jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xiaoming Luo | Zhe Hu | Qiang Zhang | Y. Pang
Source: Sustainable Energy Technologies and Assessments (article, 2025-12-01)
Field: Materials Science — Biomaterials

Recent development in thermodynamic prediction of oilfield scale: from mechanistic models to machine learning Scale (ratio) ; Selection (genetic algorithm) ; Benchmark (surveying) ; Empirical research ; Predictive modelling ; Model selection Calcium Carbonate Crystallization and Inhibition ; Enhanced Oil Recovery Techniques ; Hydrocarbon exploration and reservoir analysis
Authors: Duc P. Truong | Benjamin Nebgen | Derek DeSantis | Dimiter N. Petsev | K. Ø. Rasmussen | Boian S. Alexandrov
Source: ArXiv.org (preprint, 2025-05-27)
Field: Materials Science — Materials Chemistry

Breaking the Curse of Dimensionality: Solving Configurational Integrals for Crystalline Solids by Tensor Networks Accurately evaluating configurational integrals for dense solids remains a central and difficult challenge in the statistical mechanics of condensed systems. Here, we present a novel tensor network approach that reformulates the high-dimensional configurational integral for identical-particle crystals into a sequence of computationally efficient summations. We represent the integrand as a high-dimensional tensor and apply tensor-train (TT) decomposition together with a custom TT-cross interpolation scheme. This approach avoids the need to explicitly construct the full tensor, which would otherwise be computationally intractable. We introduce tailored rank-1 and rank-2 schemes optimized for sharply peaked Boltzmann probability densities, typical in crystalline solids. When applied to the calculation of internal energy and pressure-temperature curves for crystalline copper (Cu) and argon (Ar), as well as the alpha-to-beta phase transition in tin (Sn), our method accurately reproduces molecular dynamics simulation results using tight-binding, machine learning (HIP-NN), and MEAM potentials, all within seconds of computation time. Tensor (intrinsic definition) ; Curse of dimensionality ; Interpolation (computer graphics) ; Computation ; Phase transition ; Sequence (biology) ; Phase (matter) Machine Learning in Materials Science ; Quantum many-body systems ; Model Reduction and Neural Networks
Authors: Daniel Khaykelson (21263004) | Gabriel A. A. Diab (22754087) | Sidney R. Cohen (247318) | Tamar Kashti (22754090) | Tatyana Bendikov (1373973) | Iddo Pinkas (149095) | Ivo F. Teixeira (1408582) | Nadezda V. Tarakina (2024035) | Lothar Houben (1365690) | Boris Rybtchinski (411894)
Source: Figshare (article, 2025-12-03)
Field: Materials Science — Materials Chemistry

Elucidating Structural Disorder in a Polymeric LayeredMaterial: The Case of Sodium Poly(heptazine imide) Photocatalyst Structurally heterogeneous materials present major challenges for characterization due to their complex nanoscale order. Sodium poly(heptazine imide) (NaPHI), a layered carbon nitride photocatalyst, exemplifies this complexity, with its precise structure remaining unresolved. Here, we uncover new structural insights into NaPHI using energy-filtered four-dimensional scanning transmission electron microscopy combined with machine-learning-based diffraction image segmentation, supported by transmission electron microscopy, atomic force microscopy, X-ray diffraction, and Raman spectroscopy. At the mesoscale, NaPHI flakes display bent morphologies, while nanodiffraction patterns reveal features characteristic of stacking disorder. Based on these insights, we modeled a NaPHI-layered structure incorporating out-of-plane undulations (waves) with amplitudes of ∼0.5 Å and wavelengths of 2–3 nm. This model reproduces the observed line features in nanodiffraction patterns and agrees with powder X-ray diffraction data, thereby bridging local and bulk structural information. The introduced approach uses data-driven machine learning to identify statistically significant features, offering a robust framework for structural analysis of semi-crystalline materials. Stacking ; Transmission electron microscopy ; Raman spectroscopy ; Diffraction ; Nanoscopic scale ; Characterization (materials science) ; Electron diffraction ; X-ray crystallography ; Carbon nitride Machine Learning in Materials Science ; Advanced Photocatalysis Techniques ; 2D Materials and Applications
Authors: Amarjeet
Source: International Journal of Physics and Applications (article, 2025-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Surface plasmon dynamics in nanoscale materials: Fundamental physics, state-of-the-art, and future prospects Surface plasmon dynamics in nanoscale materials have emerged as a cornerstone of modern nanophotonics, enabling unprecedented manipulation of light at subwavelength scales. This review provides a comprehensive overview of the fundamental physics governing plasmonic excitations, including localized surface plasmons and propagating surface plasmon polaritons, and their interaction with matter at the quantum and classical levels. We critically examine the state-of-the-art developments in plasmonic sensing, nanomedicine, photocatalysis, energy harvesting, optical communication, and quantum plasmonics, highlighting their transformative potential across interdisciplinary domains. Special emphasis is given to recent advances such as ultrafast plasmon dynamics, strong coupling regimes, plasmon-exciton hybrid systems, and topological plasmonics, which pave the way for next-generation plasmonic devices. Challenges such as high intrinsic losses, fabrication limitations, and integration with complementary technologies are systematically discussed, alongside emerging strategies to address them through low-loss materials, machine learning-driven design, and sustainable plasmonic approaches. The review concludes with a forward-looking perspective on the evolving role of plasmonics in nanoscience, forecasting its critical contributions to future optoelectronics, quantum information processing, and green technologies. Plasmon ; Surface plasmon ; Quantum ; Nanoscopic scale ; Surface plasmon polariton ; Ultrashort pulse ; Metamaterial ; Nanophotonics Gold and Silver Nanoparticles Synthesis and Applications ; Plasmonic and Surface Plasmon Research
Source:  (preprint, 2025-07-17)
Field: Materials Science — Materials Chemistry

EP4: Machine Learning on Structures Chaired by Prof. Shi JIN, this 4th webinar invites Prof. Yu Guang WANG and Prof. Zheng MA to present their latest research about Machine Learning on Structures. Allen-Cahn Messsage Passing on Graphs and Hypergraphs via Particle System Theory We introduce a novel message passing framework for graphs and hypergraphs inspired by interacting particle system theory. This approach models neural message passing as a system of particles subject to attractive, repulsive, and Allen-Cahn forces, which arise in phase transition models. The resulting reaction-diffusion dynamics enable the separation of particles into class-dependent equilibria, forming the basis for Allen-Cahn Message Passing (ACMP). A key contribution of ACMP is its proven ability to mitigate the oversmoothing problem in Graph Neural Networks (GNNs) by maintaining a strictly positive lower bound on the Dirichlet energy. This allows for the construction of very deep GNNs, with hundreds of layers, implemented easily with neural ODE solvers. We extend this model to hypergraphs, where hyperedges create fields that induce shared dynamics among nodes, capturing complex higher-order interactions. Investigating both first and second-order particle system equations, we find the latter offers more stability for deeper message passing. The framework is further enhanced with stochastic elements to handle interaction uncertainties. Our models demonstrate state-of-the-art performance on a variety of real-world node classification tasks, showing strong results on both homophilic and heterophilic datasets for graphs and hypergraphs alike. Computer science ; Message passing ; Artificial neural network ; Theoretical computer science ; Ode ; Variety (cybernetics) ; Artificial intelligence ; Distributed computing ; Mathematics ; Applied mathematics Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Complex Network Analysis Techniques
Authors: Rick Oerder | Christian Hölzer | Jan Hamaekers
Source: ChemRxiv (preprint, 2025-06-04)
Field: Chemistry — Physical and Theoretical Chemistry

ConfRank+: Extending Conformer Ranking to Charged Molecules We present a machine learning model for high-throughput energetic ranking of charged molecular conformers. Based on the ConfRank approach, the model is trained in a pairwise fashion to predict energy differences for pairs of conformers. By conditioning the model on dataset embedding vectors, we are able to train our model on two different reference levels simultaneously, allowing for a larger training dataset and to emulate multiple reference methods. In particular, we train our model on a large subset of the SPICE 2.0.1 dataset with references on the ωB97M-D3(BJ)/def2-TZVPPD range-separated hybrid meta-GGA DFT-level and a self-developed conformer dataset based on the GEOM dataset including r²SCAN-3c references. The result is a single multi-fidelity model that can reproduce both reference levels up to ML-typical model errors for small- and medium-sized molecules including the following elements: H, Li, B, C, N, O, F, Na, Mg, Si, P, S, Cl, K, Ca, Br, I. By including partial atomic charges obtained from the electronegativity equilibration charge model, our model incorporates information about the charge distribution in a molecule, allowing the treatment of charged closed-shell species and explicit treatment of electrostatic interactions. We test the ranking capability of the model on various datasets, paying special attention to molecular charges of -1, 0, 1. Throughout all tests, we find our model to be as accurate as current AIMNet2 and MACE-OFF23(L) models, while requiring an order of magnitude fewer parameters and matching the robustness of the state-of-the-art semi-empirical quantum method GFN2-xTB. Conformational isomerism ; Ranking (information retrieval) ; Molecule ; Chemistry ; Computer science ; Artificial intelligence ; Organic chemistry Various Chemistry Research Topics
Authors: Yongsheng Ou
Source:  (article, 2025-08-19)
Field: Materials Science — Materials Chemistry

XGBoost-Bayesian prediction model for two-dimensional MoS2 preparation (Retraction Notice) This paper proposes a prediction model using the XGBoost algorithm to forecast the size range of product grain dimensions under experimental conditions. The study takes gas flow rate, sulfur-to-molybdenum ratio, heating temperature, and heating time as input features, categorizing the average grain size into three classes (0: 0-30 &mu;m, 1: 30-100 &mu;m, 2: above 100 &mu;m). Methodologically, the Bayesian optimization algorithm is employed to fine-tune key hyperparameters of XGBoost (such as learning rate, maximum tree depth, subsample ratio, etc.), significantly enhancing the model's generalization capability. The experimental design includes data preprocessing, test set partitioning, hyperparameter optimization, and training to ensure model robustness. The results show that the optimized XGBoost model achieves an accuracy of 80% on the test set, outperforming traditional machine learning algorithms (e.g., Support Vector Machine, Random Forest). This study provides an effective and scalable solution for optimizing material synthesis processes, though further expansion of the parameter space (e.g., pressure, impurity content) is needed to improve prediction accuracy under extreme conditions. Bayesian probability ; Computer science ; Artificial intelligence Machine Learning in Materials Science ; Catalysis and Hydrodesulfurization Studies
Authors: Alfonso; id_orcid 0009-0002-3711-4231 Annarelli | Igor Poltavsky | C. Filippi
Source: University of Twente Research Information (article, 2025-05-15)
Field: Materials Science — Materials Chemistry

Ab initio-machine learning framework for non-adiabatic dynamics Insights from azomethane photoisomerization Upon photoexcitation, molecular and condensed systems can undergo major structural changes, requiring accurate modeling of coupled electronic and nuclear dynamics over multiple electronic energy surfaces. To simulate such nonadiabatic dynamics, approximate quantum-classical schemes like surface hopping are typically employed, which require however numerous trajectories and can therefore be computationally very expensive. Here, we study azomethane, a benchmark system for photoisomerization, using high-level electronic structure methods to explore how different electronic descriptions affect relaxation pathways. To reduce the high cost of simulating many trajectories, we train a neural network on quantum data to efficiently predict energies, forces, and couplings. We are now retraining this model using quantum Monte Carlo data and plan to incorporate new, chemically intuitive wave functions developed by collaborators at the University of Groningen to better capture electronic excitations. Our goal is to extend this hybrid approach to larger systems like cyanine dyes relevant to photovoltaic applications. Electronic structure ; Surface hopping ; Quantum ; Benchmark (surveying) ; Potential energy surface ; Relaxation (psychology) ; Artificial neural network ; Photoisomerization Machine Learning in Materials Science ; Quantum many-body systems ; Spectroscopy and Quantum Chemical Studies
Authors: Logan M. Brabson | Andrew J. Medford | David S. Sholl
Source: ArXiv.org (preprint, 2025-06-10)
Field: Chemistry — Inorganic Chemistry

Comparing classical and machine learning force fields for modeling deformation of metal–organic frameworks relevant for direct air capture Direct air capture (DAC) with solid sorbents such as metal-organic frameworks (MOFs) is a promising approach for negative carbon emissions. Computational materials screening can help identify promising materials from the vast chemical space of potential sorbents. Experiments have shown that MOF framework flexibility and deformation induced by adsorbate molecules can drastically affect adsorption properties such as capacity and selectivity. Force field (FF) models are commonly used as surrogates for more accurate density functional theory (DFT) calculations when modeling sorbents, but most studies using FFs for MOFs assume framework rigidity to simplify calculations. Although flexible FFs for MOFs have been parameterized for specific materials, the generality of FFs for reliably modeling adsorbate-induced deformation to near-DFT accuracy has not been established. This work benchmarks the efficacy of several general FFs in describing adsorbate-induced deformation for DAC against DFT. Specifically, we compare a common classical FF (UFF4MOF) with several machine learning (ML) FFs: M3GNet, CHGNet, MACE-MP-0, MACE-MPA-0, eSEN, and the Equiformer V2 model developed from the recent Open DAC 2023 dataset. Our results show that current classical methods are insufficient for describing framework deformation, especially in cases of interest for DAC where strong interactions exist between adsorbed molecules and MOF frameworks. The emerging ML methods we tested -- particularly CHGNet, MACE-MP-0, and Equiformer V2 -- appear to be more promising than the classical FF for emulating the deformation behavior described by DFT but fail to achieve the accuracy required for practical predictions. Flexibility (engineering) ; Deformation (meteorology) ; Rigidity (electromagnetism) ; Parameterized complexity ; Force field (fiction) ; Generality ; Density functional theory ; Field (mathematics) Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Covalent Organic Framework Applications
Authors: Xia Liang | Johan Klarbring | Aron Walsh
Source: ArXiv.org (preprint, 2025-07-10)
Field: Materials Science — Materials Chemistry

Phase Stability and Transformations in Lead Mixed Halide Perovskites from Machine Learning Force Fields Lead halide perovskites (APbX$_3$) offer tunable optoelectronic properties but feature an intricate phase-stability landscape. Here we employ on-the-fly data collection and an equivariant message-passing neural-network potential to perform large-scale molecular dynamics of three prototypical perovskite systems: CsPbX$_3$, MAPbX$_3$, and FAPbX$_3$. Integrating these simulations with the PDynA analysis toolkit, we resolve both equilibrium phase diagrams and dynamic structural evolution under varying temperature and halide-mixing conditions. Our findings reveal that the A-site cation strongly modulates octahedral tilt modes and phase pathways: MA$^+$ effectively "forbids" the beta-to-gamma transition in MAPbX$_3$ by requiring extensive molecular rearrangements and crystal rotation, whereas the debated low-temperature phase in FAPbX$_3$ is best represented as an Im$\bar{3}$ cubic phase with $a^+a^+a^+$ tilts. Additionally, small changes in halide composition and arrangement $\unicode{x2013}$ from uniform mixing to partial segregation $\unicode{x2013}$ alter tilt correlations. Segregated domains can even foster anomalous tilting modes that impede uniform phase transformations. These results highlight the multi-scale interplay between cation environment and halide distribution, offering a rational basis for tuning perovskite architectures toward improved phase stability. Perovskite (structure) ; Halide ; Phase (matter) ; Phase diagram ; Phase transition ; Stability (learning theory) ; Mixing (physics) ; Crystal (programming language) ; Octahedron Machine Learning in Materials Science ; Perovskite Materials and Applications ; Heusler alloys: electronic and magnetic properties
Authors: Daniel Khaykelson | Gabriel A. A. Diab | Sidney Cohen | Tamar Kashti | Tatyana Bendikov | Iddo Pinkas | Ivo F. Teixeira | Nadezda V. Tarakina | Lothar Houben | Boris Rybtchinski
Source: Nano Letters (article, 2025-12-01)
Field: Materials Science — Materials Chemistry

Elucidating Structural Disorder in a Polymeric Layered Material: The Case of Sodium Poly(heptazine imide) Photocatalyst Structurally heterogeneous materials present major challenges for characterization due to their complex nanoscale order. Sodium poly(heptazine imide) (NaPHI), a layered carbon nitride photocatalyst, exemplifies this complexity, with its precise structure remaining unresolved. Here, we uncover new structural insights into NaPHI using energy-filtered four-dimensional scanning transmission electron microscopy combined with machine-learning-based diffraction image segmentation, supported by transmission electron microscopy, atomic force microscopy, X-ray diffraction, and Raman spectroscopy. At the mesoscale, NaPHI flakes display bent morphologies, while nanodiffraction patterns reveal features characteristic of stacking disorder. Based on these insights, we modeled a NaPHI-layered structure incorporating out-of-plane undulations (waves) with amplitudes of ∼0.5 Å and wavelengths of 2-3 nm. This model reproduces the observed line features in nanodiffraction patterns and agrees with powder X-ray diffraction data, thereby bridging local and bulk structural information. The introduced approach uses data-driven machine learning to identify statistically significant features, offering a robust framework for structural analysis of semi-crystalline materials. Stacking ; Transmission electron microscopy ; Raman spectroscopy ; Diffraction ; Nanoscopic scale ; Characterization (materials science) ; Electron diffraction ; Carbon nitride ; Photocatalysis Machine Learning in Materials Science ; 2D Materials and Applications ; Advanced Photocatalysis Techniques
Authors: Faiz Ahmad | Oluwayomi J. Oyejide | Shahrul Kamaruddin | Modestus O. Okwu | Francis Amadhe | Abdul Basıt | Rilwan Anjorin | Mohammed El Faghi Abdulmalek
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Novel Stacked-Model Configuration for Merox-Treated Gasoline Yield Prediction Synergized with Emms-Cfd Hydrodynamic Analysis Computational fluid dynamics ; Yield (engineering) ; Gasoline ; Materials science ; Engineering ; Aerospace engineering ; Composite material ; Waste management Petroleum Processing and Analysis ; Fault Detection and Control Systems
Authors: Shih-Hung Cheng | Er-Feng Hsieh | Ting-I Kuo | Wen–Jeng Hsueh
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Electrically Controlled Edge-Contact Spin Valves Based on Two-Dimensional Transition Metal Dichalcogenides Transition metal ; Enhanced Data Rates for GSM Evolution ; Condensed matter physics ; Materials science ; Spin (aerodynamics) ; Nanotechnology ; Chemistry ; Physics ; Computer science ; Telecommunications ; Thermodynamics 2D Materials and Applications ; Physics of Superconductivity and Magnetism ; Magnetic and transport properties of perovskites and related materials
Authors: Peña-Cano, Pablo | Piaggi, Pablo M.
Source: ArXiv.org (article, 2025-11-18)
Field: Materials Science — Materials Chemistry

Hybrid ab initio and empirical machine learning models for the potential energy surface We propose a methodology to generate hybrid machine learning models for the potential energy surface trained simultaneously on data from ab initio electronic structure calculations and on thermodynamic and/or structural observables from experiment. The approach is based on the use of a loss function that includes the mean square error of observables with respect to their experimental values, in addition to the usual terms involving the mean square error of the energies and forces with respect to ab initio data. We employ a reweighting procedure that allows for the calculation of ensemble averages of observables during training for arbitrary values of the model parameters and on the fly. The method is general and can be applied to any set of static observables. We illustrate the usefulness of this approach by applying it to the generation of hybrid models for liquid water that reproduce accurately the experimental density maximum, the density isobar at 1 bar, and the radial distribution function in molecular dynamics simulations. Observable ; Ab initio ; Function (biology) ; Surface (topology) ; Energy (signal processing) ; Potential energy surface ; Mean squared error ; Statistical ensemble ; Potential energy Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Advanced Chemical Physics Studies