jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yuma Iwasaki | Daisuke Ogawa | Masato Kotsugi | Y. K. Takahashi
Source: Science and Technology of Advanced Materials Methods (article, 2025-03-07)
Field: Materials Science — Materials Chemistry

Autonomous materials search using machine learning and ab initio calculations for L1 <sub>0</sub> -FePt-based quaternary alloys The efficient exploration of expansive material spaces remains a significant challenge in materials science. To address this issue, autonomous material search methods that combine machine learning with ab initio calculations have emerged as a promising solution. These approaches offer a systematic and rapid means of discovering new materials, particularly when the material space is too large. This requirement is particularly important in the development of L10-structured alloys as magnetic recording media. These materials require a high magnetic moment (M) and magnetocrystalline anisotropy energy (EMCA) to satisfy the demands of next-generation data storage technologies. Although autonomous search methods have been successfully applied to various material systems, quaternary L10 alloys with optimized magnetic properties remain an open and underexplored frontier. In this study, we present a simulation-based autonomous search method aimed at identifying quaternary L10 alloys with enhanced M and EMCA values. Over a continuous 100-day search, our system suggested the FeMnPtEr alloy system as a promising candidate, exhibiting superior values for both M and EMCA. Although further experimental validation is required, this study underscores the potential of autonomous search methods to accelerate the discovery of advanced materials. Ab initio ; Materials science ; Quaternary ; Ab initio quantum chemistry methods ; Chemistry ; Molecule ; Geology ; Organic chemistry Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; X-ray Diffraction in Crystallography
Authors: Lauren Way | Catalin D. Spataru | Reese E. Jones | Dallas R. Trinkle | Andrew J. E. Rowberg | Joel B. Varley | Robert B. Wexler | Christopher M. Smyth | Tyra C. Douglas | Sean R. Bishop | Elliot J. Fuller | Anthony H. McDaniel | Stephan Lany | Matthew Witman
Source: Chemistry of Materials (article, 2025-08-26)
Field: Materials Science — Materials Chemistry

Defect Diffusion Graph Neural Networks for Materials Discovery in High-Temperature Energy Applications The migration of crystallographic defects dictates material properties and performance for a plethora of technological applications. Density functional theory (DFT)-based nudged elastic band (NEB) calculations are a powerful computational technique for predicting defect migration activation energy barriers, yet they become prohibitively expensive for high-throughput screening of defect diffusivities. Without introducing hand-crafted (i.e., chemistry- or structure-specific) descriptors, we propose a generalized deep learning approach to train surrogate models for NEB energies of vacancy migration by hybridizing graph neural networks with transformer encoders and simply using pristine host structures as input. With sufficient training data, computationally efficient and simultaneous inference of vacancy defect thermodynamics and migration activation energies can be obtained to compute temperature-dependent vacancy diffusivities and to down-select candidates for more thorough DFT analysis or experiments. Thus, as we specifically demonstrate for potential water-splitting materials, candidates with desired defect thermodynamics, kinetics, and host stability properties can be more rapidly targeted from open-source databases of experimentally validated or hypothetical materials. Materials science ; Artificial neural network ; Diffusion ; Graph ; Nanotechnology ; Computer science ; Engineering physics ; Physics ; Theoretical computer science ; Artificial intelligence ; Thermodynamics Machine Learning in Materials Science ; Metal Extraction and Bioleaching ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Pan-dong Lin | Lin Yan | Hongguang Li | Shu-gang Cui | Jun-feng Nie | Binnian Zhong | Yu‐peng Lu
Source: Materials & Design (article, 2025-03-11)
Field: Materials Science — Materials Chemistry

Molecular dynamics and machine learning study of tensile behavior in single-crystal tungsten containing He bubbles • Impact of He bubbles on the tensile behavior of single-crystal tungsten was studied. • The analysis considers varying He bubble sizes, He/V ratios, temperatures, and strain rates. • Machine learning models were used to predict the peak stress. • Provide important insights into tungsten’s tensile deformation under irradiation conditions. Tungsten is commonly used in nuclear fusion plants, where irradiation defects (e.g., He bubbles) are frequently generated. This study investigates the impact of He bubbles on the tensile behavior of single-crystal tungsten through molecular dynamics (MD) simulations. The analysis considers varying He bubble sizes, He/V ratios (the number of helium atoms with respect to the number of vacancies in helium bubble), temperatures, and strain rates. The findings indicate that He bubbles significantly affect the material’s mechanical properties, with larger bubble sizes reducing tensile strength. Dislocation emission initiates from the void surface during tensile deformation. While the He/V ratio slightly influences peak stress values, it does not alter the overall stress–strain curve. Elevated temperatures lower peak stress, whereas higher strain rates increase it. Additionally, machine learning models predict the combined effects of bubble size, He/V ratio, strain rate, and temperature on the peak stress of tungsten, utilizing MD simulation data. This work offers important insights into tungsten’s behavior under irradiation conditions. Materials science ; Tungsten ; Molecular dynamics ; Single crystal ; Ultimate tensile strength ; Dynamics (music) ; Composite material ; Nanotechnology ; Metallurgy ; Crystallography ; Computational chemistry ; Psychology Nuclear Materials and Properties ; High-pressure geophysics and materials ; Nuclear Physics and Applications
Authors: Abhigyan Ojha | Ayush Tiwari | Sai Aditya Tagore Vambaravelli | Κ. K. Sahu | Sivaiah Bathula
Source: ACS Applied Energy Materials (article, 2025-07-21)
Field: Materials Science — Materials Chemistry

Exploring Phase Stability and Transport Properties of Emerging Thermoelectric Materials: Machine Learning and Experimental Insights This study investigates the thermoelectric (TE) properties of newly synthesized materials, including Heusler alloys, magnesium silicides, and tetrahedrites, identified through machine learning (ML) approaches and validated experimentally. Critical descriptors for predicting important properties of TE materials, such as the band gap, formation energy, and Seebeck coefficient (S), which strongly influence material stability and TE efficiency, are systematically assessed. Although density functional theory (DFT) remains a foundational approach for predicting TE performance, its computational expense limits its widespread application. To address this challenge, crystal graph convolutional neural networks (CGCNN) are employed to efficiently predict band gap and formation energy from graph-based representations of crystal structures. The experimentally measured band gaps of the synthesized materials show strong agreement with CGCNN predictions. Additionally, experimental measurements of S across different TE classes corroborate computational estimates, demonstrating the robustness of the ML models. For instance, the newly synthesized double half-Heusler compound Ti2FeNiSb2 exhibits an experimental S value of approximately −120 μV/K at 700 K, closely matching the Random Forest model prediction of −143.75 μV/K across the 300–700 K range. The CGCNN model achieves a mean absolute error (MAE) of 0.261 eV for band gap and 0.058 eV/atom for formation energy approximately 1.5 and 2 times lower, respectively, than those reported for earlier pretrained models. Furthermore, for the S prediction, the Random Forest algorithm, optimized using a root-mean-square error loss function, improves accuracy by ∼43.55% compared to previous ML methods. Overall, the integration of ML techniques accelerates TE material discovery, overcoming the limitations of conventional computational approaches. Stability (learning theory) ; Materials science ; Thermoelectric effect ; Phase (matter) ; Thermoelectric materials ; Computer science ; Machine learning ; Physics ; Thermodynamics Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Thermal properties of materials
Authors: Can Polat | Hasan Kurban | Erchin Serpedin | Mustafa Kurban
Source: ArXiv.org (preprint, 2025-05-17)
Field: Materials Science — Materials Chemistry

Understanding the Capabilities of Molecular Graph Neural Networks in Materials Science Through Multimodal Learning and Physical Context Encoding Molecular graph neural networks (GNNs) often focus exclusively on XYZ-based geometric representations and thus overlook valuable chemical context available in public databases like PubChem. This work introduces a multimodal framework that integrates textual descriptors, such as IUPAC names, molecular formulas, physicochemical properties, and synonyms, alongside molecular graphs. A gated fusion mechanism balances geometric and textual features, allowing models to exploit complementary information. Experiments on benchmark datasets indicate that adding textual data yields notable improvements for certain electronic properties, while gains remain limited for others. Furthermore, the GNN architectures display similar performance patterns (improving and deteriorating on analogous targets), suggesting they learn comparable representations rather than distinctly different physical insights. Exploit ; Context (archaeology) ; Encoding (memory) ; Artificial neural network ; Graph ; Benchmark (surveying) ; Focus (optics) ; Molecular graph Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: O Borgard | Natthaphol Chomsaeng | Komkrisd Wongtimnoi | Laurent Mezeix
Source: Bulletin of Materials Science (article, 2025-12-20)
Field: Materials Science — Materials Chemistry

Predicting and designing properties of twelve alloy families using artificial neural networks and generative adversarial networks Artificial neural network ; Generative grammar ; Key (lock) ; Reliability (semiconductor) ; Generator (circuit theory) ; Aerospace ; Adversarial system Machine Learning in Materials Science ; High Entropy Alloys Studies ; Additive Manufacturing Materials and Processes
Authors: Truong Nhut Huynh | Xiang He | Kim-Doang Nguyen
Source: Journal of Chemical Information and Modeling (article, 2025-11-26)
Field: Materials Science — Materials Chemistry

Predicting and Analyzing Nitrate Adsorption on High-Entropy Alloys Based on Pair Distribution Function Using a Hybrid Machine Learning Framework Incorporating five or more metals into a single structure creates a new family of alloys, high-entropy alloys (HEAs), which hold several exceptional properties, such as outstanding stabilities and continuous electronic structures, making them promising catalysts for a range of chemical conversions. Due to the high-dimensional design space, machine learning algorithms are frequently used for the optimization of high-entropy alloys to achieve enhanced catalytic performance. The precision of machine learning depends on the structural features of high-entropy alloys, and this work aims to explore whether pair distribution function (PDF) data of HEAs can be adopted as input features for the probabilistic optimization of HEA-based catalysts through machine learning. Here, we first address the challenge of the high dimensionality of PDF data through principal component analysis (PCA), and then use the PCA-reduced PDF as input features to predict the Gibbs free energy of nitrate adsorption on the FeCoNiCuZn HEA surfaces via a hybrid framework comprising a transformer-based model and a fine-tuned large language model (LLM). The results show that the as-constructed hybrid framework can accurately predict the Gibbs free energy of nitrate adsorption using PCA-reduced PDF data, with performance significantly superior to that of conventional algorithms such as random forest, support vector regression, and gradient boosting. In the meantime, the use of LLM can further improve the prediction accuracy and extract interpretable insights from the data set, eventually allowing for the predictive design of HEA-based catalysts with optimized activity and selectivity. Gibbs free energy ; Support vector machine ; Curse of dimensionality ; Principal component analysis ; Dimensionality reduction ; Probabilistic logic ; Component (thermodynamics) ; Range (aeronautics) Machine Learning in Materials Science ; Ammonia Synthesis and Nitrogen Reduction ; Electrocatalysts for Energy Conversion
Authors: Yin-Bo Tseng | Hsiu‐Wei Cheng
Source: Surfaces and Interfaces (article, 2025-07-16)
Field: Materials Science — Materials Chemistry

Application of machine learning on quantitative XPS analysis for heteroatoms (F, La and N) Alloyed HfO2 thin film Materials science ; X-ray photoelectron spectroscopy ; Thin film ; Heteroatom ; Metallurgy ; Analytical Chemistry (journal) ; Chemical engineering ; Nanotechnology ; Chromatography ; Organic chemistry Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Semiconductor materials and devices
Authors: M. J. Wang | Jingxuan Ding | Grace Xiong | Ni Zhan | Cameron J. Owen | Albert Musaelian | Yu Xie | Nicola Molinari | Ryan P. Adams | Sossina M. Haile | Boris Kozinsky
Source: ArXiv.org (preprint, 2025-03-19)
Field: Materials Science — Materials Chemistry

Revealing the proton slingshot mechanism in solid acid electrolytes through machine learning molecular dynamics In solid acid solid electrolytes CsH$_2$PO$_4$ and CsHSO$_4$, mechanisms of fast proton conduction have long been debated and attributed to either local proton hopping or polyanion rotation. However, the precise role of polyanion rotation and its interplay with proton hopping remained unclear. Nanosecond-scale molecular dynamics simulations, driven by equivariant neural network force fields, reveal a nuanced proton slingshot mechanism: protons are initially carried by rotating polyanions, followed by O$-$H bond reorientation, and the combined motion enables long-range jumps. This challenges the conventional revolving paddlewheel model and reveals significant independent proton motion that is assisted by limited rotations. Despite structural similarities, we identify qualitative differences in transport mechanisms between CsH$_2$PO$_4$ and CsHSO$_4$, caused by different proton concentrations. CsH$_2$PO$_4$ exhibits two distinct rates of rotational motions with different activation energies, contrasting with CsHSO$_4$'s single-rate behavior. The higher proton concentration in CsH$_2$PO$_4$ correlates with frustrated PO$_4$ polyanion orientations and slower rotations compared to SO$_4$ in CsHSO$_4$. Additionally, we reveal a correlation between O-sharing and proton transport in CsH$_2$PO$_4$, a unique feature due to extra proton per polyanion compared to CsHSO$_4$. Our findings suggest that reducing proton concentration could accelerate rotations and enhance conductivity. This work provides a unified framework for understanding and optimizing ionic mobility in solid-acid compounds, offering new insights into the interplay between proton hopping and disordered dynamics in polyanion rotation. Proton ; Molecular dynamics ; Proton transport ; Work (physics) ; Electrolyte ; Rotation (mathematics) ; Ionic bonding Machine Learning in Materials Science ; Fuel Cells and Related Materials
Authors: Bowen Chen | Ke Li | Yao Hou | Yifeng Luo | Zhenbing Cai | Zhanjiang Wang
Source: Materials Today Communications (article, 2025-05-20)
Field: Materials Science — Materials Chemistry

Machine learning-based prediction of wear for GH4169 alloy at different temperatures, loads, and displacements Materials science ; Alloy ; Metallurgy ; Composite material ; Forensic engineering Metal Alloys Wear and Properties ; Metal and Thin Film Mechanics ; Mechanical stress and fatigue analysis
Authors: Song Li | Songli Dai | Shiyu Xiao | Zhi Yu | Heng Wang | Zean Tian
Source: Journal of Alloys and Compounds (article, 2025-07-15)
Field: Materials Science — Materials Chemistry

Explainable machine learning-guided design of high-performance thermoelectric materials Thermoelectric effect ; Materials science ; Thermoelectric materials ; Computer science ; Mechanical engineering ; Manufacturing engineering ; Engineering ; Composite material ; Physics Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Thermal properties of materials
Authors: S. Parameshwara | Girisha Kanuvanahalli Bettaiah | Manjunath Patel Gowdru Chandrashekarappa | N.B. Pradeep | Oğuzhan Der | Chithirai Pon Selvan
Source: Engineering Reports (article, 2025-09-01)
Field: Materials Science — Materials Chemistry

Hybrid Neural Network Modeling of Mechanical Alloying Process Parameters and Alloy Performance Using Forward and Reverse Mapping ABSTRACT Mechanical alloying (MA) process synthesizes a nanostructured Ti‐Mg‐Zr ternary alloy suitable for biomedical applications. The desirable characteristics of titanium, magnesium, and zirconium (such as high biocompatibility, corrosion resistance and low density) enable the development of alloys with minimal magnetic resonance imaging (MRI) artifacts. Retaining the nanostructure (alloy performances such as crystallite size CS and lattice strain LS) in Ti‐Mg‐Zr alloys is influenced by MA parameters (milling speed: MS, duration: MD, and ball‐to‐powder‐weight ratio: BPR). The present work establishes forward and reverse modeling between the MA parameters and alloy performance using neural networks such as artificial bee colony (ABC‐NN), backpropagation (BPNN), and genetic algorithm (GA‐NN). Forward modeling predicts alloy performances from known MA parameters, while reverse modeling estimates the MA parameters for the desired alloy performances. A dataset of 1,000 samples was generated using empirical regression equations and was supplied to networks in batch mode. A detailed parametric study was conducted to optimize network parameters. The optimized network parameters resulted in reduced mean squared error (MSE) corresponding to forward and reverse modeling tasks equal to 0.0021 and 0.0065 for ABC‐NN, 0.0056 and 0.0097 for BPNN, and 0.0034 and 0.0076 for GA‐NN, respectively. The trained networks are tested against 10 randomly generated experimental cases for forward and reverse predictions, resulting in 3.41% and 3.55% for BPNN, 3.24% and 3.30% for ABC‐NN, and 3.30% and 3.33% for GA‐NN, respectively. The ABC‐NN predictions help industry personnel to scale up the synthesis of nanostructured Ti‐Mg‐Zr alloy for permanent implants requiring regular MRI examinations. Artificial neural network ; Backpropagation ; Alloy ; Parametric statistics ; Ternary operation ; Process (computing) ; Mean squared error ; Scale (ratio) Titanium Alloys Microstructure and Properties ; Additive Manufacturing Materials and Processes ; Metal and Thin Film Mechanics
Authors: D. Canadinç | E. Breitbach | A.A. Catal
Source: Shape Memory and Superelasticity (article, 2025-07-10)
Field: Materials Science — Materials Chemistry

Characterization of Two Novel NiTiHf Shape Memory Alloys Designed by Machine Learning Utilizing Novel Experimental Techniques Characterization (materials science) ; Shape-memory alloy ; Computer science ; Materials science ; Engineering drawing ; Artificial intelligence ; Nanotechnology ; Engineering Shape Memory Alloy Transformations ; Advanced Memory and Neural Computing ; Transition Metal Oxide Nanomaterials
Authors: Shun Li | Lei Fang | Tao Liu | Xuping Wang | Bing Liu | Yuanyuan Zhang | Xianshun Lv | Lei Wei
Source: Computational Materials Science (article, 2025-01-11)
Field: Materials Science — Materials Chemistry

Machine learning-accelerated molecular dynamics calculations for investigating the thermal modulation by ferroelectric domain wall in KTN single crystals Ferroelectricity ; Molecular dynamics ; Modulation (music) ; Thermal ; Materials science ; Domain (mathematical analysis) ; Dynamics (music) ; Chemical physics ; Nanotechnology ; Condensed matter physics ; Optoelectronics ; Chemistry ; Physics ; Computational chemistry ; Thermodynamics ; Dielectric Ferroelectric and Piezoelectric Materials ; Machine Learning in Materials Science ; Inorganic Chemistry and Materials
Authors: Nazir Mustapha | Mokhtar Hjiri
Source: Journal of Materials Science (article, 2025-10-14)
Field: Materials Science — Materials Chemistry

Review: machine learning-driven design and discovery of halide perovskite materials Perovskite (structure) ; Workflow ; Halide ; Feature (linguistics) ; Computation ; Band gap ; Density functional theory Machine Learning in Materials Science ; Perovskite Materials and Applications ; Magnetic and transport properties of perovskites and related materials
Authors: Li L | Haowen Wang | You Wu | Keyuan Chen | Haoxiang Zhang | Xingkao Zhang | Ju Rong | Yudong Sui | Xiaohua Yu | Jing Feng
Source: Applied Physics A (article, 2025-05-16)
Field: Materials Science — Materials Chemistry

Deep learning molecular dynamics for large-scale insights into yttrium's structural, thermal and diffusion properties Yttrium ; Molecular dynamics ; Diffusion ; Scale (ratio) ; Chemical physics ; Thermal ; Materials science ; Dynamics (music) ; Nanotechnology ; Chemistry ; Physics ; Computational chemistry ; Thermodynamics ; Metallurgy Machine Learning in Materials Science ; Nuclear Materials and Properties ; Nuclear Physics and Applications
Authors: J.M. You | Sook Young Moon | Bonchan Koo | Seung‐Yeol Jeon
Source: Journal of Industrial and Engineering Chemistry (article, 2025-11-01)
Field: Chemistry — Spectroscopy

Modeling arrhenius kinetics in CNT aerogel synthesis via FC-CVD using a CFD–machine learning hybrid framework Aerogel ; Arrhenius equation ; Decomposition ; Carbon nanotube ; Kinetics ; Catalysis ; Carbon fibers Aerogels and thermal insulation ; Thermal properties of materials ; Carbon Nanotubes in Composites
Authors: Małgorzata Łazarska | Zbigniew Ranachowski | Janusz Musiał | Tomasz Tański | Qingshan Jiang
Source: Materials (article, 2025-05-10)
Field: Materials Science — Materials Chemistry

Identification of Phase Transformations in Alloy and Non-Alloy Steel During Austempering Using Acoustic Emission and Neural Network This research was carried out for selected alloy (bearing) and non-alloy (tool) steel. The steels were subjected to austempering. The hardening temperature range was from 100 °C to 180 °C. The use of acoustic emission in connection with the artificial neural network (ANN) enabled the analysis and identification of phase changes occurring in steels during austempering. Classification of acoustic emission events was carried out with the help of their energy values and with the use of an artificial neural network. On this basis, it was observed that in the process of isothermal hardening of steel at the applied temperatures, complex transformations of austenite into martensite and bainite occur. In addition, it was found that the research methods used enabled the identification of signal components originating from the phase transformation causing the formation of thin-plate martensite midrib. The use of acoustic methods in the field of bainitic transformation creates the possibility of their application in the industry. Austempering ; Materials science ; Austenite ; Acoustic emission ; Alloy ; Metallurgy ; Alloy steel ; Bainite ; Martensite ; Hardening (computing) ; Artificial neural network ; Isothermal process ; Isothermal transformation diagram ; Microstructure ; Composite material ; Computer science ; Artificial intelligence ; Thermodynamics Metal Alloys Wear and Properties ; Microstructure and Mechanical Properties of Steels ; Advancements in Materials Engineering