jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Pavel Seredin | D. L. Goloshchapov | Tatiana Litvinova | Yaroslav A. Peshkov | Yury Ippolitov | Raul O. Freitas | Francisco C. B. Maia | Iman Mahdy
Source: ACS Biomaterials Science & Engineering (article, 2026-03-04)
Field: Materials Science — Biomaterials

Electric Field-Guided Biomimetic Mineralization of Enamel via Interfacial Engineering of Nanostructured HAp/PDA Coatings with Anisotropy and Enhanced Hardness Human enamel is a hierarchically organized hydroxyapatite biomineral, but it lacks regenerative capacity, motivating interfacial engineering strategies that control mineral growth at the enamel surface. This work examines an electric-field-assisted biomimetic mineralization approach that forms organized organo-mineral coatings composed of nanocrystalline carbonate-substituted hydroxyapatite (ncHAp), amino acids (AAs), and polydopamine (PDA). The method utilizes isolated electrodes to apply an electrostatic field, eliminating direct current through the substrate and directing the assembly at the interface. Crucially, this configuration with fully insulated electrodes prevents Faradaic current flow-through biological tissue, establishing a safe foundation for this in vitro proof-of-concept study. Grazing-incidence X-ray diffraction confirms the hydroxyapatite phase and shows a strong preferred orientation, which is reflected in a markedly increased crystallinity index due to the formation of a textured, highly oriented architecture. Electron microscopy, atomic force microscopy (AFM), and synchrotron nano-IR imaging (SINS) reveal densely packed ncHAp/AA/PDA nanoagglomerates with a core-shell architecture. Machine-learning clustering of SINS hyperspectral maps identifies nanoscale chemical heterogeneity and phosphate-rich oriented domains. Crucially, the coating exhibits enhanced mechanical properties: Vickers microhardness measurements and AFM-based nanoindentation show that the coating-substrate system exhibits a higher apparent surface hardness compared to that of native enamel under the tested indentation conditions, demonstrating the reinforcing effect of the textured, electric field-assisted composite layer. This enhancement in the composite coating-substrate system is attributed to the textured assembly of ncHAp nanocrystals reinforced by a PDA/AA interphase. The work elucidates the mechanism of electric-field-guided assembly, establishing a route for fabricating structurally organized, hard biomimetic coatings on enamel. This study establishes fundamental principles of electric-field-guided mineral assembly on enamel surfaces and provides a versatile platform for engineering high-performance biomimetic interfaces, with potential relevance to future noninvasive enamel restoration approaches. Enamel paint ; Anisotropy ; Tooth enamel ; Mineralization (soil science) ; Indentation hardness ; Electrically conductive Calcium Carbonate Crystallization and Inhibition ; Bone Tissue Engineering Materials ; Electrophoretic Deposition in Materials Science
Authors: Veera Mohana Rao Kakita (10746395) | D. Flemming Hansen (1284132)
Source: Figshare (article, 2026-03-02)
Field: Chemistry — Spectroscopy

DeepLearning Assisted Proton Pure Shift NMR Spectroscopy Nuclear magnetic resonance spectroscopy (NMR) plays a key role for the analysis of a plethora of molecules, including natural products and drug-like organic molecules. For such cases, <sup>1</sup>H NMR spectra have proven imperative because of their high sensitivity and atomic resolution. However, these spectra are complicated by overlapped complex multiplet patterns. Here we show a deep-learning approach, which transforms spin–echo modulated <sup>1</sup>H NMR spectra into highly sensitive and high-resolution singlet NMR spectra, that is, virtual homonuclear decoupled pure shift spectra. The approach was evaluated on experimental NMR spectra of complex organic compounds, where it outperforms current methods. The method also predicts uncertainties of the transformation and therefore allows for quantifications, which is a key strength of NMR. We believe that our approach will provide significant advantages when characterizing low-sensitivity samples and systems with exchangeable protons, where signals are not observed in traditional pure-shift spectra and substantial overlaps hamper analysis from conventional spectra. Homonuclear molecule ; NMR spectra database ; Spectral line ; Nuclear magnetic resonance spectroscopy ; Two-dimensional nuclear magnetic resonance spectroscopy ; Multiplet ; Spectroscopy ; Singlet state ; Carbon-13 NMR satellite Advanced NMR Techniques and Applications ; Molecular spectroscopy and chirality ; Metabolomics and Mass Spectrometry Studies
Authors: Kwame Sarkodie | Joseph Agyepong | Emmanuel Agyei | Godfred Arkoh | Rhoda Arhin | Franklin Ankomah | Caspar Daniel Adenutsi | Samuel Erzuah
Source: Petroleum (article, 2026-03-01)
Field: Chemistry — Analytical Chemistry

Machine learning-based surrogate correlation for prediction of wax deposition rate in oil and gas pipelines This study developed a data-driven surrogate-box correlation to predict wax deposition rates in crude-oil pipelines and help address the limited generalizability of empirical and mechanistic models under variable flow and thermal conditions. Literature-derived dataset of 215 experimental and field observations was compiled with inputs as oil temperature , wall temperature , dynamic viscosity , wall shear stress , flow velocity , wall temperature gradient , and wall concentration gradient , and target as wax deposition rate (g/m 2 .h). After outlier control, standardization, and ANOVA-based feature engineering, Five supervised black-box models; SVR, Random Forest, MLP, Gradient Boosting, and KNN, models were trained with an 80/20 train–test split, 5-fold cross-validated, hyperparameter-tuned and evaluated using MSE, RMSE, MAE, , and AAPRE. The top performing model, KNN, was modeled into an interpretable surrogate via Elastic Net (benchmarked against polynomial Ridge degree-2/3) to yield a closed-form correlation. KNN achieved excellent test performance ( , RMSE ⁓ 0.101, AAPRE ⁓ 5.09%), outperforming alternative models. The Elastic Net surrogate preserved predictive ability with balanced generalization ( ≈ 0.61–0.65 for train/validation/test) while exposing parameter level effects and nonlinear interactions among temperature, viscosity, shear stress, velocity, and wall-scale gradients. When compared to a physics-based correlation, the surrogate exhibited tighter clustering to measurements, which indicates improved field relevance. The principal contribution of this study is a hybrid workflow that couples high-accuracy black-box learning with transparent surrogate modeling to help enable real-time monitoring and control, proactive pigging scheduling, and optimized chemical/thermal treatments of oil flow in pipelines. The resulting correlation offers a deployable, interpretable alternative to nontransparent machine learning models or assumption-heavy empirical relations, with clear value for flow-assurance planning and operational reliability. Pipeline transport ; Wax ; Fossil fuel ; Oil viscosity ; Petroleum ; Pipeline (software) Petroleum Processing and Analysis ; Structural Integrity and Reliability Analysis ; Water Systems and Optimization
Authors: Nihad Omer Hassan | Afaf Ghais | Muhammad H.M. Ahmed | Mahmoud Adam | Razan Ahmed | Amel Abdelatti | D.E.P. Klenam | M.O. Bodunrin | Abdalrhaman Koko
Source: International Journal of Hydrogen Energy (article, 2026-02-13)
Field: Materials Science — Materials Chemistry

Density functional theory insights into the structure, electronic and mechanical properties, thermodynamics, and diffusion of high-entropy alloys for hydrogen storage: A review This review highlights the application of first principles in studying hydrogen interactions within HEAs. It explores key aspects, including electronic, mechanical stability, thermodynamics, and diffusion pathways. Most research uses SQS to simulate chemical disorder, which is paired with GGA-PBE exchange-correlation functionals and PAW pseudopotentials to ensure accurate energetic and structural predictions. Electronically, DFT descriptors such as DOS/PDOS, COHP/ICOHP, bond order, and Mulliken charges have demonstrated that hydrogen stabilization is controlled by localized metal-hydrogen bonding, charge transfer, and element-specific d-1s hybridization, all of which influence interstitial site preference and hydride stability. Mechanically, DFT-derived elastic moduli (B, G, E), Pugh's ratio, and dislocation energy factors show that hydrogen can strengthen or soften HEAs, depending on concentration, phase, and local lattice distortion, influence powder hydride decrepitation and cycle durability. Thermodynamically, hydrogen binding energy emerges as the critical descriptor, requiring an ideal intermediate binding strength for reversible absorption and desorption. DFT exhibits heterogeneous hydrogen transport regulated by competing lattice distortion (trapping) and lattice expansion (enhanced pathways), resulting in diffusion composition and phase dependence. DFT's strengths include unmatched atomistic insight, but it is limited by exchange-correlation approximations, neglect of short-range order, incomplete entropy/zero-point effects, static 0 K models, and high computing cost. Future advancement will be made by combining DFT and machine learning for high-throughput screening in rational HEA design. • Provides the first unified DFT framework for HEA hydrogen-storage mechanisms. • Identifies electronic, thermodynamic, and mechanical descriptors governing H behaviour. • Clarifies hydrogen-induced phase transitions and diffusion in complex HEAs. • Evaluates DFT limitations and required advances for accurate HEA hydride modelling. • Outlines DFT–ML pathways for rational design of high-performance HEA hydrides. Density functional theory ; Hydride ; Hydrogen ; Lattice (music) ; Diffusion ; Binding energy ; Hydrogen storage Hydrogen Storage and Materials ; High Entropy Alloys Studies ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Mrinmoy Chakraborty
Source: Open MIND (preprint, 2026-03-08)
Field: Materials Science — Materials Chemistry

CL5D HYBRID MODEL: A Multi-Phase Deterministic-Mathematical Framework for Next-Generation Hybrid Artificial Intelligence. CL5D is a novel hybrid artificial intelligence framework that fundamentally departs from the statistical-probabilistic paradigm underlying current machine learning architectures. Rather than operating exclusively at the macro level of aggregate statistical distributions, CL5D captures phenomena across the full spectrum from macro events to micro events simultaneously. The framework employs a sequential multi-agent pipeline grounded in pure mathematics: Entropy theory (At Agent), Fractal Geometry (Ab Agent), Harmonic Progression with variable region arrays (Ex Agent), and Permutation–Gamma–Valence scoring (T Agent). Each agent operates on a unified scale of 0 to 1 with a standardized five-band scorecard. The final output is expressed as a Convergence Score (Cn score) — a deterministic mathematical distance measure rather than a probabilistic confidence estimate. CL5D operates across four phases of increasing mathematical refinement. Phase I and Phase II are universal. Phase III, representing mathematical singularity (0 = ∞), applies only to specialized domains exhibiting regeneration, cosmological behavior, gene expression dynamics, or emergent systemic events. Phase IV is the natural re-emergence from singularity via quantum tunneling, returning the system to Phase I at higher refinement. A critical hardware innovation embedded in this framework is the specification of mathematical formulas for direct GPU/CPU embedding — enabling pure CL5D execution that bypasses conventional code execution entirely, dramatically reducing latency and computational overhead in deployment. Version 2.2 introduces the Phase IV Quantum Tunneling Weight equation and provides a successful validation case study using a high-energy N₁₂ molecular cluster, demonstrating the detection of a deterministic Hysteresis Coefficient (η). This report presents the complete formal algorithm, agent scorecards, mathematical proofs of micro-event capture, detailed hardware embedding specifications, and the refined mathematical foundations including the CL5D-Nyquist Granularity Constant and the Fusion Equation for Phase III singularity. Probabilistic logic ; Singularity ; Embedding ; Quantum ; Statistical model ; Fractal ; Macro ; Entropy (arrow of time) ; Quantum annealing Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Big Data and Digital Economy
Authors: Mrinmoy Chakraborty
Source: Zenodo (CERN European Organization for Nuclear Research) (preprint, 2026-03-08)
Field: Materials Science — Materials Chemistry

CL5D HYBRID MODEL: A Multi-Phase Deterministic-Mathematical Framework for Next-Generation Hybrid Artificial Intelligence. CL5D is a novel hybrid artificial intelligence framework that fundamentally departs from the statistical-probabilistic paradigm underlying current machine learning architectures. Rather than operating exclusively at the macro level of aggregate statistical distributions, CL5D captures phenomena across the full spectrum from macro events to micro events simultaneously. The framework employs a sequential multi-agent pipeline grounded in pure mathematics: Entropy theory (At Agent), Fractal Geometry (Ab Agent), Harmonic Progression with variable region arrays (Ex Agent), and Permutation–Gamma–Valence scoring (T Agent). Each agent operates on a unified scale of 0 to 1 with a standardized five-band scorecard. The final output is expressed as a Convergence Score (Cn score) — a deterministic mathematical distance measure rather than a probabilistic confidence estimate. CL5D operates across four phases of increasing mathematical refinement. Phase I and Phase II are universal. Phase III, representing mathematical singularity (0 = ∞), applies only to specialized domains exhibiting regeneration, cosmological behavior, gene expression dynamics, or emergent systemic events. Phase IV is the natural re-emergence from singularity via quantum tunneling, returning the system to Phase I at higher refinement. A critical hardware innovation embedded in this framework is the specification of mathematical formulas for direct GPU/CPU embedding — enabling pure CL5D execution that bypasses conventional code execution entirely, dramatically reducing latency and computational overhead in deployment. Version 2.2 introduces the Phase IV Quantum Tunneling Weight equation and provides a successful validation case study using a high-energy N₁₂ molecular cluster, demonstrating the detection of a deterministic Hysteresis Coefficient (η). This report presents the complete formal algorithm, agent scorecards, mathematical proofs of micro-event capture, detailed hardware embedding specifications, and the refined mathematical foundations including the CL5D-Nyquist Granularity Constant and the Fusion Equation for Phase III singularity. Probabilistic logic ; Singularity ; Embedding ; Quantum ; Statistical model ; Fractal ; Macro ; Entropy (arrow of time) ; Quantum annealing Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Big Data and Digital Economy
Authors: Xinyu Li | Menghan Wang | Yuxin Si | Zuoquan Zhu
Source: Science and Engineering (article, 2026-02-06)
Field: Materials Science — Materials Chemistry

Efficient Solution of Allen-Cahn Equation Considering Phase Boundary Thickness Variation Driven by Physics-Informed Neural Networks This study focuses on solving the Allen-Cahn (AC) equation with consideration of varying phase boundary thickness using Physics-Informed Neural Networks (PINN). Traditional numerical methods often exhibit declining computational efficiency and accuracy as problem complexity increases when handling complex phase boundary conditions; PINNs offer a novel approach to addressing such challenges. The study first conducts a detailed derivation of the AC equation and constructs a PINN-based solution model, including three key steps: designing a specific neural network architecture; building a loss function composed of partial differential equation (PDE) residual terms, boundary condition residual terms, and initial condition residual terms; and performing model training. The computational results demonstrate that the established PINN model can effectively solve the AC equation with varying phase boundary thicknesses, accurately revealing the distribution patterns of the order parameter under different phase boundary thicknesses, and adapt to the challenges brought by changes in phase boundary thickness by adjusting the number of iterations to achieve stable and efficient numerical solutions. The phase boundary thickness parameter has a significant impact on order parameter distribution, computational stability, and convergence. Additionally, a discussion is conducted between PINN and the finite difference method. This research provides support for in-depth understanding of the physical connotation of the AC equation and optimization of numerical calculation methods, and also lays a foundation for the application of PINN in solving similar complex problems in fields such as materials science and condensed matter physics. Future work can further expand the application of PINN in high-dimensional phase-field models and multi-physics coupling problems, combine other technologies to improve the accuracy and efficiency of phase-field simulations, and carry out more studies on practical application cases. 本研究聚焦于物理信息神经网络（PINN）求解不同相界厚度变化的Allen-Cahn（AC）方程。传统数值方法在处理复杂相边界条件时，其计算效率与精确度常随问题复杂度而下降；PINN为解决此类问题提供了新途径。本研究首先详细推导了AC方程，进而构建了基于PINN的求解模型，包括设计特定的神经网络架构、构建由偏微分方程残差项、边界条件残差项和初始条件残差项所组成的损失函数，并进行模型训练。计算结果表明，所建立的PINN模型能有效求解不同相界厚度变化的AC方程，准确揭示序参数在不同相界厚度下的分布规律，且能通过改变迭代次数适应相界厚度变化带来的挑战，实现稳定高效的数值求解。此外，将PINN计算结果与有限差分法进行了对比。本研究为深入理解AC方程物理内涵、优化数值计算方法提供支持，也为PINN在材料科学、凝聚态物理等领域解决类似复杂问题奠定基础。未来可进一步拓展PINN在高维相场模型、多物理场耦合问题中的应用，结合其他技术提高相场模拟精确度和效率，并开展更多实际应用案例研究。 Boundary value problem ; Boundary (topology) ; Partial differential equation ; Residual ; Artificial neural network ; Phase (matter) ; Differential equation ; Finite element method ; Function (biology) Solidification and crystal growth phenomena ; Machine Learning in Materials Science ; Model Reduction and Neural Networks
Authors: Shruti Varma | Ajay L. Vishwakarma | M. R. Sonawane | Ajay Chaudhari
Source: Food Analytical Methods (article, 2026-02-01)
Field: Chemistry — Organic Chemistry

FTIR Spectroscopy Coupled with Machine Learning for Assessment of Dielectric and Physicochemical Properties of Pure and Adulterated Coconut and Sesame Oils Partial least squares regression ; Coconut oil ; Linoleic acid ; Fourier transform infrared spectroscopy ; Iodine value ; Dielectric ; Palmitic acid ; Principal component analysis ; Oleic acid ; Chemometrics Edible Oils Quality and Analysis ; Spectroscopy and Chemometric Analyses ; Biodiesel Production and Applications
Authors: Ty Balduf | Philip A. Gerken | Mee Shelley | Mark A. Watson | M. Chandler Bennett | Mats Svensson | Abba E. Leffler | Art Bochevarov
Source: Journal of Chemical Information and Modeling (article, 2026-01-16)
Field: Materials Science — Materials Chemistry

Prediction of Atropisomerism for Drug-like Molecules A multistep computational workflow that accurately assigns organic drug-like molecules to one of three atropisomer classes on the basis of computed barrier heights has been developed. The workflow identifies rotatable bonds and applies progressively more accurate types of calculations to the eligible rotational degrees of freedom. An initial energy scan with a force field (OPLS4) is followed by a similar scan that uses an energy function driven by a neural network model (QRNN-TB) trained on density functional theory (DFT) energies. The maxima corresponding to the potentially stereogenic rotatable bonds identified at this point are further processed by applying a transition state search at the QRNN-TB level of theory. Finally, ωB97X-D3/def2-TZVP(-f) DFT energies are computed for all located extrema. The accuracy of the predicted rotational barriers was benchmarked against ωB97M-V/cc-pVTZ and DLPNO-CCSD(T)/def2-TZVPP energies with excellent correlations. The automated protocol classifies organic molecules into atropisomeric classes with a greater than 90% success rate when applied to a test set of 65 molecules containing rotationally restricted torsions (68 torsions in total). We anticipate that the balance of speed and accuracy in this method will make it conducive to production use in drug discovery programs. Atropisomer ; Molecule ; Force field (fiction) ; Stereocenter ; Field (mathematics) ; Set (abstract data type) ; Workflow ; Density functional theory ; Basis (linear algebra) ; Maxima Machine Learning in Materials Science ; Axial and Atropisomeric Chirality Synthesis ; Cyclization and Aryne Chemistry
Authors: Qi Zhang | Sicong Wan | Lei Wang
Source: arXiv (Cornell University) (preprint, 2026-03-10)
Field: Materials Science — Materials Chemistry

Ab initio simulation of the first-order proton-ordering transition in water ice Proton ordering in water ice is a paradigmatic order-disorder transition in a locally constrained system. The ice rules require exactly two hydrogens close to each oxygen, restricting the disorder to an exponentially large yet strongly correlated manifold of hydrogen-bond configurations. Within this constrained space, meV-scale energy differences drive the transition from disordered ice Ih to ordered ice XI, while distinct configurations are separated by eV-scale barriers. These barriers hinder equilibration in experiments, and efficient sampling of this space with the required energy accuracy has remained a long-standing challenge in simulation. We address this by combining a machine learning interatomic potential with loop updates that preserve the ice rules and continuous updates of atomic coordinates, enabling equilibrium sampling with ab initio accuracy and capturing configurational entropic effects. In systems of up to 360 water molecules, with over 10^6 samples retained per temperature point, the simulations reveal clear first-order transition signatures at 83 K: a negative Binder cumulant, a bimodal potential energy distribution, and a sharp step in the lattice aspect ratio. Nuclear quantum effects are estimated to lower the transition temperature by approximately 20 K, bringing the prediction closer to the experimental value of 72 K. Ab initio ; Ab initio quantum chemistry methods ; Lattice (music) ; Interatomic potential ; Potential energy ; Sampling (signal processing) ; Space (punctuation) ; Molecular dynamics ; Quantum Material Dynamics and Properties ; Quantum, superfluid, helium dynamics ; Spectroscopy and Quantum Chemical Studies