jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zaid Allal | Hassan N. Noura | Flavien Vernier | Ola Salman | Khaled Chahine
Source: Computational Materials Science (article, 2025-11-20)
Field: Materials Science — Materials Chemistry

Predicting hydrogen storage capacity of metal hydrides using novel imputation techniques and tree-based machine learning models Hydrogen is emerging as a crucial clean energy carrier, with the potential to decarbonize transportation, industry, and power generation. Among various storage methods, metal hydrides are particularly promising due to their high volumetric density, safety, and reversibility under moderate conditions. However, experimental research to optimize hydrogen absorption in metal hydrides is time-consuming and costly, motivating the use of alternative approaches to accelerate material development. In this study, we apply machine learning (ML) techniques to predict hydrogen storage capacities in metal hydrides based on compositional and structural features. The analysis used an experimental dataset covering eight material classes. To address the limited sample size, imputation techniques were applied to generate additional data points, enhancing model training without discarding existing measurements. Feature engineering and average importance analysis were used to identify the most influential variables, creating a structured input for ML models. The trained model achieved a preliminary coefficient of determination ( R 2 ) of 0.92 and a mean absolute error (MAE) of 0.043. Postprocessing further improved performance, raising R 2 to 0.99 and reducing MAE to 0.0052. While the small dataset limits generalizability, the combination of effective imputation, feature selection, and ML modeling achieves high predictive accuracy, as validated using the LIME and SHAP frameworks. These results highlight the potential of integrating ML techniques into materials science, providing a promising strategy to accelerate the development of next-generation hydrogen storage solutions while motivating further experimental validation. • Predicting hydrogen storage in metal hydrides using ML (R² = 0.99, MAE = 0.0052). • Addressing limited datasets through imputation and feature engineering. • Identifying influential compositional and structural features via ML. • Validating model interpretability using LIME and SHAP frameworks. • Accelerating metal hydride development beyond time-consuming experiments. Hydrogen storage ; Hydrogen ; Predictive modelling ; Experimental data ; Imputation (statistics) ; Mean absolute error ; Mean squared prediction error ; Metal Hydrogen Storage and Materials ; Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Aslak Fellman | Jesper Byggmästar | Fredric Granberg | Flyura Djurabekova | K. Nordlund
Source: Computational Materials Science (article, 2025-11-26)
Field: Materials Science — Materials Chemistry

Effects of training machine-learning potentials for radiation damage simulations using different pseudopotentials Machine learning (ML) has become a commonplace approach in the development of interatomic potentials for molecular dynamics simulations, and its use also for radiation effect modeling is increasing. In this work, we investigate the effects of training ML potentials on density functional theory data calculated with different pseudopotentials in nickel. We look in detail at the differences that appear in radiation damage simulations. The use of a ”harder” pseudopotential with semicore electrons has a direct impact on the short-range interactions, which in turn has implications on the radiation damage simulations. We find that despite these differences, the average threshold displacement energy is quite similar (40–50 eV for Ni). However, we find significant differences in the cumulative damage predicted by massively overlapping cascade simulations and compare them with Rutherford Backscattering Spectroscopy/channeling experiments. We also investigate approaches to modify the repulsive pair interactions after training the potentials and discuss the feasibility of such approaches. • The average threshold displacement energy quite similar (40–50 eV). • Equilibrium properties very similar. • Significant differences with regards to radiation damage simulations. • Semicore pseudopotential results closest to experiments in RBS/c simulations. • Modifications of the repulsive pair interactions after training were explored. Pseudopotential ; Radiation damage ; Molecular dynamics ; Displacement (psychology) ; Density functional theory ; Electron ; Radiation Machine Learning in Materials Science ; Fusion materials and technologies ; Advanced Electron Microscopy Techniques and Applications
Authors: Yihua Xu | Wen-Wen Xie | Shaojuan Jiang | Zhiwei Lu | Gehong Su | Chun Wu | Hanbing Rao | Mengmeng Sun | Mingxia He
Source: Journal of Materials Science (article, 2025-08-01)
Field: Materials Science — Materials Chemistry

High peroxidase-like activity of CoOx@CNz nanozymes for colorimetric and smartphone-based epinephrine sensing Materials science ; Epinephrine ; Peroxidase ; Nanotechnology ; Chemical engineering ; Analytical Chemistry (journal) ; Environmental chemistry ; Medicine ; Nuclear magnetic resonance ; Internal medicine ; Chemistry ; Physics ; Enzyme ; Engineering Advanced Nanomaterials in Catalysis ; Electrochemical sensors and biosensors ; Advanced biosensing and bioanalysis techniques
Authors: James W. Furness | Kevin B. Moore | Arteum D. Bochevarov
Source: Journal of Chemical Information and Modeling (article, 2025-09-26)
Field: Materials Science — Materials Chemistry

CSMILES: A Compact, Human-Readable SMILES Extension for Conformations While line notation schemes for molecular structure are well developed, they are generally unable to distinguish different conformations of the same molecule. CSMILES, an extension to the ubiquitous line notation scheme, SMILES, has been developed to address this issue. CSMILES are short strings of text that encode information characterizing the conformer structure in the maximally compact form. A conformer is defined by the dihedral angles associated with a structure that has a specified connectivity between atoms. The extension is straightforward: in the simplest case values for the dihedral angles of these bonds are determined from the atomic coordinates and added within a SMILES string at the location of the bond. For example, the canonical SMILES string of pentanol-1 is OCCCCC, and the CSMILES of one of its conformers is O{299}C{180}C{178}C{70}C{56}C. Evidently, the CSMILES strings remain readable, especially for smaller molecules. More difficult cases involving branching, rings, symmetry, and other complications have also been covered by our definitions. Further, CSMILES strings are canonicalized at the conformer level beyond simple connectivity. As such, canonical CSMILES strings are invariant to atom reordering, rigid translation, and rigid rotation. A two-way conversion from three-dimensional (3D) structure to CSMILES has been implemented, and the article is accompanied by a Python code which effectuates such conversions. Possible applications for CSMILES strings are discussed and include efficient storage of 3D structure information as well as development of machine learning models for conformation-dependent properties. Conformational isomerism ; Dihedral angle ; Extension (predicate logic) ; Notation ; Invariant (physics) ; String (physics) ; Atom (system on chip) Machine Learning in Materials Science ; Software Engineering Research ; Scientific Computing and Data Management
Authors: I. A. Balyakin | K. A. Arslanov | Maxim I. Vlasov
Source: Computational Materials Science (article, 2025-07-15)
Field: Materials Science — Ceramics and Composites

Simulation of structure and dynamics of phosphate glasses with fluoride additives: A hybrid approach combining universal and specialized neural network potentials Fluoride ; Artificial neural network ; Phosphate ; Dynamics (music) ; Chemical physics ; Materials science ; Nanotechnology ; Chemistry ; Computer science ; Biological system ; Physics ; Inorganic chemistry ; Artificial intelligence ; Acoustics ; Biology ; Organic chemistry Glass properties and applications ; Material Dynamics and Properties ; Phase-change materials and chalcogenides
Authors: Silei Li | Chunyan Fu | Q Li | Chengjun Zeng | Juan Xie | Hu Wang
Source: Journal of Computational Methods in Sciences and Engineering (article, 2025-08-28)
Field: Materials Science — Materials Chemistry

Research progress on nitrogen-containing heterocyclic quaternary ammonium salt corrosion inhibitors in high-temperature and high-pressure oilfield produced fluids This paper focus on the recent progress regarding nitrogen-containing heterocyclic quaternary ammonium salt corrosion inhibitors in high-temperature, high-pressure (HTHP) oilfield produced fluid environments. Under these extreme conditions, the synergistic effects of elevated temperature and aggressive media exacerbate multi-mechanism corrosion in oil and gas pipelines, posing high failure risks for conventional steel protection strategies. Nitrogen-containing heterocyclic quaternary ammonium salts form protective films primarily through coordination bonding and electrostatic adsorption, with structural optimization significantly enhancing their performance. Composite inhibitor formulations demonstrate superior efficacy in field applications. However, current studies exhibit critical gaps, including validated failure models under multi-field coupling conditions and high-temperature adsorption kinetics data. Future research should prioritize molecular design optimization, enhanced environmental compatibility, and advanced multi-scale simulations. Integrating in-situ characterization techniques with machine learning approaches will enable mechanism-driven, precision design of high-temperature corrosion inhibitors, thereby offering robust theoretical frameworks and practical engineering solutions for long-term corrosion mitigation in extreme environments. Ammonium ; Salt (chemistry) ; Corrosion ; High pressure ; Chemistry ; Nitrogen ; Quaternary ; Organic chemistry ; Biology ; Engineering Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Oriana Barros | Joaquim Castro Silva | Eurico Monteiro | Susana S. Aveiro | Pedro Domingues | Pedro Valente de Sousa | Carolina Castellanos Castro | Catarina A. B. Rodrigues | António S. Barros | Francisco Amado | Saeid Ghavami | Vito D’Agostino | Rita Ferreira | Rui Vitorino | Lúcio Lara Santos
Source:  (preprint, 2025-03-20)
Field: Chemistry — Spectroscopy

Uncovering the proteogenomic landscape of head and neck squamous cell carcinoma through urine analysis Head and neck squamous cell carcinoma (HNSCC) is a major clinical challenge due to its aggressive nature and poor prognosis in advanced stages. Late detection, often due to delayed diagnosis, limits treatment success. This study investigates non-invasive diagnostic methods to identify early-stage molecular biomarkers using a proteogenomic approach. We analyzed urine samples from 19 male HNSCC patients and identified 1427 proteins by mass spectrometry. Of these, 730 overlapped with healthy controls, highlighting prognostic markers such as RNASE1, LRG1 and CD44. Machine learning techniques, including principal component analysis and partial least squares discriminant analysis, distinguished HNSCC patients from controls and revealed unique proteomic signatures. Pathogenic variants such as GAA p.(Trp746Cys) and SIAE p.(Pro210Leu) were found to be potential indicators of advanced disease. Functional analyzes linked the identified proteins to important tumor-related processes, including epithelial-mesenchymal transition and neutrophil degranulation. These results support urinary proteomics as a promising non-invasive diagnostic tool for early detection of HNSCC and disease monitoring. Future research should validate these biomarkers in larger, more diverse cohorts to improve clinical applicability. Head and neck ; Basal cell ; Head and neck squamous-cell carcinoma ; Medicine ; Pathology ; Internal medicine ; Head and neck cancer ; Cancer ; Surgery Advanced Proteomics Techniques and Applications
Authors: Pingping Guo | Q. Wang | Wenmei HUANG | Ling Weng | Y. Zhang | Yushen Yang
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Hysteresis Measurement and Modeling under Multi-Operating Condition Considering Magnetic Domain Evolution Mechanism Hysteresis ; Magnetic hysteresis ; Magnetic domain ; Magnetostriction ; Control theory (sociology) ; Nonlinear system ; Domain (mathematical analysis) ; Domain wall (magnetism) Magnetic Properties and Applications ; Multiferroics and related materials ; Piezoelectric Actuators and Control
Authors: Xin Li (51274) | Bing Yan (170259)
Source: Figshare (article, 2025-11-11)
Field: Chemistry — Inorganic Chemistry

Tb(III)-FunctionalizedHydrogen-Bonded Organic Frameworkwith Dual-Emission for Liver Health Biomarker Detection and a Smartphone-IntegratedBionic Visual Diagnostic Platform Developing a sensitive analytical platform for monitoring tiopronin (MPG), its metabolite 2-mercaptopropionic acid (MPA), and the key liver biomarker glutathione (GSH) is crucial for liver health assessment. Here, an artificial intelligence-assisted bionic vision platform based on a dual-emission Tb<sup>3</sup><sup>+</sup>-functionalized hydrogen-bonded organic framework (Tb@HOF-dobpdc) is constructed. The ratiometric fluorescent sensor exhibits a distinct ″turn-on″ response, enabling highly sensitive detection of MPG, MPA, and GSH with low limits of detection (0.20, 0.68, and 0.31 μM, respectively). For practical application, Tb@HOF-dobpdc can achieve rapid detection of target analytes in real serum and urine samples by combining with the hydrogel. To overcome the human eye’s limitation in discerning subtle color changes, RGB channel processing is used to generate visually distinguishable pseudocolor signals. Furthermore, a backpropagation neural network (BPNN) is applied for accurately distinguishing analyte concentrations by identifying fluorescence images. Leveraging the strong correlation between these analytes and liver health, an AND logic gate diagnostic system is established, outputting ″healthy″ only when all biomarker levels are within safe ranges. This intelligent platform combines advanced material design and artificial intelligence, providing a powerful tool for point-of-care liver health diagnosis. Analyte ; Biomarker ; Image processing ; Artificial neural network ; Object detection ; Pattern recognition (psychology) ; RGB color model ; Backpropagation Metal-Organic Frameworks: Synthesis and Applications ; Sulfur Compounds in Biology ; Molecular Sensors and Ion Detection
Authors: Qiang Li | Junfeng Guo | Le W. Ma | Qiao Zhang | Tingyao Wu
Source: Geotechnical and Geological Engineering (article, 2025-09-02)
Field: Materials Science — Materials Chemistry

Residual Strength of Gas Pipelines Under Tunnel Excavation: Influence of Material Parameters Hydrogeology ; Geotechnical engineering ; Excavation ; Pipeline transport ; Residual strength ; Residual ; Engineering ; Geology ; Forensic engineering ; Mining engineering ; Environmental science ; Structural engineering ; Mathematics ; Mechanical engineering Material Properties and Failure Mechanisms ; Geotechnical Engineering and Underground Structures ; Structural Integrity and Reliability Analysis
Authors: Arpan Sur | Sudipta Saha | Chih‐Yu Lee | Ichiro Takeuchi
Source: arXiv (Cornell University) (preprint, 2025-12-22)
Field: Materials Science — Materials Chemistry

Multilevel Photonic Switching in GST-467 for Deep Neural Network Inference Phase-change materials (PCMs) have emerged as key enablers of non-volatile, ultra-compact photonic switches for energy-efficient deep neural network (DNN) applications. In this work, we investigate the recently discovered $\mathrm{Ge_{4}Sb_{6}Te_{7}}$ (GST-467) as a high-contrast optical PCM and demonstrate its suitability for multi-level photonic computing. The complex refractive indices of amorphous and crystalline GST-467 were experimentally extracted and used to propose a segmented silicon-on-insulator photonic switch optimized at 1550 nm. Three-dimensional FDTD simulations reveal that segmentation significantly enhances the extinction ratio while maintaining low insertion loss, resulting in a more than seven times higher design figure of merit than an unsegmented design. Laser-induced thermo-optical simulations further establish efficient, reversible switching with sub-nJ energy requirements for crystallization and amorphization. Compared with established GST, GSST, and GSS compositions, GST-467 provides the largest transmission contrast and supports up to 48 resolvable optical states. When deployed as multi-level weights in photonic DNN architectures, the GST-467 switch achieves superior classification accuracy on EMNIST and Fashion-MNIST benchmarks. These results position GST-467 as a highly promising PCM for scalable, low-energy photonic computing and neuromorphic hardware. Photonics ; Artificial neural network ; Neuromorphic engineering ; Optical switch ; Transmission (telecommunications) ; Photonic crystal ; Position (finance) ; Figure of merit ; Segmentation Phase-change materials and chalcogenides ; Neural Networks and Reservoir Computing ; 2D Materials and Applications
Authors: Allyn M. Xu | Dániel Szöllősi | Helmut Grubmüller | Oded Regev
Source: Journal of the American Society for Mass Spectrometry (article, 2025-12-21)
Field: Chemistry — Spectroscopy

Large Data Set Analysis Reveals Structural Origin of Peptide Collisional Cross Section Bimodal Behavior Recent advances in ion mobility spectrometry have enabled the measurement of rotationally averaged collisional cross-sectional area (CCS) for millions of peptides as part of routine proteomic mass spectrometry workflows. One of the most striking findings in recent large ion mobility data sets is that CCS exhibits two distinct modes, most notably for charge 3<sup>+</sup> peptides, with peptides predominantly exhibiting CCS in either the high or low mode. Here, using classical machine learning approaches, we identify that basic site positioning is a key sequence feature determining a peptide's CCS mode. Molecular dynamics simulations suggest that peptides in the high CCS mode tend to adopt more extended conformations and form charge-stabilized helical structures, whereas those in the low CCS mode adopt more compact, globular conformations. Further supporting this structural hypothesis, we provide evidence for preferential protonation near the C-terminus and uncover multiple position-dependent sequence determinants that all suggest the predominance of helix formation in the high CCS mode. Together, these findings will enable better integration of CCS measurements into protein identification and quantification pipelines, improving the performance of ion mobility-based proteomics. Ion-mobility spectrometry ; Mass spectrometry ; Ion ; Protonation ; Sequence (biology) ; Molecular dynamics ; Set (abstract data type) ; Peptide ; Identification (biology) Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Protein Structure and Dynamics
Authors: Jiyu Wang | Xinghua Li | Bohan Zhang | A. Mai | Taishan Hu | Jian Ou | Liuxia Li | Dongxu Jia | Gang Liu | Yi Liu
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Ceramics and Composites

RGB-Inspired CNN for goal-driven microstructure optimization of carbon-reinforced ceramic composites Convolution (computer science) ; Microstructure ; Convolutional neural network ; Bridging (networking) ; Characterization (materials science) ; Stability (learning theory) ; Ceramic ; Optimization problem Advanced ceramic materials synthesis ; Smart Materials for Construction ; Machine Learning in Materials Science
Authors: Yinqiao Wang | Jiawei Huang | Chuan Xu | Sipan Liu | Junwei Li | Kai Lan
Source: Advanced Sustainable Systems (article, 2025-10-26)
Field: Materials Science — Biomaterials

Techno‐Economic Analysis of Polylactic Acid from Corn Stover: Understanding Uncertainty and Variability via Artificial Intelligence Tools Abstract As a renewable and bio‐based material, polylactic acid (PLA) can provide long‐term sustainable environmental benefits. This study focused on analyzing the economic performance of PLA production from corn stover at a large scale in the U.S. A modeling framework is established that integrates techno‐economic analysis with the process model developed in Aspen Plus and Monte Carlo simulation that analyzes the corresponding uncertainty and variability across the system. Monte Carlo simulation is enabled by artificial intelligence (AI) models, namely Artificial Neural Network (ANN) and Random Forest. This established framework aims at streamlining the process that requires high computational time and developing an easy‐access analysis tool. Scenario analysis investigated different energy options and biorefinery lifespans. The results indicate that the mean minimum selling price of 1 t PLA is $2523 (5th percentile–95th percentile (P5–P95) $2 107–$2 940) for a 30‐year lifespan using natural gas as the energy source, $2,731 (P5–P95 $2 400–$3 076) for a 10‐year lifespan using natural gas for extra energy, and $2 637 (P5–P95 $2 370–$2 903) for a 30‐year lifespan using wood pellets for additional energy. Both AI models demonstrate the ability to predict economic performance by showing relatively high prediction accuracy, but the ANN model exhibits better performance. Monte Carlo method ; Artificial neural network ; Process (computing) ; Renewable energy ; Biorefinery ; Scale (ratio) ; Polylactic acid biodegradable polymer synthesis and properties ; Additive Manufacturing and 3D Printing Technologies ; Microplastics and Plastic Pollution
Authors: Thomas Leveau | Lisa Ventelon | Mihai‐Cosmin Marinica | Emmanuel Clouet
Source: Acta Materialia (article, 2025-12-31)
Field: Materials Science — Metals and Alloys

Segregation of hydrogen on screw dislocations in tungsten and its impact on dislocation mobility We investigate the interaction between hydrogen atoms and 1 2 〈 111 〉 screw dislocations in tungsten combining density functional theory (DFT) calculations and molecular dynamics (MD) simulations using a newly developed machine learning (ML) potential trained on ab initio data. Our study reveals two distinct hydrogen-decorated dislocation core structures: the previously known hard core configuration and a newly characterized easy core configuration. DFT calculations indicate comparable dislocation-hydrogen interaction energies for both cores, suggesting a potential competition between these configurations. Ising models, parameterized on DFT data are employed to predict hydrogen segregation profiles for both core configurations, and the results are corroborated by MD simulations. Free energy analyses and finite-temperature MD simulations indicate that the hydrogen-decorated hard core is thermodynamically favored over a wide range of temperatures and hydrogen concentrations. The mobility of this stable hydrogen-decorated hard core structure is assessed by evaluating energy barriers through DFT calculations and performing MD simulations under applied stress. Both approaches consistently show that hydrogen segregation on the hard core structure significantly reduces dislocation mobility, leading to a locking of screw dislocations. Dislocation ; Tungsten ; Density functional theory ; Hydrogen ; Core (optical fiber) ; Molecular dynamics ; Ab initio Hydrogen embrittlement and corrosion behaviors in metals ; Hydrogen Storage and Materials ; Fusion materials and technologies