jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yu-chao TANG | Bin XIAO | Jian-hui CHEN | Shui-zhou CHEN | Yi-hang LI | Fu LIU | Wan DU | Yi-heng SHEN | Xue FAN | Quan QIAN | Yi LIU
Source: Transactions of Nonferrous Metals Society of China (article, 2025-11-01)
Field: Materials Science — Materials Chemistry

Machine learning with center-environment attention mechanism for multi-component Nb alloys The graph-based representation of material structures, along with deep neural network models, often lacks locality and requires large datasets, which are seldom available in specialized materials research. To address this challenge, we developed a more data-efficient center-environment (CE) structure representation that incorporates a predefined attention-focused mechanism. This approach was applied in a machine learning (ML) study to examine the local alloying effects on the structural stability of Nb alloys. In the CE feature model, the atomic environment type (AET) method was utilized, which effectively describes the low-symmetry physical shell structures of neighboring atoms. The optimized ML-CE AET models successfully predicted double-site substitution energies in Nb with a mean absolute error of 55.37 meV and identified Si-M pairs (where M = Ta, W, Re, and lanthanide rare-earth elements) as promising stabilizers for Nb. The ML-CE AET model’s good transferability was further confirmed through accurate prediction of untrained alloying element Nb. Significantly, in cases involving small datasets, non-deep learning models with CE features outperformed deep learning models based on graph features reported in the literature. Transferability ; Stability (learning theory) ; Representation (politics) ; Artificial neural network ; Locality ; Feature (linguistics) ; Deep learning Machine Learning in Materials Science ; Titanium Alloys Microstructure and Properties ; Nuclear Materials and Properties
Authors: Dennis Delali Kwesi Wayo | Mohd Zulkifli Bin Mohamad Noor | Masoud Darvish Ganji | Camila Martins Saporetti | Leonardo Goliatt
Source: ChemRxiv (preprint, 2025-05-14)
Field: Chemistry — Physical and Theoretical Chemistry

Q-DFTNet: A Chemistry-Informed Neural Network Framework for Predicting Molecular Dipole Moments via DFT-Driven QM9 Data Predicting molecular dipole moments is essential for quantum chemistry and materials science applications. In this study, we introduce Q-DFTNet: a Chemistry-Informed Neural Network framework designed to systematically benchmark and interpret graph neural networks (GNNs) for molecular dipole prediction. Seven GNN architectures: GCN, GIN, GraphConv, GATNet, GATConv, SAGEConv, and GIN+EdgeConv, were trained on the QM9 dataset for 100 epochs and evaluated using mean squared error (MSE), mean absolute error (MAE), coefficient of determination (R$^2$), and clustering validity. GraphConv attained the best predictive performance with a test MSE of 0.8514, MAE of 0.6407, and R$^2$ of 0.621, outperforming other models with only 16.5k parameters. Attention-based models (GATNet, GATConv) exhibited faster convergence but lower accuracy, with MSEs exceeding 1.09 and R$^2$ scores below 0.52. Latent spaces were visualized via t-SNE, PCA, and UMAP projections, revealing structural encoding differences across models. Clustering quality, assessed using Silhouette Score (0.4658 for GraphConv), Davies-Bouldin Index (0.7476 for GCN), and Calinski-Harabasz Score (2411.73 for GraphConv), demonstrated how Q-DFTNet captures chemically coherent groupings. A k-means analysis of 1000 molecules in latent space revealed that GIN+EdgeConv formed the most interpretable clusters, with dipole means ranging from 2.407 to 2.778 Debye. Q-DFTNet provides a unified framework for prediction, representation learning, and molecular interpretability, offering valuable guidance for neural architecture selection in quantum machine learning pipelines. Dipole ; Artificial neural network ; Computational chemistry ; Statistical physics ; Computer science ; Chemistry ; Chemical physics ; Artificial intelligence ; Physics ; Organic chemistry Various Chemistry Research Topics ; Computational Drug Discovery Methods ; Molecular spectroscopy and chirality
Authors: Zhenyao Fang | Ting-Wei Hsu | Qimin Yan
Source: ArXiv.org (preprint, 2025-06-18)
Field: Materials Science — Materials Chemistry

A Machine Learning Framework for Modeling Ensemble Properties of Atomically Disordered Materials Disorder, though naturally present in experimental samples and strongly influencing a wide range of material phenomena, remains underexplored in first-principles studies due to the computational cost of sampling the large supercell and configurational space. The recent development of machine learning techniques, particularly graph neural networks (GNNs), has enabled the efficient and accurate predictions of complex material properties, offering promising tools for studying disordered systems. In this work, we introduce a computational framework that integrates GNNs with Monte Carlo simulations for efficient calculations of thermodynamic properties and ensemble-averaged functional properties of disordered materials. Using the surface-termination-disordered MXene monolayer \ch{Ti3C2T}$_{2-x}$ as a representative system, we investigate the effect of surface termination disorder involving \ch{-F}, \ch{-O}, and termination vacancies on the electrical and optical conductivity spectra. We find that surface termination disorder affects the temperature dependence of electrical conductivity, inducing a peak close to the order-disorder phase transition temperature that reflects the competition between scattering and electron filling effects of the surface termination groups across the phase transition. In contrast, optical conductivity remains robust to local disorder across a wide temperature range and is governed primarily by the global chemical composition of surface terminations. These results demonstrate the utility of our machine-learning-assisted framework for statistically modeling disorder effects and ensemble properties in complex materials, opening new avenues for future studies of disorder-driven phenomena in systems such as high-entropy alloys and disordered magnetic compounds. Monte Carlo method ; Surface (topology) ; Ensemble learning ; Phase transition ; Range (aeronautics) ; Scattering ; Phase (matter) Machine Learning in Materials Science
Authors: Masahiko Okumura
Source: Journal of Electronic Materials (article, 2025-08-03)
Field: Materials Science — Materials Chemistry

Machine Learning Molecular Dynamics Simulations of Materials with Complex Structures Molecular dynamics ; Solid-state physics ; Dynamics (music) ; Statistical physics ; Chemical physics ; Complex system ; Computer science ; Nanotechnology ; Materials science ; Chemistry ; Artificial intelligence ; Physics ; Computational chemistry ; Condensed matter physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Rasmus Christensen | Morten M. Smedskjær
Source: ChemRxiv (preprint, 2025-07-14)
Field: Materials Science — Materials Chemistry

Accelerating the Discovery of High-Conductivity Glass Electrolytes via Machine Learning Glass materials are promising candidates as solid electrolytes for batteries. However, the atomistic origins of variations in ionic conductivity with composition remain poorly understood. A key challenge in computationally designing high-performance glass electrolytes is the absence of an efficient general interatomic potential, particularly for mixed-anion systems, such as combinations of oxides, sulphides, and halides. To address these limitations, a machine learning interatomic potential for glass electrolytes is introduced, based on the atomic cluster expansion descriptor, covering common network formers, modifiers, and anions. Excellent agreement with experimental structure and property data across a wide range of glass compositions is observed. While the potential is broadly applicable to, e.g., lithium-ion conductivity, focus is here on the influence of glass composition on sodium-ion conductivity given the emerging importance of these conductors. By screening the diffusivity of 1,303 glass compositions, the presence of anion species, such as sulphides and halides, is found to have a large positive influence on diffusivity, enabling prediction of highly conductive glasses with mixed anions. Diffusivity and ionic conductivity are highly correlated with a set of descriptors, such as the average atomic volume. This work thus helps to accelerate predictive modelling and broader understanding of conductivity in glass electrolytes. Conductivity ; Electrolyte ; Materials science ; Artificial intelligence ; Computer science ; Chemistry ; Electrode ; Physical chemistry Machine Learning in Materials Science
Authors: M. Ghorbani | Ziyuan Li | Aleksandar Pasquini | Rajesh Vasa | N. Birbilis
Source: npj Materials Degradation (article, 2025-11-21)
Field: Materials Science — Materials Chemistry

Supervised machine learning for corrosion assessment of multi-principal element alloys using experimental and generative datasets Multi principal element alloys (MPEAs) have emerged as a novel class of materials with complex compositions and tuneable properties, making them promising candidates for corrosion-resistant applications. However, their vast compositional space complicates the establishment of clear composition-property relationships using conventional experimental and computational methods, which typically yield insufficient data for effectively mapping causal relationships. This study investigates MPEA corrosion behaviour via supervised machine learning (ML). A dataset of alloy compositions, phases, electrolyte characteristics, and corrosion properties was used to train ML models, including random forest, neural network, kernel ridge regression, and LassoIC. To address data scarcity and enhance prediction accuracy, a generative adversarial network (GAN) was employed to generate synthetic data. Retraining the ML models with augmented data led to improvements in predictive accuracy for corrosion properties. The results highlight the effectiveness of an integrated GAN-ML approach in exploring MPEA compositional complexity and accelerating the design of corrosion-resistant alloys. Generative grammar ; Artificial neural network ; Corrosion ; Kernel (algebra) ; Retraining ; Support vector machine ; Meta learning (computer science) ; Element (criminal law) Machine Learning in Materials Science ; High Entropy Alloys Studies ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Yanyan Gong | Weiwei Xie | Jun Chen
Source: Communications in Computational Chemistry (article, 2025-11-17)
Field: Materials Science — Materials Chemistry

Unraveling Li-Ion Solvation Dynamics in Ether-Based Electrolytes via High-Throughput Molecular Dynamics Simulations and Machine Learning Lithium-ion batteries (LIBs) are now widely used as energy storage systems in portable electronic devices and electric vehicles. The Li-ion solvation dynamics in electrolytes significantly influence the overall performance of LIBs. In this work, we performed high-throughput molecular dynamics smilations on thousands of ether-based electrolytes screened from a vast molecular database containing over 110 million organic molecules. Machine learning models combined with Shapley additive explanation analysis reveal that the presence of bulky alkyl groups reduce the solvent dipole moment, while enhancing the coordination ability of lithium ions with anions. Additionally, the introduction of ether-type groups (-O-) in the rings of cyclic ether molecules negatively affects the solvent dipole moment but positively influences the Li-anion coordination number. In contrast, ether-type groups (-O-) introduced in linear chains correlate positively with solvent dipole moment while negatively with the Li-anion coordination number. This work provides insights into the relation between molecular structure and Li-ion solvation dynamics in electrolyte solutions. Solvation ; Molecular dynamics ; Dipole ; Electrolyte ; Moment (physics) ; Molecule ; Lithium (medication) ; Solvent ; Ion Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Advancements in Battery Materials
Authors: Dorsa Dadashi | Marjan Kaedi | Parsa Dadashi | Suprakas Sinha Ray
Source: Molecular Informatics (article, 2025-02-01)
Field: Materials Science — Materials Chemistry

Prediction of the Appropriate Temperature and Pressure for Polymer Dissolution Using Machine Learning Models The widespread use of polymer solutions in the chemical industry poses a significant challenge in determining optimal dissolution conditions. Traditionally, researchers have relied on experimental methods to estimate the processing parameters needed to dissolve polymers, often requiring numerous iterations of testing different temperatures and pressures. This approach is both costly and time-consuming. In this study, for the first time, we present a machine learning-based approach to predict the minimum temperature and pressure required for polymer dissolution, correlating molecular weight and chemical structure of both the polymer and solvent and its weight percent. Using a dataset compiled from existing literature, which includes key factors influencing polymer dissolution, we also extracted chemical bond information from the molecular structures of polymer-solvent systems. Six different machine learning algorithms, including linear regression, k-nearest neighbors, regression trees, random forests, multilayer perceptron neural networks, and support vector regression, were employed to develop predictive models. Among these, the Random Forest model achieved the highest accuracy, with R<sup>2</sup> values of 0.931 and 0.942 for temperature and pressure predictions, respectively. This novel approach eliminates the need for repetitive experimental testing, offering a more efficient pathway to determining dissolution conditions. Dissolution ; Random forest ; Polymer ; Support vector machine ; Machine learning ; Artificial neural network ; Computer science ; Linear regression ; Regression ; Artificial intelligence ; Biological system ; Algorithm ; Materials science ; Process engineering ; Chemistry ; Mathematics ; Statistics ; Organic chemistry ; Engineering Machine Learning in Materials Science ; Injection Molding Process and Properties ; Thermal and Kinetic Analysis
Authors: Hongqiang Cui | Da Zheng | Huiying Chu | Yan Li | Guohui Li
Source: Physical Chemistry Chemical Physics (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Exploring the enantioselective synthesis mechanism of ammonium cations in solution using deep learning potential Asymmetric synthesis is fundamental to modern organic chemistry. Predicting the stereoselectivity of catalytic reactions in solution remains a challenging problem, as it requires a comprehensive understanding of the underlying mechanistic, energetic, and kinetic factors. To address this challenge, we propose an active learning workflow that integrates iterative cycles of <i>ab initio</i> molecular dynamics (AIMD) and deep learning potential molecular dynamics (DLPMD) simulations. To demonstrate its utility, this workflow is applied to investigate the enantioselective synthesis of quaternary ammonium cations catalyzed by 1,1'-bi-2-naphthol scaffolds (BINOL), with a focus on simulations of large molecular systems over extended timescales. The results of the simulations successfully reproduce the experimentally observed chirality of the major product molecules, as confirmed by <sup>1</sup>HNMR spectroscopy. Furthermore, these simulations provide detailed insight into the reaction pathways and reveal that the chirality of the primary product is thermodynamically controlled under experimental conditions. Consequently, this workflow offers a promising strategy for exploring complex reaction mechanisms and enhancing the predictive accuracy of asymmetric synthesis in complex solutions using deep learning techniques. Workflow ; Enantioselective synthesis ; Chirality (physics) ; Deep learning ; Mechanism (biology) ; Catalysis ; Molecular dynamics Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Advanced Chemical Physics Studies
Authors: Mamduh J. Aljaafreh | Sajjad H. Sumrra | Md Abrar Hassan
Source: Materials Chemistry and Physics (article, 2025-12-22)
Field: Materials Science — Materials Chemistry

A molecular graph fingerprinting based bandgap mapping of NFA polymers for next generation photovoltaic devices via machine learning Polymer ; Band gap ; Photovoltaic system ; Random forest ; Organic solar cell ; Graph Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Advanced Graph Neural Networks
Authors: Arun Sharma | W. Owen McMillan | Selsela Arsala | Supreet Gandhok | R. J. Young
Source: Digital Chemical Engineering (article, 2025-05-25)
Field: Chemistry — Analytical Chemistry

Machine learning for asphaltene polarizability: Evaluating molecular descriptors Asphaltenes are complex polycyclic organic molecules in crude oil that readily aggregate and precipitate under varying thermodynamic conditions. Their structural heterogeneity influences key physicochemical properties, including solubility, stability, and reactivity. Molecular polarizability, a crucial property governing intermolecular interactions and electronic behavior, remains challenging to predict due to this structural diversity. This study employs machine learning models to predict isotropic polarizability using two sets of molecular descriptors: WHIM and GETAWAY. A dataset of 255 asphaltene structures was analyzed using stratified sampling, generating 10 independent training (80%) and testing (20%) splits. The Wolfram Language’s Predict function evaluated multiple machine learning algorithms—including Random Forest, Decision Tree, Gradient Boosted Trees, Nearest Neighbors, Linear Regression, Gaussian Process, and Neural Network—through an automated model selection process, serving as an AutoML framework. Linear regression was the best-performing model in 9 out of 10 splits for GETAWAY descriptors. GETAWAY-based models achieved an average mean absolute deviation of 0.0920 ± 0.0030 and standard deviation of 0.113 ± 0.004, significantly outperforming WHIM-based models (MAD = 0.173 ± 0.007, STD = 0.224 ± 0.008) with paired t -tests confirming statistical significance ( p < 0.001). While R² values were reported, their interpretability was limited by heterogeneity and narrow property ranges in some test sets. These findings demonstrate the effectiveness of AutoML-guided approaches for predicting molecular properties and identify GETAWAY descriptors as a robust, efficient basis for polarizability prediction. Accurate prediction of polarizability is essential for modeling intermolecular forces and improving force field design in petroleum and materials chemistry, issues that are central to industrial and chemical applications. Polarizability ; Asphaltene ; Machine learning ; Artificial intelligence ; Computer science ; Materials science ; Chemistry ; Organic chemistry ; Molecule Petroleum Processing and Analysis ; Hydrocarbon exploration and reservoir analysis ; Computational Drug Discovery Methods
Authors: Hanqing Li | Cheng Cheng | Keyuan Chen | C. T. Hou | Li Ma | Jueyi Ye | Yongzhi Wu | Ju Rong | Xiaohua Yu | Wei Yan | Jing Feng
Source: Journal of Physics and Chemistry of Solids (article, 2025-07-22)
Field: Materials Science — Materials Chemistry

Deep learning potential-driven study of multiscale structural and thermodynamic behaviors in PtTi alloys Materials science ; Multiscale modeling ; Thermodynamics ; Statistical physics ; Chemistry ; Physics ; Computational chemistry Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; High Temperature Alloys and Creep
Authors: Jinjin Zhao | Mengwen Yan | Zeyu Shi | Chao Zhao | Lei Qi | Qing Hao | Hong Liu
Source: ACS Applied Polymer Materials (article, 2025-12-10)
Field: Materials Science — Materials Chemistry

Prediction of COF Synthesis Reaction Conditions Based on 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 R2 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 ; Predictive modelling ; Artificial neuron Covalent Organic Framework Applications ; Advanced Photocatalysis Techniques ; Carbon dioxide utilization in catalysis
Authors: José A. Moreno‐Guerra | C. Gómez-Solís | Francisco Alarcón | Javier F. Ríos-Orihuela | Marco Laurati | Ramón Castañeda-Priego
Source: Physics of Fluids (article, 2025-06-01)
Field: Materials Science — Materials Chemistry

Artificial neural network-based predictions of the viscoelastic properties of polymer matrix composites Today, the use of machine learning, especially through artificial neural networks, significantly speeds up the design of materials exhibiting unique properties. The high predictive and quantifiable capabilities, performance metrics, efficiency, and verifiability make it a powerful tool in Materials Science. Polymer matrix composites are particularly notable due to their unique chemical and physical properties, in addition to their intriguing mechanical behavior, which originate from the combination of polymers and nanoparticles. These properties make them outstanding materials suitable for a wide range of technological applications, for example, surface activation through microfilm formation, among others. This contribution explores an approach based on artificial neural networks to predict the rheological response of polymer matrix composites obtained using different types of salts. We focus on viscosity and frequency-dependent viscoelastic moduli, which are essential to understand the functional and dynamical properties of the composite, and to quantify the adhesion of composite microfilms on surfaces. Our findings reveal that trained algorithms accurately predict these viscoelastic moduli, as validated by comparison with experimental data over a broad range of compositions. This approach offers a versatile framework for studying other film-forming materials beyond polymer composites, thus representing a general methodology with promising perspective applications for research in Materials Science. Viscoelasticity ; Rheology ; Polymer ; Artificial neural network ; Viscosity ; Matrix (chemical analysis) ; Range (aeronautics) Machine Learning in Materials Science ; Composite Material Mechanics ; Polymer Nanocomposites and Properties