jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ziauddin Ursani | Dmytro Antypov | Katie Atkinson | Matthew S. Dyer | Matthew J. Rosseinsky | Sven Schewe | Andrij Vasylenko
Source:  (article, 2025-11-05)
Field: Materials Science — Materials Chemistry

Domain Independent XAI for Material Science The need for explainability in artificial intelligence cannot be overstated. Lack of explainability has been identified as the main obstacle in the implementation of many AI technologies in real-world scenarios. Recognising the different explainability requirements within different domains, we do not take a prescriptive view to try to standardise explainability. Instead, we focus on the needs of domain experts in terms of their explainability requirements. In this paper, the focus application is materials science, a field in which one lens of explainability is focussed on the connection between the features and the output, yet recognising that there is another viewpoint where domain knowledge is not always seen as essential. Complex deep learning non-interpretable methods have been shown to excel on performance metrics, which is then followed by combinatorial search of compound databases for discovery of new materials. In our work, we do not use domain knowledge, but the model is interpretable and matches the performance of complex methods. This approach offers the additional benefit of supporting the discovery of new materials by providing some explainable data. We introduce here a new approach to XAI in materials science where explainability is present from start to finish and is independent of any characteristic feature. We formalise the structure of materials science explainability in three stages: pre-modelling, modelling, and post-modelling, aligning with the AI literature. To practically implement these three stages, we use the recently introduced concepts of hierarchical learning and Monte Carlo ensemble. We call combined approach as an Explainable Hierarchical Monte Carlo Ensemble (EHME). We evaluate the performance of EHME on materials science classification datasets without using their characteristic features, with respect to machine learning parameters. Furthermore, we show that the EHME models provide some explainability that informs the materials discovery process. We also extend EHME to general machine learning classification datasets where we compare their prediction performance against contemporary methods to demonstrate generalisability of the method. Focus (optics) ; Domain (mathematical analysis) ; Field (mathematics) ; Obstacle ; Monte Carlo method ; Domain knowledge Machine Learning in Materials Science ; Explainable Artificial Intelligence (XAI) ; Advanced Graph Neural Networks
Authors: Abhijeet J. Kale | Sanjeev S. Navaratna | Pratik Sahu | Henry Chan | B. R. K. Nanda | Rohit Batra
Source: ArXiv.org (article, 2025-12-24)
Field: Materials Science — Materials Chemistry

Active Learning Guided Computational Discovery of 2D Materials with Large Spin Hall Conductivity Two-dimensional (2D) materials are promising candidates for next-generation spintronic devices due to their tunable properties and potential for efficient spin-charge interconversion. However, discovering materials with intrinsically high spin Hall conductivity (SHC) is hindered by the vast chemical space and expensive nature of conventional experimental and first-principles methods. In this work, we employ an active learning framework to accelerate the discovery of high-SHC 2D materials. Machine learning (ML) models were trained on SHC values computed from density functional theory calculations, incorporating the Kubo formalism via tight-binding Hamiltonians constructed from maximally localized Wannier functions, with explicit treatment of spin-orbit coupling. Starting from random but chemically diverse 24 2D systems, the dataset was expanded to 41 cases (from an overall pool of around 2000 materials) over three active learning loops using an expected improvement acquisition strategy. The ML technique successfully identified several high SHC candidates with the best candidate exhibiting a SHC of 271.52 (hbar/e) Ohm^-1, nearly 23 times higher than the top performer in the initial round. Beyond candidate discovery, several features such as orbital symmetry near the Fermi energy, types of atomic species, material composition, covalent radii, and electronegativity of constituent atoms were found to play critical role in shaping the spin Hall response in 2D systems. The data generated is made publicly available to facilitate further advances in 2D spintronics. Spintronics ; Wannier function ; Spin (aerodynamics) ; Density functional theory ; Spin Hall effect ; Formalism (music) ; Electronegativity ; Spin density Machine Learning in Materials Science ; 2D Materials and Applications ; Topological Materials and Phenomena
Authors: Jun Peng | Daniel Hensel | Rakshith Venugopal | Lucas Rave | Colin Schormann | Wolfram Koch | Robert H. Blick | Zierold, Robert
Source: Desy publication database (The Deutsches Elektronen-Synchrotron) (article, 2025-01-01)
Field: Materials Science — Polymers and Plastics

ALD‐Assisted VO$_2$ for Memristor Application Vanadium dioxide (VO$_2$) is a well-known candidate for memristor applications due to its insulator-to-metal transition (IMT) characteristics. The fabrication of memristor devices requires highly controlled synthesis processes concerning the material chemistry and geometry. Atomic layer deposition (ALD) offers unique advantages for the fabrication of hardware neural networks, such as miniaturization, conformality, and sub-nm thickness control. Herein, an ALD process for non-stoichiometric vanadium oxide (VO$_x$) using tetrakis(dimethylamino)vanadium (TDMAV) and water as precursors is presented. Subsequently, a tailor-made annealing process converts VO$_x$ into VO$_2$, which exhibits an IMT of about three orders of magnitude at around 70 °C, rendering it a promising memristor material. VO$_2$ thin film and Si–Al$_2$O$_3$/VO$_2$ core/shell memristors are fabricated and analyzed, both of which exhibited I–V hysteresis loops, indicating their suitability for memristor applications in both 2D and 3D morphologies. Additionally, these memristors are sensitive to the operation temperature, with the hysteresis loops narrowing and shifting toward lower voltages as temperature increases, eventually disappearing beyond VO$_2$'s intrinsic phase transition temperature. This study highlights the viability of ALD-assisted VO$_2$ for memristor applications and demonstrates its potential for advancing the three-dimensionalization of neuromorphic chips. Memristor ; Fabrication ; Neuromorphic engineering ; Annealing (glass) ; Atomic layer deposition ; Vanadium ; Thin film ; Hysteresis ; Voltage Transition Metal Oxide Nanomaterials ; Advanced Memory and Neural Computing ; Ferroelectric and Negative Capacitance Devices
Authors: G S Rekha | M. Anantha Sunil
Source: Eurasian journal of physics and functional materials (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Studies on Machine Learning-Guided Prediction and Experimental Validation of Ni-Doped BaCO₃ for Energy Applications Machine learning (ML) techniques are increasingly recognized as powerful tools for accelerating the discovery of functional materials for energy applications. In this study, multiple supervised ML algorithms, including Random Forest, Support Vector Machine, Naïve Bayes, and Logistic Regression, were implemented to classify and predict oxide-based compounds with potential piezoelectric properties. Among these, the Random Forest model exhibited the best predictive performance, achieving an accuracy of 94.15%, precision of 0.74, recall of 0.99, and a Gini coefficient of 0.93. Guided by ML predictions, BaCO₃ was identified as a promising candidate and further optimized through Ni doping. Experimental validation was carried out via wet-chemical synthesis, and X-ray diffraction (XRD) confirmed the formation of a polycrystalline orthorhombic phase. A clear peak shift to lower angles in Ni-doped samples confirmed successful lattice substitution, while higher Ni concentrations (15%) induced secondary phase formation. This combined computational–experimental approach demonstrates how ML-guided screening significantly reduces cost and time, offering a scalable pathway toward the rational design of energy materials for piezoelectric and dielectric applications. Random forest ; Support vector machine ; Energy (signal processing) ; Piezoelectricity ; Dielectric ; Scalability ; Orthorhombic crystal system ; Experimental data Machine Learning in Materials Science ; Advanced Sensor and Energy Harvesting Materials ; X-ray Diffraction in Crystallography
Authors: Frank M. Abel | Peter J. Burke | Daniel Wines | Brian Donovan | Jamer, Michelle E. | Kamal Choudhary
Source: ArXiv.org (preprint, 2025-12-15)
Field: Materials Science — Materials Chemistry

Machine Learning for Predicting Magnetization from X-ray Diffraction of Iron Oxide Nanoparticles Using Simple Physics-Based Data Generation Automation and high-throughput characterization and synthesis for material development are becoming increasingly common; these approaches require machine learning (ML) tools to assess material properties, ideally based on a single measurement. Here, ML models are developed to predict magnetization from X-ray diffraction (XRD) for iron oxide nanoparticles. Our approach is to first develop a set of simulated data that links modulated XRD, based on a crystallographic information file (CIF), to a simple magnetic model to determine magnetization at a given magnetic field, thereby enabling us to train Random Forest and Gradient Boosting regression models on a large amount of simulated data. The models are validated by synthesizing iron oxide nanoparticles and measuring their crystal structure via XRD and room-temperature magnetization curves. In doing so, we can fine-tune both the training hyperparameters and the optimal size of the simulated datasets used to train the models. Through this optimization, the best models can achieve an $R^2$ greater than 0.9 for five experimental samples, used for tuning, for predicting the max magnetization (at 2.8 T) of the measurement. Lastly, we demonstrate reasonable predictions on the full magnetization vs. magnetic field curve, showing that the RF model excels at predicting the high magnetic field values, which is key for determining the success of an iron oxide nanoparticle synthesis for applications like magnetic particle imaging (MPI), thermal magnetic particle imaging (T-MPI), and hyperthermia. Magnetization ; Iron oxide nanoparticles ; Magnetic nanoparticles ; Magnetic particle inspection ; Diffraction ; Magnetic field ; Nanoparticle ; Magnetic domain ; Characterization (materials science) Machine Learning in Materials Science ; Characterization and Applications of Magnetic Nanoparticles ; Nanoparticle-Based Drug Delivery
Authors: Zitao Chen | Y.Z. Jia | Z. Ryan Tian | Wei‐Ying Ma | Yanyan Lan
Source: ArXiv.org (preprint, 2025-06-01)
Field: Materials Science — Materials Chemistry

Manipulating 3D Molecules in a Fixed-Dimensional E(3)-Equivariant Latent Space Medicinal chemists often optimize drugs considering their 3D structures and designing structurally distinct molecules that retain key features, such as shapes, pharmacophores, or chemical properties. Previous deep learning approaches address this through supervised tasks like molecule inpainting or property-guided optimization. In this work, we propose a flexible zero-shot molecule manipulation method by navigating in a shared latent space of 3D molecules. We introduce a Variational AutoEncoder (VAE) for 3D molecules, named MolFLAE, which learns a fixed-dimensional, E(3)-equivariant latent space independent of atom counts. MolFLAE encodes 3D molecules using an E(3)-equivariant neural network into fixed number of latent nodes, distinguished by learned embeddings. The latent space is regularized, and molecular structures are reconstructed via a Bayesian Flow Network (BFN) conditioned on the encoder's latent output. MolFLAE achieves competitive performance on standard unconditional 3D molecule generation benchmarks. Moreover, the latent space of MolFLAE enables zero-shot molecule manipulation, including atom number editing, structure reconstruction, and coordinated latent interpolation for both structure and properties. We further demonstrate our approach on a drug optimization task for the human glucocorticoid receptor, generating molecules with improved hydrophilicity while preserving key interactions, under computational evaluations. These results highlight the flexibility, robustness, and real-world utility of our method, opening new avenues for molecule editing and optimization. Chemical space ; Autoencoder ; Space (punctuation) ; Key (lock) ; Latent variable ; Inpainting ; Bayesian network ; Interpolation (computer graphics) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Z. Qian | Guangwu Zhang | Zhizhu Lai | Ayaka Hanai | Yixin Xu | Guang Wang | Lü Yang | Jiaqi Gu | Yanguang Zhou | Takahiko Yanagitani | Junjun Jia | Qiye Zheng
Source: Acta Materialia (article, 2025-11-26)
Field: Materials Science — Materials Chemistry

Tailoring thermal transport in (Sc,Yb)AlN thin films to the glassy limit Amorphous solid ; Thin film ; Thermal diffusivity ; Wurtzite crystal structure ; Ionic bonding ; Ternary operation ; Thermal conductivity ; Phonon Thermal properties of materials ; Thermal Expansion and Ionic Conductivity ; Optical properties and cooling technologies in crystalline materials
Authors: A. A. Gainanova | Andrey Glushko | Alexey Neznanov | Г. М. Кузьмичева | M. O. Yaroshenko | Н. А. Лобанова
Source: Research Square (preprint, 2025-01-20)
Field: Materials Science — Materials Chemistry

Guided Synthesis of the stable sols based on nanosized titanium(IV) oxides with the predictable particle size and antimicrobial properties Particle size ; Titanium ; Antimicrobial ; Materials science ; Chemical engineering ; Particle (ecology) ; Nanotechnology ; Chemistry ; Metallurgy ; Organic chemistry ; Geology Nanoparticles: synthesis and applications ; Pigment Synthesis and Properties ; Advanced Nanomaterials in Catalysis
Authors: Shachar Fite (11036221) | Zeev Gross (1575301)
Source: Figshare (article, 2025-04-20)
Field: Materials Science — Materials Chemistry

Data-Driven Insightsinto Porphyrin Geometry: InterpretableAI for Non-Planarity and Aromaticity Analyses Porphyrins are involved in numerous and very different chemical and biological processes, due to the sensitivity of their application-relevant properties to subtle structural changes. Applying modern machine learning methodology is very appealing for discovering structure–activity relationships that can be used for design of tailor-made porphyrins for specific purposes. For achieving this goal, a high-quality set consisting of 425 metal porphyrins was established via curation of 7590 porphyrin structures from the Cambridge crystallographic database. Using data-driven techniques for analyzing nonplanarity and “structural aromaticity” allowed for validation of common knowledge in the field as well as discovery of new relations. Aromaticity was found to be influenced differently by distinct nonplanar distortions. Nonplanarity is more sensitive to macrocycle substitutions than to metal or axial ligand effects, while ruffled distortions are dominated by axial ligand size and metal properties. These findings offer new insights into structure–property relationships in porphyrins, providing a data-driven foundation for targeted synthesis to tune aromaticity and nonplanarity. Despite data set limitations, this work demonstrates the value of machine learning in uncovering complex chemical trends. Porphyrin ; Aromaticity ; Ligand (biochemistry) ; Field (mathematics) ; Quantitative structure–activity relationship ; Set (abstract data type) ; Value (mathematics) Machine Learning in Materials Science ; Synthesis and Properties of Aromatic Compounds ; Advanced Graph Neural Networks
Authors: Lei Han | Chunmei Duan | Taochuan Zhang
Source: Comptes Rendus Mécanique (article, 2025-08-07)
Field: Materials Science — Metals and Alloys

Predicting stress–strain constitutive relationships: a deep learning approach based on multi-head attention mechanism and long short-term memory networks The stress–strain constitutive relationship of materials is a critical indicator for assessing their mechanical properties and serves as a vital bridge connecting microstructures with macroscopic mechanical behaviors. This study focuses on 106 metal alloy materials, employing the finite element method to establish uniaxial tensile numerical models, thereby simulating and obtaining a comprehensive dataset of stress–strain relationship curves. Innovatively, this research proposes a MA-LSTM-DLnet prediction model that integrates multi-head attention mechanisms with long short-term memory networks, based on deep learning methodologies. The model utilizes conventional key parameters such as material strain, yield strength, tensile strength, elastic modulus, density, strength coefficient, and stress as input features to construct a multi-dimensional training dataset. It particularly explores the learning capabilities and predictive performance of the MA-LSTM-DLnet model under both complete and limited feature datasets. The results demonstrate that the trained MA-LSTM-DLnet model can accurately predict the stress–strain constitutive relationship curves of new materials by merely acquiring conventional physical parameters like yield strength, tensile strength, and elastic modulus, with a similarity exceeding 95% compared to test data. Compared to traditional empirical formulas and numerical simulation methods, the main innovation of this study lies in eliminating key parameters that are difficult to determine in mathematical models (such as the strength coefficient in the Ludwik isotropic hardening model), significantly reducing repetitive work dependent on experiments to obtain constitutive relationships, and achieving efficient and precise material performance evaluation. Constitutive equation ; Isotropy ; Ultimate tensile strength ; Stress (linguistics) ; Materials science ; Stress–strain curve ; Computer science ; Elastic modulus ; Structural engineering ; Finite element method ; Artificial intelligence ; Engineering ; Composite material Hydrogen embrittlement and corrosion behaviors in metals ; Advanced machining processes and optimization ; Microstructure and Mechanical Properties of Steels
Authors: Nathan J. Meigs (22437584) | Logan Gibb (22437587) | Nick Kurtyka (15390587) | Aaron M. Chalifoux (4225954) | Tara Mastren (4715298) | Luther W. McDonald (1423471)
Source: Figshare (article, 2025-10-15)
Field: Materials Science — Materials Chemistry

Comparative MorphologicalSignatures of Strike OrderedUranium Oxides for Nuclear Forensics Nuclear forensics remains critical in addressing threats associated with the illicit and malicious use of radioactive materials, necessitating continuous advancements in the characterization of uranium ore concentrates (UOCs). In this study, we investigate the influence of precipitation strike order on the morphological features of U<sub>3</sub>O<sub>8</sub> derived from four synthetic routes: ammonium uranyl carbonate (AUC), ammonium diuranate (ADU), uranyl peroxide (UO<sub>4</sub>), and magnesium diuranate (MDU). Machine learning classification was performed to differentiate between the synthetic route and strike order, capturing subtle variations in product morphology. Morphological signatures of scanning electron microscopy (SEM) images were analyzed using a convolutional neural network (CNN) architecture based on a ResNet34 backbone for transfer learning, with images processed into “spot” locations that combine multiple magnifications and detector modes to enhance feature extraction. While most synthetic pathways displayed marked differences in morphology between strike orders (<i>p</i> < 0.05), the magnesium diuranate (MDU) route exhibited only minor morphological differences, 42–72% discrimination accuracy, and no statistical significance, as determined by the CNN analysis. These findings indicate that the dependence of the final morphology of UOCs on strike order is dependent on the synthetic route, resulting in distinct signatures for AUC, ADU, and UO<sub>4</sub> but not MDU for the synthesis processes used. A distinct strike order fingerprint may have wider implications for forensic attribution and process monitoring. Convolutional neural network ; Ammonium carbonate ; Uranium ; Uranyl ; Feature (linguistics) ; Pattern recognition (psychology) ; Characterization (materials science) ; Process (computing) Nuclear Materials and Properties ; Radioactive contamination and transfer ; Radioactive element chemistry and processing
Authors: Shilong Li | Bengang Wei | Siyuan Shen | Xiaodong Wang | Han Zhang | Jun Liu | Yi Liu | Yakun Liu
Source: IET conference proceedings. (article, 2025-10-01)
Field: Materials Science — Materials Chemistry

A CNN-BiLSTM-Attention model of aging prediction for high-temperature superconducting cables High-temperature superconducting (HTS) cables have emerged as a promising solution for short-distance, high-current power transmission and urban electrical networks. However, traditional aging evaluation methods are inadequate for the distinctive material characteristics of HTS conductors and their complex operating environments. In this study, we leverage operational data from an in-service HTS cable project to investigate the interdependencies among cable core temperature, nitrogen cooling system temperature, and flow rate under different operating conditions. The HTS cables’ degradation trajectory is further assessed and three deep learning algorithms are compared to forecast the remaining useful life of the HTS cable. The proposed convolutional neural networks with bidirectional long short-term memory and attention mechanisms (CNN-BiLSTM-Attention) model achieves a 40–60% reduction in prediction errors. This work offers insights into effective approaches for predicting the aging process of HTS cables. Leverage (statistics) ; Electrical conductor ; Process (computing) ; Reduction (mathematics) ; Work (physics) ; Power transmission ; Power (physics) ; Superconductivity High voltage insulation and dielectric phenomena ; Fuel Cells and Related Materials ; Power System Reliability and Maintenance
Authors: Pieter Dobbelaere | Sander Vandenhaute | Véronique Van Speybroeck
Source: ArXiv.org (preprint, 2025-04-04)
Field: Chemistry — Inorganic Chemistry

Cluster-based machine learning potentials to describe disordered metal-organic frameworks up to the mesoscale Metal-organic frameworks (MOFs) are highly interesting and tunable materials. By incorporating spatial defects into their atomic structure, MOFs can be finetuned to exhibit precise chemical functionalities, extending their applicability in various technological fields. Defect engineering requires a fundamental understanding of the nature of spatial disorder and consequent changes in material properties, which is currently lacking. We introduce the cluster-based learning methodology, enabling the development of state-of-the-art machine learning potentials (MLPs) from defective systems at any length scale. Our method identifies atomic interactions in bulk structures and extracts local environments as finite molecular fragments to augment the model's training data where needed. We show that cluster-based learning delivers MLPs capable of accurately describing spatial defects in mesoscopic systems with over twenty thousand atoms. Afterwards, we select our best model to investigate some major mechanical properties of spatially disordered UiO-66-derived structures, elucidating the influence of defect concentration and composition on material behaviour. Our analysis includes large supercell structures, demonstrating that (near-) ab initio accuracy is within reach at the mesoscale. Mesoscopic physics ; Supercell ; Ab initio ; Mesoscale meteorology ; Complex system ; Divide and conquer algorithms Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography