jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Waysenson, Zacharie | France-Lanord, Arthur | SERVA, ALESSANDRA | SIMON, Patrice | Salanne, Mathieu | Saitta, A. Marco
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-12-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Data associated to the article "Electrode Flexibility Enhances Electrolyte Dynamics during Supercapacitor Charging" Contains input file used to perform the simulations of the article: Electrode Flexibility Enhances Electrolyte Dynamics during Supercapacitor Charging (DOI: 10.1021/acsnano.5c07490) These input files allow to simulate flexible carbon electrodes at fixed potential using a machine-learning potential. Supercapacitor ; Flexibility (engineering) ; Electrolyte ; Electrode ; Dynamics (music) Supercapacitor Materials and Fabrication ; Neural Networks and Applications ; Electric Power Systems and Control
Authors: Radu A. Talmazan | Christophe Chipot
Source: ArXiv.org (preprint, 2025-11-26)
Field: Materials Science — Materials Chemistry

From Static Pathways to Dynamic Mechanisms: A Committor-Based Data-Driven Approach to Chemical Reactions As computational chemistry methods evolve, dynamic effects have been increasingly recognized to govern chemical reaction pathways in both organic and inorganic systems. Here, we introduce a committor-based workflow that integrates a path-committor-consistent artificial neural network (PCCANN) with an iteratively trained hybrid-DFT-level message passing atomic convolutional encoder (MACE) potential. Beginning with a static nudged elastic band path, PCCANN extracts a committor-consistent string to represent the reactive ensemble. We illustrate the power of this methodology through two representative applications. First, we investigate an SNAr reaction using MACE trained at hybrid DFT level with implicit solvent. The mechanism is found to be concerted, and the dynamic approach reveals a lower barrier than static treatments. Second, we apply the same protocol to the isomerization of protonated isobutanol to protonated 2-butanol, yielding a quantitatively accurate free-energy landscape. We uncover three competing channels: the established concerted mechanism and two asynchronous stepwise routes mediated by water and methyl transfer, all with comparable activation barriers. Notably, the stepwise pathways traverse metastable intermediates that, to the best of our knowledge, have not been described in prior mechanistic studies. Calculated barrier heights and intermediate stabilities are in close agreement with high-level DFT benchmarks, demonstrating the framework's accuracy. Together, these studies highlight mechanistic diversity across distinct systems and establish the synergistic PCCANN-MACE protocol as a proof-of-concept approach for committor-based discovery of complex reaction dynamics. Chemical reaction ; Protonation ; Mechanism (biology) ; Reaction mechanism ; Reaction intermediate ; Metastability ; Asynchronous communication Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Various Chemistry Research Topics
Authors: Jinwei Chen | Yinshi Zhao | Yudeng Wang | Yang Yu | Yalin Li | Fan Wu | Fanyi Meng | Chang Ding | Jiafu Wang
Source:  (article, 2025-08-08)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Neural Network-Based Inverse Design of Metasurfaces with a Physics-Informed Intelligent Algorithm This paper proposes a novel physics-informed machine learning approach for metasurface inverse design, which integrates a physics-assisted genetic algorithm with a residual neural network (ResNet) to generate optimal antenna structures based on input performance requirements. To reduce parameter dimensionality and enhance training accuracy, we incorporate coupled mode theory (CMT) into the genetic algorithm for efficient parameter fitting.The proposed inverse design methodology ingeniously combines ResNet with CMT principles, significantly streamlining the traditionally cumbersome metasurface design process. The CMT framework effectively reduces network complexity and scale while elucidating the intricate relationship between geometric configurations and reflection spectra. By simply specifying target performance metrics, our system can produce optimized patch unit structures that surpass existing dataset limitations.As a test case, we applied this inverse design network to a liquid crystal-based metasurface. Experimental results demonstrate the capability to achieve phase differences exceeding 360°, thereby validating both the effectiveness and practical utility of our proposed methodology. Inverse ; Genetic algorithm ; Artificial neural network ; Curse of dimensionality ; Dimension (graph theory) ; Reflection (computer programming) ; Residual ; Inverse problem ; Representation (politics) ; Key (lock) Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Optimization
Authors: Yanhao Deng (9287145) | Tianle Jiang (18933145) | Zeyu Deng (3689377) | Yanming Wang (142016)
Source: Figshare (article, 2025-11-07)
Field: Materials Science — Materials Chemistry

luMOD: A LightweightUniversal Midrange Atomic OrientationalDescriptor Extracting structural information in atomistic simulations is crucial for applying machine learning to understand structure–property relationships and accelerate materials exploration. Existing descriptors can encode beyond-local environments and address multispecies complexities, but often suffer from high dimensionality and limited flexibility. In this work, we develop luMOD, a lightweight universal Midrange atomic Orientational Descriptor composed of only 24 features. Despite its compact size, it integrates direct and convoluted bond orientational order parameters with neighbor species distributions, enabling efficient description of heterogeneous atomic environments in several nearest-neighbor layers. luMOD demonstrates strong versatility across both single- and multispecies systems, including complex crystal structure interfaces such as perovskites and olivines. In addition to the well-maintained accuracy and applicability of luMOD, its fixed short length generally helps reduce the training time of downstream machine learning models, particularly in multispecies systems. We believe this development offers a promising step toward more efficient and scalable structural descriptors, helping to accelerate data-driven materials discovery and design. Curse of dimensionality ; ENCODE ; Scalability ; Order (exchange) ; Encoding (memory) ; Representation (politics) Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Inorganic Chemistry and Materials
Authors: Süleyman Aşır | Nemah Abu Shama | Nasir Saleem | Devrim Kayalı | Kamil Dimililer
Source: TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES (article, 2025-01-15)
Field: Chemistry — Electrochemistry

Machine Learning Models Approach for the Quantitative Classification of Ferricyanide Compound Using Electrochemical Detection with CPE-Fe3O4NPs Most common electrochemical analysis techniques used to evaluate enzymes, proteins, and heavy metals over a wide potential range include electrochemical impedance EIS, differential pulse voltammetry DPV, and square wave voltammetry SQWV. Machine leaning algorithms MLA are employed to classify the Potassium ferricyaniyde K3Fe(CN)6 concentrations using a modified carbon paste electrode CPE embedded with iron (II, III) oxide (Fe3O4) NPs. The CV, DPV, and SQWV voltametric data collected from all K3Fe(CN)6 concentrations were used as input data to the machine learning algorithms. Signaling current of K3Fe(CN)6 concentrations improved at Fe3O4 modified with nanoparticles NPs CPE in a comparison with the unmodified CPE from the results of the CV, DPV, and SQWV experiments. The high conductivity of Fe3O4 NPs led to an increase in their electrical signals in CV, DPV, and SQWV measurements. The findings demonstrated that K3Fe(CN)6 provided a linear relationship for CV, DPV, and SQWV throughout the concentrations variety of 2.0 to 10.0 mM (R2 = 0.9898, 0.9932, and 0.9937, respectively). For CV, DPV, and SQWV, the limit of detection LOD was determined to be 0.380, 0.320, and 0.300 mM, respectively. Machine learning techniques are often employed for categorization once the machine learning ML methodology has been applied to the CV, DPV, and SQWV data. To categorize the variants, various machine learning techniques have been taken into consideration. The results demonstrated that for CV, DPV, and SQWV, respectively, the ratio of analyte’s classification accuracy at CPE was determined to be 100%, 100%, and 86.7%. For CV, DPV, and SQWV, individually, the classification accuracy of analyte at modified CPE was found to be 100%, 82.5%, and 100%. The results demonstrate that ML is capable of analyzing a broad spectrum of voltammetric data, enabling the rapid and precise determination of various of redox reactions during the data processing method. Differential pulse voltammetry ; Detection limit ; Potassium ferricyanide ; Analyte ; Materials science ; Analytical Chemistry (journal) ; Cyclic voltammetry ; Dielectric spectroscopy ; Chemistry ; Electrochemistry ; Chromatography ; Electrode ; Inorganic chemistry Electrochemical Analysis and Applications ; Electrochemical sensors and biosensors ; Conducting polymers and applications
Authors: Gerrit Gerhartz | Peter Lippmann | Fred A. Hamprecht
Source: ArXiv.org (preprint, 2025-09-30)
Field: Materials Science — Materials Chemistry

Equivariance by Local Canonicalization: A Matter of Representation Equivariant neural networks offer strong inductive biases for learning from molecular and geometric data but often rely on specialized, computationally expensive tensor operations. We present a framework to transfers existing tensor field networks into the more efficient local canonicalization paradigm, preserving equivariance while significantly improving the runtime. Within this framework, we systematically compare different equivariant representations in terms of theoretical complexity, empirical runtime, and predictive accuracy. We publish the tensor_frames package, a PyTorchGeometric based implementation for local canonicalization, that enables straightforward integration of equivariance into any standard message passing neural network. Equivariant map ; Tensor (intrinsic definition) ; Representation (politics) ; Artificial neural network ; Field (mathematics) ; Algebra over a field Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Graph Neural Networks
Authors: Yuling Yin (672923) | Cheng Qian (132529) | Chao Zhao (253684) | Feng Ding (33360)
Source: Figshare (article, 2025-12-06)
Field: Materials Science — Materials Chemistry

Formation Mechanismof Phosphorus on the Ag(111) Surface:From Atomic Chains to Pentamers and Blue Phosphorene The synthesis of blue phosphorene (BLP), a two-dimensional (2D) allotrope of phosphorus, has attracted great attention in the last ten years. During the efforts of synthesizing BLP, three distinctive phases of phosphorus on the Ag(111) surface, phosphorus chains (PCs), phosphorus pentamers (PPs), and small BLP islands, have been observed while their formation mechanisms gained a deeper understanding based on previous studies. Here, by using the highly accurate machine learning force field (MLFF), we have successfully reproduced the three phases of phosphorus on the Ag(111) surface by molecular dynamic (MD) simulations. Our simulation shows that, on a Ag(111) surface, (i) PCs are more stable than both PPs and BLP at low coverage; (ii) with the increase of the coverage of phosphorus, the formation of PCs becomes less stable than the PPs at medium concentration; and (iii) further increasing the phosphorus coverage leads to the fusion of the pentamers, the nucleation of 2D BLP islands, and a transition from PPs to a BLP layer on the Ag(111) surface. The formation mechanisms revealed by the MLFF agree precisely with numerous experimental observations and thus show that the mechanisms revealed by theoretical studies empowered by the MLFF can be used to guide the experimental design of materials synthesis. Phosphorene ; Black phosphorus ; Phosphorus ; Nucleation ; Layer (electronics) 2D Materials and Applications ; Electrocatalysts for Energy Conversion ; Machine Learning in Materials Science
Authors: Ali Banjafar | Guido Falk von Rudorff
Source: Universitätsbibliothek Kassel (article, 2025-11-07)
Field: Materials Science — Materials Chemistry

Intrinsic dimensionality of molecular properties Chemical space, which encompasses all stable compounds, is unfathomably large, and its dimension scales linearly with the number of atoms considered. The success of machine learning methods suggests that many physical quantities exhibit substantial redundancy in that space, lowering their effective dimensionality. A low dimensionality is favorable for machine learning applications, as it reduces the required number of data points. It is unknown, however, how far the dimensionality of physical properties can be reduced, how this depends on the exact physical property considered, and how accepting a model error can help further reduce the dimensionality. We show that accepting a modest, nearly negligible error leads to a drastic reduction in independent degrees of freedom. This applies to several properties, such as the total energy, frontier orbital energies, and static polarizability for a wide range of neutral molecules with up to 20 atoms. We provide a method to quantify an upper bound for the intrinsic dimensionality given a desired accuracy threshold by the inclusion of all continuous variables in the molecular Hamiltonian, including the nuclear charges. We find the intrinsic dimensionality to be remarkably stable across molecules, i.e., it is a property of the underlying physical quantity and the number of atoms rather than a property of an individual molecular configuration and, therefore, highly transferable between molecules. The results suggest that the feature space of state-of-the-art molecular representations can be compressed further, leaving room for more data efficient and transferable models. Curse of dimensionality ; Dimensionality reduction ; Property (philosophy) ; Redundancy (engineering) ; Intrinsic dimension ; Dimension (graph theory) ; Feature (linguistics) ; Range (aeronautics) Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Gaussian Processes and Bayesian Inference
Authors: N. Niang | Thierry Barrière | Xavier Gabrion | Sami Holopainen | Vincent Placet
Source: Composites Science and Technology (article, 2025-08-30)
Field: Materials Science — Polymers and Plastics

Predicting the deformability of natural short-fiber reinforced polymer composites through combined constitutive mathematical and AI-based modeling approaches Biocomposites are increasingly used to reduce the use of harmful fossil plastics. Modeling and simulation tools (digital twins) have been developed to replace expensive and time-consuming physical testing during product development based on these materials. In addition to manufacturing aspects and experimentation, this study proposes a micromechanically-based constitutive mathematical model to investigate the viscoelastic–plastic deformability of biocomposites consisting of a polymer matrix and short plant fibers. Due to random fiber orientation and strong bonding between the fibers and the amorphous and crystalline phases of the polymer matrix, influence of the lattice crystalline structure was suppressed. This enables the development of a compact constitutive model. However, constitutive mathematical modeling is computationally time-consuming when applied to predict the long-term deformation behavior in large design spaces. Therefore, the proposed model is used solely to generate high-quality data for machine learning (ML) which is highly computationally efficient. The scaled-up design of new biodegradable polymeric materials, traditionally reliant on costly and time-intensive experimental procedures, is then accelerated by an advanced modeling framework that integrates constitutive mathematical models with AI-based approaches. Materials science ; Composite material ; Constitutive equation ; Natural fiber ; Fiber ; Polymer ; Synthetic fiber ; Finite element method ; Structural engineering Natural Fiber Reinforced Composites ; Innovations in Concrete and Construction Materials ; Innovative concrete reinforcement materials
Authors: Yuling Yin | Cheng Qian | Chao Zhao | Feng Ding
Source: The Journal of Physical Chemistry Letters (article, 2025-12-05)
Field: Materials Science — Materials Chemistry

Formation Mechanism of Phosphorus on the Ag(111) Surface: From Atomic Chains to Pentamers and Blue Phosphorene The synthesis of blue phosphorene (BLP), a two-dimensional (2D) allotrope of phosphorus, has attracted great attention in the last ten years. During the efforts of synthesizing BLP, three distinctive phases of phosphorus on the Ag(111) surface, phosphorus chains (PCs), phosphorus pentamers (PPs), and small BLP islands, have been observed while their formation mechanisms gained a deeper understanding based on previous studies. Here, by using the highly accurate machine learning force field (MLFF), we have successfully reproduced the three phases of phosphorus on the Ag(111) surface by molecular dynamic (MD) simulations. Our simulation shows that, on a Ag(111) surface, (i) PCs are more stable than both PPs and BLP at low coverage; (ii) with the increase of the coverage of phosphorus, the formation of PCs becomes less stable than the PPs at medium concentration; and (iii) further increasing the phosphorus coverage leads to the fusion of the pentamers, the nucleation of 2D BLP islands, and a transition from PPs to a BLP layer on the Ag(111) surface. The formation mechanisms revealed by the MLFF agree precisely with numerous experimental observations and thus show that the mechanisms revealed by theoretical studies empowered by the MLFF can be used to guide the experimental design of materials synthesis. Phosphorene ; Black phosphorus ; Phosphorus ; Nucleation ; Layer (electronics) 2D Materials and Applications ; Electrocatalysts for Energy Conversion ; Graphene research and applications
Authors: Martin Vondrák | Karsten Reuter | Johannes T. Margraf
Source: ChemRxiv (preprint, 2025-06-21)
Field: Materials Science — Materials Chemistry

Pushing Charge Equilibration Based Machine Learning Potentials to their Limits Machine learning (ML) has demonstrated its potential in atomistic simulations to bridge the gap between accurate first-principles methods and computationally efficient empirical potentials. This is achieved by learning mappings between a system's structure and its physical properties. State-of-the-art models for potential energy surfaces typically represent chemical structures through (semi-)local atomic environments. However, this approach neglects long-range interactions (most notably electrostatics) and non-local phenomena such as charge transfer, leading to significant errors in the description of molecules or materials in polar anisotropic environments. To address these challenges, ML frameworks that predict self-consistent charge distributions in atomistic systems using the Charge Equilibration (QEq) method are currently popular. In this approach, atomic charges are derived from an electrostatic energy expression that incorporates environment-dependent atomic electronegativities. Herein, we explore the limits of this concept at the example of the previously reported Kernel Charge Equilibration (kQEq) approach, combined with local short-ranged potentials. To this end we consider prototypical systems with varying total charge states and applied electric fields. We find that charge equilibration based models perform well in most situations. However, we also find that some pathologies of conventional QEq carry over to the ML variants in the form of spurious charge transfer and overpolarization in the presence of static electric fields. This indicates a need for new methodological developments. Electronegativity ; Spurious relationship ; Charge (physics) ; Electrostatics ; Statistical physics ; Range (aeronautics) ; Partial charge ; Kernel (algebra) ; Chemistry ; Electrostatic induction ; Atomic charge ; Chemical physics ; Physics ; Computer science ; Computational physics ; Molecule ; Machine learning ; Quantum mechanics ; Materials science ; Mathematics Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Electrochemical Analysis and Applications
Authors: P. F. S. Rosa
Source:  (article, 2025-02-11)
Field: Materials Science — Materials Chemistry

Challenges and Opportunities in Advanced Materials for QIS Highly tunable through gating, strain, optics, stacking, Can machine learning help?Are there tractable problems?Well-defined atomic-point defects in 2D materials hold promise for applications in QIS. Computer science ; Knowledge management ; Process management ; Business Machine Learning in Materials Science
Authors: Sanath Keshav | Julius Herb | Felix Fritzen
Source: ArXiv.org (preprint, 2025-04-01)
Field: Materials Science — Materials Chemistry

Spectral Normalization and Voigt-Reuss net: A universal approach to microstructure-property forecasting with physical guarantees Heterogeneous materials are crucial to producing lightweight components, functional components, and structures composed of them. A crucial step in the design process is the rapid evaluation of their effective mechanical, thermal, or, in general, constitutive properties. The established procedure is to use forward models that accept microstructure geometry and local constitutive properties as inputs. The classical simulation-based approach, which uses, e.g., finite elements and FFT-based solvers, can require substantial computational resources. At the same time, simulation-based models struggle to provide gradients with respect to the microstructure and the constitutive parameters. Such gradients are, however, of paramount importance for microstructure design and for inverting the microstructure-property mapping. Machine learning surrogates can excel in these situations. However, they can lead to unphysical predictions that violate essential bounds on the constitutive response, such as the upper (Voigt-like) or the lower (Reuss-like) bound in linear elasticity. Therefore, we propose a novel spectral normalization scheme that a priori enforces these bounds. The approach is fully agnostic with respect to the chosen microstructural features and the utilized surrogate model. All of these will automatically and strictly predict outputs that obey the upper and lower bounds by construction. The technique can be used for any constitutive tensor that is symmetric and where upper and lower bounds (in the Löwner sense) exist, i.e., for permeability, thermal conductivity, linear elasticity, and many more. We demonstrate the use of spectral normalization in the Voigt-Reuss net using a simple neural network. Numerical examples on truly extensive datasets illustrate the improved accuracy, robustness, and independence of the type of input features in comparison to much-used neural networks. Normalization (sociology) ; A priori and a posteriori ; Constitutive equation ; Upper and lower bounds ; Independence (probability theory) ; Artificial neural network ; Finite element method ; Cauchy elastic material Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Composite Material Mechanics