jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Chenxi Zhou (369944) | Zhe Guo (283037) | Qifeng Li (585722) | Gaojie Zhang (10783912) | Hao Wu (65943) | Jinsen Chen (2887169) | Rongxin Li (3552779) | Shuai Zhang (115662) | Cuimei Cao (5941733) | Rui Xiong (1434895) | Haixin Chang (402696) | Long You (1949395)
Source: Figshare (article, 2025-04-14)
Field: Materials Science — Materials Chemistry

Field-FreeCurrent-Induced Magnetization Switchingof a Room-Temperature van der Waals Magnet for Neuromorphic Computing Spin orbit torque (SOT) has become a promising approach to manipulating magnetization switching. The high-quality interface is essentially desired for SOT performance, which is readily acquired by two-dimensional (2D) van der Waals (vdW) materials. Recently, a 2D ferromagnetic material, Fe<sub>3</sub>GaTe<sub>2</sub>, has been discovered to possess an above-room-temperature Curie temperature and strong perpendicular magnetic anisotropy, providing an excellent candidate to build spintronic devices. On the other hand, an external magnetic field is necessary for the SOT-driven deterministic switching, hindering the real applications. Here, we realize field-free SOT switching of Fe<sub>3</sub>GaTe<sub>2</sub> at room temperature. Moreover, inspired by the superiority of 2D materials in 3D heterogeneous integration, we explore the potential in the computing in memory. The artificial synapse function is studied and used in the convolutional neural network, achieving a high-accuracy (∼92.8%) pattern recognition. Our work paves the way for magnetic memory and neuromorphic computing. Neuromorphic engineering ; Spintronics ; Ferromagnetism ; Magnetization ; Magnet ; van der Waals force ; Curie temperature ; Magnetic field ; Spin (aerodynamics) 2D Materials and Applications ; Magnetic properties of thin films ; Multiferroics and related materials
Authors: Vornholt, Tobias | Stockinger, Peter | Mutný, Mojmír | Jeschek, Markus | Nestl, Bettina | Oberdorfer, Gustav | Osuna, Silvia | Pleiss, Jürgen | Welner, Ditte Hededam | Krause, Andreas | Buller, Rebecca | Ward, Thomas R
Source: Open Access CRIS of the University of Bern (article, 2025-10-22)
Field: Materials Science — Materials Chemistry

Of Revolutions and Roadblocks: The Emerging Role of Machine Learning in Biocatalysis. Machine learning (ML) is rapidly turning into a key technology for biocatalysis. By learning patterns in amino acid sequences, protein structures, and functional data, ML models can help navigate complex fitness landscapes, uncover new enzymes in databases, and even design biocatalysts de novo. Along with advances in DNA synthesis and sequencing, laboratory automation, and high-throughput screening, ML is increasing the speed and efficiency of enzyme development. In this Outlook, we highlight recent applications of ML in the fields of enzyme discovery, design, and engineering, with a focus on current challenges and emerging solutions. Furthermore, we discuss barriers that impede a broader and faster adoption of ML-based workflows in the biocatalysis community. We conclude by suggesting best practices for fostering effective collaborations in this interdisciplinary field. Workflow ; Key (lock) ; Focus (optics) ; Active learning (machine learning) Machine Learning in Materials Science ; Enzyme Catalysis and Immobilization ; Protein Structure and Dynamics
Authors: Hamza Guelbi | Sami Chatti | Mohamed Ali Terres
Source: Springer Link (Chiba Institute of Technology) (article, 2025-10-02)
Field: Materials Science — Materials Chemistry

Optimization of V-Bending for Grade 4A Titanium: A Combined Experimental and Artificial Intelligence Approach Cold bending of Grade 4A titanium bone plates is a crucial process requiring optimization to prevent crack formation. This study integrates experimental analysis, numerical simulations, and artificial intelligence modeling. Tensile tests were performed to determine the mechanical properties of titanium, validating a finite element model for V-bending simulation. A design of experiments was conducted to assess the influence of bending parameters on key responses: equivalent plastic strain (PEEQ) and springback. Results highlight the predominant role of material thickness and die shoulder distance. To enhance prediction accuracy and process optimization, neural networks using the Bayesian regularization algorithm were applied. This approach contributes to improving the design and manufacturing of orthopedic implants. Finite element method ; Artificial neural network ; Bending ; Process (computing) ; Regularization (linguistics) ; Ultimate tensile strength ; Design of experiments ; Bayesian probability Titanium Alloys Microstructure and Properties ; Cellular and Composite Structures ; Elasticity and Material Modeling
Authors: Ahsan Javed | Muhammad Ashraf Shaheen | Muhammad Shahbaz | Muhammad Ramzan | Habib Ullah | Wei Jiang
Source: ArXiv.org (preprint, 2025-11-30)
Field: Materials Science — Materials Chemistry

Exchange-Correlation Functionals in 2D Materials: Applications, Challenges, and Limitations The rapid development of two-dimensional (2D) materials has reshaped modern nanoscience, offering properties that differ fundamentally from their bulk counterparts. As experimental discovery accelerates, the need for reliable computational techniques has become increasingly important. Within the framework of density functional theory, this review explores the critical role of exchange-correlation functionals in predicting key material properties such as structural, optoelectronic, magnetic, and thermal. We examine the challenges posed by quantum confinement, anisotropic screening, and van der Waals interactions, which conventional functionals often fail to describe. Advanced approaches, including meta-GGA, hybrid functionals, and many-body perturbation theory (e.g., GW and Bethe-Salpeter equation), are assessed for their improved accuracy in capturing electronic structure and excitonic effects. We further discuss the non-universality of functionals across different 2D material families and the emerging role of machine learning to enhance computational efficiency. Finally, the review outlines current limitations and emerging strategies, providing a roadmap for advancing exchange-correlation functionals and beyond, to enable the practical design and application of 2D materials. Key (lock) ; van der Waals force ; Density functional theory ; Perturbation theory (quantum mechanics) ; Anisotropy ; Current (fluid) 2D Materials and Applications ; Topological Materials and Phenomena ; Surface Chemistry and Catalysis
Authors: Weichao Huang | Runzhu Li | Rui Wu
Source:  (article, 2025-07-28)
Field: Materials Science — Materials Chemistry

Modeling of Quality Factor and Process Parameter Optimization in Czochralski Silicon Single Crystal Growth To address the increasingly stringent process requirements in Czochralski silicon single crystal growth, this paper proposes a hybrid process parameter optimization strategy integrating a DBN-RBF fusion model with an improved SPEA2 algorithm, taking the shape variable of solid-liquid interface <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$h$</tex> and the defect evaluation criteria <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$V / G$</tex> in quality factors as objective functions. The proposed strategy constructs a DBN-RBF fusion model by integrating Deep Belief Network (DBN) and Radial Basis Function Neural Network (RBFNN), establishing predictive models for the shape variable of solid-liquid interface and the defect evaluation criteria. Subsequently, an enhanced Strength Pareto Evolutionary Algorithm 2 with Cauchy distribution mutation strategy (SPEA2-C) is developed to optimize key process parameters including crucible temperature and crystal pulling rate. The experimental results show that the maximum difference between the numerical simulation and the optimization results is 2.9%, while the actual shape variable of solid-liquid interface measurement error is only 2.9%, which verifies the effectiveness and high precision of the strategy in the process parameter optimization, and can be applied to the growth process parameter optimization of high-quality silicon single crystal. Process variable ; Process (computing) ; Silicon ; Interface (matter) ; Artificial neural network ; Multi-objective optimization ; Genetic algorithm ; Cauchy distribution ; Quality (philosophy) ; Variable (mathematics) Solidification and crystal growth phenomena ; Silicon and Solar Cell Technologies
Authors: Lanqi Li (3375239) | Rui Luo (169614) | Xiaolu Chen (3801862) | Huapeng Wei (22178582) | Wenming Zhang (517008) | Qiang Lu (49688) | Weiming Dong (22178585) | Jianmei Lu (814665) | Bing Zhang (129) | Fan Tang (3286605)
Source: Figshare (article, 2025-09-04)
Field: Materials Science — Materials Chemistry

3D Spatial Learningfor Adsorption Energy Predictionin Multi-Temporal Solution Systems: The MTSS Data Set and a GCN-BasedNetwork Existing methods for adsorption energy prediction primarily focus on individual molecules or static molecular pairs, lacking the capabilities to model the diverse spatial configurations found in complex solution systems. While traditional data sets are static, dynamic systems explore a vast conformational space over time. This paper introduces the Multi-Temporal Solution System (MTSS) data set containing 500,000 temporally resolved configurations (3D atomic coordinates + adsorption energy labels) across five solvents. To address solution-level interactions (solute–solvent/solvent–solvent), we propose SEP-Neta dual-channel graph network integrating rotational-invariant geometric learning and molecular SMILES embeddings. Experimental validation shows SEP-Net achieves an MAE of 211.02 kJ/mol on known solvents and 507.37 kJ/mol on unseen solvents, surpassing MLP (3827.33 vs 507.37 kJ/mol on ACE solvent). This work establishes new benchmarks in system-level adsorption prediction through geometric deep learning. Set (abstract data type) ; Focus (optics) ; Graph ; Data set ; Energy (signal processing) ; Adsorption ; Work (physics) ; Space (punctuation) ; Data space Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Lanqi Li | Rui Luo | Xiaolu Chen | Huapeng Wei | Wenming Zhang | Qiang Lü | Weiming Dong | Jianmei Lu | Bing Zhang | Fan Tang
Source: Journal of Chemical Information and Modeling (article, 2025-09-03)
Field: Materials Science — Materials Chemistry

3D Spatial Learning for Adsorption Energy Prediction in Multi-Temporal Solution Systems: The MTSS Data Set and a GCN-Based Network Existing methods for adsorption energy prediction primarily focus on individual molecules or static molecular pairs, lacking the capabilities to model the diverse spatial configurations found in complex solution systems. While traditional data sets are static, dynamic systems explore a vast conformational space over time. This paper introduces the Multi-Temporal Solution System (MTSS) data set containing 500,000 temporally resolved configurations (3D atomic coordinates + adsorption energy labels) across five solvents. To address solution-level interactions (solute-solvent/solvent-solvent), we propose SEP-Net─a dual-channel graph network integrating rotational-invariant geometric learning and molecular SMILES embeddings. Experimental validation shows SEP-Net achieves an MAE of 211.02 kJ/mol on known solvents and 507.37 kJ/mol on unseen solvents, surpassing MLP (3827.33 vs 507.37 kJ/mol on ACE solvent). This work establishes new benchmarks in system-level adsorption prediction through geometric deep learning. Set (abstract data type) ; Data set ; Spatial learning ; Computer science ; Adsorption ; Energy (signal processing) ; Artificial intelligence ; Machine learning ; Chemistry ; Neuroscience ; Mathematics ; Psychology ; Statistics ; Physical chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Surface Chemistry and Catalysis
Authors: Phuong Nguyen Thi Hoang | Thinh Ngo Hung | Tuong Nguyen Huy | Hieu Nguyen Van
Source: International Journal of Advanced Computer Science and Applications (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Iterative Partition Optimization: A Novel Approach for Feature Selection in NIR Spectroscopy Machine learning for near-infrared (NIR) spectroscopy requires effective feature selection to address high dimensionality and multicollinearity. This study proposes Iterative Partition Optimization (IPO), a framework integrating Model Population Analysis with Weighted Binary Matrix Sampling through segment-wise optimization: partitioning spectra into segments, isolating one active segment while freezing others, and using adaptive weighted sampling that learns from best-performing sub-models. Validation across four diverse NIR datasets (n=54-523 samples, 100-700 wavelengths) demonstrates IPO’s consistent performance improvement over conventional methods. For agricultural products (soy flour, wheat kernels), IPO achieved lower RMSECV while reducing wavelengths. In chemical analysis (diesel fuels, manure), the method maintained high prediction accuracy (RPD&gt;3.0) using less than half the original variables. Notably in multi-component manure analysis, IPO improved predictions across seven chemical properties (N, NH4, P2O5, CaO, MgO, K2O, DM) while reducing spectral variables, consistently outperforming MCUVE in both accuracy and wavelength selection efficiency. These results establish IPO as an effective wavelength selection method for NIR spectroscopy, addressing multicollinearity while preserving spectral interpretation through optimized interval selection. Feature selection ; Curse of dimensionality ; Selection (genetic algorithm) ; Sampling (signal processing) ; Multicollinearity ; Pattern recognition (psychology) ; Feature (linguistics) ; Population Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Smart Agriculture and AI
Authors: Fengzijun Pan (22012483) | Zhoulin Liu (701725) | Guangyao Li (428714) | Yuhui Zhang (399617) | Zepeng Fan (10277532) | Yang Gang (8774915) | Dawei Wang (471687)
Source: Figshare (article, 2025-08-08)
Field: Materials Science — Materials Chemistry

Hydration Behaviorof Quartz Surfaces Revealed byMolecular Dynamics Simulations with a Novel Machine Learning Potential Understanding the hydration behavior of quartz surfaces is critical for elucidating interfacial properties in geochemical and engineering systems. However, capturing these processes via simulation poses a significant challenge, as the high computational cost of conventional density functional theory (DFT) methods renders them unsuitable for simulating large interfacial systems over long time scales. In this work, a machine learning potential (MLP) was developed using an active learning strategy and a high-quality data set to enable large-scale molecular dynamics simulations of the quartz–water interface at ab initio accuracy. The trained potential achieves high accuracy in energy and force predictions across diverse structures and significantly outperforms empirical potentials in both precision and generalization. Driven by this potential, nanosecond-scale machine learning molecular dynamics (ML-MD) simulations have been performed to elucidate the surface reconstruction behavior of cleaved α-quartz (101) and the temporal evolution of surface hydroxylation. The results show that surface reconstruction induces a pronounced structural response that varies with depth in bulk atoms, and hydroxylation proceeds via water dissociation and proton transfer mechanisms, yielding a final silanol density of 5.169 ± 0.416 nm<sup>–2</sup>, which is consistent with the thermogravimetric analysis (TGA) results. Additionally, we characterize the layered structure and hydrogen-bonding network of interfacial water, revealing a hierarchical transition from surface-anchored to bulk-like water. This study demonstrates the potential of ML-MD as a powerful and efficient framework for simulation of complex mineral–water interfacial processes with ab initio accuracy at reduced computational cost. Molecular dynamics ; Density functional theory ; Ab initio ; Dissociation (chemistry) ; Surface (topology) ; Potential energy surface ; Stability (learning theory) Machine Learning in Materials Science ; CO2 Sequestration and Geologic Interactions ; Material Dynamics and Properties
Authors: Yihang Wang | Gregory A. Voth
Source: ArXiv.org (preprint, 2025-04-09)
Field: Materials Science — Materials Chemistry

Adversarial Training for Dynamics Matching in Coarse-Grained Models Molecular dynamics (MD) simulations are essential for studying complex molecular systems, but their high computational cost limits scalability. Coarse-grained (CG) models reduce this cost by simplifying the system, yet traditional approaches often fail to maintain dynamic consistency, compromising their reliability in kinetics-driven processes. Here, we introduce an adversarial training framework that aligns CG trajectory ensembles with all-atom (AA) reference dynamics, ensuring both thermodynamic and kinetic fidelity. Our method adapts the adversarial learning paradigm, combining a physics-based generator with a neural network discriminator that differentiates between AA and CG trajectories. By adversarially optimizing CG parameters, our approach eliminates the need for predefined kinetic features. Applied to liquid water, it accurately reproduces radial and angular distribution functions as well as dynamical mean squared displacement, even extrapolating long-timescale dynamics from short training trajectories. This framework offers a new approach for bottom-up CG modeling, offering a systematic and principled way to preserve dynamic consistency in complex coarse-grained molecular systems. Adversarial system ; Discriminator ; Generator (circuit theory) ; Consistency (knowledge bases) ; Matching (statistics) ; Trajectory ; Reliability (semiconductor) ; Artificial neural network Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Protein Structure and Dynamics
Authors: J S | Horst Hahn | Shikhar Krishn Jha | Simon Schweidler | Leonardo Velasco
Source: Eng—Advances in Engineering (article, 2025-12-01)
Field: Materials Science — Materials Chemistry

A Machine Learning Approach to Determine the Band Gap Energy of High-Entropy Oxides Using UV-Vis Spectroscopy This study introduces a machine learning-based framework for the automated determination of band gap energies in high-entropy oxides (HEOs) using UV-Vis spectroscopy data. Traditionally, band gap energies are obtained from the Tauc plots by manually extrapolating the linear region to the photon energy axis, a process that is time consuming and prone to human error, particularly when dealing with large datasets. To overcome these limitations, we developed a Python-based workflow that automates the band gap evaluation process through key steps, including data preprocessing, data augmentation, hyperparameter tuning, and band gap energy prediction. Various machine learning algorithms were employed to model the relationships between UV-Vis spectra and band gap energies, resulting in significant improvements in both accuracy and efficiency. Among the tested models, Bagging, Extra Trees, and Random Forest exhibited the best predictive performance, achieving mean absolute errors (MAE) as low as 0.26–0.28 eV and coefficients of determination (R2) of 0.73–0.74, substantially outperforming conventional automated methods. Although data augmentation and hyperparameter optimization yielded only modest performance gains, they contributed to improved model robustness. Overall, the proposed ML framework provides a scalable and efficient approach for the rapid characterization of HEOs, minimizing the need for manual analysis and accelerating data-driven materials discovery. Hyperparameter ; Workflow ; Band gap ; Random forest ; Energy (signal processing) ; Hyperparameter optimization ; Spectroscopy Machine Learning in Materials Science ; High Entropy Alloys Studies ; Advancements in Solid Oxide Fuel Cells
Authors: Jannik Stebani | Tim Lewandrowski | Kilian Dremel | Simon Zabler | Volker Haag
Source: IAWA Journal - KU Leuven/IAWA Journal (article, 2025-08-05)
Field: Chemistry — Organic Chemistry

Technical framework of advanced volumetric sub-μ-CT wood imaging integrated with adaptable deep-learning-based wood species classification: initial evaluation on softwood and hardwood data Summary In an age of increasing surveillance and control of markets and trade flows, new methods for species identification of commercial timbers and other woody plant products are needed. Traditional plant anatomy has generated a vast body of knowledge on taxonomical identification. In highly processed and mixed materials such as charcoal, fibreboard board and paper, where DNA and chemotaxonomic composition is regularly destroyed or altered, the anatomical structure is often still intact. Comprehensive studies in the field of state-of-the-art μCT analysis have shown that the use of sub-micrometre CT scanning technology can be very suitable for wood identification and other structural investigations. The current state of CT technology (resolution, working speed, feasibility) opens up various options for use in combination with other relevant key technologies, in particular machine learning and other forms of digitisation in the context of wood research. Thus, the primary objective of this methodologically oriented study is to design and implement, for the first time, an automated and modular classification pipeline that integrates machine learning with volumetric high-resolution sub-μCT imaging for wood identification. To evaluate this approach, we conducted initial experiments using a simplified basal case, applying the method to sub-μCT scans of softwood ( Pinus sylvestris ) and hardwood ( Acer pseudoplatanus ). Hardwood ; Softwood ; Materials science ; Computer science ; Composite material ; Botany ; Biology Wood and Agarwood Research ; Remote Sensing and LiDAR Applications ; Forest ecology and management