jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Diksha Kadre | Sourodip Ghoshdastidar | Shreshtha Dhankar | Sudeep N. Punnathanam
Source: The Journal of Chemical Physics (article, 2025-08-08)
Field: Materials Science — Materials Chemistry

A machine learning assisted identification of optimum set of order parameters for study of gas hydrate nucleation in a molecular simulation The study of crystalline materials is of scientific and technological importance. In this regard, tools such as molecular simulations are widely used to characterize their structure and study their mechanisms of formation. In this work, we develop models for identification and classification of crystal polymorphs during a molecular simulation. The models are based on the XGBoost algorithm, which is a scalable, distributed gradient-boosted decision tree model. The inputs to the model are a set of generic order parameters that have been identified from a large pool using machine learning techniques. This study focuses on gas hydrates, which are naturally occurring crystalline compounds of light gases and water. These materials have tremendous scientific and technological importance, and their formation mechanisms under natural/laboratory conditions are areas of active scientific research. The XGBoost models developed in this work are able to accurately classify gas hydrate polymorphs and also compute nucleation rates using the mean first passage time technique. The novelty of this work is to demonstrate that the use of machine learning techniques mitigates the need for considerable expertise in crystallography while identifying crystal order parameters for polymorph classification. Nucleation ; Clathrate hydrate ; Computer science ; Machine learning ; Hydrate ; Identification (biology) ; Crystal (programming language) ; Scalability ; Decision tree ; Artificial intelligence ; Materials science ; Chemistry ; Thermodynamics ; Physics ; Database Crystallization and Solubility Studies ; Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics
Authors: An-Jun Liu | Bryan K. Clark
Source: ArXiv.org (preprint, 2025-02-26)
Field: Materials Science — Materials Chemistry

Efficient optimization of neural network backflow for ab-initio quantum chemistry The ground state of second-quantized quantum chemistry Hamiltonians is key to determining molecular properties. Neural quantum states (NQS) offer flexible and expressive wavefunction ansatze for this task but face two main challenges: highly peaked ground-state wavefunctions hinder efficient sampling, and local energy evaluations scale quartically with system size, incurring significant computational costs. In this work, we overcome these challenges by introducing a suite of algorithmic enhancements, which includes efficient periodic compact subspace construction, truncated local energy evaluations, improved stochastic sampling, and physics-informed modifications. Applying these techniques to the neural network backflow (NNBF) ansatz, we demonstrate significant gains in both accuracy and scalability. Our enhanced method surpasses traditional quantum chemistry methods like CCSD and CCSD(T), outperforms other NQS approaches, and achieves competitive energies with state-of-the-art ab initio techniques such as HCI, ASCI, FCIQMC, and DMRG. A series of ablation and comparative studies quantifies the contribution of each enhancement to the observed improvements in accuracy and efficiency. Furthermore, we investigate the representational capacity of the ansatz, finding that its performance correlates with the inverse participation ratio (IPR), with more delocalized states being more challenging to approximate. Wave function ; Quantum ; Artificial neural network ; Ab initio ; Subspace topology ; Ground state ; Delocalized electron ; Quantum chemistry ; Backflow Machine Learning in Materials Science ; Quantum many-body systems ; Quantum Computing Algorithms and Architecture
Authors: Ashley Phala | David Tshwane | Charles Siyasiya | Sisa Pityana | Regina Maphanga
Source: Springer Link (Chiba Institute of Technology) (article, 2025-11-25)
Field: Materials Science — Materials Chemistry

Prediction of the optimal hydrogen storage in TiVCrMnAlFe alloys using machine learning models The quest for efficient hydrogen storage materials is vital for the advancement of clean energy technologies. Among various candidates, high entropy alloys (HEAs) such as TiVCrFeAl have received significant attention due to their tunable structures and favorable thermodynamic properties. This study explores the application of machine learning (ML) techniques to predict the hydrogen storage capacity of the TiVCrFeAl alloy system. Using a dataset compiled HEAPS and calculated properties, multiple regression models, such as Random Forest, Gradient Boosting, Decision Tree, XGBoost, Linear Regression and Support Vector Regression, were trained to capture complex relationships between alloy composition, processing parameters, and hydrogen weight percent (wt%). Data preprocessing steps included feature selection, imputation of missing values, and standardization to ensure model robustness. The performance of the models was evaluated using cross-validation and test set metrics such as R² and mean squared error. Results show that ensemble-based models, particularly Random Forest and XGBoost, achieved high predictive accuracy, demonstrating the effectiveness of ML in modeling nonlinear property trends in HEAs. This approach offers a powerful tool for screening and optimizing hydrogen storage materials, accelerating the discovery process through computational insight. Random forest ; Support vector machine ; Data pre-processing ; Linear regression ; Hydrogen storage ; Standardization ; Nonlinear system ; Mean squared error ; Preprocessor Hydrogen Storage and Materials ; High Entropy Alloys Studies ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Xingyue Sun | Mingwei Xie | Kun Zhou | Xu Chen
Source: International Journal of Solids and Structures (article, 2025-10-23)
Field: Materials Science — Materials Chemistry

Prediction of localized plastic deformation of polycrystalline material by the multimodal neural network Feature (linguistics) ; Deformation (meteorology) ; Artificial neural network ; Ultimate tensile strength ; Encoder ; Monotonic function ; Plasticity Material Properties and Failure Mechanisms ; Metallurgy and Material Forming ; Engineering Diagnostics and Reliability
Authors: Eisenhower D. Aldemita II | Paul Rossener R. Regonia | Eduardo R. Magdaluyo Jr
Source: Kyushu University Institutional Repository (QIR) (Kyushu University) (article, 2025-10-30)
Field: Materials Science — Polymers and Plastics

Optimizing Bulletproof Natural Fiber-Reinforced Polymer Composite Materials Using a Hybrid Computational Simulation and Machine Learning Approach This study investigates the optimization of natural fiber-reinforced polymer composite (NFRPCs) creation for bulletproof applications by integrating computational simulation and machine learning (ML). We incorporate abaca (Musa textilis) or pineapple leaf fibers (Piñatex), along with aramid and carbon fibers, into layered composite plates. Ballistic performance was modeled and predicted using simulated data from ANSYS Explicit Dynamics and validated through live bullet testing. ML models, such as Support Vector Machine (SVM) and Random Forest (RF) with optimized hyperparameters, achieved up to 80% prediction accuracy and an F1-score of 82% for abaca-reinforced composites, closely aligning with experimental results. However, lower prediction accuracy was observed for Piñatex-based composites, due to fiber variability and other factors identified in the study. This hybrid methodology highlights the potential of combining simulation and ML to reduce reliance on extensive live bullet testing, providing a data-driven pathway for the efficient development of high-performance bulletproof composite materials. Composite number ; Aramid ; Support vector machine ; Random forest ; Computational simulation ; Ballistic impact ; Fiber ; Woven fabric ; Natural fiber Natural Fiber Reinforced Composites ; Machine Learning in Materials Science ; Composite Material Mechanics
Authors: Matthew S. Johnson | David H. Bross | Judit Zádor
Source: ChemRxiv (preprint, 2025-01-26)
Field: Materials Science — Materials Chemistry

Resolving the Coverage Dependence of Surface Reaction Kinetics with Machine Learning and Automated Quantum Chemistry Workflows Microkinetic models for catalytic systems require estimation of many thermodynamic and kinetic parameters that can be calculated for isolated species and transition states using ab initio methods. However, the presence of nearby co-adsorbates on the surface can dramatically alter these thermodynamic and kinetic parameters causing them to be dependent on species coverage fractions. As there are combinatorially many co-adsorbed configurations on the surface, computing the coverage dependence of these parameters is far less straightforward. We present a framework for generating and applying machine learning models to predict coverage-dependent parameters for microkinetic models. Our toolkit enables automatic calculation and evaluation of co-adsorbed configurations allowing us to sample 2,000 co-adsorbed adsorbates and transition states (TSs) for a diverse set of 9 reactions on Cu(111), a challenging surface, with four possible co-adsorbates. This dataset was then used to train subgraph isomorphic decision trees (SIDTs) to predict the stability and association energy of configurations. We were able to achieve mean absolute errors (MAEs) of 0.106 eV on adsorbates, 0.172 eV on TSs, and due to natural error cancellation in SIDTs for relative properties, 0.130 eV on reaction energies and 0.180 eV on activation barriers. We describe how to use these models to predict coverage- dependent corrections for adsorbates and TSs, and demonstrate on H∗, HO∗ and O∗ comparing the generated SIDT model with an iteratively refined version. Workflow ; Kinetics ; Quantum chemistry ; Quantum ; Chemistry ; Computer science ; Chemical kinetics ; Surface (topology) ; Nanotechnology ; Materials science ; Reaction mechanism ; Physics ; Catalysis ; Mathematics ; Database Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Scientific Computing and Data Management
Authors: Kuangpu Liu | Jian Liu | Bing Zhang | Ming Li | Yang Zhou
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Interpretable Machine Learning for Predicting Electric Spark Sensitivity of Energetic Compounds Via Molecular and Instrument Descriptors SPARK (programming language) ; Sensitivity (control systems) ; Electric spark ; Artificial intelligence ; Machine learning ; Computer science ; Chemistry ; Engineering ; Physical chemistry ; Electronic engineering Thermal and Kinetic Analysis ; Advanced Chemical Sensor Technologies ; Computational Drug Discovery Methods
Authors: Manickam Minakshi | Ashish Sharma | Ferdous Sohel | Almantas Pivrikas | Pragati A. Shinde | Katsuhiko Ariga | Lok Kumar Shrestha
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine-Learning-Assisted Material of Oxygen and Phosphorous-Rich Porous Activated Carbon for Aqueous Symmetric Supercapacitors Supercapacitor ; Activated carbon ; Aqueous solution ; Porosity ; Oxygen ; Carbon fibers ; Chemical engineering ; Materials science ; Chemistry ; Electrochemistry ; Composite material ; Engineering ; Organic chemistry ; Composite number ; Physical chemistry ; Adsorption ; Electrode Supercapacitor Materials and Fabrication
Authors: Kevin Bachelor | Sanya Murdeshwar | Dubravko Sabo | Razvan Marinescu
Source: ArXiv.org (preprint, 2025-09-21)
Field: Materials Science — Materials Chemistry

Active Learning for Machine Learning Driven Molecular Dynamics Machine-learned coarse-grained (CG) potentials are fast, but degrade over time when simulations reach under-sampled bio-molecular conformations, and generating widespread all-atom (AA) data to combat this is computationally infeasible. We propose a novel active learning (AL) framework for CG neural network potentials in molecular dynamics (MD). Building on the CGSchNet model, our method employs root mean squared deviation (RMSD)-based frame selection from MD simulations in order to generate data on-the-fly by querying an oracle during the training of a neural network potential. This framework preserves CG-level efficiency while correcting the model at precise, RMSD-identified coverage gaps. By training CGSchNet, a coarse-grained neural network potential, we empirically show that our framework explores previously unseen configurations and trains the model on unexplored regions of conformational space. Our active learning framework enables a CGSchNet model trained on the Chignolin protein to achieve a 33.05\% improvement in the Wasserstein-1 (W1) metric in Time-lagged Independent Component Analysis (TICA) space on an in-house benchmark suite. Artificial neural network ; Benchmark (surveying) ; Metric (unit) ; Oracle ; Active learning (machine learning) ; Frame (networking) ; Component (thermodynamics) ; Supervised learning ; Selection (genetic algorithm) Machine Learning in Materials Science ; Various Chemistry Research Topics
Authors: Isaac Wilfried Sanou | Julien Baderot | Ali Hallal | Léo Mazauric | Vincent Barra | Sergio Martinez | J. Foucher
Source:  (article, 2025-02-21)
Field: Materials Science — Surfaces, Coatings and Films

Metrology and segmentation in SEM/TEM imaging: accelerating semiconductor analysis through advanced deep learning techniques In the semiconductor industry, accurate and fast metrology and segmentation are crucial for complex structures analysis observed in Scanning Electron Microscopy (SEM) and Transmission Electron Microscopy (TEM) images. Traditional image analysis methods often struggle with low contrast, intricate object boundaries, and the need to distinguish between different materials composing an object. To address these challenges, we propose a deep learning model that enhances segmentation accuracy and automates metrology processes to analyze objects composed of multiple materials at advanced steps of manufacturing. Our approach proposes a transformer-based feature extractor in combination with a multi-part segmentation model. We adapted the feature extractor with SEM/TEM public images and then trained the segmentation model. This model also incorporates a novel combination of different losses to increase robustness and accuracy. These enhancements enable the model to precisely detect and segment both inner and outer objects, even in low-contrast environments, while maintaining high sensitivity to edges and contours with minimal user inputs. By integrating these advanced loss functions, our model achieves robust segmentation performance across a variety of semiconductor materials and structures. The ability to accurately delineate complex features, such as inner voids and outer shell boundaries, significantly improves the reliability of automated metrology, reducing the need for exhaustive manual annotation. This work demonstrates that our deep learning model not only accelerates the analysis process but also enhances the precision of measurements. Enabling the use of this model in semi-automatic mode with user feedback or fully automatic once validated makes it an invaluable tool for semiconductor manufacturing and research. The model's adaptability to varying image conditions and its focus on both inner and outer object detection with the possibility to design measurement strategies mark a significant step forward in the field of automated microscopy image analysis. It provides a scalable solution for the semiconductor industry's growing demands. Metrology ; Segmentation ; Computer science ; Semiconductor industry ; Image segmentation ; Semiconductor ; Artificial intelligence ; Deep learning ; Materials science ; Nanotechnology ; Engineering ; Optoelectronics ; Optics ; Manufacturing engineering ; Physics Electron and X-Ray Spectroscopy Techniques ; Integrated Circuits and Semiconductor Failure Analysis ; Advanced Electron Microscopy Techniques and Applications
Authors: Mingjun Han | Yukai Zhang | Taotao Yu | Guodong Du | ChiYung Yam | Ho-Kin Tang
Source: ACS Omega (article, 2025-07-02)
Field: Materials Science — Materials Chemistry

Enhancing Accuracy and Feature Insights in Hydration Free Energy Predictions for Small Molecules with Machine Learning Accurately predicting solvation free energy and understanding its physical determinants are essential for studying solute behavior in solution. This work employs advanced machine learning techniques to enhance predictive accuracy and extract insights into the solvation free energy of small molecules. Traditional machine learning approaches, compared to deep learning, are lightweight and require fewer computational resources. Our analysis identifies molecular geometry and topology as critical factors in predicting alchemical free energy, aligning with the theory that surface tension is a key determinant, while highlighting the role of charge distribution in improving force field designs for molecular dynamics. We propose an improved machine learning scheme that integrates K-nearest neighbors for feature processing, ensemble modeling, and dimensionality reduction. This scheme achieves a mean unsigned error of 0.53 kcal/mol on the FreeSolv data set using only two-dimensional features without pretraining on large databases, offering substantial accuracy improvements. This lightweight approach provides a viable alternative to computationally intensive deep learning models and holds promise for broad applications in chemical predictions. Feature (linguistics) ; Energy (signal processing) ; Computer science ; Molecule ; Artificial intelligence ; Nanotechnology ; Materials science ; Chemistry ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Feng Wang | Yuhang Tang | Ze-Bing Ma | Yucheng Jin | Jin Cheng
Source: ChemRxiv (article, 2025-12-22)
Field: Materials Science — Materials Chemistry

Domain Oriented Universal Machine Learning Potential Enables Fast Exploration of Chemical Space of Battery Electrolytes Electrolyte ; Battery (electricity) ; Solvation ; Field (mathematics) ; Molecular dynamics ; Computation ; Ab initio ; System dynamics Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; X-ray Diffraction in Crystallography
Authors: Mingzi Sun | Tong Wu | Baian Chen | Zikang Li | Qiuyang Lu | Lu Lu | Hon Ho Wong | Cheuk Hei Chan | Bolong Huang
Source: Advanced Intelligent Discovery (article, 2025-11-25)
Field: Materials Science — Materials Chemistry

Machine Learning‐Assisted Second‐Order Perturbation Theory for Chemical Potential Correction Toward Hubbard <i>U</i> Determination Determining the Hubbard U parameter is critical for characterizing the electronic structures of novel materials, especially atomic catalysts (ACs), where conventional linear response (CLR) and two‐way crossover linear response methods under density functional theory (DFT) often yield distinct results. This study integrates machine learning (ML) with second‐order perturbation theory to systematically derive the chemical potential in the Hubbard model, focusing on electron–electron interaction‐induced energy corrections. The framework establishes explicit relationships between perturbation energy, chemical potential, bandwidth ( W ), and particle number ( N ), with exponential decay terms quantifying bandwidth‐driven screening. It clarifies nonlinear U‐perturbation relationships and particle‐dependent corrections, which are critical for ACs with unique coordination environments. Validations in ACs and metal systems demonstrate that ML‐improved derivations resolve ambiguities in Hubbard U determination, enhancing the chemical potential prediction accuracy and linking U determinations to catalytic activity. Hubbard model ; Crossover ; Perturbation theory (quantum mechanics) ; Nonlinear system ; Density functional theory ; Perturbation (astronomy) ; Exponential function Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysis and Oxidation Reactions
Authors: Grzegorz Gwardys | Grzegorz Grodkowski | Piotr Kostusiak | Wojciech Mendelowski | Jan Slósarz | Michał Satława | Bartłomiej Śmietanka | Krzysztof Gwardys | Marcin Gołębiewski | Kamila Puppel
Source: International Journal of Molecular Sciences (article, 2025-12-10)
Field: Chemistry — Analytical Chemistry

Molecular Remodeling of Milk Fat Globules Induced by Centrifugation: Insights from Deep Learning-Based Detection of Milk Adulteration Milk adulteration through centrifugation, which artificially reduces the somatic cell count (SCC), represents a significant challenge to food authenticity and public health. This fraudulent practice alters the native molecular architecture of milk, masking inflammatory conditions such as subclinical mastitis and distorting product quality. Conventional analytical and microscopic techniques remain insufficiently sensitive to detect the subtle physicochemical changes associated with centrifugation, highlighting the need for molecular-level, data-driven diagnostics. The dataset included 128 paired raw milk samples and approximately 25,000 bright-field micrographs acquired across multiple microscopes, of which 95% were confirmed to be of high quality. In this study, advanced machine learning (ML) and deep learning (DL) approaches were applied to identify centrifugation-induced alterations in raw milk microstructure. Bright-field micrographs (pixel size 0.27 µm) of paired unprocessed and centrifuged samples were obtained under standardized optical conditions and analyzed using convolutional neural networks (ResNet-18/50, Inception-v3, Xception, NasNet-Mobile) and hybrid attention architectures (MaxViT, CoAtNet). Model performance was evaluated using the harmonic average of recalls across five micrographs per sample (HAR<sub>5</sub>). Human microscopy experts (n = 4) achieved only 18% classification accuracy-below the random baseline (25%)-confirming that centrifugation-induced modifications are not visually discernible. In contrast, DL architectures reached up to 97% accuracy (HAR<sub>5</sub>, Xception), successfully identifying subtle molecular cues. Class activation and sensitivity analyses indicated that models focused not on milk fat globule (MFG) boundaries but on high-frequency nanoscale variations related to the reorganization of casein micelles and solid non-fat fractions. The findings strongly suggest that centrifugation adulteration constitutes a molecular reorganization event rather than a morphological alteration. The integration of optical microscopy with AI-driven molecular analytics establishes deep learning as a precise and objective tool for detecting fraudulent milk processing and improving food integrity diagnostics. Raw milk ; Deep learning ; Convolutional neural network ; Nile red ; Casein ; Microscopy ; Centrifugation Spectroscopy and Chemometric Analyses ; Milk Quality and Mastitis in Dairy Cows ; Proteins in Food Systems
Authors: Jiawen Cao | Qiuling Tao | Kaihong Zheng | Fusheng Pan | Rongpei Shi | Xingjun Liu
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Biomaterials

A Deep Learning Potential for Mg-Y-Zn Alloys Uncovers the Atomistic Origin of Kink Strengthening in LPSO Structures Nucleation ; Stacking fault ; Stacking ; Slip (aerodynamics) ; Dislocation ; Plasticity ; Deformation (meteorology) ; Molecular dynamics ; Density functional theory Magnesium Alloys: Properties and Applications ; Hydrogen Storage and Materials ; Machine Learning in Materials Science