jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Junsoo Han | Kévin Ogle
Source: npj Materials Degradation (article, 2025-07-28)
Field: Materials Science — Materials Chemistry

Element-resolved electrochemical database: AESEC polarization curves of ZnAlMg alloy coating constituents Abstract An element-resolved electrochemical database of a ZnAlMg alloy coating is presented, obtained via atomic emission spectroelectrochemistry (AESEC) linear sweep voltammetry (LSV). Nominally pure Zn, Al and Mg metals as well as MgZn 2 , ZnAl intermetallic phases, and commercial ZnAl alloy coatings were investigated using AESEC-LSV to understand the complex electrochemical response of multi-phase ZnAlMg alloys. The elemental dissolution rates extrapolated from AESEC-LSV curves showed a linear relationship with spontaneous elemental dissolution rates. This demonstrates the possible use of AESEC-LSV for determining long-term elemental corrosion rates, as well as the use of element-specific electrochemical data as input parameters for more accurate machine learning based corrosion resistant alloy design. Element-resolved electrochemistry reveals important corrosion phenomena not detectable in conventional electrochemistry such as cathodic dissolution, chemical dissolution, cathodic dealloying, negative correlation effects, and anomalous hydrogen evolution. These phenomena may be significant and should be taken into account in the rate equations used for numerical modeling. Materials science ; Polarization (electrochemistry) ; Coating ; Electrochemistry ; Alloy ; Metallurgy ; Composite material ; Chemistry ; Electrode ; Physical chemistry Corrosion Behavior and Inhibition ; Electrodeposition and Electroless Coatings ; Nanoporous metals and alloys
Authors: Xuezhe Zhang | Ling Su | Cuihua Li
Source: Journal of Electronic Materials (article, 2025-12-03)
Field: Materials Science — Materials Chemistry

AI-Enhanced Materials Selection Pipeline for High-Performance Actuators in Power and Aerospace Systems: Predicting Thermal Hysteresis in High-Temperature Shape Memory Alloys Shape-memory alloy ; Pipeline (software) ; SMA* ; Interpretability ; Aerospace ; Overfitting ; Test bench ; Actuator ; Selection (genetic algorithm) ; Power (physics) Machine Learning in Materials Science ; Shape Memory Alloy Transformations ; High Entropy Alloys Studies
Authors: Yifei Zhu | Jiahui Zhang | Jiawei Peng | Mengge Li | Chao Xu | Zhenggang Lan
Source: ArXiv.org (preprint, 2025-11-15)
Field: Materials Science — Materials Chemistry

Chemistry-Enhanced Diffusion-Based Framework for Small-to-Large Molecular Conformation Generation Obtaining 3D conformations of realistic polyatomic molecules at the quantum chemistry level remains challenging, and although recent machine learning advances offer promise, predicting large-molecule structures still requires substantial computational effort. Here, we introduce StoL, a diffusion model-based framework that enables rapid and knowledge-free generation of large molecular structures from small-molecule data. Remarkably, StoL assembles molecules in a LEGO-style fashion from scratch, without seeing the target molecules or any structures of comparable size during training. Given a SMILES input, it decomposes the molecule into chemically valid fragments, generates their 3D structures with a diffusion model trained on small molecules, and assembles them into diverse conformations. This fragment-based strategy eliminates the need for large-molecule training data while maintaining high scalability and transferability. By embedding chemical principles into key steps, StoL ensures faster convergence, chemically rational structures, and broad configurational coverage, as confirmed against DFT calculations. Molecule ; Polyatomic ion ; Key (lock) ; Embedding ; Quantum chemical ; Scalability ; Molecular conformation Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Souraya Goumri‐Said | G. Abdellaoui | Mohammed Benali Kanoun
Source: Advanced Energy and Sustainability Research (article, 2025-11-04)
Field: Materials Science — Materials Chemistry

Accelerated Screening of Halide Double Perovskites via Hybrid Density Functional Theory and Machine Learning for Thermoelectric Energy Conversion A comprehensive first‐principles and machine learning study is conducted on 102 halide double perovskites to identify promising candidates for thermoelectric applications. The HSE06 hybrid functional within the Quantum ATK framework is used to accurately determine electronic structures, bandgaps, and total and partial densities of states. Boltzmann transport theory is applied to figure out important thermoelectric parameters, such as the Seebeck coefficient, electrical conductivity, and ZT values over a wide range of temperatures. Supervised machine learning models are trained on density functional theory (DFT)‐derived descriptors to speed up the discovery of new materials. These models demonstrate high predictive accuracy for thermoelectric performance across different chemical spaces. A detailed analysis of the electronic band structures and orbital contributions is carried out for Rb 2 GeI 6 , Rb 2 PbI 6 , Cs 2 SnBr 6 , and In 2 PtCl 6 , some of the best‐performing compounds. A wide range of behaviors is observed, including metallic, degenerate, and wide‐bandgap semiconducting, which correlate with distinct transport properties. This unified method shows how using accurate DFT, transport theory, and machine learning together can help find new materials with specific functions. This will lead to the development of next‐generation thermoelectric technologies based on environmentally friendly halide perovskites. Thermoelectric effect ; Density functional theory ; Thermoelectric materials ; Halide ; Range (aeronautics) ; Hybrid functional Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Perovskite Materials and Applications
Authors: Bin Li | Jian Ouyang | Yi Zou | Xiaocheng Xu | Han‐Shi Hu
Source: Nano Research (article, 2025-09-09)
Field: Chemistry — Inorganic Chemistry

Deep learning guided generation of uranyl peroxide clusters via fullerene inspired topologies Uranyl peroxide clusters, formed from uranyl ions, peroxide and other ligands, exhibit complex structural diversity and have significant potential in nuclear chemistry applications. Theoretical prediction is essential for atomic-level understanding of their formation and stability, offering insights where experiments face challenges from uranium's radioactivity and complex coordination. However, their large size and varied topologies pose challenges for theoretical study. In this work, we introduce Uni-Gen, a deep learning model based on the Uni-Mol framework, which generates uranyl cluster structures by leveraging topological similarities with carbon clusters, particularly fullerene. This newly-developed Uni-Gen not only validates the stability of the U<sub>28</sub> uranyl cluster consistent with previous experimental data, but also discovers a more stable U<sub>44</sub> uranyl cluster than the previously reported structure, and most notably, predicts the previously unreported isomers of the U<sub>38</sub> uranyl cluster. Our results demonstrate that Uni-Gen is a powerful tool for predicting diverse potentially stable uranyl cluster structures, thereby enriching our understanding of the stability landscape of uranyl clusters and offering potential for further exploration of novel uranyl materials and chemical phenomena. Uranyl ; Cluster (spacecraft) ; Fullerene ; Network topology ; Carbon fibers ; Peroxide Radioactive element chemistry and processing ; Machine Learning in Materials Science
Authors: Changhao Song
Source:  (article, 2025-09-12)
Field: Materials Science — Materials Chemistry

Exploration of Performance Prediction of Gas Separation COFs Using Multilayer Perceptron Covalent organic frameworks (COF) have shown great potential in the field of gas separation, but their vast structural space makes pure experimental screening impractical. Machine learning, especially deep learning, provides powerful computational tools for accelerating material discovery. This study aims to systematically evaluate the effectiveness of different deep learning architectures in predicting the adsorption performance of synthesized COFs for CO2, CH4, and H2S gases. We conducted a rigorous performance comparison of Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM), and Transformer models based on a published dataset containing 788 COFs and 18 structural chemical features. The results clearly indicate that MLP is significantly superior to more complex LSTM and Transformer models in terms of prediction accuracy, computational efficiency, and training stability. In addition, through the application of SHAP (Shapley Additive exPlanations) interpretability analysis, we verified that the optimal MLP model is not a "black box", and its predictive behavior is driven by physically reasonable structure performance relationships. Void fraction and Pore Limiting Diameter were identified as key factors affecting gas permeability, which is consistent with existing experimental and simulation conclusions. This study confirms that MLP, as an efficient, reliable, and interpretable tool, has significant value in the rational design and performance prediction of COFs. Interpretability ; Performance prediction ; Limiting ; Multilayer perceptron ; Artificial neural network ; Transformer ; Predictive modelling ; Perceptron Covalent Organic Framework Applications ; Carbon Dioxide Capture Technologies ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Hamdi Dağıstanlı
Source: Sivas Cumhuriyet Üniversitesi Bilim ve Teknoloji Dergisi. (article, 2025-06-26)
Field: Materials Science — Materials Chemistry

Strain Effect and Artificial Inteligence Applications on Electronic Band Structure of MoS2 Ab initio density functional theory (DFT) calculations have been used to determine the band gap of the 2D layered material MoS2 under uniaxial strain. This involves finding optimised lattice parameters and calculating the electronic band structure. We also note that by applying strains ranging from -15% to 15%, a wide range of band gaps can be obtained to study the behaviour of the semimetal and metal. The results gained are applied to machine learning. Initially, PR, which is polynomial regression, is a machine learning method that it could be studied with numpy, sklearn and scipy modules, and ANN is the applicaiton with artificial neural network with tensorflow module are applied to the optimised semi-metallic and metallic structures. For the application of ANN, Least Squares method is used. Leaky Relu (LRelu) and Elu functions are used to apply ANN. Potential dataset is obtained by using Quantum Espresso and the calculations are made by using National Center for High Performance Computing of Turkey (UYBHM). PR and ANN results are calculated using the existing data set. PR and artificial neural ANN are used only for plotting the Valance Band Maximum (VBM) and Conduction Band Minimum (CBM) graphs near the Fermi level. This study is on the basis of data which are already available and could therefore be considered to be data mining. on the electronic band structure for MoS2. Strain (injury) ; Materials science ; Computer science ; Artificial intelligence ; Biology 2D Materials and Applications ; MXene and MAX Phase Materials ; Machine Learning in Materials Science
Authors: Batalović, Katarina | Radaković, Jana | Kuzmanović, Bojana | Medić Ilić, Mirjana | Paskaš Mamula, Bojana | Vujković, Milica
Source: VinaR (Institute of Nuclear Sciences "Vinča") (article, 2025-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Multimodal Machine Learning Design of MXene/PANI Composites for Water-Based Supercapacitors Among various pseudocapacitive materials, widely investigated p-type conjugated polymer, polyaniline (PANI), excels in storage applications charges due to its high gravimetric capacity (294 mAhg⁻¹), decent electrical conductivity, simple synthesis procedures and readily available raw materials [1]. Recent research efforts combine PANI nanocomposites with appropriate materials to improve processability, scalability and electrochemical properties [2]. In this work we explore potential of applying multimodal machine learning approach to design MXene/PANI composite for water-based electrochemical double-layer capacitors, aiming toward enhanced specific capacity and stability. In this work, we investigate the potential of a multimodal machine learning approach to design MXene/PANI composites for water-based electrochemical double-layer capacitors, aiming to enhance both specific capacity and stability. We leverage the aNANt database [3], which includes over 23,000 DFT-relaxed MXene structures, and apply domain-specific constraints such as the presence of polar surface groups, high electrical conductivity, structural stability, and the use of non-critical raw materials. To predict interactions and performance of PANI–MXene combinations, we employ the universal machine learning potential SchNetPack [4]. Additional input features are derived from elemental attributes and DFT-computed properties of the MXenes. Moreover, we explore atom-level word embeddings as supplementary descriptors, utilizing pretrained models such as Mat2vec [5], Chem300, Phys300, MatSci200, MatSci300, and Mixed300 [6], which are based on large corpora of materials science literature. The resulting predictions of composite stability and electrochemical performance are discussed in the context of feature contributions and model performance. Leverage (statistics) ; Supercapacitor ; Scalability ; Composite number ; Context (archaeology) ; Nanocomposite Supercapacitor Materials and Fabrication ; MXene and MAX Phase Materials ; Ferroelectric and Negative Capacitance Devices
Authors: Ji Eun Oh | SeMin Park
Source: Korean Journal of Metals and Materials (article, 2025-09-05)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Efficient Magnetic Property Prediction and Process Optimization by Combining Generative Adversarial Networks and Auto Machine Learning in Thin-Gauge Fe-Si Alloy Sheet In order to greatly increase the energy efficiency of the motor core of electric vehicles, commercial 0.5 mm non-oriented Si steel was re-rolled to a thin-gauge of 0.1 mm, which cannot be accomplished with commercial rolling. To maximize the flux density, a key factor affecting energy efficiency, the hysteresis AI model was optimized based on the heat treatment conditions. GAN, a data augmentation technology, and deepfake technology were used. The effectiveness of GAN and AutoML were explored as a way of increasing the consistency of the hysteresis AI model with fewer experiments. The AutoML model with FLAML based on the tree model was effectively improved, and the model performance was effectively improved on 4000 data per condition, similar to one condition of the existing data without data augmentation. The R2 score was 0.9827 and the MSE was 0.0213 for various temperature and time conditions, which is less than half of the MSE of 49% compared to the MSE before applying GAN. The heatmap analysis of the flux density model showed that the maximum flux density of B8 was 1.50T under these conditions, which was about 25% better than the lowest flux density (1.14T). The value was then confirmed by actual experiment. A maximum (1.50T) of B8 in 0.1 mm re-rolled Si steel was obtained, demonstrating that the combination of GAN and FLAML can improve the consistency of continuous data material models such as hysteresis curves when experiments are insufficient. This has sufficient potential for developing magnetic data GAN technology. Consistency (knowledge bases) ; Hysteresis ; Process (computing) ; Electrical steel ; Core (optical fiber) ; Key (lock) ; Flux (metallurgy) ; Magnetic hysteresis ; Energy (signal processing) Magnetic Properties and Applications ; Welding Techniques and Residual Stresses ; Microstructure and Mechanical Properties of Steels
Authors: Yulong Li | Min Li | Bin Zi | Ting Dong
Source: Tribology International (article, 2025-08-11)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Material removal and damage mechanisms in laser-assisted polishing of β-Ga₂O₃: Atomic-scale insights Polishing ; Atomic units ; Materials science ; Laser ; Scale (ratio) ; Forensic engineering ; Nanotechnology ; Nuclear engineering ; Metallurgy ; Optics ; Engineering ; Physics Ga2O3 and related materials ; Electronic and Structural Properties of Oxides ; Semiconductor materials and devices
Authors: Mohammad Bagheri | Pekka Koskinen
Source: Nanoscale (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

Lateral graphene-metallene interfaces at the nanoscale Metallenes are atomically thin, nonlayered two-dimensional materials. While they have appealing properties, their isotropic metallic bonding makes their stabilization difficult and presents considerable challenges to their synthesis and practical applications. However, their stabilization can still be achieved by suspending them in the pores of two-dimensional template materials, making the properties of lateral interfaces of metallenes scientifically relevant. Here, we combined density-functional theory and universal machine-learning interatomic potentials to study lateral interfaces between graphene and 45 metallenes with various profiles. We optimized the interfaces and analyzed their energies, electronic structures, and stabilities at room temperature, defect formations, and structural deformations. While broad trends were identified using machine-learning analysis of all interfaces, density-functional theory was the main tool for studying the microscopic properties of selected elements. We found that the interfaces are the most stable energetically and with respect to lattice mismatch, defect formation, and lateral strain when their profiles were geometrically smooth. The most stable interfaces are found for transition metals. In addition, we demonstrate how universal machine-learning interatomic potentials now offer the accuracy required for the modeling of graphene-metallene interfaces. By systematically expanding the understanding of metallenes' interface properties, we hope these results guide and accelerate their synthesis to enable future applications and benefit from metallenes' appealing properties. Nanoscopic scale ; Interface (matter) ; Graphene ; Isotropy ; Interatomic potential ; Lattice (music) ; Molecular dynamics Machine Learning in Materials Science ; Graphene research and applications ; Surface and Thin Film Phenomena
Authors: Jingtong Zhang | Huazhang Zhang | Huanhuan Zheng | Bin Xu | Jie Wang | Xu Guo
Source: npj Computational Materials (article, 2025-10-08)
Field: Materials Science — Materials Chemistry

On-the-fly machine learning-assisted high accuracy second-principles model for BaTiO3 Second-principles method is an efficient way to build atomistic models and is widely used to simulate various properties of perovskite ferroelectric materials. However, the state-of-the-art approach to constructing training set for second-principles model still highly relies on researcher’s experience and a universal approach remains elusive. In this work, we combine machine learning and second principles method to achieve automatic generation of second-principles model. The original training set is derived from phonons and is then updated based on the uncertainties predicted by machine learning with data generated via molecular dynamics simulations. This approach allows us to obtain a machine learning assisted second-principles model for BaTiO3, which has a much-improved accuracy compared to the model in our previous work [Physical Review B, 108 134117 (2023)]. Furthermore, we investigate thermal transport properties of BaTiO3 with the new second-principles model, and find a weak wave-like contribution to the thermal conductivity. Set (abstract data type) ; Thermal ; Work (physics) ; Training set ; Experimental data ; Data set Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Ferroelectric and Negative Capacitance Devices
Authors: J. Lopez | Liliane Cristina Battirola | Joylan Nunes Maciel
Source: Journal of Materials Science (article, 2025-04-30)
Field: Materials Science — Biomaterials

Artificial neural network for predicting the mechanical behavior of extruded poly(lactic acid)/cellulose nanocrystal nanocomposites Materials science ; Nanocrystal ; Cellulose ; Nanocomposite ; Lactic acid ; Polymer science ; Composite material ; Polymer ; Solid mechanics ; Chemical engineering ; Nanotechnology Advanced Cellulose Research Studies ; biodegradable polymer synthesis and properties ; Nanocomposite Films for Food Packaging
Authors: Ben Ma | Chen Dang | Jung Hun Song | Zhaohui Chen | Yingke Zhou
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning–Accelerated Discovery of Novel High-Entropy Spinel Oxide Cathodes for Solid Oxide Fuel Cells Spinel ; Oxide ; Materials science ; Computer science ; Metallurgy Machine Learning in Materials Science ; Advancements in Solid Oxide Fuel Cells ; Electrocatalysts for Energy Conversion
Authors: Fuguang Han | Jun Li | Huantong Wu | Zhongxiang Zhou
Source:  (article, 2025-11-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Finite Element Simulation and Machine Learning of Magnetoelectric Coupling Properties of 2–2 Type Multiferroic Composites The magnetoelectric coupling properties in multiferroic materials has garnered significant attention from the research community over the past decade due to its widespread applications in spintronics, data storage, and electrically tunable microwave devices. The magnetoelectric coupling effect is the interaction between magnetic and electric polarization. This study employed finite element simulation and machine learning to analyze the magnetoelectric coupling characteristics of 2–2 type multiferroic composite materials and validated the reliability of the simulation method. Based on the constitutive equations derived from the magnetostriction of ferromagnetic components and the piezoelectric effect of ferroelectric components, the magnetoelectric coupling effect is achieved through stress transfer between the two components. The strain at the interface is simulated using an elastic mechanics model to accurately obtain key performance parameters such as magnetic flux, electric field distribution, and stress distribution. A neural network algorithm developed using finite element simulation data is combined with machine learning techniques to accelerate the optimization of performance prediction and structural design for multiferroic composites. After feature engineering optimization and Bayesian hyperparameter adjustment, the model achieved an R2 value of over 0.98, with an average absolute error (MAE) of only 0.0067. This study innovatively combines finite element simulation with machine learning, providing a theoretical framework for evaluating the magnetoelectric coupling coefficient of 2–2 type multiferroic composite materials and opening new avenues for optimizing magnetoelectric device design. Multiferroics ; Finite element method ; Magnetostriction ; Coupling (piping) ; Piezoelectricity ; Stress (linguistics) ; Ferroelectricity ; Magnetoelectric effect Multiferroics and related materials ; Magnetic Properties and Synthesis of Ferrites ; Composite Material Mechanics