection, and design process optimization. The study underscores the indispensable role of combining experimental and computational approaches in advancing this discipline. Despite the promising capabilities of these technologies, their broader implementation is hampered by challenges like the scarcity of high‐quality data, difficulties in model interpretability, and substantial computational demands. Additionally, the study stresses the necessity of standardizing methodologies and encouraging data sharing to propel advancements in the field. By applying statistical and probabilistic techniques to capitalize on existing knowledge, these technologies illuminate the fundamental principles governing material behavior, providing novel insights that facilitate the development of more efficient, customized, and sustainable solutions in composite material engineering. Generative grammar ; Field (mathematics) ; Process (computing) ; Probabilistic logic ; Convolutional neural network ; Scarcity ; Composite number ; Support vector machine Machine Learning in Materials Science ; Epoxy Resin Curing Processes ; Mechanical Behavior of Composites
Authors: Lucas Araujo | Gers Tusha | Lars V. Schäfer
Source: ChemRxiv (article, 2026-03-09)
Field: Chemistry — Physical and Theoretical Chemistry

Molecular Simulations with a Machine Learning Potential Reveal the Mechanism of Urea Macrocyclization at a Cavitand-Water Interface Urea formation is a fundamental transformation in biological, synthetic, and industrial chemistry. While its mechanism in bulk aqueous solution is well established, how supramolecular confinement modulates this reaction remains poorly understood. Here, we investigate the macrocyclization of a α,ω-aminoisocyanate substrate in a water-soluble cavitand to uncover how confinement reshapes the reaction mechanism in water. Combining classical molecular dynamics, density functional theory, and machine learning interatomic potentials coupled to enhanced sampling simulations, we construct the free-energy landscape for both the confined and unconfined reactions. The cavitand exerts two mechanistically distinct confinement effects. First, it templates a compact reactant conformation that initiates the macrocyclization, stabilizing it relative to the unconfined system without lowering the ring-closure energy barrier. Second, when confined in the cavitand, ionic intermediates that are highly transient in bulk water are converted into longer-lived species occupying distinct free-energy minima. Both effects originate from spatially heterogeneous solvation at the cavity-water interface, where one nitrogen atom of the forming urea moiety remains exposed to bulk solvent while the other nitrogen is partially buried inside the hydrophobic pocket. This anisotropic solvation selectively promotes the basic proton-transfer pathway, shifting the reaction from nearly balanced acidic and basic routes observed in bulk water to a confinement-biased basic mechanism. These findings demonstrate that molecular containers do not merely preorganize substrates geometrically but can actively reshape reaction free-energy landscapes. The molecular-level insights into supramolecular control of reactivity in aqueous environments underline confinement as a powerful design principle for controlling chemical transformations. Cavitand ; Solvation ; Supramolecular chemistry ; Aqueous solution ; Moiety ; Reactivity (psychology) ; Molecular dynamics ; Funnel ; Ionic bonding Crystallography and molecular interactions ; Spectroscopy and Quantum Chemical Studies ; Supramolecular Chemistry and Complexes
Authors: Ahmad Cahyono Adi | Romanus Hadyanto Ongan | Humairah Humairah | Elva Amalia | Deliyana Siagian | I Amal | Mozes Markus Sapari | Yayang Matira | Waffiq Maaroja | Dwi Atika Sari | Marfly Marfly
Source: Analytical Chemistry (article, 2026-03-26)
Field: Materials Science — Materials Chemistry

Shapley Additive exPlanations-Integrated Convolutional Neural Networks for Chemically Interpretable Fourier-Transform Infrared-Based Microplastic Characterization Fourier-transform infrared (FTIR) spectroscopy is a widely adopted technique for microplastic (MP) identification due to its molecular sensitivity and nondestructive nature. However, many FTIR-machine learning (ML) approaches operate as black-box classifiers, providing limited transparency regarding the spectral regions that drive model decisions and thereby constraining confidence in automated analysis. We present CNN-SHAP fusion, an explainable deep-learning framework that integrates one-dimensional convolutional neural networks (1D-CNNs) with SHapley Additive exPlanations (SHAP) to support postacquisition, attribution-based interpretation of FTIR spectra. The framework combines CNN-derived spectral embeddings with SHAP-weighted wavenumber representations as meta-features within an ensemble learning architecture, enabling the systematic evaluation of model sensitivity across the infrared spectrum. Using standardized preprocessing and a balanced dataset comprising six common polymers (HDPE, LDPE, PET, PP, PS, and PVC) measured under controlled laboratory conditions, CNN-SHAP fusion achieves a mean cross-validated classification accuracy of 99.6%. Attribution analysis indicates that model predictions are primarily influenced by spectral regions that are diagnostically relevant for polymer identification, including aliphatic C-H stretching, carbonyl-associated bands, aromatic features, and characteristic fingerprint-region patterns. These attribution profiles are consistent with established FTIR assignments for the polymers examined and remain stable across the cross-validation folds. CNN-SHAP fusion provides a transparent and reproducible framework for FTIR-based microplastic classification in laboratory and batch-processing workflows. By linking predictive performance with spectrally interpretable attribution, the approach supports the informed evaluation of model behavior and establishes a foundation for future validation under more complex and environmentally representative conditions. Characterization (materials science) ; Convolutional neural network ; Pattern recognition (psychology) ; Artificial neural network Machine Learning in Materials Science ; Additive Manufacturing and 3D Printing Technologies ; Advanced Polymer Synthesis and Characterization
Authors: Saurabh Tiwari | Aman Gupta
Source: Metals (article, 2026-03-13)
Field: Materials Science — Materials Chemistry

Machine Learning Framework for Predicting Mechanical Properties of Heat-Treated Alloys: Computational Approach Heat treatment critically controls microstructure and mechanical properties in engineering alloys, but experimental optimization is costly and time-intensive. Machine learning (ML) offers a data-driven alternative, though data scarcity and feature leakage often limit predictive reliability. A comprehensive ML framework was developed and validated using a physics-informed synthetic dataset of 332 heat-treated alloy samples covering carbon steels (AISI 4140, 1080, 4340, 5130), aluminum alloys (AlSi7Mg, AlSi10Mg, Al6061, Al2618), and stainless steels (304, 316L). Twenty-seven features describing chemical composition, heat-treatment parameters, and microstructural characteristics were initially included. Following strict data-leakage analysis, all six mechanical property features were fully removed, leaving 22 independent predictors. Five regression models—Extra Trees, Random Forest, Gradient Boosting, Ridge, and ElasticNet—were evaluated using a 70/15/15 train–validation–test split with randomized hyperparameter optimization and 3-fold cross-validation. The Random Forest model showed the best test performance for tensile strength prediction (R2 = 0.9282, RMSE = 37.24 MPa, MAE = 28.54 MPa, MAPE = 5.39%), with minimal overfitting. Tempering temperature, carbon content, and manganese content were the most influential features, aligning with established metallurgical principles. The proposed framework demonstrates robust, leakage-free prediction of mechanical properties from composition and processing parameters, offering a scalable approach for accelerated alloy design pending experimental validation. This study serves as a methodological framework demonstration; the reported performance metrics are benchmarks against the synthetic dataset, and experimental validation with real alloy data remains essential for industrial deployment. Random forest ; Hyperparameter ; Ultimate tensile strength ; Artificial neural network ; Scalability ; Alloy ; Gradient boosting ; Regression Machine Learning in Materials Science ; Additive Manufacturing Materials and Processes ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Xiaoqing Liu | Xinyu Yu | Yangshuai Wang | Zhe-Tao Sun | Zedong Luo | Kehan Zeng | Teng Zhao | Shou-Hang Bo | Zhenli Xu
Source: arXiv (Cornell University) (preprint, 2026-01-25)
Field: Materials Science — Materials Chemistry

An AI-ready fine-tuning framework for accurate machine-learning interatomic potentials in solid-solid battery interfaces Atomistic modeling of solid-solid battery interfaces is essential for understanding electro-chemo-mechanical coupling, but the complex interfacial chemistry and heterogeneous environments pose major challenges for quantum-accurate, data-efficient modeling. Herein, we propose an approach of fine-tuning with integrated replay and efficiency (FIRE), a general framework for universal machine-learning interatomic potentials by combining efficient configurational sampling with a replay-argumented continual strategy, achieving quantum-level accuracy at moderate cost. Across six solid-solid battery interface systems, FIRE consistently achieves root-mean-square errors in energy below 1 meV/atom and in force near 20 meV/angstrom, marking an order-of-magnitude improvement over existing models while requiring only 10% of the original datasets. In addition, the fine-tuned model successfully reproduces key mechanical and electrochemical properties of the materials, in close agreement with experimental data. The FIRE offers a generalizable and data-efficient approach for developing accurate interatomic potentials across diverse materials, enabling predictive simulations beyond the reach of first-principles methods. Battery (electricity) ; Interatomic potential ; Interface (matter) ; Key (lock) ; Molecular dynamics ; Energy (signal processing) Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Thermal Expansion and Ionic Conductivity
Authors: Miroslav Lebedaa | Jan Drahokoupil | Veronika Mazáčová | Petr Vlčák
Source: Journal of Materials Research and Technology (article, 2026-02-26)
Field: Materials Science — Materials Chemistry

Revealing interstitial energetics in Ti-23Nb-0.7Ta-2Zr gum metal base alloy via universal machine learning interatomic potentials Understanding the behavior of light interstitial elements in multicomponent alloys remains challenging due to the complexity of local chemical environments and the high computational cost of first-principles calculations. Here we demonstrate that three universal machine-learning interatomic potentials (uMLIPs) – MACE-MATPES-PBE-0, Orb-v3, and SevenNet-0 can efficiently map the energetics of C, N, O, and H interstitials in a Ti-23Nb-0.7Ta-2Zr (at.%) gum metal base alloy while being several orders of magnitude faster than density functional theory (DFT). All uMLIPs predict broad energy distributions (∼1-3 eV) across the four interstitial elements, reflecting their strong sensitivity to local lattice chemistry. Despite alloy disorder, MACE-MATPES-PBE-0 and Orb-v3 reproduce the expected site preferences of the bcc structure: C, N, and O relax into octahedral sites, whereas H stabilizes in tetrahedral positions. In contrast, SevenNet-0 predicts H to be most stable in octahedral coordination, indicating a limitation of this model. Correlation analysis reveals two dominant chemical trends: Ti-rich environments strongly stabilize interstitials, whereas close proximity to Nb is destabilizing. Zr and Ta show no statistically significant influence, likely due to their low concentrations. Benchmarking representative O interstitial configurations against DFT confirms that the uMLIPs reasonably reproduce the energetic ordering of chemically distinct environments. Additional DFT validation confirms that tetrahedral configurations are energetically more favorable than octahedral sites for H interstitials, further illustrating the SevenNet-0 limitation. Overall, these results demonstrate that uMLIPs enable computationally efficient, statistically broad characterization of defect energetics in Ti-23Nb-0.7Ta-2Zr gum metal base alloy and provide insight into how local chemical environments govern interstitial stability. Alloy ; Energetics ; Interstitial defect ; Octahedron ; Density functional theory ; Interatomic potential ; Tetrahedron ; Lattice (music) Machine Learning in Materials Science ; Titanium Alloys Microstructure and Properties ; Nuclear Materials and Properties
Authors: F. Adams | Daiwei Zhu | David Steuerman | A. Gilad Kusne | Ichiro Takeuchi
Source: ArXiv.org (article, 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: Daeyoung Kim | Suyue Yuan | Brandon C. Wood | Liwen F. Wan
Source: MRS Communications (article, 2026-02-18)
Field: Materials Science — Materials Chemistry

Machine-learning interatomic potentials for interfaces in all-solid-state batteries: Perspectives on training data, model selection, and validation Transferability ; Reliability (semiconductor) ; Predictability ; Interface (matter) ; Focus (optics) ; Molecular dynamics ; Transformative learning Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Advancements in Battery Materials
Authors: Zhenyun Zeng | Linmin Lao | Yuzhang Li | Jun Zhu | Yang Yue | Guangren Qian | Jia Zhang
Source: Applied Catalysis A General (article, 2026-02-13)
Field: Materials Science — Materials Chemistry

Machine learning on deconstructing contributions of atomic features determining electron transfer in B-site doping perovskite catalyst Electronegativity ; Catalysis ; Perovskite (structure) ; Atomic radius ; Doping ; Atomic units ; Dopant ; Electron transfer Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Ammonia Synthesis and Nitrogen Reduction
Authors: Wanxuan Zhang | Shuang Shan | Fanzhe Ma | Xiaoqing Liu | Peng Xu | Fuyi Chen
Source: Scientific Reports (article, 2026-01-06)
Field: Materials Science — Materials Chemistry

Fast and accurate prediction of adsorption energy of AgPd nanoalloys by deep learning potentials and neural networks The accurate prediction of adsorption energy plays a crucial role in the design of efficient catalysts. However, the high-throughput prediction of adsorption energies based on first-principles methods presents significant challenges, hindering the rapid acceleration of catalyst development. In this study, a dataset of F atom adsorption energies was constructed, consisting of 1087 adsorption configurations on AgPd clusters, using the Ag-Pd-F deep learning potential. We employed four distinct deep neural network architectures, namely, Crystal Graph Convolutional Neural Networks (CGCNN), Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and Multilayer Perceptrons (MLP), to establish an accurate predictive model for F atom adsorption on 38-atom AgPd nanoclusters. Among the models tested, the CGCNN, based on deep learning potentials, demonstrated the highest accuracy and provided the best uncertainty estimates, achieving an R² of 0.8379 and an RMSE of 0.1036 eV on the test set. This approach not only outperformed the other models in terms of predictive precision but also offered superior computational speed and cost-efficiency. The findings highlight the potential of deep learning models, particularly deep learning potentials, in predicting adsorption energies of nanoclusters, paving the way for accelerated material design and performance prediction in catalysis and materials science. Deep learning ; Artificial neural network ; Convolutional neural network ; Adsorption ; Multilayer perceptron ; Perceptron ; Atom (system on chip) Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Hydrogen Storage and Materials
Authors: Xiaoqing Liu | Xinyu Yu | Yangshuai Wang | Zhe-Tao Sun | Zedong Luo | Kehan Zeng | Teng Zhao | Shou-Hang Bo | Zhenli Xu
Source: ArXiv.org (article, 2026-01-25)
Field: Materials Science — Materials Chemistry

An AI-ready fine-tuning framework for accurate machine-learning interatomic potentials in solid-solid battery interfaces Atomistic modeling of solid-solid battery interfaces is essential for understanding electro-chemo-mechanical coupling, but the complex interfacial chemistry and heterogeneous environments pose major challenges for quantum-accurate, data-efficient modeling. Herein, we propose an approach of fine-tuning with integrated replay and efficiency (FIRE), a general framework for universal machine-learning interatomic potentials by combining efficient configurational sampling with a replay-argumented continual strategy, achieving quantum-level accuracy at moderate cost. Across six solid-solid battery interface systems, FIRE consistently achieves root-mean-square errors in energy below 1 meV/atom and in force near 20 meV/angstrom, marking an order-of-magnitude improvement over existing models while requiring only 10% of the original datasets. In addition, the fine-tuned model successfully reproduces key mechanical and electrochemical properties of the materials, in close agreement with experimental data. The FIRE offers a generalizable and data-efficient approach for developing accurate interatomic potentials across diverse materials, enabling predictive simulations beyond the reach of first-principles methods. Battery (electricity) ; Interatomic potential ; Interface (matter) ; Key (lock) ; Molecular dynamics ; Energy (signal processing) Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Thermal Expansion and Ionic Conductivity