jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Christoph Brunken | Sébastien Boyer | Saiful Omar | Martin Maarand | Olivier Peltre | Solal Attias | Bakary N'tji Diallo | A. Markina | Olaf G. Othersen | Oliver Bent
Source: ArXiv.org (preprint, 2025-03-24)
Field: Materials Science — Materials Chemistry

Universally applicable and tunable graph-based coarse-graining for Machine learning force fields Coarse-grained (CG) force field methods for molecular systems are a crucial tool to simulate large biological macromolecules and are therefore essential for characterisations of biomolecular systems. While state-of-the-art deep learning (DL)-based models for all-atom force fields have improved immensely over recent years, we observe and analyse significant limitations of the currently available approaches for DL-based CG simulations. In this work, we present the first transferable DL-based CG force field approach (i.e., not specific to only one narrowly defined system type) applicable to a wide range of biosystems. To achieve this, our CG algorithm does not rely on hard-coded rules and is tuned to output coarse-grained systems optimised for minimal statistical noise in the ground truth CG forces, which results in significant improvement of model training. Our force field model is also the first CG variant that is based on the MACE architecture and is trained on a custom dataset created by a new approach based on the fragmentation of large biosystems covering protein, RNA and lipid chemistry. We demonstrate that our model can be applied in molecular dynamics simulations to obtain stable and qualitatively accurate trajectories for a variety of systems, while also discussing cases for which we observe limited reliability. Granularity ; Graph ; Computer science ; Artificial intelligence ; Theoretical computer science Machine Learning in Materials Science
Authors: Simone Marcorini | Rocco Pomodoro | Omar Abou El Kheir | Marco Bernasconi
Source: The Journal of Chemical Physics (article, 2025-10-15)
Field: Materials Science — Materials Chemistry

Viscosity, breakdown of Stokes–Einstein relation, and dynamical heterogeneity in supercooled liquid Ge2Sb2Te5 from simulations with a neural network potential Phase change materials are exploited in non-volatile electronic memories and photonic devices that rely on a fast and reversible transformation between the amorphous and crystalline phases upon heating. Recrystallization of the amorphous phase under the operation conditions of the memories occurs in the supercooled liquid phase above the glass transition temperature Tg. The dynamics of the supercooled liquid is thus of great relevance for the operation of the devices and, close to Tg, also for the structural relaxations of the glass that affect the performances of the memories. Information on the atomic dynamics is provided by the diffusion coefficient (D) and by the viscosity (η), which are, however, both difficult to be measured experimentally under the operation conditions of the devices due to fast crystallization. In this work, we leverage a machine learning interatomic potential for the flagship phase change compound Ge2Sb2Te5 to compute η, D, and the α-relaxation time in a wide temperature range from 1200 K to about 100 K above Tg. Large-scale molecular dynamics simulations allowed quantifying the fragility of the liquid and the occurrence of a breakdown of the Stokes-Einstein relation between η and D in the supercooled phase. Isoconfigurational analysis provided a visualization of the emergence of dynamical heterogeneities responsible for the breakdown of the Stokes-Einstein relation. The analysis revealed that the regions of most mobile atoms are related to the presence of Ge atoms with particular local environments. Supercooling ; Fragility ; Amorphous solid ; Glass transition ; Polyamorphism ; Molecular dynamics ; Dynamical heterogeneity ; Interatomic potential Phase-change materials and chalcogenides ; Material Dynamics and Properties ; Nonlinear Optical Materials Studies
Authors: Thijs ter Rele | René van Roij | Marjolein Dijkstra
Source: ArXiv.org (article, 2025-12-20)
Field: Materials Science — Materials Chemistry

Machine-Learned Many-Body Potentials for Charged Colloids reveal Gas-Liquid Spinodal Instabilities only in the strong-coupling regime of Primitive Models Past experimental observations of gas-liquid and gas-crystal coexistence in low-salinity suspensions of highly charged colloids have suggested the existence of like charge attraction. Evidence for this phenomenon was also observed in primitive-model simulations of (asymmetric) electrolytes and of low-charge nanoparticle dispersions. These results from low-valency simulations have often been extrapolated to experimental parameter regimes of high colloid valency where like-charge attraction between colloids has been reported. However, direct simulations of highly charged colloids remain computationally demanding. To circumvent slow equilibration, we employ a machine-learning (ML) framework to construct ML potentials that accurately describe the effective colloid interactions. Our ML potentials enable fast simulations of dispersions and successfully reproduce the gas-liquid and gas-solid phase separation observed in primitive-model simulations at low charge numbers. Extending the ML-based simulations to higher valencies, where primitive-model simulations become prohibitively slow, also reveals like-charge attractions and gas-liquid spinodal instabilities, however only in the regime of strongly coupled electrostatic interactions and not in the weakly coupled Poisson-Boltzmann regime of the experimental observations of colloidal like-charge attractions. Spinodal ; Colloid ; Charge (physics) ; Valency ; Phase (matter) ; Colloidal particle ; Electrolyte ; Spinodal decomposition Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Material Dynamics and Properties
Authors: Caitlin Perkampus | Stefan Adams | Roger A. De Souza
Source: Journal of Materials Science Materials in Energy (article, 2025-12-16)
Field: Materials Science — Materials Chemistry

Simulating H$$^+$$, O$$^{2-}$$ and Sr$$^{2+}$$ transport in SrTiO$$_3$$ with a machine-learning potential The transport of interstitial protons, oxygen vacancies and strontium vacancies in the cubic perovskite SrTiO $$_3$$ are investigated with molecular statics (MS) and molecular dynamics (MD) simulations employing the pre-trained machine-learning (ML) potential SevenNet. In the first two cases ( $$\mathrm{H_i}$$ and $$\mathrm{v_O}$$ ), defect diffusion coefficients are obtained, and these are higher than experimental values reported in the literature. For all three cases ( $$\mathrm{H_i}$$ , $$\mathrm{v_O}$$ and $$\mathrm{v_{Sr}}$$ ), the activation enthalpies of defect migration are underestimated relative to experimental data, thus yielding a lower threshold for activation enthalpies. Without the need for subsequent modification, the SevenNet potential provides a lower limit for activation barriers of ion migration and an upper limit to diffusion coefficients. Our results thus indicate that, without fine-tuning, the SevenNet potential is unlikely to accurately reproduce experimental data, but they suggest that it can be used to compare proton, anion and cation transport rates across various perovskite oxides prior to fine-tuning and eventual experimental study. Perovskite (structure) ; Diffusion ; Strontium ; Molecular dynamics ; Limit (mathematics) ; Ion Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advancements in Solid Oxide Fuel Cells
Authors: H. Hu | Henry J. Stirrat | Adam Alayli | Akinori Saeki | Yue Huang
Source: ACS Omega (article, 2025-10-16)
Field: Materials Science — Materials Chemistry

AI-Powered Workflow for Constructing Organic Materials Databases from the Literature: Integrating Large Language Models We developed an end-to-end workflow to automate the construction of materials science databases from published literature, addressing a traditionally manual, time-intensive, and labor-intensive process. The work systematically evaluates and compares different machine learning (ML) methods to optimize each task. For identifying relevant publications, we tested various ML techniques and concluded that a combination of large language model (LLM)-based embeddings, clustering, and direct LLM queries is most effective. In the subsequent data extraction phase, we employed OpenAI's GPT-4 to extract materials and their properties, achieving accuracy comparable to manually curated data sets. Additionally, we integrated AI/ML methods to automatically generate SMILES from chemical structure images, expanding the workflow's applicability to organic materials. To validate the workflow, we applied it to studying organic donor materials in organic photovoltaic devices and benchmarked its performance against a manually curated data set derived from 503 papers. The results demonstrate the workflow's efficiency and accuracy. Finally, based on our findings, we provide recommendations for selecting the best ML methods for each task and propose further improvements for the future tool development. This workflow represents a major advancement in accelerating the development of materials science databases and enables data science applications in a broader range of research topics that were historically infeasible due to the lack of available data sets. Workflow ; Task (project management) ; Set (abstract data type) ; Data extraction ; Data modeling ; Data set ; Relational database Machine Learning in Materials Science ; Computational Drug Discovery Methods
Source: International Journal of Business and Technology Management (review, 2025-11-14)
Field: Materials Science — Biomaterials

Harnessing Artificial Intelligence for Green Innovation in Packaging: A Systematic Review of Adoption and Strategic The rising global concern about environmental sustainability drives an escalating need for sustainable packaging solutions in the packaging industry. The integration of artificial intelligence (AI) into green innovation for packaging represents a transformative approach to addressing both environmental and operational challenges. A systematic literature review (SLR) based on PRISMA 2020 framework examines recent AI-powered green packaging innovation patterns and technologies along with barriers and forecasts future developments. The analytical research included 48 peer-reviewed articles that appeared between 2020 and the present year. The findings reveal that six primary types of AI technologies are applied in packaging, with Machine Learning, general AI applications, and Deep Learning being most frequently utilized. Green packaging innovations centre around biodegradable materials and smart packaging, supported by five major AI-driven applications: process optimization, packaging monitoring, fraud detection, computer vision, and natural language processing. Despite these advancements, widespread adoption is hindered by high implementation costs, technical complexity, and regulatory uncertainties. Moreover, emerging trends highlight the importance of scalable solutions, advanced AI model development, circular economy alignment, and cross-sector collaboration. This review provides a structured knowledge base that aids academics, business leaders, and policymakers in understanding AI’s role in sustainable packaging. It also offers strategic insights and research directions to accelerate the transition toward eco-efficient and intelligent packaging systems. Sustainability ; Transformative learning ; Process (computing) ; Circular economy ; Active packaging ; Systematic review ; Sustainable development ; Emerging technologies Nanocomposite Films for Food Packaging ; Consumer Packaging Perceptions and Trends ; Sustainable Supply Chain Management
Authors: Hang Hu (1366404) | Henry J. Stirrat (22448129) | Adam Alayli (22448132) | Akinori Saeki (1280871) | Yue Huang (285485)
Source: Figshare (article, 2025-10-16)
Field: Materials Science — Materials Chemistry

AI-Powered Workflowfor Constructing Organic MaterialsDatabases from the Literature: Integrating Large Language Models We developed an end-to-end workflow to automate the construction of materials science databases from published literature, addressing a traditionally manual, time-intensive, and labor-intensive process. The work systematically evaluates and compares different machine learning (ML) methods to optimize each task. For identifying relevant publications, we tested various ML techniques and concluded that a combination of large language model (LLM)-based embeddings, clustering, and direct LLM queries is most effective. In the subsequent data extraction phase, we employed OpenAI’s GPT-4 to extract materials and their properties, achieving accuracy comparable to manually curated data sets. Additionally, we integrated AI/ML methods to automatically generate SMILES from chemical structure images, expanding the workflow’s applicability to organic materials. To validate the workflow, we applied it to studying organic donor materials in organic photovoltaic devices and benchmarked its performance against a manually curated data set derived from 503 papers. The results demonstrate the workflow’s efficiency and accuracy. Finally, based on our findings, we provide recommendations for selecting the best ML methods for each task and propose further improvements for the future tool development. This workflow represents a major advancement in accelerating the development of materials science databases and enables data science applications in a broader range of research topics that were historically infeasible due to the lack of available data sets. Workflow ; Task (project management) ; Set (abstract data type) ; Data extraction ; Data modeling ; Data set ; Relational database Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Scientific Computing and Data Management
Authors: Ardiana Topi
Source: Journal of Information Systems Engineering & Management (article, 2025-02-18)
Field: Chemistry — Analytical Chemistry

Application of Machine Learning Algorithmic Models for the Authentication of Albanian Mono-Cultivar Olive Oils Distinguished for its nutritional benefits and high economic value, olive oil has faced issues with adulteration and fraud. As production increases, the need to identify olive oil by specific cultivars and regions has become more pressing. Analyzing chemical data related to the origin and olive cultivars will facilitate advanced quality control and authenticity practices for Albanian olive oil, enhancing its competitiveness as an organic product. While traditional empirical methods have been relied upon to detect olive oil fraud and evaluate quality, this study pioneers a modern approach using machine learning algorithms to differentiate authentic products from counterfeits. Establishing effective mechanisms and best practices to trace product origins and quality indicators will raise awareness about the risks of adulteration to both consumers' health and the broader food industry. To enhance the precision of origin predictions, data pre-processing steps—especially the normalization process following the isolation of independent features from the target variable—are crucial for distance-based algorithms like kNN, which improve accuracy. Furthermore, performance metrics for all algorithms were evaluated, including k-Nearest Neighbors, Logistic Regression, Support Vector Machines, implementation hyperparameter tuning techniques, and the best-performing model. Applying supervised machine learning methods to categorize Albanian Olive Oils (OO) according to their chemical composition aids in identifying their geographical and cultivar origin. Our results indicate an accuracy of 88.88%, constrained by the limitations of the current dataset; however, we intend to expand the dataset in the future. Cultivar ; Authentication (law) ; Olive oil ; Computer science ; Horticulture ; Artificial intelligence ; Machine learning ; Botany ; Biology ; Food science ; Computer security Spectroscopy and Chemometric Analyses ; Edible Oils Quality and Analysis
Authors: Yanbang Tang
Source: Journal of Nuclear Materials (article, 2025-12-25)
Field: Materials Science — Materials Chemistry

Automated machine learning with data augmentation for predicting void swelling onset dose in irradiated metals Mean squared error ; Gaussian ; Void (composites) ; Baseline (sea) ; Support vector machine ; Mean absolute error ; Training set ; Swelling Machine Learning in Materials Science ; Fusion materials and technologies ; Nuclear Materials and Properties
Authors: Ravil Ashirmametov | А. В. Алпатов | Farrokh Yousefi | Narges Vafa | Siamac Fazli | Κωνσταντίνος Κώστας
Source: RSC Advances (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Determining the structure of functionalized graphene for tailored thermomechanical properties using ML techniques Chemical functionalization of graphene with various chemical groups unlocks an infinite number of variations for nanosheet design modifications. However, the prohibitive cost of molecular dynamics simulations and the overwhelmingly large number of design variables render the inverse design problem intractable when conventional approaches are used. To this end, we develop an MD-powered, data-driven framework to enable fast and accurate identification of the layout that exhibits a given set of user-prescribed thermomechanical properties. Specifically, we generate a dataset with 1200 records, combining the layout and thermomechanical properties (Young's modulus, thermal conductivity, maximum stress and strain at maximum stress) of functionalized graphene sheets with hydrogen and methyl groups of appropriate coverages. A variety of regression models using Label and Bag-of-Words encoding were trained with Support Vector Regression, Ridge Regression and Gaussian Process Regression models showing best predictive performance, with considerably high values for the corresponding coefficients of determination (<i>R</i> <sup>2</sup> > 0.9 for thermal conductivity, Young's modulus and maximum stress) on a hold-out test set, with mean absolute percentage error (MAPE) remaining below 1% in most cases. Finally, an evolutionary optimization process, in tandem with the trained Machine Learning (ML) models, was employed for finding graphene layouts that possess a set of user-defined target properties. MD-validations of the obtained designs confirmed the applicability of the approach while revealing acceptable deviations for thermal conductivity values and even better alignment for the mechanical properties. In summary, the proposed approach succeeds in a 7 orders of magnitude speedup in estimating the thermomechanical properties of functionalized graphene sheets when compared to pure MD simulations, and up to 6 orders of magnitude faster identification of layouts with prescribed properties, benchmarked on a nanosheet (220 × 100 Å) with 8528 atoms using a 64 core AMD EPYC workstation. Graphene ; Thermal conductivity ; Inverse ; Speedup ; Support vector machine ; Nanosheet ; Thermal ; Gaussian process Machine Learning in Materials Science ; Thermal properties of materials ; Graphene research and applications
Authors: Csirik, Mihály A. | Laestadius, Andre | Oster, Mathias
Source: ArXiv.org (article, 2025-11-23)
Field: Materials Science — Materials Chemistry

Neural network approximation of regularized density functionals Density functional theory is one of the most efficient and widely used computational methods of quantum mechanics, especially in fields such as solid state physics and quantum chemistry. From the theoretical perspecive, its central object is the universal density functional which contains all intrinsic information about the quantum system in question. Once the external potential is provided, in principle one can obtain the exact ground-state energy via a simple minimization. However, the universal density functional is a very complicated mathematical object and almost always it is replaced with its approximate variants. So far, no ``first principles'', mathematically consistent and convergent approximation procedure has been devised that has general applicability. In this paper, we propose such a procedure by first applying Moreau--Yosida regularization to make the exact functionals continuous (even differentiable) and then approximate the regularized functional by a neural network. The resulting neural network preserves the positivity and convexity of the exact functionals. More importantly, it is differentiable, so it can be directly used in a Kohn--Sham calculation. Convexity ; Artificial neural network ; Regularization (linguistics) ; Quantum ; Simple (philosophy) ; Energy functional ; Object (grammar) ; Approximation theory ; Density functional theory Machine Learning in Materials Science ; Quantum many-body systems ; Model Reduction and Neural Networks
Authors: Yapeng Zhang | Zipeng An | JingChun Wang | Yao Wang | Rui-Xue Xu | Guan Hua Chen | Xiao Zheng
Source: Chinese Journal of Chemical Physics (article, 2025-04-01)
Field: Materials Science — Materials Chemistry

Machine learning density functional compatible with dispersion correction for non-covalent interactions Machine learning (ML) has demonstrated significant potential in enhancing the predictive capabilities of density functional theory methods. In this study, we develop an ML model for correcting B3LYP-D, a density functional approximation that incorporates dispersion corrections for non-covalent interactions. This model utilizes semilocal electron density descriptors, and is trained with accurate reference data for both relative and absolute energies. Extensive benchmark tests reveal that the ML correction substantially enhances the generalization ability of the B3LYP-D functional, improving the predictions of atomization and dissociation energies for complex molecular systems. It retains the accuracy of B3LYP-D in predicting reaction barrier heights and non-covalent interactions while enabling efficient, fully self-consistent field calculations. This work signifies a promising advancement in the development of ML-corrected functionals that surpass the performance of traditional B3LYP-D. Density functional theory ; Dispersion (optics) ; Covalent bond ; Computer science ; Physics ; Chemistry ; Computational chemistry ; Optics ; Quantum mechanics Machine Learning in Materials Science