jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jiawei Yi | Rui Xu | Wenhu Tang
Source:  (article, 2025-11-07)
Field: Materials Science — Materials Chemistry

Physics-Informed Neural Network for Multi-Material Transient Temperature Field Prediction in Dry-Type Transformers Prediction of the transient temperature field in dry-type transformers is crucial for assessing their operational status and ensuring the stability of power systems. Traditional methods, such as finite element analysis, can be computationally intensive and time-consuming and may not be suitable for realtime applications. This paper proposes a novel approach that combines Proper Orthogonal Decomposition (POD) with Physics-Informed Neural Network (PINN) to efficiently predict the transient temperature field in dry-type transformers. The proposed method first employs POD to reduce the dimensionality of the temperature field data. The PINN framework is then utilized to learn the cofficients of the POD basis functions, incorporating the governing physical equations into the training process to ensure physically consistent predictions. The proposed method is validated through numerical simulations, demonstrating its effectiveness in accurately predicting the transient temperature field while significantly reducing computation time compared to traditional methods. This approach offers a promising solution for real-time monitoring and assessment of transformer health in power systems. Artificial neural network ; Transformer ; Transient (computer programming) ; Control theory (sociology) ; Field (mathematics) ; Temperature measurement Thermal properties of materials ; Model Reduction and Neural Networks ; Thermal Analysis in Power Transmission
Authors: Carlos R. Jacinto-Mejía | Loriano Storchi | Giovanni Bistoni
Source: ChemRxiv (preprint, 2025-09-23)
Field: Materials Science — Materials Chemistry

Transferable and Transparent Energy Decomposition-based Machine Learning Models for Computing Accurate Reaction Energetics Artificial neural network ; Generalization ; Mean absolute percentage error ; Random forest ; Modular design ; Linear regression ; Benchmark (surveying) ; Pipeline (software) ; Classifier (UML) Machine Learning in Materials Science
Authors: Stephanos Karafiludis | Jakob Standl | Tom William Ryll | A.J. Schwab | Carsten Prinz | Sabine Kruschwitz | Franziska Emmerling | Christoph Völker | Tomasz M. Stawski
Source: ChemRxiv (preprint, 2025-04-16)
Field: Chemistry — Inorganic Chemistry

High-Entropy Phosphate Synthesis: Advancements through Automation and Sequential Learning Optimization Transition metal phosphates (TMPs) are extensively explored for electrochemical and catalytical applications due to their structural versatility and chemical stability. Within this material class, novel high-entropy metal phosphates (HEMPs)—containing multiple transition metals combined into a single-phase structure—are particularly promising, as their compositional complexity can significantly enhance functional properties. However, the discovery of suitable HEMP compositions is hindered by the vast compositional design space and complex or very specific synthesis conditions. Here, we present a data-driven strategy combining automated wet-chemical synthesis with a Sequential Learning App for Materials Discovery (SLAMD) framework (Random Forest regression model) to efficiently explore and optimize HEMP compositions. Using a limited set of initial experiments, we identified near-equimolar multi-metal compositions in a single-phase crystalline solid. The model successfully predicted a novel Co0.3Ni0.3Fe0.2Cd0.1Mn0.1 phosphate octahydrate phase, validated experimentally, demonstrating the effectiveness of the machine learning approach. This work highlights the potential of integrating automated synthesis platforms with data-driven algorithms to accelerate the discovery of high-entropy materials, offering an efficient designing pathway to advanced functional materials. Phosphate ; Computer science ; Automation ; Entropy (arrow of time) ; Chemistry ; Artificial intelligence ; Combinatorial chemistry ; Engineering ; Biochemistry ; Physics ; Thermodynamics ; Mechanical engineering Inorganic Chemistry and Materials ; Chemical Synthesis and Characterization ; Machine Learning in Materials Science
Authors: Song, Jiahe | Wang, Chuang | Jiang, Bowen | Wang, Yinfan | Zheng, Hao | Wei, Xingjian | Liu, Chengjin | Nie, Rui | Gao, Junyuan | Sun, Jiaxing | Wang, Yubin | Wu, Lijun | Huang, Zhenhua | Wu, Jiang | Yu, Qian | He, Conghui
Source: arXiv (Cornell University) (preprint, 2025-11-04)
Field: Materials Science — Materials Chemistry

RxnCaption: Reformulating Reaction Diagram Parsing as Visual Prompt Guided Captioning Large-scale chemical reaction datasets are crucial for AI research in chemistry. However, existing chemical reaction data often exist as images within papers, making them not machine-readable and unusable for training machine learning models. In response to this challenge, we propose the RxnCaption framework for the task of chemical Reaction Diagram Parsing (RxnDP). Our framework reformulates the traditional coordinate prediction driven parsing process into an image captioning problem, which Large Vision Language Models (LVLMs) handle naturally. We introduce a strategy termed BBox and Index as Visual Prompt (BIVP), which uses our state-of-the-art molecular detector, MolYOLO, to pre-draw molecular bounding boxes and indices directly onto the input image. This turns the downstream parsing into a natural-language description problem. Extensive experiments show that the BIVP strategy significantly improves structural extraction quality while simplifying model design. We further construct the RxnCaption-15k dataset, an order of magnitude larger than prior real-world literature benchmarks, with a balanced test subset across four layout archetypes. Experiments demonstrate that RxnCaption-VL achieves state-of-the-art performance on multiple metrics. We believe our method, dataset, and models will advance structured information extraction from chemical literature and catalyze broader AI applications in chemistry. We will release data, models, and code on GitHub. Parsing ; Closed captioning ; Bounding overwatch ; Process (computing) ; Construct (python library) ; Task (project management) ; Code (set theory) ; Diagram Machine Learning in Materials Science ; Multimodal Machine Learning Applications ; Topic Modeling
Authors: Mulusew
W. Yaltaye (22019952) | Yingqi Zhao (4862782) | Kuo Zhan (21142084) | Eva Bozo (22019955) | Pei-Lin Xin (12355366) | Vahid Farrahi (21083534) | Francesco De Angelis (8459256) | Jian-An Huang (1284306)
Source: Figshare (article, 2025-08-08)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Single-MoleculeSERS Detection of Phosphorylationin Serine and Tyrosine Using Deep Learning-Assisted Plasmonic Nanopore Single-molecule detection of post-translational modifications (PTMs) such as phosphorylation plays a crucial role in early diagnosis of diseases and therapeutics development. Although single-molecule surface-enhanced Raman spectroscopy (SM-SERS) detection of PTMs has been demonstrated, the data analysis and detection accurracies were hindered by interference from citrate signals and lack of reference databases. Previous reports required complete coverage of the nanoparticle surface by analyte molecules to replace citrates, hampering the detection limit. Here, we developed a high-accuracy SM-SERS approach by combining a plasmonic particle-in-pore sensor to collect SM-SERS spectra of phosphorylation at Serine and Tyrosine, k-means-based clustering for citrate signal removal, and a one-dimensional convolutional neural network (1D-CNN) for phosphorylation identification. Significantly, we collected SM-SERS data with submonolayer analyte coverage of the particle surface and discriminated the phosphorylation in Serine and Tyrosine with over 95% and 97% accuracy, respectively. Finally, the 1D-CNN features were interpreted by a one-dimensional gradient feature weight and SM-SERS peak occurrence frequencies. Phosphorylation ; Analyte ; Serine ; Nanopore ; Tyrosine ; Surface-enhanced Raman spectroscopy ; Plasmon ; Tyrosine phosphorylation Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Degang Zhao | Qingquan Bao | Robert A. Riggleman
Source: The Journal of Chemical Physics (article, 2025-10-28)
Field: Materials Science — Materials Chemistry

Accelerating self-consistent field theoretic simulations for disordered systems with deep learning Polymer science holds a pivotal role in areas such as advanced materials design, drug delivery systems, and biological systems, where being able to efficiently predict polymer thermodynamics and self-assembly is crucial. Self-consistent field theory (SCFT) offers a theoretical framework with many successful predictions that have guided experiments. However, there are classes of systems that are challenging to describe with SCFT due to their computational expense, such as anisotropic systems and worm-like chain models. In this study, we take a first step toward alleviating these challenges by developing a machine-learning approach that can predict density fields directly from the potential fields without the need for computing chain propagators, which is typically the computationally demanding step of a field theory. By integrating different types of neural network models into SCFT, we compared the performance of the models and developed a robust and computationally efficient model for Gaussian chain models that form disordered, inhomogeneous (microphase separated) structures. Our model is able to achieve a speedup of more than three times for the same size systems and up to 100 times for larger systems in our tested systems. The results of this work demonstrate one strategy for how deep learning can be leveraged to improve the efficiency of large-scale SCFT simulations, and the methods herein could be readily extended to other, more computationally demanding models. Field (mathematics) ; Deep learning ; Chain (unit) ; Artificial neural network ; Speedup ; Complex system ; Work (physics) Block Copolymer Self-Assembly ; Machine Learning in Materials Science ; Material Dynamics and Properties
Authors: Austin Rodriguez (21646866) | Justin S. Smith (5436020) | Jose L. Mendoza-Cortes (1288968)
Source: Figshare (article, 2025-07-02)
Field: Materials Science — Materials Chemistry

Does Hessian Data Improve the Performance of MachineLearning Potentials? The integration of machine learning into reactive chemistry, materials discovery, and drug design is transforming the development of novel molecules and materials. Machine Learning Interatomic Potentials (MLIPs) predict potential energies and forces with quantum chemistry accuracy, surpassing traditional approaches. Incorporating force fitting in MLIP training enhances potential-energy surface predictions and improves model transferability and reliability. This paper introduces and evaluates the integration of Hessian matrix training in MLIPs, which encodes second-order information about the PES curvature. Our evaluation focuses on models trained only to equilibrium geometries and first-order saddle points (i.e., critical points on the potential surface), demonstrating their ability to extrapolate to nonequilibrium geometries. This integration improves extrapolation capabilities, allowing MLIPs to accurately predict energies, forces, and Hessian predictions for nonequilibrium geometries. Hessian-trained MLIPs enhance reaction pathway modeling, transition state identification, and vibrational spectra predictions, benefiting molecular dynamics (MD) simulations and Nudged Elastic Band (NEB) calculations. By analyzing models trained with varying combinations of energy, force, and Hessian data on a small molecule reactive data set, we demonstrate that models including Hessian information not only extrapolate more accurately to unseen molecular systems, improving accuracy in reaction modeling and vibrational analysis, but also reduce the total amount of data required for effective training. However, the primary trade-off is increased computational expense, as Hessian training requires more resources than conventional energy-force training. Our findings provide comprehensive insights into the advantages and limitations of Hessian integration in MLIP training, allowing practitioners in computational chemistry to make informed decisions about employing this method in accordance with their specific research objectives and computational constraints. Hessian matrix ; Extrapolation ; Non-equilibrium thermodynamics ; Saddle point ; Overhead (engineering) ; Data point ; Benchmark (surveying) ; Molecular dynamics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Physical and Chemical Molecular Interactions
Authors: José
A. Pérez (22593620) | María M. Zanardi (8708721) | Ariel M. Sarotti (1366479)
Source: Figshare (article, 2025-11-10)
Field: Materials Science — Materials Chemistry

Low-Cost, High-AccuracyReactivity Modeling: IntegratingGenetic Algorithms and Machine Learning with Multilevel DFT Calculations Accurate prediction of Gibbs activation energies (Δ<i>G</i><sup>‡</sup>) for Diels–Alder (DA) reactions remains a critical challenge in computational chemistry, as conventional density functional theory (DFT) methods often fail to consistently achieve chemical accuracy (<1 kcal mol<sup>–1</sup>). In this work, we demonstrate that no single method reliably meets this threshold through a systematic evaluation of 720 DFT approaches across 24 DA reactions. Here, we introduce a proof-of-concept framework that integrates a genetic algorithm and machine learning (GA-ML) to intelligently select cost-effective, multilevel DFT combinations. Our optimized GA1 model identified four low-cost DFT combinations that yield Δ<i>G</i><sup>‡</sup> predictions with a mean absolute error (MAE) of 0.4 kcal mol<sup>–1</sup> in both training and external validation sets, matching the accuracy of high-level CCSD(T) calculations at a fraction of the computational cost. To further enhance adaptability, we introduce Dynamic Generalization-Driven Transfer Learning (DGDTL), a novel method that adaptively optimizes linear coefficients, ensuring robust predictions for both known and unseen reactions. The integration of GA-ML and DGDTL establishes a scalable, high-fidelity paradigm for reactivity prediction, with potential applications in catalysis, drug design, and materials science. Matching (statistics) ; Genetic algorithm ; Density functional theory ; Computational learning theory ; Training set ; Fraction (chemistry) Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Computational Drug Discovery Methods
Authors: Om Prakash Hota | Pragyan Mohanty | Ranjita Mahapatra
Source: Brazilian Journal of Physics (article, 2025-09-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Future Research Direction on Rare-Earth and Alkaline-Earth Metal-Doped LaMnO3 Perovskites Dopant ; Doping ; Lanthanum manganite ; Perovskite (structure) ; Manganite ; Nanoscopic scale ; Oxide ; Variety (cybernetics) ; Lanthanum Magnetic and transport properties of perovskites and related materials ; Electronic and Structural Properties of Oxides ; Advanced Condensed Matter Physics
Authors: Li-Hui Mou (5007050) | Gui-Duo Jiang (9357637) | Chao Wang (146527) | Xin Cheng (179584) | Jia Yu (220212) | Zi-Yu Li (1360101) | Jun Jiang (149215)
Source: Figshare (article, 2025-04-08)
Field: Materials Science — Materials Chemistry

Unveiling UniversalReactivity Descriptors of MetalClusters toward Dinitrogen Activation: A Machine Learning Protocolwith Three-Level Feature Extraction The activation of dinitrogen (N<sub>2</sub>) by metal clusters is a fundamental challenge in chemistry and has been extensively studied. However, previous studies have primarily focused on case-by-case interpretations of experimental reactivity, resulting in a lack of universal reactivity descriptors and predictive models that can quantitatively estimate reaction rates and elucidate structure–activity relationships. In this study, we develop predictive, interpretable, and transferable machine learning models for metal cluster reactivity toward N<sub>2</sub>, employing a systematic and hierarchical feature extraction strategy that generates electronic and intrinsic features from three structural levels. Crucial features influencing cluster reactivity were identified, and the mechanisms by which these features govern reactivity were elucidated. To enhance model robustness and transferability, pairwise learning (data augmentation) and feature interaction (feature augmentation) strategies were employed, leading to a deep neural network model that correlates feature differences with reaction rate differences. Trained on 159 homonuclear metal clusters, the model demonstrates satisfactory transferability to 57 heteronuclear metal clusters, enabling reaction rate predictions based on their feature differences. This work presents an expert-guided machine learning protocol for developing generalizable models to predict and understand metal cluster reactivity. Homonuclear molecule ; Reactivity (psychology) ; Robustness (evolution) ; Heteronuclear molecule ; Feature (linguistics) ; Cluster (spacecraft) ; Artificial neural network Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Inorganic Chemistry and Materials
Authors: Xiao Yuan
Source:  (article, 2025-11-27)
Field: Materials Science — Materials Chemistry

Cross-scale deep learning from microstructure to macro-mechanics: predicting strength and durability from SEM/CT images This paper presents a novel cross-scale deep learning framework for predicting macroscopic mechanical properties, including strength and durability parameters, directly from microstructural images of materials. Unlike traditional approaches that rely on manual feature extraction or phenomenological modeling, our method integrates convolutional neural networks (CNN) with attention mechanisms to establish a direct mapping between multiscale microstructural characteristics and mechanical responses. The framework processes Scanning Electron Microscopy (SEM) and Micro-Computed Tomography (Micro-CT) images through a dual-path architecture that captures both local texture information and global morphological features. We evaluate our approach on diverse material systems, including commercial steels, AZ80 magnesium alloys, and carbon fiber reinforced polymers (CFRP). Experimental results demonstrate that our model achieves less than 2.2% prediction error for yield strength and ultimate tensile strength, while accurately estimating fatigue-related parameters and damage progression. The integration of physical constraints and multi-task learning further enhances the model's generalizability and physical interpretability, providing a powerful tool for accelerating materials design and performance assessment. Convolutional neural network ; Durability ; Deep learning ; Ultimate tensile strength ; Generalizability theory ; Texture (cosmology) ; Microstructure ; Carbon fiber reinforced polymer ; Segmentation Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Electron and X-Ray Spectroscopy Techniques
Authors: Jianhu Gong | Zhengming Zhang | Fan Zhou | H. S. Fu | Hongchang Wang | Dunhui Wang
Source: Advanced Science (article, 2025-09-26)
Field: Materials Science — Materials Chemistry

A Generative Framework for Predicting Antiferromagnets Predicting antiferromagnets (AFMs) is crucial for advancing ultrafast spintronics. However, traditional methods are constrained by the complexity of correlated electrons and magnetic ordering, limiting systematic exploration of new chemical spaces. Herein, an AFM design framework integrating a crystal diffusion variational autoencoder is presented with data augmentation (CDVAE-DA), crystal graph convolutional neural networks (CGCNNs), a genetic algorithm (GA), and density functional theory (DFT) validation. CDVAE-DA generates chemically diverse and structurally valid candidates, achieving a composition validity rate of 90.68%. Leveraging transfer learning on an AFM dataset, the CDVAE-DA is biased toward generating AFM structures. The CGCNNs efficiently screen for potential AFMs using three properties: formation energy (FE), total magnetic moment (TMM), and band gap (BG). The GA regulates the direction of structure generation using these properties, while DFT calculations rigorously validate the lattice stability and AFM order of generated structures. Employing this framework with GA yields three AFM semiconductors (MnS, FeO<sub>4</sub>P, and MnO) from 2000 generated structures. In contrast, omitting GA identifies two metallic AFMs (LiVO<sub>2</sub>, LiFeN) from 5000 structures. This underscores the GA's role in optimizing latent vectors to facilitate efficient AFM generation. This work establishes a design paradigm for AFMs, accelerating next-generation spintronic material discovery. Spintronics ; Density functional theory ; Autoencoder ; Stability (learning theory) ; Lattice (music) ; Electron ; Skew ; Magnetic moment Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advanced Condensed Matter Physics
Authors: Moon‐ki Choi | Daniel Palmer | Harley T. Johnson
Source: ArXiv.org (preprint, 2025-10-18)
Field: Materials Science — Materials Chemistry

Graph Neural Network for Unified Electronic and Interatomic Potentials: Strain-tunable Electronic Structures in 2D Materials We introduce UEIPNet, an equivariant graph neural network designed to predict both interatomic potentials and tight-binding (TB) Hamiltonians for an atomic structure. The UEIPNet is trained using density functional theory calculations followed by Wannier projection to predict energies and forces as node-level targets and Wannier-projected TB matrices as edge-level targets. This enables physically consistent modeling of coupled mechanical electronic responses with near-DFT accuracy. Trained on bilayer graphene and monolayer MoS2 DFT data, UEIPNet captures key deformation-electronic effects: in twisted bilayer graphene, it reveals how interlayer spacing, in-plane strain, and out-of-plane corrugation drive isolated flat-band formation, and further shows that modulating substrate interaction strength can generate flat bands even away from the magic angle. For monolayer MoS2, the UEIPNet accurately reproduces phonon dispersions, strain-dependent band-gap evolution, and local density of states modulations under non-uniform strain. The UEIPNet offers a generalized, efficient, and scalable framework for studying deformation-electronic coupling in large-scale atomistic systems, bridging classical atomistic simulations and electronic-structure calculations. Bilayer ; Wannier function ; Density functional theory ; Monolayer ; Electronic structure ; Bridging (networking) ; Phonon ; Artificial neural network ; Coupling (piping) 2D Materials and Applications ; Graphene research and applications ; Topological Materials and Phenomena