jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Abdou Wahidi Bello | Jamal Adetola | Said Amana ABDILLAH | Charbel Mamlankou
Source: International Journal of Numerical Methods and Applications (article, 2025-10-29)
Field: Materials Science — Materials Chemistry

CONSTRAINED NEURAL NETWORKS APPROACH OF THE CAHN-HILLIARD EQUATION WITH -DEPENDENT MOBILITY This paper presents a comparative study of numerical methods for the Cahn-Hilliard equation with concentration dependent mobility, contrasting a classical finite element approach with a modern deep learning framework. We introduce a constrained Physics-Informed Neural Network (c-PINNs), which directly incorporates the physical bounds of the concentration field into the learning process. Our results demonstrate that this approach not only captures the complex dynamics of phase separation with high visual fidelity but also achieves strong quantitative agreement with a high fidelity finite element reference solution. The c-PINNs method adeptly handles the high order spatial derivatives inherent to the problem via automatic differentiation, bypassing the complexities of traditional discretization. While the initial training phase is computationally intensive, the resulting model acts as a fast and accurate surrogate, capable of instantaneous predictions. This work highlights the potential of constrained neural networks as a robust and flexible alternative for simulating complex physical phenomena, paving the way for more efficient exploration of phase separation dynamics in materials science and beyond. Artificial neural network ; Fidelity ; Finite element method ; High fidelity ; Phase (matter) ; Field (mathematics) ; Representation (politics) ; Work (physics) Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Block Copolymer Self-Assembly
Authors: Xin Li | Meng Xu | Tao Zheng | Chen Sun | Xin Lan | Jun Hu | Zechuan Yu
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Fast Probe of Hydrogen Molecules Binding on Alpha-Iron Surface Via a Machine-Learning Method and High-Throughput Molecular Dynamics Simulations Throughput ; Alpha (finance) ; Molecular dynamics ; Molecule ; Dynamics (music) ; Hydrogen molecule ; Hydrogen ; Surface (topology) ; Chemistry ; Nanotechnology ; Chemical physics ; Biophysics ; Biological system ; Materials science ; Computer science ; Computational chemistry ; Physics ; Biology ; Telecommunications ; Organic chemistry ; Mathematics Machine Learning in Materials Science ; Nuclear Physics and Applications ; Advanced Chemical Physics Studies
Authors: Anna C. M. Thöni | William E. Robinson | Yoram Bachrach | Wilhelm T. S. Huck | Tal Kachman
Source: arXiv (Cornell University) (preprint, 2025-02-11)
Field: Materials Science — Materials Chemistry

Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations In chemical reaction network theory, ordinary differential equations are used to model the temporal change of chemical species concentration. As the functional form of these ordinary differential equations systems is derived from an empirical model of the reaction network, it may be incomplete. Our approach aims to elucidate these hidden insights in the reaction network by combining dynamic modelling with deep learning in the form of neural ordinary differential equations. Our contributions not only help to identify the shortcomings of existing empirical models but also assist the design of future reaction networks. Ordinary differential equation ; Artificial neural network ; Differential equation ; Chemical reaction ; Differential (mechanical device) ; Delay differential equation ; Differential algebraic equation Machine Learning in Materials Science ; Gene Regulatory Network Analysis ; Model Reduction and Neural Networks
Authors: Raghav Dangayach (14625334) | Nohyeong Jeong (10290883) | Yongsheng Chen (632502)
Source: Figshare (article, 2025-10-29)
Field: Materials Science — Materials Chemistry

Machine LearningAnalysis and Monomer Screening ofPolyamide Nanofiltration Membranes for Lithium Separation Nanofiltration (NF) membranes are increasingly being used to achieve precise solute–solute separation. These membranes are commonly synthesized using interfacial polymerization, offering great potential to separate lithium from magnesium. In this study, we have developed machine learning models that relate fabrication conditions, membrane properties, and operational conditions of NF membranes to predict water permeability and lithium/magnesium selectivity. Morgan fingerprints (MFs) and molecular descriptors (MDs) are used to represent the chemical and physical properties of the monomers. Explainable artificial intelligence tools such as Shapley additive explanations (SHAP) and partial dependence plots are used to evaluate the effects of the synthesis conditions and membrane properties on membrane performance. Based on the insights obtained from SHAP analysis, we developed a material screening approach to find promising monomers from a list of amines and cation-based ionic liquids. We construct a reference MF using the functional groups that positively contribute to membrane performance and compute a screening score that favors potential candidates with more desirable MDs. Finally, the synthesizability of these monomers is assessed using the synthetic accessibility score to find the most promising candidates. We compared the performance of screened monomers against traditional ones to validate the reliability of our approach. The results of this study provide critical insights into the relationships between synthesis conditions, membrane properties, and performance and establishes a novel, strategic framework for rational screening of monomers for NF membrane synthesis. This approach holds promise to accelerate the discovery of high-performance membranes tailored for specific separation challenges, thereby advancing the field of membrane technology. Membrane ; Nanofiltration ; Monomer ; Polyamide ; Synthetic membrane ; Molecular descriptor Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Membrane Separation Technologies
Authors: Xinhu Sha (21175310) | Zhuo Chen (135585) | Daiqian Xie (1313829) | Yanzi Zhou (1292469)
Source: Figshare (article, 2025-04-24)
Field: Materials Science — Materials Chemistry

Modeling EnzymeReaction and Mutation by Direct MachineLearning/Molecular Mechanics Simulations Accurately modeling enzyme reactions through direct machine learning/molecular mechanics simulations remains challenging in describing the electrostatic coupling between the QM and MM subsystems. In this work, we proposed a reweighting ME (mechanic embedding) REANN (recursively embedded atom neural network) method that trains the potential and point charges of the QM subsystem in vacuo. The charge equilibration approach has been encoded into REANN to ensure conservation of the total charge of the QM subsystem. Electrostatic coupling is measured by point charges, and the polarization of the MM subsystem on the coupling can be corrected by thermodynamic perturbation after molecular dynamics simulations. We first constructed the REANN surfaces of potential energy and charges for the acylation of cyclooxygenase-1 (COX-1) and cyclooxygenase-2 (COX-2) by aspirin. These surfaces allowed us to reproduce the free energy curves of B3LYP/MM-MD with a chemical accuracy. Subsequently, they were successfully applied to R513A of COX-2, reproducing the free energy barrier simulated by B3LYP/MM MD with a difference of less than 0.5 kcal mol<sup>–1</sup> and a speedup of 80-fold, revealing our method can predict the activity of mutants accurately and rapidly. This method is expected to be applied in virtual screening in the future. Molecular dynamics ; Molecular mechanics ; Coupling (piping) ; QM/MM ; Thermodynamic integration ; Potential energy ; Free energy perturbation ; Charge (physics) ; Kinetic energy Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Molecular Junctions and Nanostructures
Authors: Zhiduo Liu | Haoyu Zhang | Linfan Sun | Chuan Wang | Ge Zhou | Ximin Zang | Lijia Chen
Source: Materials & Design (article, 2025-12-01)
Field: Materials Science — Materials Chemistry

A machine learning multitasking integrated framework and its derived [Mo]eq-[Fe]eq map for designing TRIP titanium alloys • A high-precision multitasking integrated framework for TRIP titanium alloy design was developed. • The integrated framework is interpretability, enabling the identification of key influencing factors for the TRIP effect. • First proposed, the [Mo] eq -[Fe] eq map enables quick judgement of TRIP effect occurrence in titanium alloys. To enable rapid and accurate design of TRIP titanium alloys, an integrated multitasking framework based on the Stacking algorithm was developed, for the first time integrating the Sparrow Search Algorithm (SSA) to optimize hyperparameters in the framework. The SSA-Stacking framework exhibited satisfactory predictive accuracy on the test set and outperformed traditional base models. A TRIP titanium alloy Ti-5Mo-5 V-3Cr-3Al-2Nb (wt. %) was designed based on the SSA-Stacking framework. It was experimentally verified that the TRIP effect occurred after the alloy was solution-treated at 870 °C for 0.5 h, which in turn achieved a good strength-plasticity matching with a tensile strength of 864 MPa, a yield strength of 748 MPa, and an elongation of 28 %. To provide researchers with a more convenient semi-empirical method for designing TRIP titanium alloys, a [Mo] eq -[Fe] eq map was constructed based on the dataset, SSA-Stacking framework, and SHAP analysis. Researchers can directly utilize the [Mo] eq -[Fe] eq map established in this study to rapidly determine whether the TRIP effect can occur. The [Mo] eq -[Fe] eq map demonstrates higher accuracy than the traditional semi-empirical design methods. Further combined with interaction analysis between [Mo] eq , [Fe] eq , and solution temperature, this study provides quantitative guidance for optimizing the chemical composition and heat treatment processes in TRIP titanium alloy development. Human multitasking ; Hyperparameter ; Titanium ; Titanium alloy ; Set (abstract data type) ; Matching (statistics) Titanium Alloys Microstructure and Properties ; Additive Manufacturing Materials and Processes ; Machine Learning in Materials Science
Authors: Yuxiang Gao | Zhicheng Zhong
Source: ArXiv.org (preprint, 2025-08-09)
Field: Materials Science — Materials Chemistry

Mechanism of Anisotropic Crystallization and Phase Transitions under Van der Waals Squeezing Mechanical confinement strategies, such as van der Waals (vdW) squeezing, have emerged as promising routes for synthesizing non-vdW two-dimensional (2D) layers, surprisingly yielding high-quality single crystals with lateral sizes approaching 100 micrometer. However, the underlying mechanisms by which such a straightforward approach overcomes the long-standing synthesis challenges of non-vdW 2D materials remains a puzzle. Here, we investigate the crystallization dynamics and phase evolution of Bi under vdW confinement through molecular dynamics (MD) simulations powered by a machine-learning force filed fine-tuned and distilled from a pre-trained model with DFT-level accuracy. We reveal that pressure-dependent layer modulation arises from a quantum confinement-driven anisotropic crystallization mechanism, in which out-of-plane layering occurs nearly two orders of magnitude faster than in-plane ordering. Two critical transitions are identified: an alpha-to-beta phase transformation at 1.64 GPa, and a subsequent collapse into a single-atomic layer at 2.19 GPa. The formation of large-area single crystals is enabled by substrate-induced orientational selection and accelerated grain boundary migration, driven by atomic diffusion at elevated temperatures. These findings resolve the mechanistic origin of high-quality 2D crystal growth under confinement and establish guiding principles for the controlled synthesis of metastable 2D single crystals, with implications for next-generation quantum and nanoelectronic devices. van der Waals force ; Metastability ; Crystallization ; Anisotropy ; Phase transition ; Phase (matter) ; Crystal (programming language) ; Quantum 2D Materials and Applications ; Topological Materials and Phenomena ; Graphene research and applications
Authors: Quentin Bizot | N. Jakse
Source: arXiv (Cornell University) (preprint, 2025-06-10)
Field: Materials Science — Materials Chemistry

Crystal Nucleation in Eutectic Al-Si Alloys by Machine-Learned Molecular Dynamics Solidification control is crucial in manufacturing technologies, as it determines the microstructure and, consequently, the performance of the final product. Investigating the mechanisms occurring during the early stages of nucleation remains experimentally challenging as it initiates on nanometer length and sub-picoseconds time scales. Large scale molecular dynamics simulations using machine learning interatomic potential with quantum accuracy appears the dedicated approach to complex, atomic level, multidimensional mechanisms with local symmetry breaking. A potential trained on a high-dimensional neural network on density functional theory-based ab initio molecular dynamics (AIMD) trajectories for liquid and undercooled states for Al-Si binary alloys enables us to study the nucleation mechanisms occurring at the early stages from the liquid phase near the eutectic composition. Our results indicate that nucleation starts with Al in hypoeutectic conditions and with Si in hypereutectic conditions. Whereas Al nuclei grow in a globular shape, Si ones grow with polygonal faceting, whose underlying mechanisms are further discussed. Nucleation ; Eutectic system ; Molecular dynamics ; Phase (matter) ; Interatomic potential ; Supercooling ; Microstructure ; Ab initio ; Crystal (programming language) Machine Learning in Materials Science ; Solidification and crystal growth phenomena ; Aluminum Alloy Microstructure Properties
Authors: Lalit Kaushik
Source: Materials Letters (article, 2025-12-30)
Field: Materials Science — Materials Chemistry

Lankford coefficient predictions using convolutional neural networks on images of sections of orientation distribution function Convolutional neural network ; Orientation (vector space) ; Representation (politics) ; Texture (cosmology) ; Pattern recognition (psychology) ; Pixel ; Function (biology) ; Artificial neural network ; Distribution function Machine Learning in Materials Science ; Microstructure and mechanical properties ; Powder Metallurgy Techniques and Materials
Authors: Pengwei Tao | Lin Lin | Mengzhi Han
Source:  (article, 2025-11-07)
Field: Materials Science — Materials Chemistry

PaiNN-LS-Elec: Machine Learning Force Field Integrating Linear Attention and Electronegativity Correction In the field of molecular dynamics simulation of machine learning force field, most of the existing methods focus on the study of neutral singlet structure particles and short-range interactions, while the study of non neutral singlet structure particles and long-range interactions still faces many challenges. Therefore, a new linear-sigmoid attention is proposed and introduced into the PaiNN(Polarizable Atom Interaction Neural Network) model to deal with the non neutral singlet structure particles and correct the long-range interaction force. Moreover, the linear attention takes into account both computational efficiency and feature diversity. In addition, from the point of view of physical chemistry, this paper also enhanced the expression ability of atomic local charge in the model, that is, the electronegativity of atoms was introduced as a priori knowledge to modulate the expression of atomic local charge, so as to further improve the expression ability of the model. The experimental results show that on the carbene and Ag3 datasets, the accuracy of the new linear attention is similar to that of softmax attention with high computational complexity, and the electronegativity correction further improves the performance of the model; On the carbon chain and Ag clusters datasets, compared with the existing machine learning force field, the proposed machine learning force field combining linear attention and electronegativity correction reduces the root mean square error (RMSE) of force by 6.27% and 64.2%, respectively. Electronegativity ; Softmax function ; Force field (fiction) ; Atom (system on chip) ; Field (mathematics) ; A priori and a posteriori Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Computational Drug Discovery Methods
Authors: Dhanalaxmi Ananthula | K Kishore | P. Usha Sri | Bala Narasimha Guniputi
Source: Journal of Polymer & Composites (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

AI-Assisted Defect Detection in Polymer Composite Insulators Using an Optimised Ensemble Deep Learning Framework for Structural Health Monitoring Structural health monitoring ; Deep learning ; Pattern recognition (psychology) ; Artificial neural network ; Ensemble learning Machine Learning in Materials Science ; High voltage insulation and dielectric phenomena ; Advanced Sensor and Energy Harvesting Materials
Authors: Sanghyun Lee
Source: Ceramist (article, 2025-06-30)
Field: Materials Science — Surfaces, Coatings and Films

Energy material characterization techniques with using hard X-ray absorption spectroscopy Hard X‑ray absorption spectroscopy (Hard XAS) offers element‑specific bulk sensitivity combined with sub‑second time resolution, making it an indispensable tool for next‑generation energy materials. This article outlines the fundamentals of X-ray Absorption Near Edge Structure and Extended X-ray Absorption Fine Structure, details a reproducible Athena–Artemis workflow for calibration, background subtraction, &lt;i&gt;χ&lt;/i&gt;(k) extraction, and FEFF‑based fitting, and highlights operando Hard XAS studies that unravel redox and structural dynamics in lithium‑ion battery electrodes and water‑splitting catalysts. Representative cases demonstrate how Hard XAS decouples capacity fade from voltage decay in Li‑rich layered oxides, stabilises Ruδ+ sites during high‑current acidic Oxygen evolution reaction, and guides interface engineering through real‑time redox mapping. Finally, we discuss forthcoming advances—including fourth‑generation synchrotrons, lab‑scale high‑harmonic sources and machine‑learning fitting engines—that will enable autonomous closed‑loop materials discovery. Characterization (materials science) ; Spectroscopy ; Materials science ; Absorption (acoustics) ; X-ray absorption spectroscopy ; Absorption spectroscopy ; X-ray ; Analytical Chemistry (journal) ; X-ray spectroscopy ; Optics ; Nanotechnology ; Physics ; Chemistry ; Composite material ; Environmental chemistry ; Astronomy Electron and X-Ray Spectroscopy Techniques ; Nuclear Physics and Applications ; X-ray Spectroscopy and Fluorescence Analysis
Authors: Rinske M. Alkemade | Rastko Sknepnek | Frank Smallenburg | Laura Filion
Source: The Journal of Chemical Physics (article, 2025-06-18)
Field: Materials Science — Materials Chemistry

Machine learning short-ranged many-body interactions in colloidal systems using descriptors based on Voronoi cells Machine learning (ML) strategies are opening the door to faster computer simulations, allowing us to simulate more realistic colloidal systems. Since the interactions in colloidal systems are often highly many-body, stemming from, e.g., depletion and steric interactions, one of the challenges for these algorithms is capturing the many-body nature of these interactions. In this paper, we introduce a new ML-based strategy for fitting many-body interactions in colloidal systems where the many-body interaction is highly local. To this end, we develop Voronoi-based descriptors for capturing the local environment and fit the effective potential using a simple neural network. To test this algorithm, we consider a simple two-dimensional model for a colloid-polymer mixture, where the colloid-colloid interactions and colloid-polymer interactions are hard-disk like, while the polymers themselves interact as ideal gas particles. We find that a Voronoi-based description is sufficient to accurately capture the many-body nature of this system. Moreover, we find that the Pearson correlation function alone is insufficient to determine the predictive power of the network emphasizing the importance of additional metrics when assessing the quality of ML-based potentials. Voronoi diagram ; Colloid ; Computer science ; Artificial neural network ; Biological system ; Colloidal particle ; Simple (philosophy) ; Artificial intelligence ; Statistical physics ; Machine learning ; Mathematics ; Physics ; Chemistry ; Geometry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Phase Equilibria and Thermodynamics
Authors: Shinya Fujiwara | Machiko Ode | Yuta Kojima | Mayu Muramatsu
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Measurement of Secondary Dendrite Arm Spacing Using Deep Learning Dendrite (mathematics) ; Deep learning ; Noise (video) ; Computation ; Pattern recognition (psychology) ; Key (lock) Solidification and crystal growth phenomena ; Machine Learning in Materials Science ; Metallurgical Processes and Thermodynamics
Authors: Yu. G. Kabaldin
Source: Russian Engineering Research (article, 2025-06-01)
Field: Materials Science — General Materials Science

Digital Materials Science: Prediction of Structure and Mechanical Properties of New Materials The article considers the fundamentals of control of the structure of new metals based on iron, aluminum, etc. synthesized by melting using conventional technologies and technologies that employ quantum-mechanical calculations of the alloying chemical element. Optimal compositions, average grain sizes, and structural stability of new metals are determined using neural networks, employing fractal analysis and nonlinear dynamics. A digital twin of the target metal is developed, including a series of twins. The results of predicting the structure and mechanical properties of new materials according to the developed methodology are presented. Materials science ; Engineering design process ; Mechanical engineering ; Engineering Material Properties and Applications
Authors: N. A. Shymanski | А. В. Баглов | Kh. B. Musaev | Olim Ruzimuradov | Л. С. Хорошко
Source: Репозиторий БГУИР (BSUIR Repository) (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Express Analysis of the Structural and Electronic Properties of Nanomaterials Using Big Data, Large Language Models and Generative Artificial Intelligence The possibility of using large language models, generative machine learning and big data methods for predictive analysis of the electronic properties of nanostructures based on semiconductor materials is considered, without limiting the generality of this approach for other crystalline materials. The conceptual solution in the form of a cross-platform software application with an advanced search generation capability for applying this approach to working with scientific data arrays is described. Preliminary test results showed a positive result of using the developed solution to predict semiconductor properties (bandgap width, Fermi energy) of a reference material. Prospects for the development and implementation of big data methods, large language models, and generative artificial intelligence in the context of modern trends in materials science are discussed. Generative grammar ; Generality ; Computer science ; Context (archaeology) ; Big data ; Artificial intelligence ; Generative model ; Data mining Machine Learning in Materials Science