jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Olesya Dan'kiv | Andrii Stolyarchuk | Oleksandr Viychuk | І.D. Stolyarchuk | Oleh Kuzyk
Source: Physics and Chemistry of Solid State (article, 2025-12-22)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A study of acousto-electron effects in semiconductor quantum dots and bionanocomplexes based on them using deep machine learning An artificial neural network architecture has been developed that is capable of predicting changes in the energy spectrum of semiconductor quantum dots and their bionanocomplexes under the influence of an acoustic wave and interaction with human serum albumin molecules based on its specified geometric sizes, elastic constants and deformation potential constants of the allowed energy bands of quantum dot materials, as well as the frequency and amplitude of the ultrasonic wave and the surface concentration of human serum albumin. Two artificial neural network architectures have been implemented and optimized. Both models contain three hidden layers; however, the second architecture involves expanding the input space by adding harmonic functions. Both approaches to neural network modeling demonstrate good agreement with the results of mathematical modelling, provided that the input parameters lie within the training range. In the case when the input parameters lie outside the training sample, the model using additional harmonic functions at the input demonstrates a much better result. Within the framework of the developed model for the CdSe/ZnS/CdS/ZnS QD–human serum albumin bionanocomplex, the dependence of the energy shift of the radiation quantum on the frequency of the acoustic wave was investigated for different values ​​of the surface concentration of albumin and different geometric sizes of the core and shell of the quantum dot. Artificial neural network ; Harmonic ; Quantum ; Quantum dot ; Energy (signal processing) ; Surface acoustic wave ; Quantum system ; Surface acoustic wave sensor ; Semiconductor Gold and Silver Nanoparticles Synthesis and Applications ; Quantum Dots Synthesis And Properties ; Nonlinear Optical Materials Research
Authors: Kunjie SONG | Rongjun XIE
Source: Journal of Inorganic Materials (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Research Advances in Machine Learning-driven Development of Novel Luminescent Materials Phosphor materials are essential functional components in modern optoelectronics, playing a crucial role in next-generation displays, solid-state lighting, biomedical imaging, and sensing applications.However, the properties of materials are stringently required in multiple dimensions for practical applications.The conventional trial-and-error approach impedes the development of novel high-performance phosphors due to its long cycles and high costs.In recent years, the rapidly advancing machine learning (ML) methodology presents an innovative solution to addressing these bottlenecks.It not only dramatically accelerates the high-throughput virtual screening of candidate materials by establishing complex mapping among "composition-structure-property", substantially improving efficiency, but also provides novel theoretical insights into structure-property correlations through algorithm-derived importance weights.This review systematically outlines the methodological framework for ML-driven phosphor development, covering key stages including data preparation, feature engineering, model selection, model evaluation and application.It further discusses specific considerations and corresponding strategies for each stage of the process.Subsequently, it reviews recent research progress in applying ML to predict key phosphor properties, encompassing critical metrics such as emission wavelength, full width at half-maximum (FWHM), thermal stability, fluorescence lifetime, centroid shift, and process optimization.Finally, this review addresses existing challenges in current research, such as scarcity of reliable data, complexity of materials properties, and difficulties in parameter quantification.The article concludes by outlining future development trends for the deep integration of artificial intelligence (AI) technology in luminescent materials research, aiming to provide a valuable reference for promoting the establishment of an "AI for Science" paradigm in the field of phosphor materials. Luminescence ; Research development ; Development (topology) ; Optical materials Machine Learning in Materials Science ; Luminescence Properties of Advanced Materials ; Luminescence and Fluorescent Materials
Authors: Sen Gao | Han Mu | Tianchi Li
Source: IEEE Transactions on Automation Science and Engineering (article, 2025-01-01)
Field: Materials Science — Polymers and Plastics

Model Predictive Control of PVC Polymerization Temperature Based on the Koopman Operator Extracted From Feedforward Neural Network Due to the fact that the Polyvinyl Chloride (PVC) polymerization process is a time-varying, nonlinear process with large time delays, there are many uncertainties and external disturbances during production, making it difficult to establish precise mathematical models. Traditional control methods often fail to meet the control requirements under such circumstances. Therefore, from a data-driven perspective, a Model Predictive Control (MPC) method for PVC polymerization temperature based on the Koopman Operator and Feedforward Neural Networks has been proposed. This method first uses a Feedforward Neural Network(FNN) to extract the Koopman Operator from the temperature data of the PVC polymerization process, thereby constructing the state-space equation of the PVC polymerization process. Finally, it employs MPC to control the temperature of the PVC polymerization process. This method does not require prior knowledge or a mechanistic model of the system, achieving data-driven modeling and control. Simulation results have confirmed the effectiveness of this method in controlling the temperature of the PVC polymerization reaction. Polymerization ; Model predictive control ; Control theory (sociology) ; Feed forward ; Feedforward neural network ; Artificial neural network ; Temperature control ; Polyvinyl chloride Polymer Science and PVC ; Model Reduction and Neural Networks ; Advanced Control Systems Optimization
Authors: Shaojie Zheng | Jijiang Hu | Zhen Yao
Source: Journal of Polymer Science (article, 2025-11-22)
Field: Materials Science — Materials Chemistry

Machine Learning‐Driven Prediction of Polypropylene Alloy Properties: A Bayesian Network– <scp>XGBoost</scp> Framework Robust to Missing Data ABSTRACT Polypropylene alloys exhibit a complex multiscale structure, posing challenges for traditional analytical methods in uncovering the relationships between structural and mechanical properties. To address this, a novel machine learning framework that integrates graphical modeling with regression techniques is proposed to enhance interpretability and predictive accuracy. Specifically, a Bayesian network is constructed to elucidate the dependencies among structural variables, guiding feature selection in the regression stage, and a hierarchical XGBoost regression model based on this network (BNXGB) is developed to predict impact strength (IS) and flexural modulus (FM). Compared to a conventional XGBoost model (XGB), BNXGB demonstrates superior performance, achieving higher R 2 score for both IS and FM. More importantly, BNXGB handles missing input features by leveraging the network to impute them from parent nodes, thereby maintaining robust performance under incomplete data. Even under the extreme condition when all features except the four root nodes are missing, BNXGB still preserves reasonable R 2 scores for both IS and FM, whereas XGB's performance drops sharply. These results demonstrate the robustness and practical applicability of the BNXGB framework for polymer property prediction under missing data, potentially reducing the need for exhaustive material characterization. Interpretability ; Missing data ; Robustness (evolution) ; Feature selection ; Bayesian network ; Regression ; Overfitting ; Bayesian probability ; Feature (linguistics) Machine Learning in Materials Science ; Polymer crystallization and properties ; Composite Material Mechanics
Authors: Christoph Brunken | Olivier Peltre | Héloise Chomet | Łukasz Walewski | Megan J. McAuliffe | Valentin Heyraud | Solal Attias | Martin Maarand | Yessine Khanfir | Edan Toledo | Fabio Falcioni | Marie Bluntzer | Silvia Acosta‐Gutiérrez | Jules Tilly
Source: ArXiv.org (preprint, 2025-05-28)
Field: Materials Science — Materials Chemistry

Machine Learning Interatomic Potentials: library for efficient training, model development and simulation of molecular systems Machine Learning Interatomic Potentials (MLIP) are a novel in silico approach for molecular property prediction, creating an alternative to disrupt the accuracy/speed trade-off of empirical force fields and density functional theory (DFT). In this white paper, we present our MLIP library which was created with two core aims: (1) provide to industry experts without machine learning background a user-friendly and computationally efficient set of tools to experiment with MLIP models, (2) provide machine learning developers a framework to develop novel approaches fully integrated with molecular dynamics tools. The library includes in this release three model architectures (MACE, NequIP, and ViSNet), and two molecular dynamics (MD) wrappers (ASE, and JAX-MD), along with a set of pre-trained organics models. The seamless integration with JAX-MD, in particular, facilitates highly efficient MD simulations, bringing MLIP models significantly closer to industrial application. The library is available on GitHub and on PyPI under the Apache license 2.0. MIT License ; Set (abstract data type) ; Molecular dynamics ; Property (philosophy) ; Development (topology) ; Core (optical fiber) ; Core model ; License Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Physical and Chemical Molecular Interactions
Authors: Sushma Dahal | Bhupal Guragain | S. K. Yadav | Narayan Prasad Adhikari
Source: Physica Scripta (article, 2025-12-08)
Field: Materials Science — Materials Chemistry

DFT validated machine learning model for elastic property prediction and mechanical classification of ternary cubic alloys Abstract Elastic constants govern how crystalline solids respond to stress, resist deformation, and transmit elastic waves. For cubic crystals, the mechanical behavior can be fully described using three independent elastic constants, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi>C</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>11</mml:mn> </mml:mrow> </mml:msub> </mml:math> , <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi>C</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>12</mml:mn> </mml:mrow> </mml:msub> </mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi>C</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>44</mml:mn> </mml:mrow> </mml:msub> </mml:math> . These constants can be determined experimentally or through computational approaches such as molecular dynamics or density functional theory (DFT). However, experimental measurements are often challenging, while DFT calculations, though accurate, are computationally expensive. To address this, we have used a machine learning (ML) framework to predict and classify the elastic properties of ternary cubic alloys. A multi-output XGBoost model was trained on Matminer - featurized data from the Materials Project and validated against DFT calculations for 12 half-Heusler alloys, showing excellent agreement. We then applied the model to predict the elastic constants of 972 ternary materials, finding that 970 are mechanically stable and exhibit strong internal correlations among their elastic properties. Additionally, we employed unsupervised learning via K-means clustering to categorize these materials into four mechanical classes such as brittle and soft, ductile with moderate stiffness, stiff and strong, and highly ductile and soft. This integrated approach establishes a high-throughput pathway for accelerating the discovery and targeted design of materials with tailored mechanical performance. Ternary operation ; Brittleness ; Density functional theory ; Elastic modulus ; Cluster analysis ; Unsupervised learning ; Elasticity (physics) Machine Learning in Materials Science ; Microstructure and mechanical properties ; Model Reduction and Neural Networks
Authors: Yuxuan Wan | Xuan Zhang | Liang Zhang
Source: Nanomaterials (article, 2025-10-22)
Field: Materials Science — Materials Chemistry

Development of a Machine Learning Interatomic Potential for Zirconium and Its Verification in Molecular Dynamics Molecular dynamics (MD) can dynamically reveal the structural evolution and mechanical response of Zirconium (Zr) at the atomic scale under complex service conditions such as high temperature, stress, and irradiation. However, traditional empirical potentials are limited by their fixed function forms and parameters, making it difficult to accurately describe the multi-body interactions of Zr under conditions such as multi-phase structures and strong nonlinear deformation, thereby limiting the accuracy and generalization ability of simulation results. This paper combines high-throughput first-principles calculations (DFT) with the machine learning method to develop the Deep Potential (DP) for Zr. The developed DP of Zr was verified by performing molecular dynamic simulations on lattice constants, surface energies, grain boundary energies, melting point, elastic constants, and tensile responses. The results show that the DP model achieves high consistency with DFT in predicting multiple key physical properties, such as lattice constants and melting point. Also, it can accurately capture atomic migration, local structural evolution, and crystal structural transformations of Zr under thermal excitation. In addition, the DP model can accurately capture plastic deformation and stress softening behavior in Zr under large strains, reproducing the characteristics of yielding and structural rearrangement during tensile loading, as well as the stress-induced phase transition of Zr from HCP to FCC, demonstrating its strong physical fidelity and numerical stability. Molecular dynamics ; Zirconium ; Interatomic potential ; Grain boundary ; Softening ; Crystal structure ; Lattice (music) ; Phase transition ; Deformation mechanism Machine Learning in Materials Science
Authors: Shinnosuke Hattori | Kohei Shimamura | Aiichiro Nakano | Rajiv K. Kalia | Priya Vashishta | Ken‐ichi Nomura
Source: ArXiv.org (preprint, 2025-06-09)
Field: Materials Science — Materials Chemistry

Beyond Scaling: Chemical Intuition as Emergent Ability of Universal Machine Learning Interatomic Potentials Machine Learning Interatomic Potentials (MLIPs) have successfully demonstrated scaling behavior, i.e. the power-law improvement in training performance, however the emergence of novel capabilities at scale remains unexplored. We have developed Edge-wise Emergent Decomposition (E3D) framework to investigate how an MLIP develops the ability to derive physically meaningful local representations of chemical bonds without explicit supervision. Employing an E(3)-equivariant network (Allegro) trained on molecular data (SPICE~2), we found that the trained MLIP spontaneously learned representations of bond dissociation energy (BDE) by decomposing the global potential energy landscape. The learned BDE values quantitatively agree with literature and its scalability are found to be robust across diverse training datasets, suggesting the presence of underlying representation that captures chemical reactions faithfully beyond given training information. Our E3D analysis utilizing Shannon's entropy reveals a close interplay between the decomposability of potential energy learning, scalability of learning, and emergent chemical reactivity, thus providing novel insights of scaling limitations and pathways toward more physically interpretable and predictive simulations. Scaling ; Intuition ; Scalability ; Dissociation (chemistry) ; Training set ; Entropy (arrow of time) ; Representation (politics) ; Artificial neural network Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Gaussian Processes and Bayesian Inference
Authors: Raúl Duque | Arman Duha | Mario F. Borunda
Source: ArXiv.org (preprint, 2025-02-03)
Field: Materials Science — Materials Chemistry

Machine Learning-Driven Analytical Models for Threshold Displacement Energy Prediction in Materials Understanding the behavior of materials under irradiation is crucial for the design and safety of nuclear reactors, spacecraft, and other radiation environments. The threshold displacement energy (Ed) is a critical parameter for understanding radiation damage in materials, yet its determination often relies on costly experiments or simulations. This work leverages the machine learning-based Sure Independence Screening and Sparsifying Operator (SISSO) method to derive accurate, analytical models for predicting Ed using fundamental material properties. The models outperform traditional approaches for monoatomic materials, capturing key trends with high accuracy. While predictions for polyatomic materials highlight challenges due to dataset complexity, they reveal opportunities for improvement with expanded data. This study identifies cohesive energy and melting temperature as key factors influencing Ed, offering a robust framework for efficient, data-driven predictions of radiation damage in diverse materials. Displacement (psychology) ; Energy (signal processing) ; Computer science ; Artificial intelligence ; Machine learning ; Statistical physics ; Physics ; Psychology Machine Learning in Materials Science
Authors: Tien-Thinh Le | Quoc Dat Ha | Huan Thanh Duong
Source: Vietnam Journal of Mechanics (article, 2025-03-24)
Field: Materials Science — General Materials Science

Convolutional neural network for homogenization of particulate composite materials based on finite element data This study develops a convolutional neural network model to predict the apparent mechanical properties of particulate composite materials based on finite element data. The particulate composite material is considered with random inclusions in size and position. The datasets for training and testing processes are generated by using a validated finite element simulation. Various parametric studies are then investigated, including model efficiency and uncertainty propagation. Moreover, the influence of the constituents and microstructure is numerically revealed based on the proposed convolutional neural network model. It is shown that the developed convolutional neural network model is capable of capturing the microstructural features and provides accurate predictions of apparent mechanical properties of particulate composite materials. Homogenization (climate) ; Convolutional neural network ; Composite number ; Finite element method ; Particulates ; Artificial neural network ; Materials science ; Structural engineering ; Composite material ; Computer science ; Engineering ; Artificial intelligence ; Chemistry Material Properties and Applications ; Industrial Engineering and Technologies ; Advanced Theoretical and Applied Studies in Material Sciences and Geometry
Authors: Khalid Reggab | Houssam Eddine Hailouf | Kingsley Onyebuchi Obodo | Mohammed Benali Kanoun | Souraya Goumri‐Said
Source: Physics Open (article, 2025-09-17)
Field: Materials Science — Materials Chemistry

Klein-Gordon equation and machine learning-enhanced functional analysis: Insights into diatomic molecular systems via analytical and predictive modeling approaches This study investigates the behavior of spinless particles under the influence of scalar and vector potentials by analytically solving the Klein-Gordon equation using the Nikiforov-Uvarov functional analysis method, coupled with the Hellmann and modified Kratzer potentials, employing the Greene-Aldrich approximation for the centrifugal term. The analytical energy eigenvalues and eigenfunctions were utilised to examine the energy spectra of specific diatomic molecules (CO, NO, N 2 , and CH), demonstrating the correlation of these properties with potential parameters and quantum numbers. In addition to the analytical results, machine learning techniques like Random Forest and Neural Network regressors were used to model and predict the energy spectra based on the calculated data. This made it possible to swiftly explore energy landscapes. The ML models showed great agreement with the analytical results and were better at extrapolating to new quantum numbers and molecular types. This hybrid analytical-ML approach is a strong way to speed up the study of diatomic molecular systems. It combines the rigour of quantum mechanics with data-driven predictions and makes it possible to efficiently screen molecular energy spectra in theoretical and computational chemistry. Diatomic molecule ; Eigenfunction ; Eigenvalues and eigenvectors ; Potential energy ; Spectral line ; Quantum ; Energy (signal processing) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Wise Herowati | Muhamad Akrom
Source: Journal of Multiscale Materials Informatics (article, 2025-12-11)
Field: Materials Science — Materials Chemistry

Hybrid Quantum Neural Network for Predicting Corrosion Inhibition Efficiency of Organic Molecules Corrosion inhibition efficiency (IE%) prediction plays a central role in the computational discovery of high-performance organic inhibitors. Classical machine learning has shown promising results; however, its performance often deteriorates when learning non-linear interactions between quantum chemical descriptors. Meanwhile, quantum machine learning (QML) provides enhanced expressivity through quantum feature mapping but remains limited by NISQ-era hardware. In this study, we propose a Hybrid Quantum Neural Network (HQNN) integrating classical dense layers with variational quantum circuits (VQC) to predict the inhibition efficiency of organic corrosion inhibitors. Using a curated dataset of 660 molecules with DFT descriptors, the HQNN achieves an RMSE of 3.41 and R² of 0.958, outperforming classical regressors and pure VQC. The results demonstrate that hybrid quantum models offer a balanced trade-off between quantum advantage and practical feasibility in materials informatics. Quantum ; Artificial neural network ; Quantum chemical ; Feature (linguistics) ; Molecule ; Quantum machine learning ; Organic molecules ; Quantum computer Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks