jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jun Fujima | Hideki Yoshikawa | H. Nagao | Hiroaki Tosaka | Takuya Kadohira | Masahiko Demura
Source: Science and Technology of Advanced Materials Methods (article, 2025-12-12)
Field: Materials Science — Materials Chemistry

Research data express: a data accumulation and sharing system for digital transformation in materials research To accelerate the digital transformation (DX) in materials science, foundational technology for effectively managing vast amounts of data from diverse experiments and calculations and sharing them in a reusable format is essential. However, a significant barrier is that data formats, description styles, and terminologies differ among research fields, instrument manufacturers, and models, and are often not publicly available as readily usable schemas, making them unreadable. Research Data Express (RDE), introduced in this paper, is a highly flexible and scalable data accumulation and sharing system developed to solve these challenges. The core feature of RDE is the ‘Dataset Template’, which defines the format for data description and translation. Users can flexibly define data structures tailored to their research content using these templates. During data registration, raw files from experimental instruments and manually entered experimental conditions are automatically interpreted, integrated, and structured by a processing program defined in the template. This automated process includes metadata extraction, translation into common domain-specific terms, data visualization, and even the calculation of feature values for machine learning applications. In this paper, we detail the basic design and system architecture of RDE and explain the data management methodology based on Dataset Templates. RDE significantly reduces the burden of routine data processing for researchers and enhances data findability, interoperability, reusability (the FAIR principles), and traceability, thereby strongly promoting data-driven materials research. Metadata ; USable ; Raw data ; Scalability ; Process (computing) ; Data sharing ; Feature (linguistics) ; Data transformation ; Reusability ; Data management Machine Learning in Materials Science ; Research Data Management Practices ; Scientific Computing and Data Management
Authors: Guocheng Ding (14186401) | Wei Liu (20030) | Mengxuan Ling (20577520) | Feiyu Chen (7479809) | Zien Liu (22405771) | Qinqin Yuan (3944921) | Longjiu Cheng (1391263)
Source: Figshare (article, 2025-10-10)
Field: Chemistry — Organic Chemistry

Prediction of SF<sub>6</sub> Replacement Gases Basedon Machine Learning Global Warming Potential (GWP) is a crucial criterion for assessing the impact of SF<sub>6</sub> replacement gases on climate change, which is determined by radiative efficiency (RE) and atmospheric lifetime (τ). This work leverages six modified machine learning (ML) methods to estimate GWP and its critical parameters (τ and RE), as well as explore their correlations via molecular descriptors, aiming at high-throughput screening and evaluation of potential SF<sub>6</sub> replacement gases. Based on the coefficient of determination, mean absolute error, and root-mean-square error, the optimal ML methods are Histogram Gradient Boosting Regression for GWP (0.95, 0.28, and 0.33), Gradient Boosting Regression for τ (0.92, 0.28, and 0.40), and Extreme Tree for RE (0.90, 0.04, and 0.05). Furthermore, molecular descriptors analysis reveals that τ and RE are mainly influenced by the energy of the highest occupied molecular orbitals and the gap between the highest occupied molecular orbitals and the lowest unoccupied molecular orbitals, respectively, which jointly affect GWP. Ultimately, the developed models are applied to screen the GWP<sub>100</sub> of 853 molecules from the QuanDB data set, yielding six SF<sub>6</sub> replacement gases with low boiling points, low GWP, and high electrical strength. This work not only predicts viable alternatives to SF<sub>6</sub> replacement gases but also provides insight into intrinsic correlations between GWP and its critical parameters (τ and RE). Gradient boosting ; Boosting (machine learning) ; Regression ; Radiative transfer ; Histogram ; Regression analysis ; Work (physics) ; Greenhouse gas Chemical Thermodynamics and Molecular Structure ; Phase Equilibria and Thermodynamics ; Machine Learning in Materials Science
Authors: Kaiyang Wang | Vincenzo Lordi | Stephan Friedrich | Amit Samanta
Source: Computational Materials Science (article, 2025-12-23)
Field: Materials Science — Materials Chemistry

Development of an interatomic potential for the Ta–Li system Grain boundary ; Interatomic potential ; Embedded atom model ; Molecular dynamics ; Atom (system on chip) ; Melting point ; Ab initio quantum chemistry methods ; Phase (matter) ; Range (aeronautics) Microstructure and mechanical properties ; Metallurgical and Alloy Processes ; Fusion materials and technologies
Authors: Isaiah A. Moses | Chen Chen | Joan M. Redwing | Wesley F. Reinhart
Source: ArXiv.org (preprint, 2025-05-29)
Field: Materials Science — Materials Chemistry

Cross-Modal Characterization of Thin Film MoS$_2$ Using Generative Models The growth and characterization of materials using empirical optimization typically requires a significant amount of expert time, experience, and resources. Several complementary characterization methods are routinely performed to determine the quality and properties of a grown sample. Machine learning (ML) can support the conventional approaches by using historical data to guide and provide speed and efficiency to the growth and characterization of materials. Specifically, ML can provide quantitative information from characterization data that is typically obtained from a different modality. In this study, we have investigated the feasibility of projecting the quantitative metric from microscopy measurements, such as atomic force microscopy (AFM), using data obtained from spectroscopy measurements, like Raman spectroscopy. Generative models were also trained to generate the full and specific features of the Raman and photoluminescence spectra from each other and the AFM images of the thin film MoS$_2$. The results are promising and have provided a foundational guide for the use of ML for the cross-modal characterization of materials for their accelerated, efficient, and cost-effective discovery. Characterization (materials science) ; Raman spectroscopy ; Thin film ; Metric (unit) ; Atomic force microscopy ; Experimental data Machine Learning in Materials Science ; 2D Materials and Applications
Authors: Yuan Chiang | Youngsoo Choi | Daniel Osei-Kuffuor
Source: ArXiv.org (preprint, 2025-08-29)
Field: Materials Science — Materials Chemistry

Generative Latent Space Dynamics of Electron Density Modeling the time-dependent evolution of electron density is essential for understanding quantum mechanical behaviors of condensed matter and enabling predictive simulations in spectroscopy, photochemistry, and ultrafast science. Yet, while machine learning methods have advanced static density prediction, modeling its spatiotemporal dynamics remains largely unexplored. In this work, we introduce a generative framework that combines a 3D convolutional autoencoder with a latent diffusion model (LDM) to learn electron density trajectories from ab-initio molecular dynamics (AIMD) simulations. Our method encodes electron densities into a compact latent space and predicts their future states by sampling from the learned conditional distribution, enabling stable long-horizon rollouts without drift or collapse. To preserve statistical fidelity, we incorporate a scaled Jensen-Shannon divergence regularization that aligns generated and reference density distributions. On AIMD trajectories of liquid lithium at 800 K, our model accurately captures both the spatial correlations and the log-normal-like statistical structure of the density. The proposed framework has the potential to accelerate the simulation of quantum dynamics and overcome key challenges faced by current spatiotemporal machine learning methods as surrogates of quantum mechanical simulators. Autoencoder ; Quantum ; Divergence (linguistics) ; Generative model ; Regularization (linguistics) ; Key (lock) ; Electron density ; Density estimation ; Molecular dynamics Machine Learning in Materials Science ; Quantum, superfluid, helium dynamics ; Quantum many-body systems
Authors: Donald Hlungwani | Raesibe Sylvia Ledwaba | Phuti Ngoepe
Source: Springer Link (Chiba Institute of Technology) (article, 2025-11-25)
Field: Materials Science — Materials Chemistry

Development of machine-learned force fields for lithium-manganese-oxide (Li-Mn-O) spinel. Density functional theory accuracy at a lower computational cost The on-the-fly machine learning implemented in the VASP code was utilized to generate interatomic potentials for LiMn2O4 spinel. The potentials will be further expanded to incorporate the Li2MnO3 layered character to enable accurate large-scale atomistic simulations of layered-spinel composite structures at practical computational cost. Li2MnO3 is incorporated in LiMn2O4 structures, which are plagued by capacity fading due to the loss of manganese caused by irreversible structural changes, to improve the electrochemical performance of this material. As such, the generated MLFFs in the current study are a significant step in that direction. The MLFFs were able to reproduce the DFT data with a percentage difference of less than 2%, showing impressive accuracy. The generated MLFFs will be further expanded and improved in a future study. Density functional theory ; Current (fluid) ; Development (topology) ; Code (set theory) ; Computational complexity theory ; Interatomic potential ; Experimental data ; Simplicity Machine Learning in Materials Science ; Advancements in Battery Materials ; Advanced Battery Materials and Technologies
Authors: Quirin Göttl | Natalie Rosen | Jakob Burger
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Convex Envelope Method for T, P Flash Calculations for Chemical Systems with an Arbitrary Number of Components and Arbitrary Aggregate States Envelope (radar) ; Aggregate (composite) ; Regular polygon ; Flash (photography) ; Mathematics ; Applied mathematics ; Statistical physics ; Materials science ; Mathematical analysis ; Physics ; Combinatorics ; Geometry ; Computer science ; Nanotechnology ; Optics ; Telecommunications Advanced Physical and Chemical Molecular Interactions
Authors: E. Häbel
Source: Journal of Information Systems Engineering & Management (article, 2025-05-10)
Field: Materials Science — Materials Chemistry

Optimal Design of Medium Voltage Composite Insulator Profiles Using Finite Element Method and Artificial Neural Networks Polymeric insulators are extensively used in high-voltage applications due to their performance hydrophobic properties. Electrical field distribution along the insulator surface is considered one of the important parameters for the performance evaluation of outdoor insulators. Reliability of medium voltage composite insulators depends on achieving an optimal electrical and geometrical design that produces the lowest electric field strength. This paper proposes a method to find the optimal geometrical design of composite insulators for different voltage levels. This method combined Finite Element Method (FEM) and Artificial Neural Networks (ANN).First, it was used the FEM to evaluate the influence of insulator geometrical parameters on the electric field strength such as shed radius, core diameter, shed inclination angle and distance between the first shed and end fitting. The electric field strength was calculated by using the commercial software package COMSOL Multiphysics. Then, the ANN algorithm has been implemented using the MATLAB program to improve the electric field distribution of composite insulators by reducing the value of maximum electric field strength in order to optimize the original profile that produces the lowest electric field strength. The results of this study are able to provide theoretical support to design and select the suitable profile of composite insulators in order to obtain better performance. Finite element method ; Electric field ; Voltage ; Insulator (electricity) ; Artificial neural network ; Optimal design ; MATLAB ; Reliability (semiconductor) High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Thermal Analysis in Power Transmission
Authors: Prachi Garg | Baishakhi Mazumder
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Ceramics and Composites

Revealing Degradation Mechanisms in YSZ Ceramics Through Machine Learning-Guided Aging and Multiscale Characterization Grain boundary ; Microstructure ; Characterization (materials science) ; Degradation (telecommunications) ; Yttria-stabilized zirconia ; Ceramic ; Cubic zirconia ; Phase (matter) ; Monoclinic crystal system Advanced ceramic materials synthesis ; Advancements in Solid Oxide Fuel Cells ; Recycling and utilization of industrial and municipal waste in materials production
Authors: Jeong Min Choi | Jaehoon Kim | Ji‐Hwan Lee | Won‐Joon Son | Seungwu Han
Source: ArXiv.org (preprint, 2025-09-25)
Field: Materials Science — Materials Chemistry

Atomistic Insights into Cu/amorphous-Ta$_x$N Interfacial Adhesion via Machine Learning Interatomic Potentials: Effects of Stoichiometry and Interface Construction Accurate understanding and control of interfacial adhesion between Cu and Ta$_x$N diffusion barriers are essential for ensuring the mechanical reliability and integrity of Cu interconnect systems in semiconductor devices. Amorphous tantalum nitride (a-Ta$_x$N) barriers are particularly attractive due to their superior barrier performance, attributed to the absence of grain boundaries. However, a systematic atomistic investigation of how varying Ta stoichiometries influences adhesion strength at Cu/a-Ta$_x$N interfaces remains lacking, hindering a comprehensive understanding of interface optimization strategies. In this study, we employ machine learning interatomic potentials (MLIPs) to perform steered molecular dynamics (SMD) simulations of Cu/a-Ta$_x$N interfaces. We simultaneously evaluate three distinct interface construction approaches--static relaxation, high-temperature annealing, and simulated Cu deposition--to comprehensively investigate their influence on adhesion strength across varying Ta compositions ($x=1, 2, 4$). Peak force and work of adhesion values from SMD simulations quantitatively characterize interface strength, while atomic stress and strain analyses elucidate detailed deformation behavior, highlighting the critical role of interfacial morphologies. Additionally, we explore the atomistic mechanisms underlying cohesive failure, revealing how targeted incorporation of Ta atoms into Cu layers enhances the cohesive strength of the interface. This study demonstrates how MLIP-driven simulations can elucidate atomic-scale relationships between interface morphology and adhesion behavior, providing insights that can guide future atomistic engineering strategies toward enhancing intrinsic barrier adhesion, potentially enabling liner-free interconnect technologies. Molecular dynamics ; Interatomic potential ; Adhesion ; Deformation (meteorology) ; Diffusion barrier ; Interface (matter) ; Stress (linguistics) ; Diffusion Block Copolymer Self-Assembly ; Copper Interconnects and Reliability ; Nanofabrication and Lithography Techniques
Authors: Shuang Liang | Liang Yu | Jia Liu | Yang Song | Zhengbo Chen
Source: Food Chemistry (article, 2025-10-24)
Field: Materials Science — Materials Chemistry

Colorimetric sensor array for flavonoids recognition based on peroxidase-like FeCo nanosheet Nanosheet ; Absorbance ; Sensor array ; Flavonoid ; Linear discriminant analysis ; Fingerprint (computing) ; Redox Advanced Nanomaterials in Catalysis ; Advanced biosensing and bioanalysis techniques ; Electrochemical sensors and biosensors
Authors: Jelena Tamulienė | Jonas Šarlauskas
Source: Energies (article, 2025-04-05)
Field: Chemistry — Physical and Theoretical Chemistry

Role of -SF5 Groups in Modulating the Stability and Energy Characteristics of Fluorinated Molecules In this paper, we present our investigations into the detonation performance and stability variations caused by replacing the -CF3 or -OCF3 group with -SF5. The widely applied DFT B3LYP/cc-pVTZ approach was employed to evaluate the HOMO–LUMO gap, cohesive energy, chemical hardness, and electronegativity. Based on these parameters, we predict the changes in chemical and thermal stability resulting from the inclusion of -SF5 instead of -CF3 or -OCF3. Our results indicate that, in some cases, the density of fluorine-containing nitro compounds decreases due to the presence of the pentafluorosulfanyl group. Additionally, machine learning techniques were used to determine the detonation pressure and velocity of fluorine–sulfur-containing compounds. Our findings suggest that fluorine-containing nitro compounds exhibit better detonation performance and stability than fluorine–sulfur-containing ones. Overall, the pentafluorosulfanyl groups inclusion of aromatic polynitro compounds improved neither the stability nor the detonation properties such as -CF3 or -OCF3 groups. Molecule ; Stability (learning theory) ; Energy (signal processing) ; Chemistry ; Political science ; Computer science ; Physics ; Organic chemistry ; Quantum mechanics Chemical Reactions and Mechanisms ; Fullerene Chemistry and Applications ; Energetic Materials and Combustion
Authors: Jeong Min Choi | Jaehoon Kim | Ji‐Hwan Lee | Won‐Joon Son | Seungwu Han
Source: ACS Applied Electronic Materials (article, 2025-12-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Atomistic Insights into Cu/Amorphous-Ta <sub> <i>x</i> </sub> N Interfacial Adhesion via Machine Learning Interatomic Potentials: Effects of Stoichiometry and Interface Construction Accurate understanding and control of interfacial adhesion between Cu and TaxN diffusion barriers are essential for ensuring the mechanical reliability and integrity of Cu interconnect systems in semiconductor devices. Amorphous tantalum nitride (a-TaxN) barriers are particularly attractive due to their superior barrier performance, attributed to the absence of grain boundaries. However, a systematic atomistic investigation of how varying Ta stoichiometries influences adhesion strength at Cu/a-TaxN interfaces remains lacking, hindering a comprehensive understanding of interface optimization strategies. In this study, we employ machine learning interatomic potentials (MLIPs) to perform steered molecular dynamics (SMD) simulations of Cu/a-TaxN interfaces. We simultaneously evaluate three distinct interface construction approaches─static relaxation, high-temperature annealing, and simulated Cu deposition─to comprehensively investigate their influence on adhesion strength across varying Ta compositions (x = 1, 2, 4). Peak force and work of adhesion values from SMD simulations quantitatively characterize interface strength, while atomic stress and strain analyses elucidate detailed deformation behavior, highlighting the critical role of interfacial morphologies. Additionally, we explore the atomistic mechanisms underlying cohesive failure, revealing how targeted incorporation of Ta atoms into Cu layers enhances the cohesive strength of the interface. This study demonstrates how MLIP-driven simulations can elucidate atomic-scale relationships between interface morphology and adhesion behavior, providing insights that can guide future atomistic engineering strategies toward enhancing intrinsic barrier adhesion, potentially enabling liner-free interconnect technologies. Molecular dynamics ; Interatomic potential ; Adhesion ; Deformation (meteorology) ; Interface (matter) ; Diffusion barrier ; Stress (linguistics) ; Diffusion Copper Interconnects and Reliability ; Metal and Thin Film Mechanics ; Semiconductor materials and devices
Authors: Xinliang Zhang | Chenyu Li | Jingtao Liu | Lijie Jia
Source: Advanced Theory and Simulations (article, 2025-07-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A Physics‐Informed Hysteretic Neural Network for Duhem Hysteresis Modeling and Parameters Identification Abstract The Duhem model is widely used for modeling hysteresis in smart materials due to its explicit differential equations and accuracy in capturing Preisach‐like nonlinearities. However, its non‐smooth switching operator presents challenges in parameter identification due to the nonconvex optimization involved. This paper introduces a physics‐informed hybrid neural network, namely hysteretic neural network‐long short‐term memory (HNN‐LSTM), for identifying Duhem model parameters and modeling the hysteresis nonlinearity. First, a LSTM network with an expanded input is constructed to describe the nonlinear hysteresis between input and output. The resulting LSTM sub‐model achieves universal approximation of the Duhem hysteresis and accurate estimation of the internal state. Second, a physics‐informed sub‐model, HNN, is then introduced to impose physical constraints on LSTM training by embedding Duhem model parameters into network weights. Then, by minimizing a composite loss combining numerical and physical errors, the model achieves both numerical accuracy and physical consistency. Thus, parameter identification is thereby accomplished by solving the inverse problem of the HNN‐LSTM while ensuring consistency with the original differential model. Finally, simulation and experimental results confirm the effectiveness of the proposed method. This approach offers a promising solution for modeling non‐smooth systems and estimating their parameters accurately. Hysteresis ; Artificial neural network ; Identification (biology) ; Statistical physics ; Physics ; Computer science ; Artificial intelligence ; Condensed matter physics ; Biology Magnetic Properties and Applications ; Model Reduction and Neural Networks ; Piezoelectric Actuators and Control
Authors: Deepak Kumar (63095) | Vinayak Dixit (6448814) | Manojkumar Ramteke (2094313) | Hariprasad Kodamana (7454270)
Source: Figshare (article, 2025-05-27)
Field: Materials Science — Materials Chemistry

Learning System Physics Using Symbolic Neural Integration(SyNISM) with Applications to Chemical Processes Learning the underlying physics of complex systems is a fundamental step in advancing data-driven modeling and engineering design. Symbolic regression (SR) is crucial for deriving data-driven, interpretable mathematical models that provide insights into complex systems, particularly in chemical engineering. While neural networks (NNs) excel at modeling nonlinear relationships, they often lack interpretability, making it difficult to extract explicit mathematical expressions. SR addresses this challenge, but combining it with NNs presents difficulties in handling complex mathematical operations, such as multiplication, division, and transcendental functions. To address these challenges, we propose a hybrid framework, SyNISM, that learns system physics by combining SR and NNs. Using a parameter hypernetwork to dynamically generate sparse parameters, SyNISM ensures interpretability while learning physically consistent representations of the system dynamics. This approach resolves challenges related to the differentiability and parameter selection using custom activation functions and probabilistic sampling. The framework is validated through case studies on batch reactors, continuous stirred tank reactors (CSTRs), series reactions, unsteady-state heat transfer, and thermophysical property modeling, where it accurately learns underlying physics and produces symbolic equations aligned with analytical solutions. SyNISM also performed better than traditional SR approaches in more than 75% of the applied cases in predicting beyond the training data. Interpretability ; Symbolic regression ; Artificial neural network ; Probabilistic logic ; Nonlinear system ; Complex system ; Property (philosophy) ; Dynamical systems theory ; Differentiable function Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Evolutionary Algorithms and Applications