jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sourav Sarkar | Nepal Sahu | Mohammed Hasan Ali | Chandrashekhar Azad | Uday Kumar
Source:  (article, 2025-06-11)
Field: Materials Science — Materials Chemistry

Interpretable machine learning approach for prediction of the vibrational free energy of materials The vibrational free energy (F<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">vib</inf>) of the material is one of the significant parameters to explain the thermal stability of materials. The development of highly accurate machine learning (ML) models is an essential platform for explaining the thermal stability of materials. In this study, we used a dataset of 1357 records and employed four ML models (GBR, LR, RR and GPR) to predict F<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">vib</inf>. Furthermore, the SHAP technique was applied for the interpretation of the best performing model. The performance of all the models was achieved in terms of R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ≥ 0.999. Out of the four models, GBR was the best performer with R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.99978. This result was better than the highest reported accuracy of R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.9955. In the SHAP analysis, S<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">vib</inf> (vibrational entropy) was the most contributing feature among all the input features in the predictive output of the GBR model. Energy (signal processing) ; Computer science ; Machine learning ; Artificial intelligence ; Mathematics ; Statistics Machine Learning in Materials Science ; Neural Networks and Applications ; Mineral Processing and Grinding
Authors: Mustafa Dağ | Ercan Aydoğmuş | Zehra Gülten Yalçın | Hasan Arslanoğlu
Source: Polymer Engineering and Science (article, 2025-11-09)
Field: Materials Science — Materials Chemistry

Valorization of Industrial Waste in Polymer Composites: Enhancing Mechanical and Thermal Properties for Insulation Applications Using Machine Learning Analysis ABSTRACT This study investigates the incorporation of industrial waste materials into polyurethane‐based composites and evaluates their mechanical, thermal, and microstructural properties. The polyurethane matrix was synthesized from methylene diphenyl diisocyanate (MDI) and polyether polyol, into which various waste fillers, including ulexite, colemanite, tincal, and Kırka clay, were introduced in different proportions. Mechanical testing revealed that specific wastes significantly enhance compressive strength, with ulexite‐ and clay‐reinforced composites achieving improvements of 42.19% and 43.54%, respectively, compared to the pure polymer. The ulexite–clay composite exhibited the highest mechanical strength (38.67 kN), whereas tincal‐containing samples demonstrated the weakest performance. Shore A hardness values generally decreased with waste incorporation, indicating that filler addition reduces polymer rigidity. Thermal conductivity results showed property variations within ±25%, where ulexite increased conductivity while Kırka clay reduced it, thereby improving thermal insulation potential. Microstructural analysis using scanning electron microscopy (SEM) confirmed heterogeneous morphologies with dense filler distribution that intensified with increasing filler ratios. Fourier transform infrared spectroscopy (FTIR) indicated both physical and chemical interactions between the polymer matrix and boron‐containing fillers, highlighting the complex interfacial bonding mechanisms. To complement the experimental analyses, machine learning (ML) models were applied to predict composite performance based on waste type and ratio. Among the tested algorithms, Random Forest (RF) demonstrated the highest predictive accuracy (R 2 &gt; 0.90), confirming its suitability for modeling composite properties. The integration of ML provided quantitative insights into the role of individual and combined waste fillers, aligning closely with experimental observations. This research demonstrates that the controlled selection and optimization of waste fillers can enhance the performance of polyurethane composites, promote recycling of industrial byproducts, and support the development of sustainable materials for applications such as thermal insulation and structural components. Filler (materials) ; Fourier transform infrared spectroscopy ; Polymer ; Shore durometer ; Polyurethane ; Composite number ; Compressive strength ; Thermal conductivity Thermal properties of materials ; Polymer Nanocomposites and Properties ; Smart Materials for Construction
Authors: А. А. Соловых | Nikita Rybin | Ivan S. Novikov | Alexander V. Shapeev
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Path-Integral Molecular Dynamics with Actively-Trained and Universal Machine Learning Force Fields Dynamics (music) ; Path integral formulation ; Path (computing) ; Molecular dynamics ; Computer science ; Force field (fiction) ; Classical mechanics ; Statistical physics ; Artificial intelligence ; Physics ; Quantum mechanics ; Acoustics Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Protein Structure and Dynamics
Authors: Pengcheng Guo | Hanfeng Song | Yunhui Tang | Bing Wang | Bo Wang
Source: Journal of Intelligent Manufacturing (article, 2025-08-02)
Field: Materials Science — Materials Chemistry

Application of physics-informed neural network to calculate heat transfer and deuterium diffusion-trapping in tungsten plasma facing materials Tungsten ; Trapping ; Deuterium ; Plasma ; Diffusion ; Heat transfer ; Materials science ; Nuclear engineering ; Nuclear physics ; Statistical physics ; Physics ; Mechanics ; Thermodynamics ; Engineering ; Metallurgy Fusion materials and technologies ; Nuclear Materials and Properties ; Nuclear Engineering Thermal-Hydraulics
Authors: Jiaxian Han | Baoqiang Xu | Yun Lei | Feng Hui | Yongbing He | ShunJin J. Wang | Jicai Liu | Shiqiang Li | Kangzhong Tang
Source: ACS Omega (article, 2025-12-22)
Field: Materials Science — Materials Chemistry

Investigating the Growth of GaSb Single Crystals through Optimized LEC Method Utilizing Finite Element Simulation and Machine Learning Techniques In this study, CGsim and liquid-encapsulated Czochralski (LEC) growth experiments were employed to handle the challenges associated with growing large-sized compound semiconductor single crystals. CGsim, a simulation software integrating the finite element method with machine learning (ML) techniques, was utilized to optimize the heat flux and crystallization front morphology at the solid-liquid interface during GaSb crystal growth. ML validation, performed across various crucible rotation speeds and crystal position (CP) configurations, enabled the optimization of the moving front shape at the solid-liquid interface, reducing the protrusion angle to 0.086°. The crystal quality of GaSb single crystal slices was evaluated through X-ray double crystal rocking curves and optical microscopy. The results indicate that the optimized growth parameters reduced the dislocation density in 6-in. GaSb single crystals from 1039 to 369 cm<sup>-2</sup> and narrowed the X-ray rocking curve full width at half-maximum (fwhm) from 29 to 28.5 arcsec. The steady-state/unsteady-state simulations conducted in CGsim, combined with ML-optimized growth parameters, significantly lowered the likelihood of defect formation, involving dislocation clustering, vacancy defects, twins, undercooling striations, and small-angle grain boundaries. Dislocation ; Crucible (geodemography) ; Crystal (programming language) ; Finite element method ; Supercooling ; Crystal growth ; Rotation (mathematics) ; Crystallization Solidification and crystal growth phenomena ; Machine Learning in Materials Science ; Semiconductor Quantum Structures and Devices
Authors: Motmans, Brent | Ghogare, Digvijay | van Wijk, Thijs G. I. | Van Herck, Joren | De Meyer, Pieter | Smit, Berend | Hardy, An | Vanpoucke, Danny E. P.
Source: arXiv (Cornell University) (preprint, 2025-12-18)
Field: Materials Science — Materials Chemistry

Predictive Inorganic Synthesis based on Machine Learning using Small Data sets: a case study of size-controlled Cu Nanoparticles Copper nanoparticles (Cu NPs) have a broad applicability, yet their synthesis is sensitive to subtle changes in reaction parameters. This sensitivity, combined with the time- and resource-intensive nature of experimental optimization, poses a major challenge in achieving reproducible and size-controlled synthesis. While Machine Learning (ML) shows promise in materials research, its application is often limited by scarcity of large high-quality experimental data sets. This study explores ML to predict the size of Cu NPs from microwave-assisted polyol synthesis using a small data set of 25 in-house performed syntheses. Latin Hypercube Sampling is used to efficiently cover the parameter space while creating the experimental data set. Ensemble regression models successfully predict particle sizes with high accuracy ($R^2 = 0.74$), outperforming classical statistical approaches ($R^2 = 0.60$). Additionally, classification models using both random forests and Large Language Models (LLMs) are evaluated to distinguish between large and small particles. While random forests show moderate performance, LLMs offer no significant advantages under data-scarce conditions. Overall, this study demonstrates that carefully curated small data sets, paired with robust classical ML, can effectively predict the synthesis of Cu NPs and highlights that for lab-scale studies, complex models like LLMs may offer limited benefit over simpler techniques. Small data ; Random forest ; Set (abstract data type) ; Nanoparticle ; Data set ; Latin hypercube sampling ; Regression ; Ensemble learning ; Scarcity Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Computational Drug Discovery Methods
Authors: Hao Dong | Yang Lou | Jiale Linghu | Yufeng Nie | Junzhi Cui
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

A wavelet-neural network method for efficient and high-accuracy prediction of thermal responses in random composite materials Finite element method ; Curse of dimensionality ; Artificial neural network ; Residual ; Noise (video) ; Gaussian ; Random field ; Range (aeronautics) ; Thermal Thermal properties of materials ; Composite Material Mechanics ; Epoxy Resin Curing Processes
Authors: Daniel Kaplan | Alexander C. Tyner | Eva Y. Andrei | J. H. Pixley
Source: arXiv (Cornell University) (preprint, 2025-12-18)
Field: Materials Science — Materials Chemistry

Machine learning assisted high throughput prediction of moiré materials The world of 2D materials is rapidly expanding with new discoveries of stackable and twistable layered systems composed of lattices of different symmetries, orbital character, and structural motifs. Often, however, it is not clear a priori whether a pair of monolayers twisted at a small angle will exhibit correlated or interaction-driven phenomena. The computational cost to make accurate predictions of the single particle states is significant, as small twists require very large unit cells, easily encompassing 10,000 atoms, and therefore implementing a high throughput prediction has been out of reach. Here we show a path to overcome this challenge by introducing a machine learning (ML) based methodology that efficiently estimates the twisted interlayer tunneling at arbitrarily low twist angles through the local-configuration based approach that enables interpolating the local stacking for a range of twist angles using a random forest regression algorithm. We leverage the kernel polynomial method to compute the density of states (DOS) on large real space graphs by reconstructing a lattice model of the twisted bilayer with the ML fitted hoppings. For twisted bilayer graphene (TBG), we show the ability of the method to resolve the magic angle DOS at a substantial improvement in computational time. We use this new technique to scan through the database of stable 2D monolayers (MC2D) and reveal new twistable candidates across the five possible points groups in two-dimensions with a large DOS near the Fermi energy, with potentially exciting interacting physics to be probed in future experiments. Leverage (statistics) ; Stacking ; Twist ; Bilayer graphene ; Random forest ; Lattice (music) ; Kernel (algebra) ; A priori and a posteriori ; Computational complexity theory Graphene research and applications ; Machine Learning in Materials Science ; Topological Materials and Phenomena
Authors: Pratik De Sarkar | Harsh Sharma | K. K. Ghosh | Debashis De
Source:  (article, 2025-12-09)
Field: Materials Science — Materials Chemistry

Machine Learning-Based Prediction of Perovskite Solar Cell Material Suitability Under Varying Weather Conditions Peroskite Solar Cells(PSC) can generate electricity with the help of sunlight and they are very cheap to produce but very unstable in real life conditions. This paper suggests an Machine Learning based recommendation system for PSC materials under different weather conditions like temperature, humidity, UV light and PM2.5 pollution. We used Random Forest Classifier that adapt on a synthetic dataset and based on it cagtogories their performance as “poor”, “moderate” and “good”. To make the output more reliable and stop it from overfitting it uses cross validation and cheaks the answers against simple models like Logistic Regression which shows linear method doesn't work well against complex patterns well. SHAP is used to make sure the predictions are easy to understand and explain. A simple web app made with Flask lets users enter their city see the weather data and get material suggestions that fit their local conditions. The results show that the Random Forest model reaches over 90% accuracy on made up data which proves that machine learning can help choose better PSC materials that last longer and work well in different environments. Perovskite (structure) ; Solar cell ; Temperature measurement ; Solar energy Machine Learning in Materials Science ; Perovskite Materials and Applications ; Solar Radiation and Photovoltaics
Authors: Freederia AI Researcher
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-08-18)
Field: Materials Science — Materials Chemistry

High-Throughput Screening of Al-Li Alloys via Automated Compositional Optimization and Microstructural Prediction Using Bayesian Neural Networks **Abstract:** This paper presents a novel framework for accelerating the discovery and optimization of aluminum-lithium (Al-Li) alloys for aerospace applications. Utilizing a closed-loop Bayesian Neural Network (BNN) predictive model, we automate the process of compositional design and microstructural prediction, dramatically reducing the time and resources required for traditional trial-and-error experimentation. The BNN model rapidly predicts the performance characteristics of Al-Li alloys based on their elemental composition, incorporating established thermodynamic and kinetic principles. Coupled with a Bayesian optimization algorithm, the framework iteratively refines alloy compositions to meet targeted mechanical strength and density requirements, circumventing the limitations of traditional alloy development methodologies. This approach promises a ten-fold reduction in experimental iterations, accelerating the introduction of high-performance Al-Li alloys into the aerospace industry. Bayesian optimization ; Artificial neural network ; Aerospace ; Process (computing) ; Bayesian probability ; Bayesian network Machine Learning in Materials Science ; Aluminum Alloy Microstructure Properties ; Metallurgy and Material Forming
Authors: Sebastien Röcken | Julija Zavadlav
Source: ArXiv.org (preprint, 2025-02-19)
Field: Materials Science — Materials Chemistry

Enhancing Machine Learning Potentials through Transfer Learning across Chemical Elements Machine Learning Potentials (MLPs) can enable simulations of ab initio accuracy at orders of magnitude lower computational cost. However, their effectiveness hinges on the availability of considerable datasets to ensure robust generalization across chemical space and thermodynamic conditions. The generation of such datasets can be labor-intensive, highlighting the need for innovative methods to train MLPs in data-scarce scenarios. Here, we introduce transfer learning of potential energy surfaces between chemically similar elements. Specifically, we leverage the trained MLP for silicon to initialize and expedite the training of an MLP for germanium. Utilizing classical force field and ab initio datasets, we demonstrate that transfer learning surpasses traditional training from scratch in force prediction, leading to more stable simulations and improved temperature transferability. These advantages become even more pronounced as the training dataset size decreases. The out-of-target property analysis shows that transfer learning leads to beneficial but sometimes adversarial effects. Our findings demonstrate that transfer learning across chemical elements is a promising technique for developing accurate and numerically stable MLPs, particularly in a data-scarce regime. Transfer of learning ; Computer science ; Artificial intelligence ; Chemistry Machine Learning in Materials Science
Authors: Kaihua Zhang (320124) | Yongzhi Ren (12290571) | Shuanhu Qi (20480234) | Ying Jiang (5910)
Source: Figshare (article, 2025-09-30)
Field: Materials Science — Materials Chemistry

IdentifyingMemory Effects in Short-Time Dynamicsof Polymer Systems by Machine Learning Memory effects are intrinsic properties of flexible macromolecular systems emerging originally from their hierarchical microstructures and multiscale dynamic behaviors of polymer chains during nonequilibrium processes. The nonlocal temporal dependence of transport coefficients (e.g., the Onsager coefficient), a fundamental manifestation of memory effects that govern the dynamic evolution of collective observables, still poses significant challenges for analytical formulations. Herein, we propose a machine-learning-based framework to extract the history-dependent Onsager coefficient directly from the density evolution data, generated from particle-based single-chain in mean-field simulations, thereby enabling these coefficients to accurately capture underlying microscopic characteristics. This framework, termed the dynamic density functional theory-ordinary differential equation (DDFT-ODE) Net, embeds equations of DDFT into the ODE network, enhancing the physical consistency and computational efficiency of the framework. Our study of the relaxation of density fluctuations in an ideal symmetric diblock copolymer melt reveals that memory effects play a dominant role in determining density evolution processes at early times. Further analysis of the memory kernel function of the Onsager coefficient identifies two characteristic relaxation times, which are both smaller than the Rouse time, a signature suggesting that memory effects are closely related to short-time segmental motions. By defining two types of time averages and comparing their asymptotic behaviors at the long-time limit, we establish connections linking the history-dependent Onsager coefficient, the instantaneous Onsager coefficient, and the memory-free one, which provide a theoretical foundation for quantitatively evaluating the reliability of various DDFT implementations. Our data-driven framework is easily extensible to other different systems, combining different particle-based simulations and continuum field-based DDFT schemes, thereby providing a useful framework for developing structure–property correlations in complex polymer systems. Non-equilibrium thermodynamics ; Relaxation (psychology) ; Kernel (algebra) ; Active matter ; Diffusion ; Consistency (knowledge bases) ; Probability density function ; Function (biology) Block Copolymer Self-Assembly ; Machine Learning in Materials Science ; Rheology and Fluid Dynamics Studies
Authors: Taoyong Cui | Y. L. Han | Haojun Jia | Chenru Duan | Qiyuan Zhao
Source: ArXiv.org (preprint, 2025-05-18)
Field: Materials Science — Materials Chemistry

HORM: A Large Scale Molecular Hessian Database for Optimizing Reactive Machine Learning Interatomic Potentials Transition state (TS) characterization is central to computational reaction modeling, yet conventional approaches depend on expensive density functional theory (DFT) calculations, limiting their scalability. Machine learning interatomic potentials (MLIPs) have emerged as a promising approach to accelerate TS searches by approximating quantum-level accuracy at a fraction of the cost. However, most MLIPs are primarily designed for energy and force prediction, thus their capacity to accurately estimate Hessians, which are crucial for TS optimization, remains constrained by limited training data and inadequate learning strategies. This work introduces the Hessian dataset for Optimizing Reactive MLIP (HORM), the largest quantum chemistry Hessian database dedicated to reactive systems, comprising 1.84 million Hessian matrices computed at the $ω$B97x/6-31G(d) level of theory. To effectively leverage this dataset, we adopt a Hessian-informed training strategy that incorporates stochastic row sampling, which addresses the dramatically increased cost and complexity of incorporating second-order information into MLIPs. Various MLIP architectures and force prediction schemes trained on HORM demonstrate up to 63% reduction in the Hessian mean absolute error and up to 200 times increase in TS search compared to models trained without Hessian information. These results highlight how HORM addresses critical data and methodological gaps, enabling the development of more accurate and robust reactive MLIPs for large-scale reaction network exploration. Hessian matrix ; Leverage (statistics) ; Scale (ratio) ; Limiting ; Reduction (mathematics) ; Training set ; Data point ; Density functional theory Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Computational Drug Discovery Methods
Authors: Judah Immanuel | Avik Mahata | Aniruddha Maiti
Source: ArXiv.org (article, 2025-12-26)
Field: Materials Science — Materials Chemistry

Toward Generalizable Surrogate Models for Molecular Dynamics via Graph Neural Networks We present a graph neural network (GNN) based surrogate framework for molecular dynamics simulations that directly predicts atomic displacements and learns the underlying evolution operator of an atomistic system. Unlike conventional molecular dynamics, which relies on repeated force evaluations and numerical time integration, the proposed surrogate model propagates atomic configurations forward in time without explicit force computation. The approach represents atomic environments as graphs and combines message-passing layers with attention mechanisms to capture local coordination and many-body interactions in metallic systems. Trained on classical molecular dynamics trajectories of bulk aluminum, the surrogate achieves sub angstrom level accuracy within the training horizon and exhibits stable behavior during short- to mid-horizon temporal extrapolation. Structural and dynamical fidelity are validated through agreement with reference radial distribution functions and mean squared displacement trends, demonstrating that the model preserves key physical signatures beyond pointwise coordinate accuracy. These results establish GNN-based surrogate integrators as a promising and computationally efficient complement to traditional molecular dynamics for accelerated atomistic simulations within a validated regime. Molecular dynamics ; Pointwise ; Surrogate model ; Complement (music) ; Graph ; Artificial neural network ; Operator (biology) ; Surrogate data Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Quantum many-body systems