jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Joseph Oche Agada | Andrew McAninch | Haley A. Day | Yasemin Tanyu | Ewan McCombs | S. M. Koohpayeh | Brian H. Toby | Yishu Wang | Arpan Biswas
Source: ArXiv.org (article, 2025-12-29)
Field: Materials Science — Materials Chemistry

Physically-Constrained Autoencoder-Assisted Bayesian Optimization for Refinement of High-Dimensional Defect-Sensitive Single Crystalline Structure Physical properties and functionalities of materials are dictated by global crystal structures as well as local defects. To establish a structure-property relationship, not only the crystallographic symmetry but also quantitative knowledge about defects are required. Here we present a hybrid Machine Learning framework that integrates a physically-constrained variational-autoencoder (pcVAE) with different Bayesian Optimization (BO) methods to systematically accelerate and improve crystal structure refinement with resolution of defects. We chose the pyrochlore structured Ho2Ti2O7 as a model system and employed the GSAS2 package for benchmarking crystallographic parameters from Rietveld refinement. However, the function space of these material systems is highly nonlinear, which limits optimizers like traditional Rietveld refinement, into trapping at local minima. Also, these naive methods don't provide an extensive learning about the overall function space, which is essential for large space, large time consuming explorations to identify various potential regions of interest. Thus, we present the approach of exploring the high Dimensional structure parameters of defect sensitive systems via pretrained pcVAE assisted BO and Sparse Axis Aligned BO. The pcVAE projects high-Dimensional diffraction data consisting of thousands of independently measured diffraction orders into a lowD latent space while enforcing scaling invariance and physical relevance. Then via BO methods, we aim to minimize the L2 norm based chisq errors in the real and latent spaces separately between experimental and simulated diffraction patterns, thereby steering the refinement towards potential optimum crystal structure parameters. We investigated and compared the results among different pcVAE assisted BO, non pcVAE assisted BO, and Rietveld refinement. Rietveld refinement ; Diffraction ; Bayesian optimization ; Scaling ; Crystal structure ; Crystal structure prediction ; Powder diffraction ; Tetrahedron Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Model Reduction and Neural Networks
Authors: Zezhou Wu
Source: Advances in Engineering Technology Research (article, 2025-07-28)
Field: Materials Science — Materials Chemistry

Predictive Modeling in High-Temperature Superconductors: Comparative Insights from Density Functional Theory and Machine Learning Superconductivity, defined by the disappearance of electrical resistance below a critical temperature, holds significant promise for future technologies such as quantum computing, maglev transport, and energy-efficient power grids. Among the many superconducting materials, nickel and copper oxide stand out due to their comparable layered structures but distinct electronic properties. Understanding and predicting their superconducting behavior is essential for discovering new high-temperature superconductors. Density functional theory and machine learning have become indispensable tools in this effort. While DFT offers insights into band structure and orbital interactions, ML models enable high-throughput screening of potential materials. However, data scarcity, model interpretability, and limited generalizability remain significant barriers to progress. This review critically evaluates the effectiveness and limitations of these predictive techniques, identifies unresolved issues, and discusses integrative research strategies that combine theory, simulation, and experimentation to accelerate discoveries in high-Tc superconductivity. This comparative analysis offers a roadmap for building interpretable, efficient, and scalable predictive models in the next phase of high-Tc materials research. Density functional theory ; Superconductivity ; Artificial intelligence ; Computer science ; Machine learning ; Physics ; Statistical physics ; Condensed matter physics ; Quantum mechanics Machine Learning in Materials Science ; Superconductivity in MgB2 and Alloys ; Physics of Superconductivity and Magnetism
Authors: Xing Chen | Weichen Liu | Tianyu Ruan | Jinsong Shao | S. Sundara Pandiyan | Min Yao | Li Wang
Source: ACS Omega (article, 2025-07-11)
Field: Materials Science — Materials Chemistry

MCST-AFN: A Multichannel Spatiotemporal Feature Adaptive Fusion Network Framework Based on a Low-Fidelity Molecular Dynamics Model The capability of predicting molecular properties plays a crucial role in drug development, and the learning of molecular representations stands as the primary step in tasks aimed at predicting molecular properties. Static three-dimensional (3D) structural information has been shown to significantly aid in molecular representation; however, molecules are in constant motion and change, implying that their properties should be closely linked with dynamic molecular conformations. Traditional four-dimensional (4D) Quantitative Structure-Property Relationship (QSPR) methods, while incorporating time as a dimension, have high computational costs and fail to fully integrate the temporal dimension, leading to ineffective integration of molecular conformation ensembles. Inspired by deep learning-based molecular dynamics (DLMD) techniques and multifidelity learning (MFL) strategies, in this work, a multichannel spatiotemporal feature adaptive fusion network framework (MCST-AFN) based on a low-fidelity molecular dynamics model is proposed. This framework integrates deep learning technology with molecular dynamics (MD) simulations, effectively enhancing molecular representation while significantly reducing computational costs. Initially, a low-fidelity molecular dynamics simulation model is trained using real molecular dynamics simulation data. Compared to existing tools such as Amber, this low-fidelity model can update atomic coordinates at a lower computational cost and output multichannel atom-level embeddings that encapsulate information across different time scales. Subsequently, an attention-based network is constructed to achieve adaptive fusion of multichannel spatiotemporal features, and a self-supervised learning task for atom masking prediction is designed to further enhance molecular representation. The MCST-AFN was tested on 13 benchmark data sets for molecular property prediction, achieving an average performance improvement of 2.10% across 12 data sets. The most significant enhancement was seen in the ESOL data set, with a performance boost of 19.70%. Feature (linguistics) ; Dynamics (music) ; Fidelity ; Fusion ; Computer science ; Biological system ; Artificial intelligence ; Physics ; Biology ; Telecommunications Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Complex Network Analysis Techniques
Authors: Hasan M. Sayeed | Casey Clark | Taylor D. Sparks
Source: ChemRxiv (preprint, 2025-06-22)
Field: Materials Science — Materials Chemistry

KnowMat: Transforming Unstructured Material Science Literature into Structured Knowledge The rapid expansion of scientific literature in materials science presents challenges for efficiently extracting and analyzing experimental data. To address this, we introduce KnowMat, an accessible pipeline that transforms unstructured texts into structured, machine-readable datasets. Leveraging lightweight open-source Large Language Models (LLMs), such as Llama 3.1 (8B) and Llama 3.2 (3B) through the Ollama platform, KnowMat automatically extracts key materials information including composition, processing conditions, characterization methods, and properties. Implemented via an intuitive Flask-based web interface, users can easily upload documents, manage extraction tasks, and save structured outputs directly to CSV files, facilitating database creation and integration into machine learning workflows. Evaluation on real-world materials science papers demonstrates KnowMat’s accuracy, efficiency, and usability on consumer-grade hardware, significantly reducing barriers to data-driven materials research. Workflow ; Computer science ; Upload ; Pipeline (software) ; Usability ; Key (lock) ; Unstructured data ; Interface (matter) ; World Wide Web ; Database ; Information retrieval ; Data science ; Software engineering ; Human–computer interaction ; Data mining ; Programming language ; Big data Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques
Authors: Huadong Yang | Yuxiao Zhu | Harold M. H. Chong | Ruomeng Huang
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Accurate and Efficient Design and Optimization of Thermoelectric Cooler Via Machine Learning Technologies Thermoelectric effect ; Thermoelectric cooling ; Computer science ; Industrial engineering ; Manufacturing engineering ; Mathematical optimization ; Mechanical engineering ; Artificial intelligence ; Engineering ; Mathematics ; Physics Advanced Thermoelectric Materials and Devices ; Thermal Radiation and Cooling Technologies ; Advanced Thermodynamics and Statistical Mechanics
Authors: Abdalaziz Rashid Al-Maeeni | Д. Деркач | A. Ustyuzhanin
Source: ArXiv.org (preprint, 2025-01-28)
Field: Materials Science — Materials Chemistry

Engineering Point Defects in MoS2 for Tailored Material Properties using Large Language Models The tunability of physical properties in transition metal dichalcogenides (TMDCs) through point defect engineering offers significant potential for the development of next-generation optoelectronic and high-tech applications. Building upon prior work on machine learning-driven material design, this study focuses on the systematic introduction and manipulation of point defects in MoS2 to tailor their properties. Leveraging a comprehensive dataset generated via density functional theory (DFT) calculations, we explore the effects of various defect types and concentrations on the mate rial characteristics of TMDCs. Our methodology integrates the use of pre-trained large language models to generate defect configurations, enabling efficient predictions of defect-induced property modifications. This research differs from traditional methods of material generation and discovery by utilizing the latest advances in transformer model architecture, which have proven to be efficient and accurate discrete predictors. In contrast to high-throughput methods where configurations are generated randomly and then screened based on their physical properties, our approach not only enhances the understanding of defect-property relationships in TMDCs but also provides a robust framework for designing materials with bespoke properties. This facilitates the advancement of materials science and technology. Point (geometry) ; Computer science ; Mathematics Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Xiaohua Li | Yi Liu | Wenbin Zhao | Yikun Zhao | Long Fu | Zhiyuan Zheng
Source: IET Electric Power Applications (article, 2025-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Research on core loss prediction of low‐frequency transformer based on Grey Wolf optimisation algorithm optimised Back Propagation neural network Abstract In this paper, a prediction model of 20 Hz low‐frequency transformer core loss based on the grey wolf optimisation algorithm‐optimised back propagation neural network is proposed. Firstly, the loss characteristics of silicon steel sheet materials at different low‐frequency temperatures and normal temperatures at different frequencies were compared. The general law of the variation of no‐load iron loss with frequency and temperature is analysed. Finally, the BP neural network prediction model of low‐frequency transformer core loss is established. The loss data obtained by experiment and simulation are used as training and verification samples to predict transformer core loss. The results show that the GWO‐BP neural network loss model proposed in this paper successfully predicted the no‐load loss of the transformer at different temperatures. When the prediction effect of the GWO‐BP model was optimal, the determination coefficient R 2 reached 0.9169, and the mean relative error and root mean square error were only 1.15% and 0.0085, respectively. Moreover, the MRE of the GWO‐BP model is within 9%. Compared with the BP model and whale optimization algorithm‐BP model, the prediction accuracy of the loss is improved by the GWO‐BP model, and the calculation time of the loss is reduced by the finite element method. Artificial neural network ; Backpropagation ; Transformer ; Algorithm ; Computer science ; Artificial intelligence ; Engineering ; Electrical engineering ; Voltage Magnetic Properties and Applications ; Non-Destructive Testing Techniques
Authors: Radaković, Jana | Batalović, Katarina
Source: VinaR (Institute of Nuclear Sciences "Vinča") (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Universal, target-focused, and target-adapted word embeddings as autonomous predictors in materials design Static word embeddings derived from scientific literature offer untapped potential as autonomous machine learning predictors in materials design. By treating the joined embeddings of atoms constituting a chemical compound as a repository of physicochemical information, we investigated the robustness of encoded domain-specialized statistical patterns to the inherent variability of embeddings occurring due to information heterogeneity within the language model training corpus. Results demonstrate that observed substantial variability does not inhibit the information transfer enabled by mapping composite predictors of materials onto physicochemical properties. Furthermore, regardless of the lack of direct interpretability, statistical properties integral to computing the real-world targets are distributed throughout these information-rich arrays, with the transitive character of encoded information depending on the target itself. High-order feature interactions facilitate access to the fine-grained atomic properties; accordingly, before merging atom embeddings into a composite vector, we can calibrate predictors to remove any dimensions irrelevant to the target. Robustness (evolution) ; Transitive relation ; Word (group theory) ; Bridging (networking) ; Character (mathematics) ; Feature (linguistics) ; Statistical learning Machine Learning in Materials Science ; Material Selection and Properties ; Advanced Graph Neural Networks
Authors: Libin Wu | Shenghang Zhang | Haiyong Weng | Shangpeng Sun | Dapeng Ye
Source: Measurement (article, 2025-07-31)
Field: Chemistry — Analytical Chemistry

Integrating micro-hyperspectral imaging and machine learning to investigate mie scattering for early detection of microbial contamination in liquid fermentation cultures Hyperspectral imaging ; Contamination ; Fermentation ; Liquid culture ; Artificial intelligence ; Computer science ; Microbiology ; Biology ; Food science ; Ecology ; Botany Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Huiya Wang | Lu Zhou | Pengnan Zhao | Yujie Huang | Yaofeng Zhu | Guangsheng Wang
Source: Advanced Materials Technologies (article, 2025-11-05)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Compatibility Challenges and Strategies of Multispectral Stealth Materials: From Microwave‐Terahertz Absorption to Infrared Camouflage Abstract The rapid advancement of radar detection and communication technologies, coupled with the increasing complexity of electromagnetic (EM) environments, has driven the demand for multispectral‐compatible stealth materials that can function across microwave (MW), terahertz (THz), and infrared (IR) bands. This review systematically summarizes the recent progress in the design and development of ultrabroadband EM waves (EMWs) stealth materials. First, it clarifies the fundamental principle of achieving single‐spectrum stealth and the inherent challenges of multi‐spectrum compatibility, especially the contradictory requirements in different frequency bands. Designing multispectral compatible stealth materials requires a rational design of the multi‐scale structure and composition at both macro/micro level and atomic/molecular level. Subsequently, an analysis of the state‐of‐the‐art materials and structures, such as aerogels, multilayer composites, and macroscopic metamaterials, is carried out. Particular emphasis is placed on heterogeneous interface engineering, gradient design, bioinspired concepts, and intelligent responsive systems. Ultimately, potential solutions, including deepening the understanding of the loss mechanism and machine learning‐assisted material design, are presented to develop multifunctional, adaptive stealth systems. This review aims to provide valuable insights and inspiration to researchers in next‐generation multispectral‐compatible stealth technologies. Camouflage ; Multispectral image ; Terahertz radiation ; Radar ; Cloaking ; Infrared ; Reflectivity ; Microwave Metamaterials and Metasurfaces Applications ; Electromagnetic wave absorption materials ; Advanced Antenna and Metasurface Technologies
Authors: Yash Barhate | Yun-Sun Kang | Neda Nazemifard | Bernhard Renner | Yihui Yang | Charles D. Papageorgiou | Zoltan K. Nagy
Source: Systems and Control Transactions (article, 2025-06-30)
Field: Materials Science — Materials Chemistry

Data-driven Digital Design of Pharmaceutical Crystallization Processes Mechanistic population balance modeling (PBM) has advanced the design of pharmaceutical crystallization processes, enabling the production of active pharmaceutical ingredient (API) crystals with desired critical quality attributes (CQAs), such as purity and crystal size distribution. However, PBM development can sometimes be resource-intensive, requiring extensive design of experiments (DoE) and high-quality process data, making it impractical under fast-paced industrial development timelines. This study proposes a machine learning (ML)-based workflow for developing �fit-for-purpose� digital twins of crystallization processes, leveraging industrially available DoE data to link operating conditions with CQAs. Validated on industrial data for a commercial API with complex crystallization challenges, the workflow efficiently identifies optimal operating conditions, demonstrating the potential of data-driven digital twins to accelerate the development of pharmaceutical processes. Crystallization ; Computer science ; Chemical engineering ; Engineering Crystallization and Solubility Studies ; Computational Drug Discovery Methods ; Machine Learning in Materials Science
Authors: Yuangui Yang | Yuanzhong Wang
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2025-12-29)
Field: Chemistry — Analytical Chemistry

Qualitative and quantitative analysis of Paris polyphylla var. yunnanensis from different origin using multi-dimensions FT-MIR and NIR spectroscopy, data fusion combined with machine learning Partial least squares regression ; Principal component analysis ; Pattern recognition (psychology) ; Linear discriminant analysis ; Fusion ; Sensor fusion ; Artificial neural network ; Support vector machine Spectroscopy and Chemometric Analyses ; Traditional Chinese Medicine Analysis ; Phytochemical Studies and Bioactivities
Authors: Suhwan Song | Heejae Kim | Jaehee Jang | H.S. Cho | Gunhee Kim | Geonu Kim
Source: ArXiv.org (preprint, 2025-09-30)
Field: Materials Science — Materials Chemistry

Scalable Reactive Atomistic Dynamics with GAIA Groundbreaking advances in materials and chemical research have been driven by the development of atomistic simulations. However, the broader applicability of atomistic simulations remains limited, as they inherently depend on energy models that are either approximate or computationally prohibitive for large-scale simulations. Machine learning interatomic potentials (MLIPs) have recently emerged as a promising class of energy models, but their deployment also remains challenging due to the scarcity of systematic protocols for generating training data spanning diverse structural regimes. Here we introduce GAIA, an end-to-end automated framework that streamlines dataset construction for the development of general-purpose reactive MLIPs. GAIA combines a metadynamics-based exploration scheme with closed-loop data expansion for the efficient sampling of a broad spectrum of atomic arrangements, thereby addressing the reliance on heuristics in conventional dataset generation. Using GAIA, we constructed Titan25, a benchmark-scale dataset, and trained an MLIP that closely matches both static and dynamic density functional theory results. The resulting model reproduces key experimental observations across distinct modes of reactivity, including detonation, coalescence, and catalytic processes. GAIA thus helps bridge the gap between simulation and experiment, paving the way toward scalable and general MLIPs capable of describing a wide range of materials and chemical processes. Heuristics ; Scalability ; Key (lock) ; Streamlines, streaklines, and pathlines ; Range (aeronautics) ; USable ; Bridge (graph theory) ; Field (mathematics) Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Quantum, superfluid, helium dynamics
Authors: Liangkai Ma | Brian Clark | Joe Sootsman
Source:  (article, 2025-05-27)
Field: Materials Science — Materials Chemistry

Polymer-Based Thermal Interface Material Modeling and Selection Polymer-based thermal interface materials (TIMs) are polymeric matrix materials with heat-conducting fillers used to lower the operating temperatures of microelectronics by reducing the thermal resistance between contact surfaces. These thermal interface materials have experienced accelerated growth in recent years driven by the ever-increasing performance and functions of electronic systems pushing the thermal management requirements of devices at an unprecedented pace aligned with several megatrends such as the rapid global adoption of electric vehicles, autonomous driving, 5G, machine learning (ML), and artificial intelligence (AI). A proper selection of thermally conductive composites is critical to the thermal management performance, reliability, and safety of the devices in targeted applications. In this paper, we introduce a hybrid modeling approach that combines thermal finite element analysis (FEA) with statistical modeling. This approach was used to study and identify the critical factors impacting microelectronic device temperature and to guide the selection of TIMs for different applications. A 2D steady state heat transfer FEA model was first developed and validated using experimental data obtained from an in-house thermal test vehicle (TTV). A second FEA model was developed based on a land grid array (LGA) package design from literature. Parametric studies and numerical design of experiments (DOEs) were conducted using the second FEA model to build statistical models for analyzing the factors affecting device thermal management and TIM selection for different applications. An inverse model was developed from the statistical model using statistical software JMP to gain insights into TIM development and selection based on targeted applications. The insights gained from this work are valuable for engineers and researchers working in the field of thermally conductive composites and microelectronic device development and application. Finite element method ; Microelectronics ; Thermal grease ; Interface (matter) ; Material selection ; Thermal ; Model selection ; Statistical model ; Parametric statistics Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Electronic Packaging and Soldering Technologies