jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Wenbin Liu | Lei Jia | Huike Zhang | Peng Shi
Source: Journal of Intelligent & Fuzzy Systems (article, 2026-04-01)
Field: Materials Science — Materials Chemistry

Atmospheric Corrosion Prediction of Low-Alloy Carbon Steel Based on Dual-Correction Dynamic Ensemble Framework Accurate forecasting of atmospheric corrosion for low-alloy carbon steel is critical for lifecycle management of infrastructure, yet data-driven models often exhibit systematic bias under highly variable environments. To address the limitations of atmospheric corrosion prediction—particularly systematic prediction bias and residual skewness commonly observed in gradient-boosting-based learners—this study proposes a Dual-Correction Dynamic Ensemble (DCDE) framework. DCDE integrates XGBoost and CatBoost in a two-layer architecture, where a primary prediction layer captures baseline nonlinear degradation behavior and a residual correction mechanism compensates structured errors. A unified Bayesian optimization protocol is adopted to jointly configure model structures, correction depth, and fusion parameters, enabling synergistic interaction between the boosting components. Experimental validation on industrial atmospheric corrosion datasets using 5-fold cross-validation demonstrates the effectiveness of DCDE: the proposed method achieves RMSE = 0.2988 ± 0.0473 and Pearson R = 0.9627 ± 0.0138, and reduces the mean relative error by approximately 10.6% compared with the baseline XGBoost model. The main contributions include (i) synergistic integration of heterogeneous boosting architectures, (ii) a dual-correction ensemble design that improves error compensation and stability, and (iii) a joint Bayesian optimization scheme that enhances predictive accuracy and interpretability for corrosion-driven lifespan prediction in complex degradation environments. Interpretability ; Residual ; Skewness ; Boosting (machine learning) ; Bayesian probability ; Corrosion ; Baseline (sea) ; Ensemble forecasting Corrosion Behavior and Inhibition ; Structural Integrity and Reliability Analysis ; Concrete Corrosion and Durability
Authors: Soham Choudhuri | Subinoy Adhikari | Jagannath Mondal
Source: bioRxiv (Cold Spring Harbor Laboratory) (article, 2026-03-05)
Field: Materials Science — Materials Chemistry

From Prefix to Path: Learning Temporally Consistent Biomolecular Dynamics from Limited Initial Data Molecular dynamics (MD) simulations provide detailed insights into biomolecular motion but are often limited by the prohibitive cost of sampling long-timescale behavior. Here, we present a Transformer-based framework that reconstructs temporally continuous dynamical trajectories from only a small fraction of the initial data, directly targeting time-ordered evolution rather than independent ensemble snapshots. Using three systems spanning distinct dynamical regimes (intrinsically disordered α-Synuclein, Cytochrome P450-substrate recognition, and a synthetic three-well potential), we show that the model learns both local fluctuations and long-range temporal structure. At inference time, the model generates full trajectories autoregressively from an initial prefix as prompt, capturing metastable transitions, basin-to-basin movements, and system-specific dynamical signatures. Free-energy surfaces computed from generated trajectories closely match ground-truth landscapes and, in several cases, we observe enhanced sampling in generated trajectories relative to the trained trajectories-while preserving kinetically meaningful transition patterns. These results demonstrate that Transformer architectures can serve as efficient, system-agnostic tools for time-continuous molecular trajectory prediction, offering a data-driven complement to long MD simulations and enabling accelerated exploration of conformational space. Trajectory ; Inference ; Sampling (signal processing) ; Dynamical systems theory ; Granularity ; Complement (music) ; Dynamics (music) Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Ilia Toli
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2026-04-08)
Field: Materials Science — Materials Chemistry

447 Terabytes per Square Centimetre at Zero Retention Energy: Non-Volatile Memory at the Atomic Scale on Fluorographane The memory wall -- the widening gap between processor throughput and memory bandwidth -- has become the defining hardware constraint of the artificial intelligence era, now compounded by a structural NAND flash supply crisis driven by AI demand. We propose a post-transistor, pre-quantum memory architecture built on single-layer fluorographane (CF), in which the bistable covalent orientation of each fluorine atom relative to the sp3-hybridized carbon scaffold constitutes an intrinsic, radiation-hard binary degree of freedom. The C-F inversion barrier of ~4.6 eV (B3LYP-D3BJ/def2-TZVP, this work; verified transition state with one imaginary frequency; confirmed at 4.8 eV by DLPNO-CCSD(T)/def2-TZVP; rigorous lower bound from the fluorophenalane molecular model) yields a thermal bit-flip rate of ~10^{-65} s^{-1} and a quantum tunneling rate of ~10^{-76} s^{-1} at 300 K, simultaneously eliminating both spontaneous bit-loss mechanisms. The barrier lies below the C-F bond dissociation energy (5.6 eV) at both levels of theory, so the covalent bond remains intact throughout the inversion. A single 1 cm^2 sheet encodes 447 TB of non-volatile information at zero retention energy. Volumetric nanotape architectures extend this to 0.4-9 ZB/cm^3. We present a tiered read-write architecture progressing from scanning-probe validation (Tier 1, achievable with existing instrumentation) through near-field mid-infrared arrays (Tier 2) to a dual-face parallel configuration governed by a central controller, with a projected aggregate throughput of 25 PB/s at full Tier 2 array scale. A scanning-probe prototype already constitutes a functional non-volatile memory device with areal density exceeding all existing technologies by more than five orders of magnitude. Throughput ; Quantum entanglement ; Resistive random-access memory ; Binary number ; Flash memory ; Non-volatile memory ; Memory architecture ; Quantum tunnelling ; Atom (system on chip) ; Atomic units ; Terabyte 2D Materials and Applications ; Semiconductor materials and devices ; Chemical and Physical Properties of Materials
Authors: S.B. Akinpelu | S.A. Abolade | E. Okafor | D.O. Obada | A.M. Ukpong | S. Kumar R | J. Healy | A. Akande
Source: Results in Chemistry (article, 2026-02-22)
Field: Materials Science — Materials Chemistry

Explainable ensemble learning for predicting mechanical properties of ABX₃ perovskites using elemental composition descriptors This study proposes a data-driven modelling alternative to conventional Density Functional Theory (DFT) calculations by employing three explainable ensemble learning algorithms (CatBoost, XGBoost, and Random Forest) to predict and interpret mechanical properties (bulk, shear, and Young's moduli) of ABX₃ perovskites based on elemental descriptors. All models demonstrated strong predictive performance, achieving R 2 values of ≥0.93 in both the training and testing phases. Among them, the Random Forest model consistently outperformed the others, yielding the highest R 2 scores, along with the lowest mean absolute error (MAE) and root mean squared error (RMSE), indicating superior generalization and robustness across all target properties. To enhance interpretability, Shapley Additive Explanations (SHAP) was used to quantify the contribution of individual features to model predictions. Feature importance analysis revealed that the melting points of the A- and B-site elements are among the most influential predictors of bulk modulus, likely due to their correlation with the material's resistance to thermal vibrations and mechanical deformation. Overall, this work demonstrates that explainable ensemble learning models, particularly Random Forest, can serve as scalable and cost-effective tools for accurately predicting and interpreting mechanical properties in perovskite materials. The integration of model interpretability with predictive insight offers a promising pathway for accelerating materials discovery and design in computational materials science. Correlation plots for the training phase for each of the ensemble learning methods. (a) Bulk; (b) shear; (c) Young moduli. • This study introduces an alternative approach to DFT by evaluating three ML models to predict and interpret the mechanical properties of perovskites. • CatBoost, XGBoost, and Random Forest models exhibited strong predictive performance. • The Random Forest model demonstrated superior performance comparatively. • The SHAP algorithm was applied to analyze the trained ensemble learning models for interpretability using a novel holistic method. Ensemble learning ; Interpretability ; Random forest ; Robustness (evolution) ; Ensemble forecasting ; Mean squared error ; Correlation ; Generalization ; Density functional theory Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Advancements in Solid Oxide Fuel Cells
Authors: Collin R. Johnson | Kerstin Wohlgemuth | Sergio Lucia
Source: Journal of Process Control (article, 2026-01-15)
Field: Materials Science — Materials Chemistry

A tutorial overview of model predictive control for continuous crystallization: Current possibilities and future perspectives This paper presents a systematic approach to the advanced control of continuous crystallization processes using model predictive control. We provide a tutorial introduction to controlling complex particle size distributions by integrating population balance equations with detailed models of various continuous crystallizers. Since these high-fidelity models are often too complex for online optimization, we propose the use of data-driven surrogate models that enable efficient optimization-based control. Through two case studies, one with a low-complexity system allowing direct comparison with traditional methods and another involving a spatially distributed crystallizer, we demonstrate how our approach enables real-time model predictive control while maintaining accuracy. The presented methodology facilitates the use of complex models in a model-based control framework, allowing precise control of key particle size distribution characteristics, such as the median particle size $d_{50}$ and the width $d_{90} - d_{10}$. This addresses a critical challenge in pharmaceutical and fine chemical manufacturing, where product quality depends on tight control of particle characteristics. Model predictive control ; Control (management) ; Key (lock) ; Population ; Quality (philosophy) ; Current (fluid) ; Control system ; Term (time) Crystallization and Solubility Studies ; Process Optimization and Integration ; Advanced Control Systems Optimization
Authors: Biqing Chen | Gao Ji | Haizhu Sun | Zhi Chen | Xiaohong Qiu
Source: International Immunopharmacology (article, 2026-01-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

SERS-enabled immune monitoring: Decoding the tumor microenvironment for precision Cancer immunotherapy Immune system ; Tumor microenvironment ; Immunotherapy ; Cancer immunotherapy ; Immune checkpoint ; Cytokine ; Precision medicine Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Nanoplatforms for cancer theranostics
Authors: Quinn M. Gallagher | Ryan J. Szukalo | Nicolas Giovambattista | Pablo G. Debenedetti | Michael A. Webb
Source: arXiv (Cornell University) (preprint, 2026-01-24)
Field: Materials Science — Materials Chemistry

A Local Structural Basis to Resolve Amorphous Ices Phases with distinct thermodynamic properties must differ in their underlying distributions of microscopic structures. While ordered phases are readily distinguished by unit cells and space groups, the local structural basis differentiating amorphous phases is less apparent. Here, using a new probabilistic data-driven framework applied to molecular simulation data on water, we identify local collective variables that discriminate low-density and high-density amorphous (LDA and HDA) ices and characterize pressure-induced transitions between these phases. As expected, descriptors related to local density capably distinguish LDA and HDA; however, phase identity is surprisingly encoded within the first coordination shell. Furthermore, LDA transitions to HDA by a simple redistribution of LDA- and HDA-like environments with no evident intermediate structures, in accordance with a first-order-like transition that contrasts with the gradual evolution observed in other amorphous systems such as metallic glasses. These findings are robust across force fields, which themselves exhibit structural differences, and exemplify how other systems lacking obvious distinguishing features can be characterized. Amorphous solid ; Basis (linear algebra) ; Probabilistic logic ; Phase transition ; Molecular dynamics ; Redistribution (election) ; Space (punctuation) Material Dynamics and Properties ; nanoparticles nucleation surface interactions ; Metallic Glasses and Amorphous Alloys
Authors: Quinn M. Gallagher | Ryan J. Szukalo | Nicolas Giovambattista | Pablo G. Debenedetti | Michael A. Webb
Source: ArXiv.org (article, 2026-01-24)
Field: Materials Science — Materials Chemistry

A Local Structural Basis to Resolve Amorphous Ices Phases with distinct thermodynamic properties must differ in their underlying distributions of microscopic structures. While ordered phases are readily distinguished by unit cells and space groups, the local structural basis differentiating amorphous phases is less apparent. Here, using a new probabilistic data-driven framework applied to molecular simulation data on water, we identify local collective variables that discriminate low-density and high-density amorphous (LDA and HDA) ices and characterize pressure-induced transitions between these phases. As expected, descriptors related to local density capably distinguish LDA and HDA; however, phase identity is surprisingly encoded within the first coordination shell. Furthermore, LDA transitions to HDA by a simple redistribution of LDA- and HDA-like environments with no evident intermediate structures, in accordance with a first-order-like transition that contrasts with the gradual evolution observed in other amorphous systems such as metallic glasses. These findings are robust across force fields, which themselves exhibit structural differences, and exemplify how other systems lacking obvious distinguishing features can be characterized. Amorphous solid ; Basis (linear algebra) ; Probabilistic logic ; Phase transition ; Molecular dynamics ; Redistribution (election) ; Space (punctuation) Material Dynamics and Properties ; nanoparticles nucleation surface interactions ; Metallic Glasses and Amorphous Alloys
Authors: Phillip Hillen | Nolan Miller | Feras El Yaakobi
Source:  (article, 2026-02-09)
Field: Materials Science — Materials Chemistry

Applying Design of Experiment (DOE) to Optimize Corrosion Inhibitor Component Ratios for the Montney Basin and Beyond Abstract This study explores the optimization of corrosion inhibitor formulations using a Design of Experiments (DOE) approach. Despite the widespread use of corrosion inhibitors in oil and gas production, limited research has focused on the impact of varying ratios of active components. The objective was to determine the optimal blend of three common active ingredients in continuous inhibitors to enhance corrosion protection. DOE is a system for planning tests and analyzing results to determine the relationship between factors affecting a process and the output of that process. An augmented 2-dimensional simplex lattice design was used to test ten initial formulations. This design was later refined based on performance outcomes. Corrosion inhibitor performance was assessed under conditions like those found in the Montney Basin. Some variables were manipulated to test the inhibitor response, including testing both sweet and sour gas environments, as well as brine-only and brine/oil partitioned fluid matrices. This study underscores the effectiveness of DOE as a systematic approach to chemical formulation and highlights its value in enhancing product performance through data-driven decision-making. Corrosion ; Corrosion inhibitor ; Design of experiments ; Fossil fuel ; Simplex algorithm Corrosion Behavior and Inhibition ; Material Selection and Properties ; Process Optimization and Integration
Authors: Jody Albertazzi | Ajinkya Pandit | Giovanni Aprile | Maria del Carme Pons Royo | Valentina Busini | R. Rota | Allan S. Myerson | Torsten Stelzer
Source: Industrial & Engineering Chemistry Research (article, 2026-02-07)
Field: Materials Science — Materials Chemistry

Computational Fluid Dynamic Study in a Multistage Small-Scale Tower Crystallizer The solid–liquid hydrodynamics in a novel tower crystallizer consisting of seven vertically stacked spherical mixed-suspension, mixed-product removal crystallizers is reported. Mixing in spherical geometries remains underexplored. Therefore, computational fluid dynamics (CFD) simulations (Eulerian–Eulerian multiphase approach with Reynolds-Averaged Navier–Stokes closure at steady state) were performed to systematically assess in silico the impact of stirring rate, impeller type, blade geometry and number, dual-impeller configurations, and baffles on the mixing performance. Results demonstrate the best suspension mixing for representative pharmaceutical crystals (100 μm, 20% solids loading) using an axial-flow pitched blade impeller (four blades, 30° blade angle). Adding baffles further improves mixing, achieving homogeneity (relative standard deviation, σ < 0.2). While mixing is often overlooked in lab-scale crystallizers, possibly due to their small volumes (≤100 mL), this study highlights the critical role of mixing as a fundamental parameter for achieving suspension uniformity, which significantly alters the crystallization performance. The developed and generalizable framework provides mechanistic insights into the solid–liquid mixing, offering a data-driven foundation toward the rational design and scale-up of crystallizers. Ultimately, integrating these CFD findings with population balance modeling in future work will contribute to model-predictive crystallizers, bridging the gap from the lab to industrial scale. Baffle ; Impeller ; Computational fluid dynamics ; Mixing (physics) ; Suspension (topology) ; Homogeneity (statistics) ; Agitator ; Rushton turbine ; Paddle Crystallization and Solubility Studies ; Fluid Dynamics and Mixing ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Junyi Chen | Chen Cao | Shaoning Shen | Tianyuan Guo | Jingang Liang
Source: Radiation Physics and Chemistry (article, 2026-01-08)
Field: Materials Science — Materials Chemistry

A multi-scale Mamba framework for advanced multi-layer photon shielding analysis Electromagnetic shielding ; Photon ; Monte Carlo method ; Sequence (biology) ; Flux (metallurgy) ; Photon energy ; Core (optical fiber) ; Radiation shielding Radiation Shielding Materials Analysis ; Radiative Heat Transfer Studies ; Gas Dynamics and Kinetic Theory
Authors: Chuyu Liu | Nan Gao | Haiqi Ren | Hao Liu | Juxiang Hou | Zhongsen Li | Benqi Zhao | Yibei Yu | Xiaowei He | Zhuozhao Zheng | Xiaolei Song
Source: Nature Communications (article, 2026-03-31)
Field: Materials Science — Materials Chemistry

Single-breath-hold 3D abdominal metabolic MRI enables label-free diagnosis of liver cancer Chemical exchange saturation transfer (CEST) MRI could detect proteins/peptides, creatine, glucose, and glycogen by labeling their exchangeable amide, amine, and hydroxyl groups respectively, via frequency-selective RF pulses. Without the need for contrast agents or specialized hardware, CEST can be conveniently integrated into existing clinical MR protocols. However, its abdominal application is limited by long scan time (> 5 min) and susceptibility to respiratory motion (60-70% successful scan rate). We develop an ultra-fast 3D CEST MRI approach using spatial-spectral encoding (SSE), enabling a full spectral scan of whole-liver 3D images within a single breath-hold. SSE-CEST employs an efficient z-ω encoding pattern by applying a saturation gradient, followed by a data-driven spatial spectral reconstruction based on the low-rankness of CEST spectra. SSE-CEST is comprehensively evaluated in glycogen phantoms, ex vivo porcine liver, healthy volunteers and patients. Single breath-hold SSE-CEST largely improves successful rate, with a correlation of 0.95 between two repeated scans. SSE-CEST enables the detection of multi-metabolite changes in the liver and pancreas after an overnight fasting, and the dynamic mapping of hepatic glucose metabolism during an oral glucose test. For liver cancer patients, SSE could differentiate active lesions from post-treatment necrosis, featuring superior in-slice spatial resolution and motion-stabilized images. SSE-CEST MRI potentially could facilitate the diagnosis and patient management for liver and other abdominal diseases. Magnetic resonance imaging ; Liver cancer ; Ex vivo ; Pancreas ; Pancreatic cancer ; Glycogen ; In vivo ; Dynamic contrast-enhanced MRI Lanthanide and Transition Metal Complexes ; Advanced MRI Techniques and Applications ; Molecular Sensors and Ion Detection
Authors: Mojtaba Khakpour Komarsofla | Amirkianoosh Kiani
Source: Energy and AI (article, 2026-04-05)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Accelerating supercapacitor lifetime optimization using quantum machine learning surrogates • Quantum ML surrogates improve supercapacitor cycle-life prediction • Quantum kernels outperform classical models in low-data regimes • Bayesian optimization accelerates lifetime design search • Unified electrochemical and materials dataset enables robust modeling • Experimental validation confirms optimized device performance Supercapacitor lifetime optimization is limited by the high cost and duration of long-cycle experiments, which constrains the amount of training data available for data-driven design. This study proposes and validates an experimental–computational pipeline that couples device-level cycling data with electrode physical measurements and material characterization, then uses kernel-based surrogate modeling and Bayesian optimization to maximize cycle life. A unified dataset was constructed by merging constant-current charge–discharge records with fixed physical/material descriptors, yielding more than one million raw data points across fourteen commercial supercapacitors tested under multiple operating protocols. Two surrogate models were evaluated under matched settings: classical kernel ridge regression (C-KRR) and quantum-kernel ridge regression (Q-KRR). In data-limited regimes, Q-KRR achieved higher predictive accuracy, with the clearest advantage at small training sizes (e.g., at n = 75, R 2 ≈ 0.871 for Q-KRR versus ≈ 0.700 for C-KRR). When embedded in Bayesian optimization, the quantum-driven pipeline (Q-KRR-BO) consistently proposed higher predicted cycle-life candidates than the classical pipeline (C-KRR-BO) under the same iteration budget (e.g., at n = 75, best predicted cycle life increased from 4609 to 6521 cycles). The optimized outcomes were experimentally validated using an independent device, which achieved 10198 cycles at the 90% retention threshold and was closer in feature space to Q-KRR-BO recommendations than to C-KRR-BO recommendations. Overall, the results indicate that quantum kernels can reduce the amount of required experimental training data for lifecycle optimization, while classical KRR remains preferable when runtime is the dominant constraint. Pipeline (software) ; Bayesian optimization ; Kernel (algebra) ; Bayesian probability ; Surrogate model ; Feature (linguistics) ; Regression ; Feature selection Supercapacitor Materials and Fabrication ; Electrocatalysts for Energy Conversion ; Machine Learning in Materials Science