jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zhan-Xing Li | Tian-Xing Yang | Akihiko Kimura | Peng Dou
Source: Intermetallics (article, 2025-08-23)
Field: Materials Science — Metals and Alloys

Prediction of the tensile properties of ODS ferritic/martensitic steels under various temperature conditions using interpretable machine learning models It is crucial to accurately and reliably predict the mechanical properties of oxide dispersion strengthened (ODS) steels for their application in Generation IV nuclear reactors. In this work, six machine learning models were utilized to predict the ultimate tensile strength and total elongation of ODS steels under various temperature conditions. The extreme gradient boosting (XGBoost) models exhibited the best predictive performance, with R 2 , RMSE, and MAE values of 0.96, 79 MPa, and 57 MPa for ultimate tensile strength prediction, respectively, and 0.90, 1.9%, and 1.6% for total elongation prediction, respectively. Shapley Additive Explanations (SHAP) method was used to interpret the model by analyzing feature importance and its corresponding impact trends. The key chemical compositions affecting ultimate tensile strength are Ti, Y 2 O 3 , Cr, and W, whereas significant process parameters include heat treatment temperature (HT) and consolidation temperature (CT). Among these, Ti, Y 2 O 3 , and W positively correlate with ultimate tensile strength, whereas HT and CT negatively correlate. However, the key chemical compositions affecting total elongation are Y 2 O 3 , W, Cr, and Ti, whereas significant process parameters include HT and CT. Among these, W, HT, and CT positively correlate with total elongation, whereas Ti negatively correlates. Furthermore, the interactions between important features obtained through SHAP analysis were discussed. This study established relationships among composition, processing, and tensile properties of ODS steels, thereby supporting the optimization of composition design and process improvements. • ODS steel tensile data were systematically collected at different temperatures. • Key features were selected to train six machine learning models. • Feature importance, trends, and interactions were derived via model interpretation. Materials science ; Ultimate tensile strength ; Martensite ; Metallurgy ; Microstructure Hydrogen embrittlement and corrosion behaviors in metals ; Fusion materials and technologies ; Nuclear Materials and Properties
Authors: Bowen Deng | Thijs Stuyver
Source: Journal of Chemical Information and Modeling (article, 2025-01-09)
Field: Materials Science — Materials Chemistry

Graph-Based Deep Learning Models for Thermodynamic Property Prediction: The Interplay between Target Definition, Data Distribution, Featurization, and Model Architecture In this contribution, we examine the interplay between target definition, data distribution, featurization approaches, and model architectures on graph-based deep learning models for thermodynamic property prediction. Through consideration of five curated data sets, exhibiting diversity in elemental composition, multiplicity, charge state, and size, we examine the impact of each of these factors on model accuracy. We observe that target definition, i.e., using formation instead of atomization energy/enthalpy, is a decisive factor, and so is a careful selection of the featurization approach. Our attempts at directly modifying model architectures result in more modest, though not negligible, accuracy gains. Remarkably, we observe that molecule-level predictions tend to outperform atom-level increment predictions, in contrast to previous findings. Overall, this work paves the way toward the development of robust graph-based thermodynamic model architectures with more universal capabilities, i.e., architectures that can reach excellent accuracy across data sets and compound domains. Computer science ; Graph ; Property (philosophy) ; Artificial intelligence ; Theoretical computer science ; Data mining Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Grazielle Oliveira | Pedro Henrique Wegner | P. Carvalho | Fernando Augusto Pedersen Voll | Agnes de Paula Scheer | Rafael de P. Soares | Fabiane Oliveira Farias
Source: Fluid Phase Equilibria (article, 2025-07-23)
Field: Materials Science — Materials Chemistry

Machine learning-enhanced COSMO-SAC for accurate solubility predictions Chemistry ; Solubility ; COSMO-RS ; Thermodynamics ; Organic chemistry Crystallization and Solubility Studies ; Analytical Chemistry and Chromatography ; Phase Equilibria and Thermodynamics
Authors: Chenggong 成功 Zhao 赵 | Yifan 一凡 Li 李 | Chunying Chen | Yuanzheng 元政 Tang 唐 | Yan 燕 He 何 | Wei 伟 Yu 于 | Bingyang 炳阳 Cao 曹
Source: Chinese Physics B (article, 2025-06-17)
Field: Materials Science — Materials Chemistry

Phase change thermal interface materials: From principles to applications and beyond Abstract Phase change thermal interface materials (PC-TIMs) have emerged as a promising solution to address the increasing thermal management challenges in electronic devices. This is attributed to their dual mechanisms of latent heat absorption and phase change-induced interfacial wettability. This review explores the fundamental principles, material innovations, and diverse applications of PC-TIMs. The heat transfer enhancement mechanisms are first underlined with key factors such as thermal carrier mismatch at the microscale and contact geometry at the macroscale, emphasizing the importance of material selection and design for optimizing thermal performance. Section 2 focuses on corresponding experimental approaches provided, including intrinsic thermal conductivity improvements and interfacial heat transfer optimization. Section 3 discusses common methods such as physical adsorption via porous materials, chain-crosslinked network designs, and core–shell structures, and their effects on leakage prevention, heat transfer enhancement, and application flexibility. Furthermore, the extended applications of PC-TIMs in thermal energy storage are explored in Section 4, suggesting their potential in diverse technological fields. The current challenges in interfacial heat transfer research and the prospect of PC-TIMs are also discussed. The data-driven machine learning technologies will play an increasingly important role in addressing material development and performance prediction. Phase change ; Materials science ; Interface (matter) ; Phase-change material ; Thermal ; Thermal grease ; Phase (matter) ; Engineering physics ; Thermodynamics ; Composite material ; Thermal conductivity ; Physics Thermal properties of materials ; Phase Change Materials Research ; Thermal Expansion and Ionic Conductivity
Authors: Xiao Hu | Xiuyuan Zhao | Jiyuan Wang | Yongbin Yang
Source: PLoS ONE (article, 2025-10-06)
Field: Materials Science — Materials Chemistry

Information-theoretic multi-scale geometric pre-training for enhanced molecular property prediction Maximizing information transfer across different structural scales is critical for effective molecular representation learning. Current molecular graph neural networks fail to fully capture the multi-scale nature of molecular geometry, leading to suboptimal information propagation between local and global structural features. We propose Multi-Scale Geometric Pre-training (MSG-Pre), an information-theoretic framework that hierarchically integrates molecular information across atomic, functional group, and conformer levels through entropy-guided mechanisms. Our approach employs a scale-adaptive attention mechanism that dynamically weights geometric features based on their information content, coupled with a hierarchical contrastive learning scheme that maximizes mutual information between complementary structural views. This is further reinforced by a geometric regularization strategy that minimizes information loss of essential conformational properties. Rigorous empirical validation on 14 molecular benchmark datasets demonstrates state-of-the-art performance with improvements up to 5.2% over previous methods. Notably, MSG-Pre significantly enhances information extraction for nanomedicine applications including nanoparticle-protein interactions and surface functionalization efficacy. Theoretical analysis reveals that MSG-Pre effectively maximizes cross-scale mutual information while minimizing intra-scale redundancy, maintaining an optimal information-entropy balance in molecular representations. Our work establishes an information-theoretic foundation for geometric pre-training that improves molecular understanding and enhances prediction capabilities for both drug discovery and nanomaterial design applications. Mutual information ; Molecular graph ; Representation (politics) ; Benchmark (surveying) ; Information theory ; Molecular dynamics ; Interaction information ; Graph ; Molecular descriptor Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Pengcheng Fu | Meng Qi | Chunshuang Liu | Cheng Zhang | Yuhao Zhao | Zhibao Liu | Hao Wang | Dongfeng Zhao
Source: Journal of Environmental Management (article, 2025-10-28)
Field: Materials Science — Materials Chemistry

Machine learning-based prediction of PAHs thermal desorption efficiency: Model optimization, boundary correction, and mechanistic insights Desorption ; Environmental remediation ; Thermal desorption ; Monte Carlo method ; Bayesian inference ; Process (computing) Thermal and Kinetic Analysis ; Phase Change Materials Research ; Thermal Expansion and Ionic Conductivity
Authors: M. Engelhardt | Markus O. Zimmermann | Finn Mier | Frank M. Boeckler
Source: Journal of Chemical Theory and Computation (article, 2025-06-09)
Field: Chemistry — Physical and Theoretical Chemistry

Comparison of QM Methods for the Evaluation of Halogen−π Interactions for Large-Scale Data Generation Halogen-π interactions play a pivotal role in molecular recognition processes, drug design, and therapeutic strategies, providing unique opportunities for enhancing and fine-tuning the binding affinity and specificity of pharmaceutical agents. The present study systematically benchmarks various combinations of quantum mechanical (QM) methods and basis sets to characterize halogen-π interactions in model systems. We evaluate both density functional theory (DFT) methods and wave function-based post-HF methods in terms of accuracy to reference calculations at the CCSD(T)/CBS level of theory and runtime efficiency. By balancing these crucial aspects, we aim to identify an optimal configuration suitable for high-throughput applications. Our results indicate that MP2 using the reasonably large TZVPP basis set is in excellent agreement with reference calculations, striking a balance between accuracy and computational efficiency. This allows us to generate large, reliable data sets, which will serve as a basis to develop and train machine-learning models capable of accurately capturing the strength of halogen-π interactions, thereby providing a robust data-driven foundation for medicinal chemistry analysis. Computer science ; Density functional theory ; Set (abstract data type) ; Halogen ; Scale (ratio) ; Basis (linear algebra) ; Basis set ; Training set ; Artificial intelligence ; Machine learning ; Computational chemistry ; Chemistry ; Physics ; Mathematics Crystallography and molecular interactions ; Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science
Authors: Sridatri Nandy | K. V. Jovan Jose
Source: The Journal of Physical Chemistry A (article, 2025-06-13)
Field: Materials Science — Materials Chemistry

Geometric Guidance Integrated with Directed Electrostatics Strategy within a Graph Neural Network Approach for Nanocluster Structure Prediction We introduce the Geometric-DESIGNN method, which integrates Geometric Guidance with Directed Electrostatics Strategy within a Graph Neural Network framework to predict the stable configuration of nanoclusters on their potential energy surfaces. This approach merges the geometric and electronic strategies using graph neural network-based models to predict structures of large atomic clusters with specific size and point-group symmetries. This approach aids in constructing atomic metal cluster structures by predicting building frames through a geometric approach and locating the minima in the molecular electrostatic potential (MESP) landscape. By following alternate geometric and DESIGNN building strategies for each shell of parent clusters, we efficiently achieve close-packed daughter structures along their evolutionary paths. The geometric-DESIGNN approach is validated on the prototype Mg<sub><i>n</i></sub> clusters, by building structures for sizes up to <i>n</i> < 561. Furthermore, constraining the point-group symmetry of the parent clusters, we identify new symmetric isomers of medium to large Mg<sub><i>n</i></sub> clusters with <i>n</i> < 150. This methodology is also employed to construct stable Mg<sub><i>n</i></sub> nanoclusters with <i>n</i> = 332, 338, and 561. Benchmarking results show that the geometric-DESIGNN approach is an efficient tool for accelerated prediction of the nanocluster structure. Electrostatics ; Artificial neural network ; Graph ; Computer science ; Artificial intelligence ; Theoretical computer science ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Nanocluster Synthesis and Applications
Authors: Mohammad Hossein Nargesi | jafar amiriparian | Kamran Kheiralipour
Source: Applied Food Research (article, 2025-06-01)
Field: Chemistry — Analytical Chemistry

Detection of wheat, chickpea, and sea foam in black pepper using hyperspectral imaging technique Black pepper is the most important spice and used as medicinal plant. Hyperspectral imaging is a fast, accurate, and cheap method for the assessment of food material. The present research offers the possibility of using hyperspectral imaging for the detection of chickpea flour, wheat flour, and sea foam adulterants in black pepper powder. The adulterated samples were prepared with 0-50% levels. The principal component analysis method was applied to select the efficient wavelengths of the acquired hypercubes of the samples. After feature extraction, sequential feature selection method was used to find efficient features and then artificial neural network was used for classification and prediction. The classification and prediction accuracy of the best model was 100 %. The obtained results in the present research demonstrated the high capability of the developed system for detecting adulterants in black pepper powder. Hyperspectral imaging ; Pepper ; Environmental science ; Horticulture ; Remote sensing ; Biology ; Geology Spectroscopy and Chemometric Analyses ; GABA and Rice Research ; Identification and Quantification in Food
Authors: Muhamad Akrom | Supriadi Rustad | Hermawan Kresno Dipojono | Ryo Maezono | Hideaki Kasai
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Comprehensive Prediction of Abx3 Perovskite Formation Energy Via Quantum Circuit Learning Perovskite (structure) ; Quantum ; Energy (signal processing) ; Materials science ; Computer science ; Optoelectronics ; Physics ; Engineering ; Quantum mechanics ; Chemical engineering Electronic and Structural Properties of Oxides ; Machine Learning in Materials Science
Authors: Yunhua Lu | Guo Y | Qingwei Zhang | Chao Zhang | Shiai Xu | Zhang Jun-an
Source: ACS Sustainable Chemistry & Engineering (article, 2025-06-10)
Field: Materials Science — Materials Chemistry

Unlocking Adsorption Potential: Machine-Learning-Assisted Design of Doped Magnesium Oxide Structures for CO<sub>2</sub> Capture Magnesium oxide (MgO) is widely regarded as a promising CO2 capture material due to its stability, low cost, and high theoretical adsorption capacity. However, the actual adsorption performance of intrinsic MgO is hindered by the limited exposure of alkaline sites. Doping modification has been demonstrated as an effective method to improve its adsorption capacity. Unfortunately, detailed configurations of these doped MgO structures and their correlation with adsorption energy remain little explored and are unclear. This study systematically constructs a total of 142 doped MgO structures (Element–Concentration–Adsorption sites, ECA) involving 17 elements across the periodic table. Through a synergistic approach combining density functional theory (DFT) and machine learning, optimal doping elements and configurations are identified. Nonmetals (N, P, S, Cl) and metalloids (Si, Al) are identified as the most effective dopants, enhancing CO2 adsorption energy by up to 720% compared to undoped MgO. Most of the dopant atoms exhibit site-dependent performance, with oxygen adsorption sites generally favorable. Key electronic descriptors, such as s- and d-orbital electron differences of the dopant atom (NE-s-dA) and band gap variations (BGD-d), strongly correlate with adsorption energy. Machine-learning models further validate these findings, highlighting the critical role of dopant-induced charge redistribution and orbital interactions. This work provides a direct guide for designing high-performance MgO-based adsorbents, emphasizing the efficacy of nonmetal and metalloid doping for efficient CO2 capture. Adsorption ; Magnesium ; Doping ; Oxide ; Materials science ; Chemistry ; Nanotechnology ; Chemical engineering ; Computer science ; Inorganic chemistry ; Engineering ; Physical chemistry ; Optoelectronics ; Metallurgy Catalytic Processes in Materials Science ; Carbon Dioxide Capture Technologies ; Magnesium Oxide Properties and Applications
Authors: Md. Bayazid Rahman | Ahmad Avatar Tulsi | Abdul Momin
Source: AgriEngineering (article, 2025-08-25)
Field: Chemistry — Analytical Chemistry

Spectral-Spatial Fusion for Soybean Quality Evaluation Using Hyperspectral Imaging Accurate postharvest quality evaluation of soybeans is essential for preserving product value and meeting industry standards. Traditional inspection methods are often inconsistent, labor-intensive, and unsuitable for high-throughput operations. This study presents a non-destructive soybean classification approach using a simplified reflectance-mode hyperspectral imaging system equipped with a single light source, eliminating the complexity and maintenance demands of dual-light configurations used in prior studies. A spectral–spatial data fusion strategy was developed to classify harvested soybeans into four categories: normal, split, diseased, and foreign materials such as stems and pods. The dataset consisted of 1140 soybean samples distributed across these four categories, with spectral reflectance features and spatial texture attributes extracted from each sample. These features were combined to form a unified feature representation for use in classification. Among multiple machine learning classifiers evaluated, Linear Discriminant Analysis (LDA) achieved the highest performance, with approximately 99% accuracy, 99.05% precision, 99.03% recall and 99.03% F1-score. When evaluated independently, spectral features alone resulted in 98.93% accuracy, while spatial features achieved 78.81%, highlighting the benefit of the fusion strategy. Overall, this study demonstrates that a single-illumination HSI system, combined with spectral–spatial fusion and machine learning, offers a practical and potentially scalable approach for non-destructive soybean quality evaluation, with applicability in automated industrial processing environments. Hyperspectral imaging ; Fusion ; Environmental science ; Remote sensing ; Quality (philosophy) ; Computer science ; Artificial intelligence ; Geography ; Physics ; Linguistics Spectroscopy and Chemometric Analyses
Authors: Jiale Liu | Hantian You | Zheng Guo | Qin Xu | Changsheng Zhang | Luhua Lai
Source: Proceedings of the National Academy of Sciences (article, 2025-10-10)
Field: Materials Science — Materials Chemistry

GeoEvoBuilder: A deep learning framework for efficient functional and thermostable protein design While deep learning has advanced protein sequence and function design, engineering highly active and stable proteins still requires labor-intensive iterative computational design and experimentation. There is a critical need for methods capable of directly generating protein sequences with the required properties. Here, we present GeoEvoBuilder, an advanced deep learning framework that adaptively integrates structural and evolutionary constraints for protein sequence design. GeoEvoBuilder accurately recapitulates functional sites and generates sequences that fold correctly with enhanced activity and thermal stability. GeoEvoBuilder has been applied to redesign green fluorescent protein, glutathione peroxidase 4 (GPX4), and dihydrofolate reductase (DHFR), yielding variants with significantly improved thermal stability and activity. Notably, the top DHFR design demonstrated a 20-fold increase in catalytic efficiency and a 10 °C gain in thermal stability. Crystal structure determination confirmed that the designed proteins form correct structures. Further analysis of residue dynamic correlations in GPX4 variants provides insights into how remote sites regulate enzymatic activity. Unlike conventional methods that focus on single mutation and their combinations with iterative design and experiment cycles, GeoEvoBuilder explores a large sequence space that enables successful designs with over 30% residue changes in one run. GeoEvoBuilder not only provides a transformative tool for protein engineering but also can be applied to uncover the intricate relationships between protein sequence, structure, function, and evolution. GeoEvoBuilder is publicly available at https://github.com/PKUliujl/GeoEvoBuilder. Protein design ; Protein engineering ; Deep learning ; Dihydrofolate reductase ; Directed Molecular Evolution ; Synthetic biology ; Directed evolution ; Protein function ; Sequence (biology) ; Sequence space Enzyme Structure and Function ; Protein Structure and Dynamics ; Bacterial Genetics and Biotechnology
Authors: Qingyi Lin | Chuang Zhang | Xuhui Meng | Zhaoli Guo
Source: Journal of Computational Physics (article, 2025-09-11)
Field: Materials Science — Materials Chemistry

Monte Carlo physics-informed neural networks for multiscale heat conduction via phonon Boltzmann transport equation Boltzmann equation ; Thermal conduction ; Monte Carlo method ; Heat equation ; Sampling (signal processing) ; Artificial neural network ; Boltzmann constant ; Phonon ; Boltzmann machine Thermal properties of materials ; Model Reduction and Neural Networks ; Heat Transfer and Optimization