jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Seunghyeon Oh | Jiwon Roh | Hyundo Park | Donggyun Lee | Chonghyo Joo | Jinwoo Park | Il Moon | Insoo Ro | Junghwan Kim
Source: ACS Sustainable Chemistry & Engineering (article, 2025-01-31)
Field: Materials Science — Materials Chemistry

Enhancing Catalyst Performance Prediction with Hybrid Quantum Neural Networks: A Comparative Study on Data Consistency Variation Data consistency affects the robustness of machine learning-based models. Most experimental and industrial data have low consistency, leading to poor generalization performance. In this study, a hybrid Quantum Neural Network (hybrid QNN) with superior generalization capabilities, was compared with established machine learning models, including artificial neural networks and decision-tree-based methods such as CatBoost and XGBoost. We evaluated these models by predicting the catalyst performance across different data-consistency scenarios using two catalyst data sets: a low-consistency preferential oxidation of CO (PROX) catalyst and a high-consistency oxidation coupling of methane (OCM) catalyst. The hybrid QNN performed better in both low- and high-consistency environments, demonstrating robust generalization capabilities. In the regression tasks, the hybrid QNN achieved a 6.7% lower mean absolute error (MAE) for the PROX catalyst and a 35.1% lower MAE for the OCM catalyst compared with the least-performing model. Adaptability is crucial in catalysis, where data scarcity and variability are common. Our research confirms the potential of the hybrid QNN as a comprehensive tool for advancing catalyst design and selection by achieving high accuracy and predictive power under diverse conditions. Artificial neural network ; Variation (astronomy) ; Consistency (knowledge bases) ; Catalysis ; Quantum ; Computer science ; Artificial intelligence ; Chemistry ; Physics ; Organic chemistry Machine Learning in Materials Science ; Topic Modeling ; Cloud Computing and Resource Management
Authors: Runzhong Wang | Robert Wang | Mrunali Manjrekar | Connor W. Coley
Source: ArXiv.org (preprint, 2025-02-25)
Field: Materials Science — Materials Chemistry

Neural Graph Matching Improves Retrieval Augmented Generation in Molecular Machine Learning Molecular machine learning has gained popularity with the advancements of geometric deep learning. In parallel, retrieval-augmented generation has become a principled approach commonly used with language models. However, the optimal integration of retrieval augmentation into molecular machine learning remains unclear. Graph neural networks stand to benefit from clever matching to understand the structural alignment of retrieved molecules to a query molecule. Neural graph matching offers a compelling solution by explicitly modeling node and edge affinities between two structural graphs while employing a noise-robust, end-to-end neural network to learn affinity metrics. We apply this approach to mass spectrum simulation and introduce MARASON, a novel model that incorporates neural graph matching to enhance a fragmentation-based neural network. Experimental results highlight the effectiveness of our design, with MARASON achieving 28% top-1 accuracy, a substantial improvement over the non-retrieval state-of-the-art accuracy of 19%. Moreover, MARASON outperforms both naive retrieval-augmented generation methods and traditional graph matching approaches. Code is publicly available at https://github.com/coleygroup/ms-pred Artificial neural network ; Matching (statistics) ; Graph ; Deep learning ; Deep neural networks ; Pattern recognition (psychology) Machine Learning in Materials Science ; Machine Learning in Bioinformatics ; Advanced Graph Neural Networks
Authors: Kai Gu | Yingping Liang | Jiaming Su | Peihan Sun | Peng Jia | Naihua Miao | Zhimei Sun | Ying Fu | Haizheng Zhong | Jun Zhang
Source: ACS Nano (article, 2025-11-05)
Field: Materials Science — Materials Chemistry

Deep Learning Models for Colloidal Nanocrystal Synthesis Colloidal synthesis of nanocrystals usually includes complex chemical reactions and multistep synthesis processes. Despite the great success in the past 30 years, it remains challenging to clarify the correlations between the synthetic parameters of the chemical reaction and the physical properties of nanocrystals. Here, we developed a deep learning-based nanocrystal synthesis model that correlates synthetic parameters with the final size and shape of target nanocrystals, using a data set of 3508 recipes covering 348 distinct nanocrystal compositions. The size and shape labels were obtained from transmission electron microscope images using a segmentation model trained with a semi-supervised algorithm on a data set comprising around 1.2 million nanocrystals. By applying the reaction intermediate-based data augmentation method and elaborated descriptors, the synthesis model was able to predict the nanocrystal's size with a mean absolute error of 1.39 nm, while reaching an 89% average accuracy for shape classification. The synthesis model shows knowledge transfer capabilities across different nanocrystals with the input of new recipes. With that, the influence of chemicals on the final size of nanocrystals was further evaluated, revealing the descending order of importance of the nanocrystal composition, precursor or ligand, and solvent. Overall, the deep learning-based nanocrystal synthesis model offers a powerful tool to expedite the development of high-quality nanocrystals. Nanocrystal ; Deep learning ; Colloid ; Nanoparticle ; Set (abstract data type) ; Transmission electron microscopy Machine Learning in Materials Science ; Quantum Dots Synthesis And Properties ; Nanocluster Synthesis and Applications
Authors: Radu A. Talmazan | Jakob Gamper | Iván Castillo | Thomas S. Hofer | Maren Podewitz
Source: Digital Discovery (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Coupling causality and interpretable machine learning to reveal the reaction coordinate of C–N coupling with a supramolecular Cu-calix[8]arene catalyst Supramolecular 3d transition-metal catalysts are large, flexible systems with intricate interactions, resulting in complex reaction coordinates. To capture their dynamic nature, we developed a broadly applicable, high-throughput workflow, that leverages quantum mechanics/molecular mechanics molecular dynamics (QM/MM MD) in explicit solvent, to investigate a Cu(i)-calix[8]arene-catalysed C-N coupling reaction. The system complexity and high amount of data generated from sampling the reaction requires automated analyses. To identify and quantify the reaction coordinate from noisy simulation trajectories, we applied interpretable machine learning techniques (Lasso, Random Forest, Logistic Regression) in a consensus model, alongside dimensionality reduction methods (PCA, LDA, tICA). By employing a Granger Causality model, we move beyond the traditional view of a reaction coordinate, by defining it instead as a sequence of molecular motions leading up to the reaction. Supramolecular chemistry ; Catalysis ; Coupling (piping) ; Causality (physics) ; Materials science ; Crystallography ; Chemistry ; Polymer chemistry ; Computational chemistry ; Chemical physics ; Physics ; Organic chemistry ; Quantum mechanics ; Crystal structure ; Composite material Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Ling Tang | Weiyi Xia | Gayatri Viswanathan | Ernesto Soto | Kirill Kovnir | Cai‐Zhuang Wang
Source: The Journal of Chemical Physics (article, 2025-08-25)
Field: Materials Science — Materials Chemistry

Developing a neural network machine learning interatomic potential for molecular dynamics simulations of La–Si–P systems While molecular dynamics (MD) is a very useful computational method for atomistic simulations, modeling the interatomic interactions for reliable MD simulations of real materials has been a long-standing challenge. In 2007, Behler and Parrinello first proposed and demonstrated an artificial neural network machine learning (ANN-ML) scheme, opening a new paradigm for developing accurate and efficient interatomic potentials for reliable MD simulation studies of the thermodynamics and kinetics of materials. In this paper, we show that an accurate and transferable ANN-ML interatomic potential can be developed for MD simulations of the La-Si-P system. The crucial role of training data in the ML potential development is discussed. The developed ANN-ML potential accurately describes not only the energy vs volume curves for all the known elemental, binary, and ternary crystalline structures in the La-Si-P system but also the structures of La-Si-P liquids with various compositions. Using the developed ANN-ML potential, the melting temperatures of several crystalline phases in the La-Si-P system are predicted by the coexistence of solid-liquid phases from MD simulations. While the ANN-ML model systematically underestimates the melting temperatures of these phases, the overall trend agrees with experiment. The developed ANN-ML potential is also applied to study the nucleation and growth of LaP as a function of different relative concentrations of Si and P in the La-Si-P liquid, and the obtained results are consistent with experimental observations. Interatomic potential ; Molecular dynamics ; Nucleation ; Ternary operation ; Thermodynamics ; Artificial neural network ; Chemistry ; Materials science ; Statistical physics ; Physics ; Computer science ; Computational chemistry ; Machine learning Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Electronic and Structural Properties of Oxides
Authors: Yudai Iwamizu | Kota Suzuki | Michiyo Kamiya | Naoki Matsui | K. Nomoto | Satoshi Hori | Masaaki Hirayama | Ryoji Kanno
Source: Electrochemistry (article, 2025-02-19)
Field: Materials Science — Materials Chemistry

Chemical Composition-Driven Machine Learning Models for Predicting Ionic Conductivity in Lithium-Containing Oxides A machine learning model that can predict the ionic conductivity of lithium-containing oxides using chemical composition and ionic conductivity data was previously developed. However, this model revealed several limitations, leading to less-than-ideal prediction accuracy. Thus, new models demonstrating improved prediction ability must be developed. This study presents the development of machine learning models for the accurate prediction of ionic conductivity in lithium-containing materials based solely on their chemical composition. The models constructed using the NGBoost and LightGBM algorithms show high compatibility with the training and test data, resulting in high predictive accuracy. The constructed models identify “entropy,” which is considered a key factor in developing ionic conductors, as an important feature. This finding highlights the potential utility of this property from a solid-state chemistry perspective. The developed models demonstrate high predictive accuracy even for previously reported lithium superionic conductor-type materials that were not included in the training dataset. The established models are expected to facilitate efficient material discovery for the development of all-solid-state lithium batteries. Ionic conductivity ; Lithium (medication) ; Ionic bonding ; Composition (language) ; Conductivity ; Materials science ; Chemical composition ; Chemical engineering ; Inorganic chemistry ; Chemistry ; Ion ; Physical chemistry ; Psychology ; Electrode ; Engineering ; Electrolyte ; Organic chemistry Machine Learning in Materials Science ; Fuel Cells and Related Materials
Authors: Qian Zhang | Hengyue Zhang | Zhiyao Su | Yajing Sun | Wenping Hu
Source: Journal of Chemical Theory and Computation (article, 2025-07-10)
Field: Materials Science — Materials Chemistry

Discovering Molecular Insights in Organic Optoelectronics with Knowledge-Informed Interpretable Deep Learning Deep learning holds significant promise for accelerating molecular screening and materials design. However, the black-box nature of current models limits their ability to generate fundamentally new chemical knowledge and insights. Here, we propose LUMIA (Learning and Understanding Molecular Insights with Artificial Intelligence), an innovative interpretable deep learning framework integrating chemistry-informed contrastive learning and Monte Carlo tree search (MCTS). LUMIA is pretrained on approximately 1.4 million organic molecules, using knowledge-informed augmentations that embed π-conjugation and substituent effects explicitly. This allows it to effectively capture hierarchical molecular representations aligned with chemical intuition. Critically, the explicit integration of chemical knowledge enables LUMIA to achieve state-of-the-art performance across multiple organic optoelectronic property prediction tasks. Leveraging its intrinsic interpretability through MCTS, LUMIA directly uncovers previously unexplored substructure patterns influencing reorganization energy, enabling rational molecular design beyond the training data set. Furthermore, LUMIA reveals novel chemical insights, including synergistic effects of substituent positions in pyrazole derivatives. This study highlights the pivotal role of knowledge embedding in interpretable deep learning, transforming molecular design, and accelerating chemical discovery. Computer science ; Data science ; Deep learning ; Artificial intelligence ; Knowledge management Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Do Hung Manh | Sang Uck Lee
Source: Chemical Engineering Journal (article, 2025-09-01)
Field: Materials Science — Materials Chemistry

Local environment-aware prediction of catalytic activity for the hydrogen evolution reaction in alloy nanoparticles via machine learning interatomic potentials Catalysis ; Alloy ; Nanoparticle ; Hydrogen ; Interatomic potential ; Materials science ; Nanotechnology ; Chemical engineering ; Computer science ; Chemistry ; Molecular dynamics ; Computational chemistry ; Metallurgy ; Engineering ; Organic chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Computational Drug Discovery Methods
Authors: Amirehsan Ghasemi | Murat Barışık
Source: Nanomaterials (article, 2025-10-31)
Field: Chemistry — Spectroscopy

Machine Learning for Thermal Transport Prediction in Nanoporous Materials: Progress, Challenges, and Opportunities Predicting the thermal properties of nanoporous materials is a major challenge that affects their applications in efficient thermal insulation and energy storage. This narrative review discusses the application of machine learning models in nanoporous materials, including covalent organic frameworks, metal-organic frameworks, aerogels, and zeolites. It discusses model advancements, with a focus on predictive accuracy and computational efficiency. This includes models such as convolutional neural networks, graph neural networks, and physics-informed neural networks. This study also addresses the limitations of these data-driven models, including data availability, challenges in maintaining physical consistency, and difficulties in generalizing across various material families. Additionally, it covers emerging approaches such as multimodal and transfer learning, which are explored for their potential to reduce computational costs. Moreover, the benefits of interpretable machine learning methods for understanding underlying physical mechanisms are introduced and highlighted. This review provides comprehensive and practical guidelines for researchers using machine learning approaches in the study and design of nanoporous materials. Nanoporous ; Convolutional neural network ; Predictive modelling ; Artificial neural network ; Computational model ; Deep learning ; Graph Aerogels and thermal insulation ; Thermal properties of materials ; Covalent Organic Framework Applications
Authors: Oleh Yasniy | Dmytro Tymoshchuk | Iryna Didych | Volodymyr Iasnii | Iaroslav Pasternak
Source: Procedia Structural Integrity (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Modelling the properties of shape memory alloys using machine learning methods In this paper, the properties of shape memory alloys (SMA), in particular nickel-titanium alloy (Nitinol), were modelled using machine learning methods. The strain of the material ε was predicted depending on the applied stress σ and the number of loading-unloading cycles N by boosted trees, random forests, support vector machines (SVM), k-nearest neighbors (KNN), and artificial neural networks (ANN) algorithms. Experimental data were used to train the models. The highest accuracy was achieved with the ANN, for which the mean absolute percentage error (MAPE) was 0.29% for the loading period and 0.38% for the unloading period. Additional model validation at 127 cycles showed an error of 0.75% for the loading period and 0.92% for the unloading period. These results confirm the high efficiency of ANNs for predicting complex nonlinear material behavior, which can significantly reduce the number of experiments required to study SMA properties. Shape-memory alloy ; Materials science ; Computer science ; Artificial intelligence ; Composite material ; Machine learning Shape Memory Alloy Transformations ; Polymer composites and self-healing
Authors: Ninghan Tang | P. Hao | Juan Miguel Tiscar | F.A. Gilabert
Source: Polymers (article, 2025-04-01)
Field: Materials Science — Polymers and Plastics

Predicting Mechanical Responses in Polymer Blends with Unintended Polymer Fractions Using an Efficient Neural Network-Based Constitutive Material Model Current mechanical recycling procedures often fall short of achieving 100% purity in recycled thermoplastics, which typically consist of mixed polymer types. These other polymers, though typically present in small amounts, can significantly affect the mechanical properties of the recycled material. Addressing this issue, this study introduces a neural network (NN) approach combined with a physically-based constitutive model to accurately predict the mechanical behavior of polymer blends of varying compositions. The NN-based method relies on the training of a crucial internal variable controlling the nonlinear response. This variable is derived from the physical model, which minimizes the dependence on extensive experimental data. We evaluated this approach on polymer blends of LLDPE/PET, LLDPE/PA6, and LDPE/PS at various weight fraction ratios. The results demonstrate that the NN-based model effectively aligns with experimental outcomes, enhancing our ability to predict how different blend ratios influence the mechanical properties of polymer blends. This capability is crucial for optimizing the use of recycled polymers in various applications. Materials science ; Polymer ; Polymer blend ; Linear low-density polyethylene ; Artificial neural network ; Composite material ; Nonlinear system ; Mass fraction ; Low-density polyethylene ; Biological system ; Computer science ; Copolymer ; Artificial intelligence Polymer crystallization and properties ; Composite Material Mechanics ; Mechanical Behavior of Composites
Authors: Yuchen Guo | Chaokun Guo | Yuanji Xu | Bing Zheng | Xiaodong Ni | Fuyang Tian
Source: Journal of Magnetism and Magnetic Materials (article, 2025-07-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine learning investigation of chemical disorder effects in 24-valence-electron full-Heusler alloys with single magnetic element Materials science ; Valence electron ; Valence (chemistry) ; Condensed matter physics ; Heusler compound ; Electron ; Electronic structure ; Physics ; Quantum mechanics Heusler alloys: electronic and magnetic properties ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Jaemin Wang | Hyeonseok Kwon | Sang‐Ho Oh | Jae Heung Lee | Dae Won Yun | Hyungsoo Lee | Seong-Moon Seo | Young-Soo Yoo | Hi Won Jeong | Hyoung Seop Kim | Byeong‐Joo Lee
Source: npj Computational Materials (article, 2025-07-15)
Field: Materials Science — Materials Chemistry

Multiscale computational framework linking alloy composition to microstructure evolution via machine learning and nanoscale analysis Achieving targeted microstructures through composition design is a core challenge in developing structural materials for high-performance applications. This study introduces a multiscale Integrated Computational Materials Engineering (ICME) framework that combines CALPHAD-based thermodynamic modeling, machine learning, molecular dynamics, and diffusion kinetics to link alloy chemistry to microstructural evolution. Machine learning models trained on 750,000 CALPHAD-derived datapoints enabled rapid screening of two billion compositions based on thermodynamic criteria. An advanced screening step incorporated nanoscale physical descriptors that capture mechanisms governing precipitate coarsening and dynamic recrystallization. Applied to wrought Ni-based superalloys, the framework identified twelve compositions predicted to form fine intragranular γ′ precipitates within coarse γ grains; one was experimentally validated, with microscopy confirming the predicted microstructure. While demonstrated for Ni-based systems, the methodology is broadly generalizable. This work highlights the power of integrating high-throughput composition screening with atomistic-scale evaluation to accelerate microstructure-driven materials design beyond equilibrium thermodynamics. Microstructure ; Nanoscopic scale ; Materials science ; Alloy ; Composition (language) ; Nanotechnology ; Computer science ; Artificial intelligence ; Metallurgy Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Xue‐liang Yin | Ruipeng Li | Xun Sun | Qi Feng | Bo Zhang | Shuyuan Zhao
Source: JOM (article, 2025-02-24)
Field: Materials Science — Ceramics and Composites

Prediction of Composite Performance of Refractory and Ceramic Materials Based on Deep Learning Refractory (planetary science) ; Ceramic ; Composite number ; Materials science ; Composite material Advanced ceramic materials synthesis ; Advanced materials and composites ; Machine Learning in Materials Science
Authors: Sadaf Noreen | Mamduh J. Aljaafreh | Sajjad Hussain Sumrra
Source: Coatings (article, 2025-06-26)
Field: Materials Science — Materials Chemistry

Modeling Enthalpy of Formation with Machine Learning for Structural Evaluation and Thermodynamic Stability of Organic Semiconductors The enthalpy of formation (dHm) is a crucial parameter in evaluating the structural stability of organic semiconductors. In this study, we employed machine learning (ML) models to predict the dHm of organic semiconductors. The current results show that Kappa2 and NumRotableBonds are highly correlated with the dHm, indicating their importance in determining the stability of these materials. Using Gradient Boosting, Random Forest, and Extra Trees models, we achieved a high R2 value of 0.68–0.70, demonstrating the effectiveness of these models in predicting the dHm. Further analysis using SHAP values revealed that Kappa2 and fr_uncrch_alkane are the most important descriptors in determining the dHm. These findings provide valuable insights into the structural evaluation and stability of organic semiconductors and highlight the potential of ML models in predicting key properties of these materials. Enthalpy ; Stability (learning theory) ; Chemical stability ; Semiconductor ; Standard enthalpy change of formation ; Materials science ; Thermodynamics ; Organic semiconductor ; Physical chemistry ; Chemistry ; Computer science ; Machine learning ; Physics ; Optoelectronics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Organic Electronics and Photovoltaics