jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Seiki Saito | Masato Iida | Hiroaki Nakamura | K. Sawada | K. Hoshino | M. Kobayashi | Masahiro Hasuo | Y. Homma | S. Yamoto
Source: Nuclear Materials and Energy (article, 2025-05-04)
Field: Materials Science — Materials Chemistry

Machine learning-based hydrogen recycling model for predicting rovibrational distributions of released molecular hydrogen on tungsten materials via molecular dynamics simulations Understanding the hydrogen recycling process is crucial for comprehending the behavior of detached plasma in nuclear fusion devices. To achieve this, a molecular dynamics (MD) model is being developed to predict the distribution of translational energies and rovibrational states of hydrogen atoms and molecules released from the plasma-facing materials. Neutral transport simulations, utilizing distributions obtained from the MD model as boundary conditions, are also a powerful tool for analyzing the impact of recycled hydrogens on edge plasma. However, the MD model requires significant computational resources to obtain distributions under varying material and irradiation conditions such as material temperature and incident energy. Therefore, developing effective models that seamlessly integrate neutral transport simulation with hydrogen recycling models is crucial. Machine learning techniques are employed to develop predictive models capable of forecasting distributions of energies and rovibrational states of released hydrogen atoms and molecules. Furthermore, a model considering the incident energy distribution (shifted-Maxwellian) is developed by integrating the monochromatic distribution with the shifted-Maxwellian distribution. • Hydrogen Recycling in Nuclear Fusion: Understanding hydrogen recycling is crucial for comprehending detached plasma behavior in fusion devices. • Development of Molecular Dynamics (MD) Model: A MD model calculates the distribution of transrational energies and rovibrational states of hydrogen released from plasma-facing materials. The distribution are used for the boundary condition of neutral transport simulation for edge plasma. • Computational Challenges: MD model requires significant computational resources, urging the need for seamless integration with neutral transport simulations. • Machine Learning Approach: Machine learning techniques predict distributions of released hydrogen’s energies and states, addressing computational challenges. • Shifted-Maxwellian Distribution: Integration of machine learning model with shifted-Maxwellian distribution incorporates incident energy distribution. • Impact of Electron Temperature: High electron temperature favors hydrogen atom emission, while low temperature favors hydrogen molecule emission. Rotational–vibrational spectroscopy ; Tungsten ; Molecular dynamics ; Hydrogen ; Materials science ; Hydrogen molecule ; Dynamics (music) ; Chemical physics ; Chemistry ; Molecule ; Computational chemistry ; Physics ; Metallurgy ; Organic chemistry Fusion materials and technologies ; Nuclear Physics and Applications ; Nuclear Materials and Properties
Authors: Colter Ellis | Xinyi Fang | Christopher Balzer | Timothy Quah | M. Scott Shell | Glenn H. Fredrickson | Mengyang Gu
Source: ArXiv.org (preprint, 2025-09-08)
Field: Materials Science — Materials Chemistry

Fast phase prediction of charged polymer blends by white-box machine learning surrogates Compatibilized polymer blends are a complex, yet versatile and widespread category of material. When the components of a binary blend are immiscible, they are typically driven towards a macrophase-separated state, but with the introduction of electrostatic interactions, they can be either homogenized or shifted to microphase separation. However, both experimental and simulation approaches face significant challenges in efficiently exploring the vast design space of charge-compatibilized polymer blends, encompassing chemical interactions, architectural properties, and composition. In this work, we introduce a white-box machine learning approach integrated with polymer field theory to predict the phase behavior of these systems, which is significantly more accurate than conventional black-box machine learning approaches. The random phase approximation (RPA) calculation is used as a testbed to determine polymer phases. Instead of directly predicting the polymer phase output of RPA calculations from a large input space by a machine learning model, we build a parallel partial Gaussian process model to predict the most computationally intensive component of the RPA calculation that only involves polymer architecture parameters as inputs. This approach substantially reduces the computational cost of the RPA calculation across a vast input space with nearly 100% accuracy for out-of-sample prediction, enabling rapid screening of polymer blend charge-compatibilization designs. More broadly, the white-box machine learning strategy offers a promising approach for dramatic acceleration of polymer field-theoretic methods for mapping out polymer phase behavior. Polymer blend ; Polymer ; Gaussian process ; Binary number ; Polymer architecture ; Component (thermodynamics) ; Random phase approximation ; Phase (matter) ; Field (mathematics) Machine Learning in Materials Science ; Fuel Cells and Related Materials
Authors: Enda Xiao | Terumasa Tadano
Source: Research Square (preprint, 2025-09-22)
Field: Materials Science — Materials Chemistry

Accurate Screening of Functional Materials with Machine-Learning Potential and Transfer-Learned Regressions: Heusler Alloy Benchmark Magnetocrystalline anisotropy ; Workflow ; Magnetic moment ; Density functional theory ; Alloy ; Benchmark (surveying) ; Characterization (materials science) ; Energy (signal processing) ; Austenite Machine Learning in Materials Science
Authors: Luigi Bonati | Pietro Novelli | Antonio Rossi
Source: 2D Materials (article, 2025-12-02)
Field: Materials Science — Materials Chemistry

From trial-and-error to intelligent workflows: machine learning and simulations for scalable 2D material synthesis Abstract Wafer-scale growth of two-dimensional (2D) materials remains an unsolved challenge. Current synthesis still depends on trial-and-error, limiting reproducibility and slowing progress toward practical technologies. This perspective argues that machine learning (ML) and atomistic simulations can provide the foundation for intelligent synthesis workflows. Gradient-free optimization methods, including Bayesian optimization and adaptive Monte Carlo, already demonstrated that high-quality growth conditions can be learned efficiently from experimental feedback. At the same time, atomistic simulations deliver physical priors, synthetic data, and mechanistic insights that focus the search and mitigate data scarcity. We propose a synergistic framework where experiments, simulations, and ML form a closed loop: experiments provide validation, simulations constrain exploration, and ML uncovers patterns and proposes new recipes. Embedding this intelligence into growth protocols can turn 2D materials synthesis into a reproducible and scalable methodology, moving beyond intuition-driven experimentation, accelerating their translation into wafer-scale technologies. While current demonstrations remain system-specific, this perspective provides a pathway toward wafer-scale technologies and data-driven discovery. Scalability ; Bayesian optimization ; Embedding ; Perspective (graphical) ; Limiting ; Translation (biology) ; Focus (optics) Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; 2D Materials and Applications
Authors: Wei-Yi Li (8194494) | Xi-Yao Yun (22604129) | Xing-Han Gu (22604132) | Rong Liu (202610) | Yi-Lin Fang (8736153) | Wan-Xiao Guo (22604135) | Jin-Tao Wang (3627560) | Tao Wang (12008) | Ning Gao (153779)
Source: Figshare (article, 2025-11-12)
Field: Materials Science — Materials Chemistry

HlightReaxMD: A Machine Learning-Augmented MultiscaleAnalysis Framework for Radiation Chemistry Dynamics and Damage Prediction Molecular dynamics (MD) simulations are currently widely used to study large-scale displacement cascades based on massive simulation trajectories. However, when the irradiation process involves the complex chemical reactions, effectively analyzing and extracting features from these data becomes a challenge. Here, we introduce a new cross-platform toolkit, HlightReaxMD, designed to directly obtain information about the irradiation damage process and chemical reactions from MD trajectories and further achieve the prediction of irradiation damage. The analysis tools in HlightReaxMD include chemical reaction analysis and calculation of reaction kinetic parameters, analysis of the collision cascade process, and calculation of necessary physicochemical properties. HlightReaxMD supports the analysis of all elements used in reactive force fields by reading ReaxFF potential file parameters and provides an automated solution for tracking atomic-scale collision events and analyzing chemical reaction mechanisms through constructing cascade trees and reaction network paths. A machine learning-driven model using the analysis results has been included in HlightReaxMD, which can predict irradiation damage by considering various factors, rather than relying solely on the Norgett–Robinson–Torrens displacements per atom (NRT-dpa) model. It enables researchers to automatically obtain dynamic processes and reaction information from atomic to microscale defects from terabyte-level trajectory data. Thus, HlightReaxMD can promote systematic research on irradiation effects in materials science. ReaxFF ; Microscale chemistry ; Cascade ; Molecular dynamics ; Process (computing) ; Trajectory ; Chemical reaction ; Irradiation ; Collision Machine Learning in Materials Science ; Nuclear Materials and Properties ; Catalysis and Oxidation Reactions
Authors: Jiao Huang | Qianli Xing | Jinglong Ji | Bo Yang
Source:  (article, 2025-06-30)
Field: Materials Science — Materials Chemistry

ADA-GNN: Atom-Distance-Angle Graph Neural Network for Crystal Material Property Prediction Property prediction is a fundamental task in crystal material research. To model atoms and structures, structures represented as graphs are widely used and graph learning-based methods have achieved significant progress. Bond angles and bond distances are two key structural information that greatly influence crystal properties. However, most of the existing works only consider bond distances and overlook bond angles. The main challenge lies in the time cost of handling bond angles caused by the large cutoff, which leads to a significant increase in inference time. To address this issue, we first propose a flexible dual-scale neighbor partitioning mechanism with consistent receptive fields in point graphs and edge graphs, which reduces redundant information unrelated to modeling periodicity and improves prediction speed without affecting the modeling of graph periodicity. Then, we propose a novel Atom-Distance-Angle Graph Neural Network (ADA-GNN) for property prediction tasks, which processes node information and structural information separately. The accuracy of predictions and the speed of inference process are both improved with the flexible dual-scale modeling and the specially designed architecture of ADA-GNN. The experimental results validate that our approach achieves state-of-the-art results in two large-scale material benchmark datasets on property prediction tasks. Bond graph ; Inference ; Property (philosophy) ; Artificial neural network ; Graph ; Computation ; Node (physics) ; Benchmark (surveying) Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Crystallography and molecular interactions
Authors: Sheila E. Whitman | Marat I. Latypov
Source: ArXiv.org (preprint, 2025-01-28)
Field: Materials Science — Materials Chemistry

Machine learning of microstructure--property relationships in materials leveraging microstructure representation from foundational vision transformers Machine learning of microstructure--property relationships from data is an emerging approach in computational materials science. Most existing machine learning efforts focus on the development of task-specific models for each microstructure--property relationship. We propose utilizing pre-trained foundational vision transformers for the extraction of task-agnostic microstructure features and subsequent light-weight machine learning of a microstructure-dependent property. We demonstrate our approach with pre-trained state-of-the-art vision transformers (CLIP, DINOv2, SAM) in two case studies on machine-learning: (i) elastic modulus of two-phase microstructures based on simulations data; and (ii) Vicker's hardness of Ni-base and Co-base superalloys based on experimental data published in literature. Our results show the potential of foundational vision transformers for robust microstructure representation and efficient machine learning of microstructure--property relationships without the need for expensive task-specific training or fine-tuning of bespoke deep learning models. Microstructure ; Artificial intelligence ; Transformer ; Property (philosophy) ; Computer vision ; Computer science ; Engineering ; Materials science ; Composite material ; Electrical engineering ; Philosophy ; Epistemology Machine Learning in Materials Science ; Industrial Vision Systems and Defect Detection
Authors: Berna Alemdag | Azra Kocaarslan | Gözde Kabay
Source: Research Square (preprint, 2025-09-09)
Field: Materials Science — Materials Chemistry

Leveraging Molecular Descriptors and Explainable Machine Learning for Monomer Conversion Prediction in Photoinduced Electron Transfer-Reversible Addition- Fragmentation Chain Transfer Polymerization Interpretability ; Chain transfer ; Monomer ; Polymerization ; Polymer ; Component (thermodynamics) ; Stability (learning theory) ; Molecular recognition Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization ; Photopolymerization techniques and applications
Authors: Tzu‐Hsuan Chao | Alexander Foncerrada | Patrick J. Shamberger | Daniel P. Tabor
Source: Soft Matter (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Low-data machine learning models for predicting thermodynamic properties of solid–solid phase transformations in plastic crystals Plastic crystals, many of which are globular small molecules that exhibit transitions between rotationally ordered and rotationally disordered states, represent an important subclass of colossal barocaloric effect materials. The known set of plastic crystals is notably sparse, which presents a challenge to developing predictive thermodynamic models to describe new molecular structures. To predict the transformation entropy of plastic crystals, we developed a comprehensive database of tetrahedral plastic crystal molecules (neopentane analogs) and used several types of features, including chemical functional groups, molecular symmetry, DFT-calculated vibrational entropy, and energy decomposition analysis to train a machine learning model. To select the most relevant features, we used a correlation matrix to screen out highly correlated features and ran sure independence screening and sparsifying operator (SISSO) regression on the remaining features. The SISSO regression samples over combinatorial spaces, including operations and features, to find the relationship between material properties. Using a dataset of 49 plastic crystals and 37 non-plastic crystals based on a common tetrahedral geometry, we have demonstrated the effectiveness of this strategy. Furthermore, we applied this strategy to develop a regression model to predict transition entropy and enthalpy. The top 100 models from the operation space showed that the overall distribution of performance became narrower, sacrificing the top-performing model but avoiding the worst models. Using this approach, we identified the top-performing descriptors to further clarify the underlying mechanisms of the plastic crystal transformation. Plastic crystal ; Materials science ; Phase (matter) ; Thermodynamics ; Phase transition ; Chemical physics ; Chemistry ; Physics ; Organic chemistry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; X-ray Diffraction in Crystallography
Authors: Jinjin Zhao (3777973) | Mengwen Yan (6200741) | Zeyu Shi (8689485) | Chao Zhao (253684) | Lei Qi (173882) | Qing Hao (566198) | Hong Liu (50694)
Source: Figshare (article, 2025-12-10)
Field: Materials Science — Materials Chemistry

Prediction of COFSynthesis Reaction Conditions Basedon Data Mining and Machine Learning Covalent organic frameworks (COFs) have demonstrated superior physical/chemical properties and application potential, while the use of machine learning (ML) methods to predict COF synthesis parameters has not been fully explored. This work developed an ML prediction network model for predicting COF synthesis conditions using monomers as input, which guided the synthesis of real COF materials and their application in NIR-II photothermal antibacterial applications. First, we accurately extracted COF synthesis conditions from 609 references using the large language model Deepseek-R1 and established a COF synthesis database SYN-COF. Subsequently, the XGBoost-based ML model was trained and optimized using the COF synthesis database, achieving <i>R</i><sup>2</sup> values of 0.88 for the synthesis temperature and 0.47 for the synthesis time, and demonstrated higher accuracy in competition with 7 human experts. With the assistance of the ML prediction network model, we successfully synthesized previously unreported COF materials, leading to a good NIR-II photothermal antibacterial performance after iodine doping. Artificial neural network ; Work (physics) ; Training set ; Covalent organic framework ; Artificial neuron ; Predictive modelling Covalent Organic Framework Applications ; Advanced Photocatalysis Techniques ; Nanoplatforms for cancer theranostics
Authors: Junqi Yu | Changliang Lin | Qingli Wang | Jin‐Liang Wang
Source:  (review, 2025-06-20)
Field: Materials Science — Materials Chemistry

A Review of Advances in the Application of Machine Learning Methods for Intelligent Detection of Composite Materials Fiber-reinforced composites are widely used in aerospace due to their superior mechanical properties, including high strength-to-weight ratio and corrosion resistance, which significantly enhance aircraft performance and fuel efficiency. However, the increasing complexity of their composition-such as diverse fiber types, matrix materials, and hierarchical structures-along with more intricate structural designs, poses significant challenges for traditional research methods. Experimental testing is often costly and timeconsuming, while numerical simulations demand massive computational resources and struggle to accurately capture nonlinear damage behaviors, making them both resource-heavy and data-intensive. Machine learning offers an efficient, datadriven alternative for addressing nonlinear damage mechanisms and performance prediction in composites. This review summarizes recent advances in the application of machine learning techniques to the structural health monitoring of composites, focusing on damage localization, damage identification, and mechanical property prediction. It compares commonly used supervised algorithms like neural networks and support vector machines as well as unsupervised techniques such as clustering, discussing their advantages, such as high prediction accuracy of supervised models, and limitations, such as the reliance of many algorithms on large labeled datasets, in various applications. Finally, the paper outlines future research directions, including multi-source data fusion from sensors and simulations, hybrid deep learning frameworks for enhanced feature extraction, and digital twin-based lifecycle management, aiming to advance intelligent monitoring systems for composite structures in aerospace. Structural health monitoring ; Artificial neural network ; Aerospace ; Support vector machine ; Feature (linguistics) ; Nonlinear system ; Deep learning ; Supervised learning Machine Learning in Materials Science ; Smart Materials for Construction ; Mechanical Behavior of Composites
Authors: Т. В. Лаптева | S.A. Laptev | Yuri S. Chistov
Source:  (article, 2025-03-24)
Field: Chemistry — Analytical Chemistry

Determination of Chemical Mixture Viscosity by Means of Machine Learning Methods Mathematical modeling of petrochemical, oil refining and oil treatment processes is an integral part of the systems of pre-project analysis of newly created, operability of operating plants, as well as systems of automated control of technological processes. Chemical-technological processes are multi-level complex systems, the hierarchy of which varies depending on the process in progress. Each of the hierarchy levels is represented by a system of equations of varying complexity, which is further reduced to a system of nonlinear equations including transcendental functions. The solution of such systems requires considerable time even when using modern computational methods, which is unacceptable, especially when used in advanced process control systems. In this paper we propose a method of calculating the viscosity of a broad fraction of hydrocarbons based on the use of machine learning models, namely neural networks. In order to accelerate training and obtain the result, the decomposition of the initial problem into a set of tasks, less parametric, which facilitates the process of training the model. Thanks to this decomposition, for a number of regions of the feature space, there is no need to calculate viscosity. To solve the problem of calculating the viscosity of a chemical mixture, a program complex was created to ensure the interaction of trained neural networks. Viscosity ; Computer science ; Process engineering ; Machine learning ; Materials science ; Engineering ; Composite material Spectroscopy and Chemometric Analyses ; Chemical Thermodynamics and Molecular Structure ; Analytical Chemistry and Chromatography
Authors: Yuqing He | Pierre-Paul De Breuck | Hongming Weng | Matteo Giantomassi | Gian‐Marco Rignanese
Source: ArXiv.org (preprint, 2025-03-20)
Field: Materials Science — Materials Chemistry

Machine Learning on Multiple Topological Materials Datasets A dataset of 35,608 materials with their topological properties is constructed by combining the density functional theory (DFT) results of Materiae and the Topological Materials Database. Thanks to this, machine-learning approaches are developed to categorize materials into five distinct topological types, with the XGBoost model achieving an impressive 85.2% classification accuracy. By conducting generalization tests on different sub-datasets, differences are identified between the original datasets in terms of topological types, chemical elements, unknown magnetic compounds, and feature space coverage. Their impact on model performance is analyzed. Turning to the simpler binary classification between trivial insulators and nontrivial topological materials, three different approaches are also tested. Key characteristics influencing material topology are identified, with the maximum packing efficiency and the fraction of $\textit{p}$ valence electrons being highlighted as critical features. Topological data analysis ; Topology (electrical circuits) ; Categorization ; Generalization ; Binary number ; Topological insulator ; Topological space ; Feature (linguistics) Machine Learning in Materials Science
Authors: Raashiq Ishraaq | Siddhartha Das
Source: Trends in Chemistry (article, 2025-11-20)
Field: Materials Science — Surfaces, Coatings and Films

Machine learning for probing polymer and polyelectrolyte brushes Polyelectrolyte ; Polymer ; Molecular recognition Polymer Surface Interaction Studies ; Advanced Polymer Synthesis and Characterization ; Hydrogels: synthesis, properties, applications