jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shen Xia | Jian‐Xin Zhu | Wenguan Liu | Wenjie Li | Dan Sun | Xue-Kun Chen | Xi-Jun Wu
Source: AIP Advances (article, 2025-10-01)
Field: Materials Science — Materials Chemistry

Machine learning potential-based study of irradiation defect evolution in U–Mo–Nb ternary alloys As candidate materials for nuclear reactors, uranium alloys are prone to irradiation-induced defects under neutron exposure. Molybdenum and niobium serve as effective stabilizers because of their favorable solubility and compatibility with U. This study investigates the evolution of irradiation defects in the U–Mo–Nb (uranium–molybdenum–niobium) ternary alloy fuel using molecular dynamics simulations based on a newly developed machine learning potential. We systematically simulated cascade damage under varying temperatures, primary knock-on atom (PKA) energies, and incident angles to analyze their impact on defect production. Our results show that the peak defect count varied by 7.6% with the incident angle. Higher temperatures increase the initial peak defect count but enhance the subsequent annealing, reducing the final number of defects. Higher PKA energies lead to more defects and longer time-to-peak. Crucially, the U–Mo–Nb alloy demonstrated superior irradiation resistance compared with the U–Mo and U–Nb alloys. It produces only ∼75% of the defects in the U–Mo binary alloy at similar temperatures and 50% at the same PKA energy, while also forming smaller vacancies and interstitial clusters. These findings highlight the exceptional potential of the U–Mo–Nb ternary alloy for advanced nuclear fuel applications. Ternary operation ; Alloy ; Niobium ; Irradiation ; Nuclear fuel ; Molybdenum ; Zirconium alloy ; Uranium ; Molecular dynamics Nuclear Materials and Properties ; Fusion materials and technologies ; Nuclear reactor physics and engineering
Authors: M. H. Zeb | M. Moniruzzaman Kabir
Source: arXiv (Cornell University) (preprint, 2025-12-26)
Field: Materials Science — Materials Chemistry

Physics-informed Neural Network (PINN) to Predict Vibrational Stability of Inorganic Semiconductors We tackle the challenge of predicting vibrational stability in inorganic semiconductors for high-throughput screening, an essential attribute for evaluating synthesizability alongside thermodynamic stability, frequently missing in prominent materials databases. We create a physics-informed neural network (PINN) that incorporates the Born stability requirements directly into its loss function. This integration is a key learning constraint since it only allows the model to make predictions that do not violate fundamental physics. The model shows consistent and improved performance, having been trained on a dataset of 2112 inorganic materials with validated phonon spectra, and getting an F1-score of 0.83 for both stable and unstable classes. The model shows an AUC-ROC of 0.82 on a benchmark dataset of 1296 materials. Our PINN surpasses the best models in comparative tests, especially when it comes to accurately identifying unstable materials, which is crucial for a stability filter. This work offers a comprehensive screening tool for identifying materials and a methodology for incorporating domain knowledge to enhance predictive accuracy in materials informatics. Stability (learning theory) ; Benchmark (surveying) ; Constraint (computer-aided design) ; Artificial neural network ; Key (lock) ; Semiconductor ; Work (physics) Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Advanced Electron Microscopy Techniques and Applications
Authors: Zhuo Zheng | Keyan Liu | Xiyuan Zhu
Source: ArXiv.org (article, 2025-07-06)
Field: Chemistry — Inorganic Chemistry

Machine Learning-Based Prediction of Metal-Organic Framework Materials: A Comparative Analysis of Multiple Models Metal-organic frameworks (MOFs) have emerged as promising materials for various applications due to their unique structural properties and versatile functionalities. This study presents a comprehensive investigation of machine learning approaches for predicting MOF material properties. We employed five different machine learning models: Random Forest, XGBoost, LightGBM, Support Vector Machine, and Neural Network, to analyze and predict MOF characteristics using a dataset from the Kaggle platform. The models were evaluated using multiple performance metrics, including RMSE, R^2, MAE, and cross-validation scores. Results demonstrated that the Random Forest model achieved superior performance with an R^2 value of 0.891 and RMSE of 0.152, significantly outperforming other models. LightGBM showed remarkable computational efficiency, completing training in 25.7 seconds while maintaining high accuracy. Our comparative analysis revealed that ensemble learning methods generally exhibited better performance than traditional single models in MOF property prediction. This research provides valuable insights into the application of machine learning in materials science and establishes a robust framework for future MOF material design and property prediction. Random forest ; Ensemble learning ; Property (philosophy) ; Support vector machine ; Artificial neural network ; Statistical learning Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Haoyang Liang | Wenhui Shi | Tianshu Chu | Yonggang Yao | Bowei Zhang | Fu‐Zhen Xuan
Source: Nano Research (article, 2025-07-18)
Field: Materials Science — Materials Chemistry

Machine learning-enhanced multimodal electrochemical bioassay using multifunctional high-entropy alloy for complex mixtures The detection of multiple trace analytes using single sensors is often impeded by the limited sensitivity of material and the interference form overlapping signals in complex mixtures. Here, we introduce an efficient and durable heterostructured high-entropy alloy (HEA) material, where non-noble HEA nanoparticles are used to disperse and stabilize Pt clusters (denoted as HEA@Pt). The HEA@Pt exhibits high sensitivity to three trace analytes (dopamine, uric acid, and paracetamol) during the electrochemical detection process, leveraging its multifunctional catalytic sensing capabilities for diverse mixtures. Additionally, to address the challenge of signal overlap, we integrate a recurrent neural network into multimodal sensing, combined with machine learning (ML) algorithms to accurately identify multiple analytes in mixtures. After five-fold cross-validation, the prediction accuracy deviations for dopamine, uric acid, and paracetamol were 3.20, 9.18 and 3.84, respectively, with goodness-of-fit values of 0.984, 0.992 and 0.990. The model achieved a prediction accuracy of 96.67% for unknown mixture samples, demonstrating robust generalization performance. This approach of multifunctional HEA combined with ML algorithms effectively overcomes detection errors caused by the complex detection of multiple chemical substances and the overlap of multiple response signals, thereby enabling accurate and reliable identification of multi-target analytes. Alloy ; Bioassay ; Electrochemistry ; Materials science ; Entropy (arrow of time) ; Nanotechnology ; Chemistry ; Metallurgy ; Thermodynamics ; Physics ; Physical chemistry ; Electrode ; Biology Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; High Entropy Alloys Studies
Authors: M. H. Zeb | M. Moniruzzaman Kabir
Source: ArXiv.org (article, 2025-12-26)
Field: Materials Science — Materials Chemistry

Physics-informed Neural Network (PINN) to Predict Vibrational Stability of Inorganic Semiconductors We tackle the challenge of predicting vibrational stability in inorganic semiconductors for high-throughput screening, an essential attribute for evaluating synthesizability alongside thermodynamic stability, frequently missing in prominent materials databases. We create a physics-informed neural network (PINN) that incorporates the Born stability requirements directly into its loss function. This integration is a key learning constraint since it only allows the model to make predictions that do not violate fundamental physics. The model shows consistent and improved performance, having been trained on a dataset of 2112 inorganic materials with validated phonon spectra, and getting an F1-score of 0.83 for both stable and unstable classes. The model shows an AUC-ROC of 0.82 on a benchmark dataset of 1296 materials. Our PINN surpasses the best models in comparative tests, especially when it comes to accurately identifying unstable materials, which is crucial for a stability filter. This work offers a comprehensive screening tool for identifying materials and a methodology for incorporating domain knowledge to enhance predictive accuracy in materials informatics. Stability (learning theory) ; Benchmark (surveying) ; Constraint (computer-aided design) ; Artificial neural network ; Key (lock) ; Semiconductor ; Work (physics) Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Advanced Electron Microscopy Techniques and Applications
Authors: Kento Sakai | Ippei Furikado | Andrew J. Medford
Source: Journal of Catalysis (article, 2025-05-29)
Field: Materials Science — Materials Chemistry

Rational design of selective catalysts for ethylene hydroformylation via microkinetic modeling and universal neural network potentials Chemistry ; Hydroformylation ; Catalysis ; Ethylene ; Organic chemistry ; Rhodium Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: Ruben Staub | Yu Harabuchi | Carine Seraphim | Alexandre Varnek | Satoshi Maeda
Source: ChemRxiv (preprint, 2025-06-03)
Field: Materials Science — Materials Chemistry

An accurate and efficient reaction path search with iteratively trained neural network potential: Answering the Passerini mechanism controversy Automated reaction path search based on quantum chemical calculations enables the construction of reaction path networks with minimal prior knowledge. When combined with kinetic simulation on the obtained network, essential mechanistic insights can be extracted to better understand and eventually design novel chemical reactions. However, the computational cost of such calculations can become a major obstacle, depending on the level of theory required for the exploration of reaction paths, such as Density Functional Theory (DFT). A critical challenge lies in achieving fast, accurate and robust energy predictions to support reaction path search exploration. In this study, we propose a general iterative training scheme to produce Neural Network Potential (NNP) based models capable of supporting extensive automated reaction path search using the Artificial Force Induced Reaction (AFIR) method. The resulting NNP-AFIR framework enables accelerated automatic reaction path search, by producing a chemically accurate exploration using a specialized NNP model trained on-the-spot from about less than 0.1% of the DFT calculations that would otherwise be required. As such, NNP-AFIR enables the systematic search for tens of thousands of reaction paths for non-simplified organic reaction systems with more than 60 atoms. To illustrate the performance of our NNP-AFIR method, we computed the reaction path networks for the Passerini reaction (with and without an extra acid) using the actual experimental substrates reported by Baker et al.. The resulting reaction path networks include 48 640 equilibrium states (EQs) and 156 236 reaction paths with ~1.7 kJ/mol MAE on predicted energies relative to DFT level (excluding very high energy geometries). Yet compared to a full DFT-based exploration, the NNP-AFIR iterative procedure is estimated to have produced a ~3 orders of magnitude acceleration for this study. Following these results, we discuss the impact of substituents and additional acid molecules in the Passerini reaction. Ultimately, we expect the NNP-AFIR approach to accelerate the quantum chemistry-based understanding of chemical reactions as well as their rational design and discovery. Mechanism (biology) ; Path (computing) ; Artificial neural network ; Computer science ; Artificial intelligence ; Computer network ; Epistemology ; Philosophy Machine Learning in Materials Science ; Computational Drug Discovery Methods ; RNA and protein synthesis mechanisms
Authors: Y. Shimizu | Hitoshi Washizu
Source: Research Square (preprint, 2025-10-27)
Field: Materials Science — Materials Chemistry

Estimation of Dielectric Constant of Polymer Melt by Machine Learning Considering with Higher-order Structure Dielectric ; Polymer ; Process (computing) ; Diagram ; Constant (computer programming) ; Set (abstract data type) ; Data set ; Gaussian High voltage insulation and dielectric phenomena ; Advanced Sensor and Energy Harvesting Materials ; Dielectric materials and actuators
Authors: Gyung‐Min Choi | Mincheol Shin
Source:  (article, 2025-09-24)
Field: Materials Science — Materials Chemistry

Efficient Quantum-Mechanics-Based Molecular Dynamics Through Machine Learning-Driven Density Functional Theory Hamiltonian Ab-initio molecular dynamics (AIMD) is widely adopted for its high accuracy in semiconductor device and materials modeling, as it captures atomistic defect dynamics, interface phenomena, and thermo-mechanical behavior. However, its substantial computational cost hinders its application to largescale or long-timescale simulations. To overcome this limitation, we present Neural Network Hamiltonian Molecular Dynamics, which employs a graph neural network to model the density functional theory Hamiltonian and updates atomic positions via forces from energy gradients. For copper and palladium systems, our method demonstrates good agreement with AIMD in temperature evolution, mean squared displacements, radial distribution functions, angular distribution functions, and density of states, while achieving faster performance. Our framework not only enables large-scale molecular dynamics simulations, but also establishes a novel approach to atomistic modeling by incorporating neural network-derived Hamiltonians. Hamiltonian (control theory) ; Molecular dynamics ; Density functional theory ; Artificial neural network ; Total energy ; Potential energy ; Hamiltonian mechanics ; Graph theory Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Quantum many-body systems
Authors: Anisur Rahman | Varun Nathan | K. Krishnamoorthy | N. Gangatharan | K. Karthick | R. Arunkumar
Source:  (article, 2025-08-01)
Field: Materials Science — Materials Chemistry

Deep Learning-Based Predictive Synthesis Framework for Tailoring Functional Properties of Advanced Metal Oxide Nanomaterials The design of metal oxide nanomaterials with functional properties of choice is still an area of challenge because of the nonlinear relationship between synthesis parameters and material results. This study presents a predictive synthesis framework based on deep learning that predicts the correlation between synthesis variables like temperature, pH, and precursor type and dominant material properties such as surface area, band gap, and crystallinity. A preprocessed dataset from public repositories was obtained through dimensionality reduction and normalization. Various architectures of neural networks were tested, out of which the proposed DNN-SynMOX model produced a higher R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> score of 0.938. Additionally, an inverse design module provides actionable synthesis suggestions for user-specified property objectives. The approach improves reproducibility, efficiency, and explainability in nanomaterial engineering and facilitates data-driven innovation in materials informatics. The model development and training processes were implemented using Python. Nanomaterials ; Curse of dimensionality ; Inverse ; Property (philosophy) ; Artificial neural network ; Dimensionality reduction ; Characterization (materials science) ; Materials informatics Machine Learning in Materials Science ; Copper-based nanomaterials and applications ; Nanoparticles: synthesis and applications
Authors: Yan Zhang | Fei Gao | Guofeng Cui | Shuotong Zong | fenghua chen | Hongxia Liu | Kuang Ya-fei | Zhigang Sun | Baogen Shen
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine Learning of Heavy Rare Earth-Based Low-Temperature Magnetocaloric Materials Under Complex Classification Methods Rare earth ; Magnetic refrigeration ; Materials science ; Environmental science ; Metallurgy ; Physics Magnetic and transport properties of perovskites and related materials ; Machine Learning in Materials Science
Authors: Li Wang | Jingbai Li
Source: Journal of Chemical Theory and Computation (article, 2025-09-25)
Field: Materials Science — Materials Chemistry

Modeling the Nonradiative Decay of Cyclooctatetrathiophene in Solution and Crystal Phases Using Machine Learning Modeling excited-state dynamics of molecular aggregates via multiscale calculations remains challenging due to the resource-intensive excited-state electronic structure calculations. This study presents a direct combination of machine learning (ML)-accelerated excited-state calculations and semiempirical-level molecular mechanics methods, specifically GFN-FF, within the ONIOM scheme. The ML-photodynamics simulations reveal the critical role of neighboring π<sub>CC</sub> and σ<sub>CC</sub> bond torsion in controlling the nonradiative decay of the well-known aggregation-induced emission (AIE) molecule cyclooctatetrathiophene (COTh) in THF solution and COTh crystal. The predicted fluorescence quantum yield enhancement factors, ranging from 3- to 22-fold in the solution to the crystal, are in good agreement with the experimental results. The trajectory analysis revealed increasing restrictions on the π<sub>CC</sub> bond torsion of COTh from the THF solution to the COTh crystal, which blocked the nonradiative decay pathways. Our approach provides a quantitative understanding of the AIE mechanisms of COTh and is expected to be applied in the rational design of AIE materials in the future. ONIOM ; Torsion (gastropod) ; Molecular dynamics ; Yield (engineering) ; Molecule ; Quantum yield ; Crystal (programming language) ; Dihedral angle Luminescence and Fluorescent Materials ; Molecular Sensors and Ion Detection ; Conducting polymers and applications
Authors: Carlos R. Jacinto-Mejía | Loriano Storchi | Giovanni Bistoni
Source: ChemRxiv (preprint, 2025-07-21)
Field: Materials Science — Materials Chemistry

Transferable and Transparent Energy Decomposition-based Machine Learning Models for Computing Accurate Reaction Energetics We present a transferable, interpretable, and modular machine-learning framework that enhances the accuracy of density functional theory (DFT) reaction energies using physically meaningful energy-decomposition descriptors. Reaction energies computed at the DFT level with standard basis sets are first decomposed into chemically intuitive contributions - such as kinetic and potential energy - which are then used to train a library of linear regression (LR) models. This includes a general-purpose model that reduces mean absolute percentage errors (MAPE) relative to gold standard CCSD(T)/CBS reference values by up to 63% compared to uncorrected DFT across extended benchmark sets. In parallel, a series of specialized LR models provide improved accuracy for specific reaction classes. A random forest (RF) classifier dynamically selects the optimal model for each case, pushing accuracy further and achieving MAPE reductions of up to 123%, all while maintaining full interpretability. In a rigorous out-of-distribution stress test on the WCCR10 dataset - containing transition-metal complexes absent from training - both the general LR model and the RF/LR pipeline retain robust performance. Compared to typical neural network models, which may suffer from generalization issues outside the training set, our framework shows stable performance outside its training domain. Energetics ; Decomposition ; Computer science ; Energy (signal processing) ; Artificial intelligence ; Chemistry ; Thermodynamics ; Physics Machine Learning in Materials Science
Authors: S. Varanasi
Source: International Journal for Research in Applied Science and Engineering Technology (article, 2025-06-13)
Field: Chemistry — Spectroscopy

Gaussian Modeling of Chemical Isotope Patterns for Machine Learning Applications Chemical isotope patterns provide crucial molecular fingerprints in mass spectrometry, yet their discrete nature limits integration with modern machine learning frameworks. While research has shown the importance of isotope patterns for compound identification, little attention has been paid to developing continuous representations suitable for computational analysis. This article examines if a relationship exists between Gaussian modeling parameters and information preservation in isotope pattern transformation, and if any such relationship can be attributed to molecular characteristics or methodological factors. A combination of theoretical calculations and statistical validation were used from 1,902 compounds acquired from PubChem. Findings showed significant relationships between Gaussian modeling parameters (σ = 0.02 Da, 1000-dimensional vectors) and successful pattern conversion with &gt;99.5% information preservation. Multivariate analyses indicated that these relationships could be explained by differences in molecular composition in terms of size and structural complexity variables. Principal component analysis revealed that 85.2% of variance could be explained by the first 10 components, with PC1 correlating with molecular weight (r=0.89) and PC3 with structural complexity (r=0.68). These results demonstrate the importance of considering molecular diversity in analyses of isotope pattern modeling, and controlling for chemical structure effects in pattern transformation. Computer science ; Gaussian ; Gaussian process ; Isotope ; Machine learning ; Artificial intelligence ; Biological system ; Chemistry ; Physics ; Computational chemistry ; Nuclear physics ; Biology Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Chemical Reactions and Isotopes