jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tomas Klinavičius (6984422) | Asta Tamulevičienė (18762694) | Tomas Tamulevičius (2151037)
Source: Figshare (article, 2025-09-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

How to Select theProper Gold Permittivity for GeneratingTraining Data for Deep Neural Networks for Analysis of the NanoparticleColloid Size Distributions from Their UV–Vis–NIR ExtinctionSpectra? The investigation of colloidal nanoparticle size distribution has emerged as one of the most important yet routinely executed measurements due to its wide range of applications. Deep learning has recently become a valuable tool for spectroscopic data interpretation including localized surface plasmon resonances in the UV–vis–NIR extinction spectra. However, deep neural networks (DNNs) require a large number of training data instances to make accurate predictions. Researchers frequently turn to computational methods, such as Mie theory, for this task. Unfortunately, the influence of permittivity selection on neural network prediction results for plasmonic metal nanoparticles is often underinvestigated or even entirely overlooked. In this article, we thoroughly examined the relation between gold nanoparticle size distributions, their extinction spectra, and permittivities provided by different authors in the scientific literature. We investigated the effects of using numerically generated training data originating from different permittivities on DNN predictions in detail. The relationship between permittivity and extinction spectra for wet-chemistry-synthesized Au nanoparticles was verified, which directly applies to any spherical or near-spherical plasmonic nanoparticles. Permittivity ; Extinction (optical mineralogy) ; Plasmon ; Artificial neural network ; Nanoparticle ; Plasmonic nanoparticles ; Colloidal gold ; Range (aeronautics) Gold and Silver Nanoparticles Synthesis and Applications ; Machine Learning in Materials Science ; Nanocluster Synthesis and Applications
Authors: Alexis Geslin
Source: ChemRxiv (preprint, 2025-09-01)
Field: Materials Science — Materials Chemistry

Materials properties predictions: the role of numerical tokens in Large Language Models. Developing computational approaches to accurately predict materials properties has the potential to greatly accelerate materials research by enabling large-scale screening. However, for crystal materials, capturing their 3D structure and their periodicity is critical to make accurate properties predictions. While traditional deep learning approaches have showed significant improvements, they lack the ability to properly convey structure, periodicity and complex crystallographic information. Conversely, since crystal text descriptions can be very expressive, LLMs-based properties prediction models are promising. Here, I finetuned a state- of-the-art LLM-based model developed by Rubungo et al. [1] that predicts crystal bandgaps based on crystal text descriptions and used it as my baseline. I then developed an architecture that explicitly leverages numerical tokens in the text descriptions. I showed a 15meV improvement in the test error (MAE) over my baseline, more than the gain showcased by Rubungo et al. over other state-of-the- art models [1]. I also investigated newer embedding models to test their capabilities to extract information from text descriptions with numerical information. The best of these embeddings-based architectures, obtained with the E5-large model, yielded results comparable to my baseline, without the need to finetune the LLM embedding part. These newly developed architectures offer ways to push the limits of materials properties prediction. Computer science ; Linguistics ; Natural language processing ; Philosophy Machine Learning in Materials Science
Authors: Usama Saleem (21184464) | Shaama Mallikarjun Sharada (8989958)
Source: Figshare (article, 2025-04-26)
Field: Materials Science — Materials Chemistry

Anomalous Diffusionof Metal Atoms on Oxide Surfaces:A Machine Learning Molecular Dynamics Study of Pt<sub>1</sub>/TiO<sub>2</sub> We investigate the high-temperature evolution of the metal–support interface in atomically dispersed catalysts to probe the dynamical evolution of the metal atom. Since high computational costs limit the time scales of ab initio molecular dynamics (AIMD) trajectories to a few picoseconds, we deploy a machine learned interatomic potential (MLIP), trained on AIMD data, using the FLARE program. We generate multiple trajectories spanning at least 50 ps each for atomically dispersed Pt on rutile TiO<sub>2</sub> (110), initiated at different positions on the stoichiometric surface. We find that the motion of Pt is subdiffusive (not Brownian) in most cases, quantified by the parameters constituting the fractional Fokker-Planck equation. The subdiffusive behavior originates in strong interactions of Pt with bridging oxygen atoms of the support. Several trajectories show that Pt mobility is quenched when it coordinates with two bridging oxygen atoms to form a near-linear O–Pt–O complex. Diffusion that resembles Brownian motion is observed only at the highest simulation temperature examined (1000 K) for sites at which Pt is far from a bridging oxygen. The study therefore shows that in the absence of surface defects or adsorbates, the thermal stability of the metal atom is determined by coordination with bridging oxygen atoms. Bridging (networking) ; Molecular dynamics ; Oxide ; Atom (system on chip) ; Metal ; Stoichiometry ; Oxygen ; Ab initio Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysis and Oxidation Reactions
Authors: Anna C. M. Thöni (21162407) | William E. Robinson (5465822) | Yoram Bachrach (792062) | Wilhelm T. S. Huck (8666409) | Tal Kachman (21162410)
Source: Figshare (article, 2025-04-22)
Field: Materials Science — Materials Chemistry

Modeling ChemicalReaction Networks Using Neural OrdinaryDifferential 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 equation 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 modeling 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 ; Exact differential equation ; Differential (mechanical device) ; Chemical equation Machine Learning in Materials Science ; Gene Regulatory Network Analysis ; Model Reduction and Neural Networks
Authors: Chenxin Zhang | Qian Wang | Puru Jena
Source: Small (article, 2025-12-12)
Field: Materials Science — Materials Chemistry

Twisting‐Induced Phonon Localization and Ultralow Thermal Conductivity in Penta‐PdTe <sub>2</sub> Bilayer Revealed by a Universal Machine‐Learning Potential The recent synthesis of penta-PdTe<sub>2</sub> sheet highlights the growing interest in pentagonal topologies for developing new 2D materials. Distinct from previously synthesized 2D pentagonal materials, penta-PdTe<sub>2</sub> possesses the heaviest elemental composition to date, rendering its phonon transport properties particularly intriguing. In this study, the universal machine-learning potential, NEP89, is fine-tuned using high-accuracy first-principles data spanning monolayer, bilayer, and twisted configurations of penta-PdTe<sub>2</sub> and achieving an energy prediction error within 2.3 meV atom<sup>-1</sup>. Leveraging the homogeneous non-equilibrium molecular dynamics simulations with the fine-tuned machine-learning potential and Wigner transport theory, the phonon transport behavior and lattice thermal conductivity is systematically investigated. These results reveal that interlayer stacking reduces the bilayer thermal conductivity to 28.00% of the monolayer value, while interlayer twisting induces a further reduction to 76.19%, leading to an ultralow thermal conductivity of (0.30 ± 0.059) W m<sup>-1</sup> K<sup>-1</sup>. These findings demonstrate that the interplay of pentagonal topology, strategic elemental composition, and twist engineering provides an effective route for tuning phonon transport in 2D materials. Thermal conductivity ; Phonon ; Stacking ; Bilayer ; Lattice (music) ; Molecular dynamics Thermal properties of materials ; 2D Materials and Applications ; Topological Materials and Phenomena
Authors: Dat P. Nguyen (4345360) | Quyet M. Le (22431883) | Hoa T. P. Tran (22431886) | Phuc T. Le (22431889) | Tin V. T. Nguyen (11219077)
Source: Figshare (article, 2025-10-14)
Field: Materials Science — Materials Chemistry

Enhanced Prediction of Absorption and Emission Wavelengthsof Organic Compounds through Hybrid Graph Neural Network Architectures The prediction of absorption and emission spectra is a crucial task in chemistry and materials science, traditionally addressed with computational methods, such as density functional theory (DFT). While DFT can deliver with high accuracy, its substantial computational cost and long run times hinder its scalability for large molecular systems. In this study, we present an efficient graph neural network (GNN)-based approach for predicting optical properties. Our method combines molecular graph representations with molecular fingerprints, allowing the model to capture detailed structural and electronic features, as well as solvent effects. This simple yet effective GNN framework achieves <i>R</i><sup>2</sup> values of up to 0.92 for the maximum absorption wavelength and 0.83 for the maximum emission wavelength, significantly improving prediction accuracy while requiring substantially fewer computational resources. Including explicit solvent fingerprints further improves absorption predictions to <i>R</i><sup>2</sup> = 0.96 on the test set, with solvent-specific subsets reaching <i>R</i><sup>2</sup> ≈ 0.99 (e.g., methanol). The results highlight the potential of GNNs to transform spectral prediction workflows and pave the way for accelerated molecular design and materials discovery. Scalability ; Workflow ; Absorption (acoustics) ; Artificial neural network ; Molecular absorption ; Graph ; Graph theory ; Absorption spectroscopy Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Наталія Фоменко | Олексій Олександрович Ларін
Source: Scientific periodicals of Ukraine (article, 2025-12-29)
Field: Materials Science — Materials Chemistry

ІНТЕГРАЦІЯ МЕХАНІЧНИХ ОБМЕЖЕНЬ У ГЛИБОКУ СИМВОЛЬНУ ОПТИМІЗАЦІЮ ДЛЯ ВІДКРИТТЯ В’ЯЗКО-ПРУЖНИХ МОДЕЛЕЙ The current issue of mathematically modelling the mechanical behavior of polymer materials produced using additive technologies, specifically thermoplastic polyurethane (TPU), is addressed in the paper. The widespread use of 3D printing to create flexible structures and biomedical implants necessitates accurate constitutive models that can describe complex nonlinear phenomena, such as hysteresis, stress relaxation, and strain rate dependence. Traditional phenomenological approaches, based, for example, on hyperelastic potentials (Mooney-Rivlin, Ogden), have significant limitations: the need for a priori selection of the model structure, such as fixing the number of relaxation branches, and the complexity of identifying a large number of parameters. This often leads to the problem of solution inconsistency, which complicates the prediction of material behavior under conditions of complex anisotropy caused by layer-by-layer printing. On the other hand, modern machine learning methods, such as neural networks, although providing high approximation accuracy, act as “black boxes” and do not provide physically interpretable dependencies suitable for further analysis. The goal of this work is to develop a method for the automated search for determining equations of viscoelasticity that combines the flexibility of data-driven approaches with physical meaningfulness. We propose the use of a modified deep symbolic optimization (DSO) framework with the integration of “mechanical content.” The algorithm searches for the analytical form of the elastic deformation potential and relaxation function. Applying the method to experimental data has yielded a compact determining equation that provides high approximation accuracy and accurately describes the relaxation behavior of 3D-printed materials. The results indicate the promise of symbolic regression for automating the creation of transparent (“white-box”) material models in finite element analysis. Hyperelastic material ; Viscoelasticity ; Nonlinear system ; Relaxation (psychology) ; Flexibility (engineering) ; Experimental data ; Artificial neural network ; Symbolic regression ; A priori and a posteriori Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Topology Optimization in Engineering
Authors: Kawsar Ali | Sultan Hameed | Helmi Ben Saad
Source: METHODS AND OBJECTS OF CHEMICAL ANALYSIS (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Prediction of Solvatochromic λmax of Pb(II) Complexes Using Gaussian Process Regression: A Tool for Analytical Applications This study presents the application of a Gaussian Process Regression (GPR) model for predicting the maximum absorption wavelength (λmax) of lead(II) complex with organic reagent [3-((2-chloro-4- hydroxyphenyl) diazenyl)-5nitrobenzene-1,2-diol] (CHPDND) extracted using various organic solvents. The proposed model utilizes key solvatochromatic parameters, including dielectric constant (ε), polarity/ polarizability (π*), hydrogen bond donor acidity (α), hydrogen bond acceptor basicity (β), and a custom polarity scale (ξ), to model the complex solvent effects on electronic transitions. The GPR model, equipped with a squared exponential kernel, demonstrated high predictive accuracy and efficiency, capturing the nonlinear relationships between solvent properties and λmax. The model underwent automatic hyperparameter optimization, achieving convergence within minimal iterations and exhibiting stable performance. The findings were validated using both experimental and aggregated datasets, showing a strong agreement between predicted and actual values. The results emphasize the potential of machine learning approaches, particularly GPR, as reliable and efficient alternatives to traditional computational methods for optical property prediction. This work provides a practical framework for solvent selection and molecular design in the fields of analytical chemistry and materials science. Solvatochromism ; Polarizability ; Dielectric ; Gaussian process ; Gaussian ; Hyperparameter ; Nonlinear system ; Convergence (economics) ; Exponential function Machine Learning in Materials Science ; Spectroscopy and Chemometric Analyses ; Computational Drug Discovery Methods
Authors: Marcin Kirsz | Abayomi Daramola | Aymeric Hermann | Hongxiang Zong | Graeme J. Ackland
Source: ArXiv.org (preprint, 2025-02-04)
Field: Materials Science — Materials Chemistry

Tadah! A Swiss Army Knife for Developing and Deployment of Machine Learning Interatomic Potentials The Tadah! code provides a versatile platform for developing and optimizing Machine Learning Interatomic Potentials (MLIPs). By integrating composite descriptors, it allows for a nuanced representation of system interactions, customized with unique cutoff functions and interaction distances. Tadah! supports Bayesian Linear Regression (BLR) and Kernel Ridge Regression (KRR) to enhance model accuracy and uncertainty management. A key feature is its hyperparameter optimization cycle, iteratively refining model architecture to improve transferability. This approach incorporates performance constraints, aligning predictions with experimental and theoretical data. Tadah! provides an interface for LAMMPS, enabling the deployment of MLIPs in molecular dynamics simulations. It is designed for broad accessibility, supporting parallel computations on desktop and HPC systems. Tadah! leverages a modular C++ codebase, utilizing both compile-time and runtime polymorphism for flexibility and efficiency. Neural network support and predefined bonding schemes are potential future developments, and Tadah! remains open to community-driven feature expansion. Comprehensive documentation and command-line tools further streamline the development and application of MLIPs. Software deployment ; Computer science ; Artificial intelligence ; Software engineering Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; Computational Drug Discovery Methods
Authors: Vikas Tiwari | Tarak Karmakar
Source: ChemRxiv (preprint, 2025-01-20)
Field: Materials Science — Materials Chemistry

Understanding the Mechanism of Nanocluster Formation from Machine-Learned Potential-based Simulations Understanding the mechanism for the formation of metal nanoclusters is an open challenge in the nanoscience field. Computational modeling can provide molecular details of nanocluster formation that are otherwise inaccessible. However, even with advanced computational resources, simulating the nucleation of a nanocluster in solution presents significant challenges, including inaccurate energy predictions and limitations on system size and timescale. This work addresses these challenges by integrating deep neural networks (DNN) with well-tempered metadynamics (WT-Metad) to model the nucleation of an Ag$_6$(SCNH$_2$)$_6$ (a prototypical example) in solution. An unbiased neural network potential (NNP)--based molecular dynamics (MD) simulation captured the cluster's dynamic behavior, while WT-Metad simulations revealed an almost barrierless downhill transition from dispersed precursors to a nucleated state. Scaling up the system to 30 randomly distributed precursors demonstrated spontaneous nucleation at multiple sites, underscoring the method's robustness. This study presents the first successful DNN model of nanocluster formation in solution with DFT-level accuracy, paving the way for advancements in the field. Mechanism (biology) ; Computer science ; Epistemology ; Philosophy Machine Learning in Materials Science
Authors: Rathna Priya Thangaraj Sundaramurthy | Thiruppathi Senthilkumar | C. B. Singh | Annamalai Manickavasagan
Source: Research Square (preprint, 2025-05-07)
Field: Chemistry — Analytical Chemistry

Application of Non-destructive and Chemical-free Short-Wave Infrared Hyperspectral Imaging (SWIR-HSI) coupled with Machine Learning Regression for Rapid Quantification of Deoxynivalenol (DON) in Individual Corn Kernels Hyperspectral imaging ; Infrared ; Chemical imaging ; Regression ; Artificial intelligence ; Remote sensing ; Pattern recognition (psychology) ; Computer science ; Environmental science ; Materials science ; Optics ; Mathematics ; Statistics ; Physics ; Geology Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis
Authors: Carlos
R. Jacinto-Mejía (22522536) | Loriano Storchi (1674586) | Giovanni Bistoni (290623)
Source: Figshare (article, 2025-10-29)
Field: Materials Science — Materials Chemistry

Transferable andTransparent Energy Decomposition-BasedMachine Learning Models for Computing Accurate Reaction Energetics We present a transferable, interpretable, and modular machine-learning framework that enhances the accuracy of density functional theory (DFT) reaction energies using physically meaningful energy-decomposition descriptors. Reaction energies computed at the DFT level with standard basis sets are first decomposed into chemically intuitive contributionssuch as kinetic and potential energywhich are then used to train a library of linear regression (LR) models. This includes a general-purpose model that reduces mean absolute percentage errors (MAPE) relative to gold standard CCSD(T)/CBS reference values by up to 63% compared to uncorrected DFT across extended benchmark sets. In parallel, a series of specialized LR models provide improved accuracy for specific reaction classes. A random forest (RF) classifier dynamically selects the optimal model for each case, pushing accuracy further and achieving a MAPE reduction of up to 123 percentage points, all while maintaining full model interpretability. In a rigorous out-of-distribution stress test on the WCCR10 data setcontaining transition-metal complexes absent from trainingboth the general LR model and the RF/LR pipeline retain robust performance. Unlike typical neural network models, which often face generalization challenges beyond their training set, our framework maintains stable performance outside its training domain. Artificial neural network ; Generalization ; Mean absolute percentage error ; Random forest ; Modular design ; Linear regression ; Benchmark (surveying) ; Pipeline (software) ; Density functional theory ; Approximation error Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Okolie, Jude A. | Mukherjee, Alivia
Source: Bucknell Digital Commons (Bucknell University) (article, 2025-06-01)
Field: Materials Science — Materials Chemistry

Activated Carbon : Synthesis, Analysis, and Industrial Applications Activated Carbon: Synthesis, Analysis, and Industrial Applications explores the fundamentals of activated carbon production and characterization, modification techniques and applications of machine learning in the field of activated carbon synthesis and applications. The book is divided into three parts to enable readers and researchers of all levels easy access to the information herein. Section 1 is on Synthesis methods and characterization techniques. The next six chapters on Section 2 focus on diverse industrial applications of activated carbon. The last section is on machine learning applications as well as research progress in activated carbon synthesis, modification, and diverse applications.Written for researchers, graduate and undergraduate students, academics, and industry professionals in the fields of sustainable environmental science and chemical engineering, this book will be a welcomed reference for those who wish to better understand the role of activated carbon in solving sustainability challenges in the world related to energy shortage, greenhouse gas emissions, and environmental issues. -- back cover Activated carbon ; Sustainability ; Greenhouse gas ; Field (mathematics) ; Carbon fibers ; Section (typography) ; Production (economics) Machine Learning in Materials Science ; Carbon dioxide utilization in catalysis ; Ammonia Synthesis and Nitrogen Reduction