jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yuxun Zheng | K.-W. A. Chee | Lang Ding | Zuanming Jin
Source: IET conference proceedings. (article, 2025-08-01)
Field: Materials Science — Surfaces, Coatings and Films

Deep learning-based object detection in electron microscopy for semiconductor analysis: advances, challenges, and perspectives With the continuous miniaturization of semiconductor devices, nanoscale defect detection has become a key fundamental and technological requirement. Electron microscopes, with their high resolution, play a crucial role in structural and electrical analysis. In recent years, deep learning has significantly improved the accuracy and automation level of semiconductor defect detection and characterization. This paper reviews the application progress of deep learning in electron microscope imaging for semiconductor analysis and research, focusing on the characteristics and performance of CNN, Transformer, and their hybrid models. Despite significant achievements, data scarcity, insufficient generalization ability, and computational costs remain major challenges. Proposing solutions to these challenges is expected to further promote the development of intelligent detection and facilitate high-precision and high-yield semiconductor manufacturing. Semiconductor ; Microscopy ; Object (grammar) ; Nanotechnology ; Electron microscope ; Materials science ; Engineering physics ; Computer science ; Data science ; Optoelectronics ; Artificial intelligence ; Physics ; Optics Electron and X-Ray Spectroscopy Techniques ; Industrial Vision Systems and Defect Detection
Authors: Jingwei Zhang (288128) | Siyu Chen (278663) | Haisheng Ren (1609681) | Zerong Li (11486116)
Source: Figshare (article, 2025-05-10)
Field: Materials Science — Materials Chemistry

Enhancing the PredictiveKinetics of IntramolecularH‑Migration Reactions of Ether Peroxy Radicals by IntegratingMachine Learning with Quantum Chemistry: A Comparative Study of GenericRate Rules and Machine Learning Techniques Accurate reaction rate constants are essential for automated mechanism generators, yet high-level quantum chemistry methods remain computationally prohibitive. This study addresses this challenge for intramolecular H-migration in ether peroxy radicals, which are important in low-temperature combustion mechanisms. We investigated 102 reactions using M06-2X/6-311++G­(2df,2p) calculations and canonical transition state theory. While we developed generic rate rules for 42 reaction classes, they proved inadequate for accurate predictions. Therefore, we built machine learning models with reaction-specific descriptors to predict the rate constants. We compared tree-based algorithms (eXtreme Gradient Boosting and Light Gradient Boosting Machine) against nontree-based algorithms (Multivariate Linear Regression and Gaussian Process Regression). The Light Gradient Boosting Machine emerged as superior with a root mean square error of 0.396 in ln <i>k</i> values. Feature importance and SHapley Additive exPlanation analysis revealed that structural characteristics and energetic properties were the most influential factors affecting reaction rates. Our systematic comparison demonstrates that machine learning methods significantly outperform generic rate rules for kinetic modeling of ether peroxy radical H-migration reactions. Boosting (machine learning) ; Gradient boosting ; Intramolecular force ; Reaction rate constant ; Quantum machine learning ; Radical ; Quantum chemistry ; Gaussian process Machine Learning in Materials Science ; Advanced Combustion Engine Technologies ; Catalysis and Oxidation Reactions
Authors: Patryk Tajs | Mateusz Skarupski | Jakub Rydzewski
Source: ArXiv.org (preprint, 2025-05-22)
Field: Materials Science — Materials Chemistry

NeuralTSNE: A Python Package for the Dimensionality Reduction of Molecular Dynamics Data Using Neural Networks Unsupervised machine learning has recently gained much attention in the field of molecular dynamics (MD). Particularly, dimensionality reduction techniques have been regularly employed to analyze large volumes of high-dimensional MD data to gain insight into hidden information encoded in MD trajectories. Among many such techniques, t-distributed stochastic neighbor embedding (t-SNE) is particularly popular. A parametric version of t-SNE that employs neural networks is less commonly known, yet it has demonstrated superior performance in dimensionality reduction compared to the standard implementation. Here, we present a Python package called NeuralTSNE with our implementation of parametric t-SNE. The implementation is done using the PyTorch library and the PyTorch Lightning framework and can be imported as a module or used from the command line. We show that NeuralTSNE offers an easy-to-use tool for the analysis of MD data. Python (programming language) ; Dimensionality reduction ; Artificial neural network ; Isomap ; Curse of dimensionality ; Embedding ; Parametric statistics ; Reduction (mathematics) ; R package Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Quantum many-body systems
Authors: Tingting Zhou | Dan Jian | Meiqi Wei | Guan Zhang | Yuhan Zhou | Yuqing Huang | Qi Wang | Maobing Shuai
Source: Inorganic Chemistry (article, 2025-07-01)
Field: Materials Science — Materials Chemistry

Quantitative Mapping of Oxygen Affinity to Local Chemical Environments in Ti–Zr–Nb–Ta Alloys via Machine Learning Simultaneously enhancing strength and ductility is a longstanding challenge in materials science. Recent studies show that incorporating oxygen into Ti-Zr-family refractory high-entropy alloys (HEAs) can overcome this trade-off, with improved properties stemming from interstitial oxygen occupancy. However, oxygen occupancy is inherently site-specific and strongly influenced by local chemical environments, complicating quantitative predictions of oxygen solution energies at individual sites. Here, we address this challenge in the Ti-Zr-Nb-Ta system by combining high-throughput first-principles calculations with machine learning (ML). Representing local environments with Smooth Overlap of Atomic Positions features, our ML model accurately predicts oxygen solution energies from initial, unrelaxed atomic configurations (<i>R</i><sup>2</sup> = 0.93, mean absolute error = 0.11 eV), enabling analysis of oxygen occupancy trends and spatial correlations over extensive compositional ranges. Two critical descriptors─the average oxygen solution energy and its standard deviation─are proposed to quantify overall oxygen affinity and distribution heterogeneity within each composition. Notably, these descriptors correlate closely with experimentally reported strength and ductility enhancements, highlighting that controlled oxygen interstitial occupancy is crucial for optimizing mechanical properties. Our findings provide fundamental insights into oxygen solution behaviors in HEAs and facilitate the design of oxygen-containing HEAs with controlled oxygen incorporation and distribution. Chemistry ; Oxygen ; Niobium ; Chemical engineering ; Organic chemistry Machine Learning in Materials Science ; Titanium Alloys Microstructure and Properties ; Nuclear Materials and Properties
Authors: P. Djagba | V. Rakotonarivo | A. Zeleke
Source:  (article, 2025-12-03)
Field: Materials Science — Materials Chemistry

Hybrid Deep Learning Models for the Prediction of Some Experimental Quantities of Molecules The prediction of experimental molecular properties is a critical task in computational chemistry and drug discovery that enables a faster and a more accurate assessment of molecular behavior without the need for costly laboratory experiments. Deep learning has emerged as a state-of-the-art approach across various domains, such as cheminformatics, due to its capacity to learn complex, nonlinear patterns. Along this line, recent research has shown that hybrid architectures combining different deep learning models can significantly enhance model performance. In this study, we investigate a hybrid modeling framework that integrates Graph Neural Networks (GNNs) and Probabilistic Graphical Models (PGMs) for the prediction of critical experimental molecular quantities, including IC<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">50</inf>, logP, water solubility, and hydration free energy. The proposed models are trained and evaluated on multiple large-scale experimental datasets and compared against standard deep learning baselines, such as Long Short-Term Memory (LSTM) networks and transformer-based models such as ChemBERTa. Deep learning ; Artificial neural network ; Pattern recognition (psychology) ; Feature (linguistics) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Gaussian Processes and Bayesian Inference
Authors: Hongyu Gao (158323) | Minghe Zhu (21475920) | Jia Ma (438820) | Marc Honecker (21475923) | Kexian Li (9403873)
Source: Figshare (article, 2025-06-04)
Field: Materials Science — Materials Chemistry

Probing Rate-DependentLiquid Shear Viscosity UsingCombined Machine Learning and Nonequilibrium Molecular Dynamics Accurately measuring liquid dynamic viscosity across a wide range of shear rates, from the linear-response to shear-thinning regimes, presents significant experimental challenges due to limitations in resolving high shear rates and controlling thermal effects. In this study, we integrated machine learning (ML) with nonequilibrium molecular dynamics (NEMD) simulations to address these challenges. A supervised artificial neural network (ANN) model was developed to predict viscosity as a function of shear rate, normal pressure, and temperature, effectively capturing the complex interplay among these variables. The model reveals distinct trends in shear viscosity, characterized by the shear-thinning exponent, and highlights nonmonotonic behavior in the radius of gyration components, reflecting molecular morphological changes driven by rate-dependent volume expansion. Notably, temperature effects diminish at higher shear rates, where molecular alignment and spacing dominate the response to shear. By implementing the ‘fix npt/sllod’ command in LAMMPS, we achieve precise constant-pressure control in NEMD simulations, ensuring accurate representation of system dynamics. This study demonstrates the potential of ML-enhanced NEMD for efficient and accurate viscosity prediction, providing a robust framework for future research in complex fluid dynamics and material design. Shear (geology) ; Viscosity ; Artificial neural network ; Shear rate ; Molecular dynamics ; Non-equilibrium thermodynamics ; Representation (politics) ; Rheology Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Material Dynamics and Properties
Authors: Edoardo Tamiazzo | Alberto Biasin | Pierantonio Facco
Source: IFAC-PapersOnLine (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Improved Understanding of Experimental Campaigns in Catalyst Development through machine learning The development of new catalysts is typically carried out by performing extended experimental campaigns of dynamic experiments through high-throughput miniature reactors in which the sequence of the experiment is often managed based on the experience of the scientists and the developers. In these systems, the sequential nature of experiments introduces complex effects that may propagate to successive experimental batches at different conditions which are difficult to model and interpret. Big amounts of data are typically collected from experimental campaigns, which provide the opportunity to develop data-driven models that extract valuable information on the system. In this study, we propose a new machine-learning methodology that allows the in-depth understanding of the experiment dynamics, associated with both the experiment batch itself and the catalyst history (namely, the sequence of multiple experiments performed in different conditions of temperature, composition, etc.). In particular, multiway multivariate latent variables techniques are used to capture the dynamic within the single experimental batch and the high auto-and cross-correlation between variables, two-dimensional dynamic modelling is used to deal with the dynamics of the catalyst history and orthogonalization is used to remove information redundancy. The methodology is validated in the case study of the development of catalyst for ammonia production. We show that the model captures the correlation between variables which describe the reaction kinetics and thermodynamics within each experimental batch, as well as the influence of catalyst history, especially in terms of feed composition. Furthermore, the model captures the contributions of both the dynamics of the single experimental batches and the catalyst history, ensuring very good predictive performance on the ammonia productivity. Catalysis ; Computer science ; Chemistry Machine Learning in Materials Science ; Fault Detection and Control Systems ; Advanced Data Processing Techniques
Authors: Daniel Wines (9455510) | Kamal Choudhary (2813026)
Source: Figshare (article, 2025-05-05)
Field: Materials Science — Materials Chemistry

CHIPS-FF: Evaluating Universal Machine Learning ForceFields for Material Properties We introduce CHIPS-FF (Computational High-Performance Infrastructure for Predictive Simulation-based Force Fields), an open-source benchmarking platform for machine learning force fields (MLFFs). This platform focuses on complex properties including elastic constants, phonon spectra, defect formation energies, surface energies, and interfacial and amorphous phase properties. Utilizing 16 graph-based MLFFs including ALIGNN-FF, CHGNet, MatGL, MACE, SevenNet, ORB, MatterSim, and OMat24, CHIPS-FF integrates the Atomic Simulation Environment (ASE) with JARVIS-Tools to facilitate high-throughput simulations. Our framework is tested on a set of 104 materials, including metals, semiconductors, and insulators representative of those used in semiconductor components, with each MLFF evaluated for convergence, accuracy, and computational cost. Additionally, we evaluate the force-prediction accuracy of these models for close to 2 million atomic structures. By offering a streamlined, flexible benchmarking infrastructure, CHIPS-FF aims to guide the development and deployment of MLFFs for real-world semiconductor applications, bridging the gap between quantum mechanical simulations and large-scale device modeling. Benchmarking ; Bridging (networking) ; Material properties ; Semiconductor ; Field (mathematics) ; Software deployment ; Set (abstract data type) ; Phonon Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Advanced Materials Characterization Techniques
Authors: Songjia Kong | Ziheng Yu | Naoki Matsui | Michiyo Kamiya | Yudai Iwamizu | Yūki Tanaka | N. Kubota | K. Nomoto | Satoshi Hori | Masaaki Hirayama | Kota Suzuki | Ryoji Kanno
Source: Small (article, 2025-11-18)
Field: Materials Science — Materials Chemistry

From Composition to Ionic Conductivity: Machine Learning‐Guided Discovery and Experimental Validation of Argyrodite‐Type Lithium‐Ion Electrolytes The discovery of solid-state electrolytes (SSEs) with high lithium-ion conductivities is critical for advancing all-solid-state batteries. However, prior efforts have largely focused on structure-driven design. This study presents a composition-based machine learning framework, Elements-To-Ionics (E2I), for the accurate prediction and optimization of the ionic conductivities of argyrodite-type SSEs using only their elemental compositions. Guided by these predictions, a series of Si-Sn, Ge-Si, and Ge-Sn co-substituted argyrodites are synthesized. Li<sub>6.7</sub>Ge<sub>0.595</sub>Si<sub>0.105</sub>P<sub>0.3</sub>S<sub>5</sub>I achieves the highest ionic conductivity (7.2 × 10<sup>-3</sup> S cm<sup>-1</sup>) with a low activation energy (0.20 eV). Using hot-pressing to optimize the conductivity, values comparable to those of Li<sub>10</sub>GeP<sub>2</sub>S<sub>12</sub>-type superionic conductors are achieved (>10<sup>-2</sup> S cm<sup>-1</sup>). The developed model reliably identifies both high- and low-conductivity regions and significantly reduces the experimental workload. These results highlight the potential of composition-based informatics for accelerating the discovery of high-performance SSEs within complex chemical spaces, and provide a valuable methodology for the development of next-generation solid-state battery technologies. Electrolyte ; Ionic bonding ; Ionic conductivity ; Fast ion conductor ; Battery (electricity) ; Electrical conductor ; Conductivity Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Thermal Expansion and Ionic Conductivity
Authors: Kazuki Ishizaki | Toru Asahi | Takuya Taniguchi
Source: Digital Discovery (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Machine learning-driven optimization of the output force in photo-actuated organic crystals We employed two types of machine learning for molecular design and experimental optimization to efficiently maximize the blocking force. Materials science ; Computer science ; Nanotechnology ; Artificial intelligence Machine Learning in Materials Science ; Photopolymerization techniques and applications ; Advanced Polymer Synthesis and Characterization
Authors: Michele Lo Giudice | Alessandro Salvini | Marco Stella | F. Sargeni | Silvia Licciardi | Guido Ala | Pietro Romano | Vittorio Bertolini | Antonio Faba
Source: IET Science Measurement & Technology (article, 2025-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Efficient hysteresis characterization and prediction in 3D‐printed magnetic materials using deep learning Abstract This research proposes a data processing pipeline employing Fourier analysis and deep neural networks to replicate the phenomenon of magnetic hysteresis, in particular frequency components derived from experimental data gathered using a newly developed 3D‐printed material. The characterisation of hysteresis is essential for enhancing material performance and constructing precise models to anticipate material behaviour under diverse operating circumstances, especially in 3D‐printed materials where properties can be meticulously regulated to ensure successful applications. The experimental signals were used for training and testing a neural network, exploiting Fourier coefficients to condense signals into the frequency components. This compression extracts fewer parameters and thus reduces and optimises the resources required by the neural network. It also improves the generalisation performance of the model, allowing it to make more accurate predictions on unseen data. This therefore optimises traditional modelling that requires a complete representation of hysteresis loops in the time domain, which must be addressed with the use of complex neural networks and large datasets. The experimental results show lower computational costs during the prediction process and a smaller memory footprint. Furthermore, the proposed model is easily adaptable for the loss estimation in different types of materials and input signals. Characterization (materials science) ; Hysteresis ; Materials science ; Magnetic hysteresis ; Computer science ; Nanotechnology ; Condensed matter physics ; Magnetic field ; Physics ; Magnetization Magnetic Properties and Applications ; Non-Destructive Testing Techniques ; Magnetic properties of thin films
Authors: Jiaqi Wu | Ye Liu | Xue‐Qing Gong
Source: Pure and Applied Chemistry (article, 2025-05-22)
Field: Materials Science — Materials Chemistry

Machine learning predictions for regioselectivity of hydroformylation reactions: leveraging limited data for high-precision results Abstract The development of robust machine learning models to assist the prediction and optimization of homogeneously catalyzed reactions has attracted wide interests. In this work, we propose a workflow to estimate the linear to branched ratio of the products in hydroformylation reactions using a stacking ensemble method that integrates Random Forest, eXtreme Gradient Boosting, and Light Gradient Boosting Machine algorithms, leveraging physicochemically significant features from small-batch experimental data. The stacking model achieves superior performance with R 2 and Root Mean Square Error values of 0.918 and 0.078, respectively. Moreover, the SHapley Additive exPlanations analysis and density functional theory calculations reveal the significant impact of the gap values between the highest occupied molecular orbital and lowest unoccupied molecular orbital of alkenes on the regioselectivity of hydroformylation reactions, indicating that larger gap values tend to result in a higher proportion of the linear products. This study illustrates that the combination of physicochemically significant features and interpretable ensemble models can serve as a useful strategy for predicting regioselectivity in homogeneously catalyzed reactions. Chemistry ; Hydroformylation ; Regioselectivity ; Combinatorial chemistry ; Biochemical engineering ; Organic chemistry ; Catalysis ; Rhodium Machine Learning in Materials Science ; Organometallic Complex Synthesis and Catalysis ; Computational Drug Discovery Methods
Authors: Xiangying Zhang | Sitan Wang
Source: E3S Web of Conferences (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Study on the electrocatalytic CO<sub>2</sub> reduction performance of covalent organic framework materials based on machine learning In order to accurately predict the catalytic performance of covalent organic framework materials (COFs) for electrocatalytic carbon dioxide and analyze the influencing factors affecting the catalytic effect, this study collected COFs structure data and experimental data from 44 literatures, and used machine learning methods. Six regression models were trained and evaluated with COFs structure data and experimental data as features and Faraday efficiency as output. By evaluating the fitting coefficient, mean absolute error and the fitting effect of the test set, the extreme gradient boosting (XGB) model has the best performance. Through the visual analysis of the partial dependence diagram and the individual expectation condition diagram of the XGB model, the coordination metal is Ni, the coordination metal content is greater than 10 %, the pore limit diameter is in the range of 2.5nm-12.5nm. The COFs with tetragonal crystal system have high Faraday efficiency. This research method can not only accurately predict the catalytic performance of covalent organic framework materials (COFs) for electrocatalytic carbon dioxide, but also provide a reference for the screening of catalysts according to the structural characteristics. Reduction (mathematics) ; Covalent bond ; Computer science ; Electrocatalyst ; Materials science ; Process engineering ; Chemistry ; Engineering ; Electrochemistry ; Electrode ; Organic chemistry ; Mathematics ; Physical chemistry Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; CO2 Reduction Techniques and Catalysts