jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Anna Grünebohm | Matous Mrovec | Maxim N. Popov | L.‐S. Hsu | Yury Lysogorskiy | Anton Bochkarev | Ralf Drautz
Source: Physical Review Materials (article, 2025-10-10)
Field: Materials Science — Materials Chemistry

Efficient local atomic cluster expansion for <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>BaTiO</mml:mi><mml:mn>3</mml:mn></mml:msub></mml:math> close to equilibrium Barium titanate (BTO) is a representative perovskite oxide that undergoes three first-order ferroelectric phase transitions related to exceptional functional properties. In this work, we develop two atomic cluster expansion (ACE) models for BTO to reproduce fundamental properties of bulk and defective BTO phases. The two ACE models do not target full transferability but rather aim to examine the influence of implicit and explicit treatment of long-range Coulomb interactions. We demonstrate that both models describe equally well the temperature-induced phase transitions and field-induced polarization switching. Even though the parametrizations are based on a limited number of configurations that are mostly not far away from the equilibrium, the ACE models are also able to capture properties of important crystal defects, such as oxygen vacancies, stacking faults, and domain walls. The phase transitions and the fundamental properties of the investigated defects can be described with similar accuracy with or without explicit treatment of charges and Coulomb interactions, allowing for efficient short-range machine learning potentials. Coulomb ; Transferability ; Phase transition ; Barium titanate ; Cluster (spacecraft) ; Ferroelectricity ; Perovskite (structure) ; Polarization (electrochemistry) Electronic and Structural Properties of Oxides ; Machine Learning in Materials Science ; Ferroelectric and Piezoelectric Materials
Authors: Wei Zhang | Tinghong Gao | Yutao Liu | Guo‐Li Shen | Bei Wang | Zhongzhong Zhu | Jin Huang | Shipeng Zhang | Shuang Li
Source: Advanced Theory and Simulations (article, 2025-11-27)
Field: Materials Science — Materials Chemistry

Thermal Conductivity Modulation in Carbon Nanotubes via Silicon Nanowire Encapsulation Investigated Using Neuroevolution Potential ABSTRACT One‐dimensional van der Waals heterostructures based on single‐walled carbon nanotubes (SWCNT) have recently attracted increasing attention because of their structural flexibility and tunable thermal transport properties. Therefore, this study proposes a silicon nanowire (SiNW) encapsulation approach to construct a SiNW@CNT composite and systematically investigate its thermal transport behavior. Using an efficient neuroevolution potential model, we developed a high‐precision machine learning potential tailored for the SiNW@CNT structure. Using homogeneous non‐equilibrium molecular dynamics, equilibrium molecular dynamics, and heterogeneous nonequilibrium molecular dynamics simulations combined with spectral heat flux analysis, we found that SiNW encapsulation markedly reduces the thermal conductivity of SWCNT. The reduction in thermal conductivity becomes more pronounced as the SiNW filling ratio increases. At the maximum filling ratio, SiNW@CNT exhibits a thermal conductivity approximately 50% that of hollow SWCNTs. This reduction is attributed to SiNW encapsulation, which enhances phonon scattering within the SWCNT, shortens the phonon mean free path and lifetimes, and decreases overall thermal transport efficiency. In addition, as the system size increases, the thermal conductivity difference between SWCNT and SiNW@CNT widens, highlighting a clear size dependence and a transition from ballistic to diffusive transport. These findings provide a crucial theoretical basis for designing novel nanocomposites with tunable thermal conductivity. Thermal conductivity ; Carbon nanotube ; Nanowire ; Silicon ; Mean free path ; Phonon ; van der Waals force ; Molecular dynamics ; Non-equilibrium thermodynamics Thermal properties of materials ; Thermal Radiation and Cooling Technologies ; Carbon Nanotubes in Composites
Authors: Muhamad Akrom | Muhammad Reesa Rosyid | Lubna Mawaddah | Akbar Priyo Santosa
Source: Jurnal Online Informatika (article, 2025-04-01)
Field: Materials Science — Materials Chemistry

Variational Quantum Circuit-Based Quantum Machine Learning Approach for Predicting Corrosion Inhibition Efficiency of Expired Pharmaceuticals This study examines the potential of quantum machine learning (QML) to predict the corrosion inhibition capacity of expired pharmaceutical compounds. The investigation employs a QSPR model, using features generated from density functional theory (DFT) calculations as input. At the same time, corrosion inhibition efficiency (CIE) values obtained from experimental data serve as the target output. The VQC model demonstrates varied performance across evaluation metrics, especially with encoding and ansatz design. The model achieves fine scores in evaluation metrics, with root mean square error (RMSE) of 6.15, mean absolute error (MAE) of 5.63, and mean absolute deviation (MAD) of 5.50. The research underscores the significance of larger datasets for enhancing predictive accuracy and points to QML's potential in exploring anti-corrosion materials. Although there are some limitations, this study provides a foundational framework for using QML to predict anti-corrosive properties. Quantum ; Corrosion ; Computer science ; Materials science ; Physics ; Quantum mechanics ; Metallurgy Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Surface and Thin Film Phenomena
Authors: Gunaram | Ashu Choudhary | Gaurav Sharma
Source: Journal of Condensed Matter (article, 2025-05-04)
Field: Chemistry — Analytical Chemistry

Harnessing Machine Learning for Comparative Analysis of Nanomaterials in Agro-Environmental Applications This article explores the transformative potential of integrating nanomaterials (NM) and machine learning (ML) to address critical global challenges, particularly in agriculture sustainability and climate change mitigation. By conducting a comparative analysis of various nanomaterials and their applications in agriculture and environmental protection, we demonstrate how ML techniques can optimize the properties and functionalities of these materials. In agriculture, nanomaterials are used in developing nanofertilizers, nanopesticides, and nanosensors, which enhance crop yield, pest control, and soil health monitoring. In environmental applications, nanofilters help mitigate climate change-related issues. This research underscores the value of combining NM and ML to advance sustainable agro-environmental solutions, highlighting the role of interdisciplinary approaches in creating smarter, more efficient technologies. By leveraging advanced ML algorithms and AI, we can improve the specificity, sensitivity, and accuracy of nanomaterials, offering innovative solutions to challenges such as food security and environmental conservation. Nanomaterials ; Computer science ; Nanotechnology ; Materials science Spectroscopy and Chemometric Analyses
Authors: Shuya Chen | Fushuang Dai | Mengqi Guo | Chunwang Dong
Source: Foods (article, 2025-08-22)
Field: Chemistry — Analytical Chemistry

Assessment of Tenderness and Anthocyanin Content in Zijuan Tea Fresh Leaves Using Near-Infrared Spectroscopy Fused with Visual Features Focusing on the characteristic tea resource Zijuan tea, this study addresses the difficulty of grading on production lines and the complexity of quality evaluation. On the basis of the fusion of near-infrared (NIR) spectroscopy and visual features, a novel method is proposed for classifying different tenderness levels and quantitatively assessing key anthocyanin components in Zijuan tea fresh leaves. First, NIR spectra and visual feature data were collected, and anthocyanin components were quantitatively analyzed using UHPLC-Q-Exactive/MS. Then, four preprocessing techniques and three wavelength selection methods were applied to both individual and fused datasets. Tenderness classification models were developed using Particle Swarm Optimization-Support Vector Machine (PSO-SVM), Random Forest (RF), and Convolutional Neural Networks (CNNs). Additionally, prediction models for key anthocyanin content were established using linear Partial Least Squares Regression (PLSR), nonlinear Support Vector Regression (SVR) and RF. The results revealed significant differences in NIR spectral characteristics across different tenderness levels. Model combinations such as TEX + Medfilt + RF and NIR + Medfilt + CNN achieved 100% accuracy in both training and testing sets, demonstrating robust classification performance. The optimal models for predicting key anthocyanin contents also exhibited excellent predictive accuracy, enabling the rapid and nondestructive detection of six major anthocyanin components. This study provides a reliable and efficient method for intelligent tenderness classification and the rapid, nondestructive detection of key anthocyanin compounds in Zijuan tea, holding promising potential for quality control and raw material grading in the specialty tea industry. Anthocyanin ; Tenderness ; Near infrared reflectance spectroscopy ; Food science ; Near-infrared spectroscopy ; Chemistry ; Biology Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Phytochemicals and Antioxidant Activities
Authors: Mohsen Farshad | Fathya Y. M. Salih | Dinis O. Abranches | Yamil J. Colón
Source: The Journal of Chemical Physics (article, 2025-10-24)
Field: Materials Science — Materials Chemistry

Accelerating phase diagram construction through activity coefficient prediction Obtaining phase diagrams from molecular simulations remains computationally demanding due to the need for extensive sampling of coexistence conditions and large system sizes. This study demonstrates a novel methodology for efficiently predicting phase behavior in Lennard-Jones mixtures by leveraging machine learning. Here, we train a Gaussian process (GP) model on Kirkwood-Buff Integrals (KBIs) to establish a predictive link between KBIs and activity coefficients-a key thermodynamic quantity encoding deviations from ideality. Through the incorporation of KBI trends, the GP model leads to the prediction of the activity coefficients of two new systems without prior knowledge of their phase behavior, eliminating the need for direct coexistence simulations and significantly reducing computational cost. This framework has broad applicability in computational thermodynamics, offering a scalable strategy for studying complex mixtures with tunable interactions. Scalability ; Key (lock) ; Process (computing) ; Gaussian process ; Phase diagram ; Phase (matter) ; Encoding (memory) ; Gaussian Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Computational Drug Discovery Methods
Authors: Panagiotis Mougkogiannis | Andrew Adamatzky
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Biomaterials

Magnetic Field-Modulated Boolean Logic in Proteinoid-Fe3o4 Hybrid Materials Field (mathematics) ; Computer science ; Chemistry ; Nanotechnology ; Materials science ; Mathematics ; Pure mathematics Diatoms and Algae Research
Authors: Yuhan Zhao | Shaoxiang Yang
Source: ChemistrySelect (article, 2025-11-01)
Field: Materials Science — Materials Chemistry

Research on the Application Status of Artificial Intelligence in Covalent Organic Frameworks Abstract In recent years, the popularity of artificial intelligence (AI) has been rising, and its application fields have been expanding. In the field of covalent organic frameworks (COFs), AI can efficiently and quickly assist the screening, prediction, design, and optimization of COFs, thus expanding the application field of COFs. This paper systematically reviews the research progress in the application of AI in COFs in recent 5 years (2021–2025), and focuses on how machine learning (ML) and other algorithms can empower the structural design, performance prediction, and function optimization of COFs, so as to efficiently prepare target materials. And focus on its cross‐disciplinary applications in life and health, food, environment, energy and so on. In the end, the research trend of AI technology in COFs is prospected, and the core challenges faced by this cross‐field research (such as the shortage of interdisciplinary talents) are analyzed, so as to provide some reference for related research. Field (mathematics) ; Applications of artificial intelligence ; Popularity ; Economic shortage ; Function (biology) ; Focus (optics) ; Core (optical fiber) Covalent Organic Framework Applications ; Advanced Photocatalysis Techniques ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Chen Hao Xia | Manasa Kaniselvan | Marko Mladenović | Mathieu Luisier
Source:  (article, 2025-09-24)
Field: Materials Science — Materials Chemistry

Machine-Learned Hamiltonians for Quantum Transport Simulation of Valence Change Memories The construction of the Hamiltonian matrix <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{H}$</tex> is an essential, yet computationally expensive step in ab-initio device simulations based on density-functional theory (DFT). In homogeneous structures, the fact that a unit cell repeats itself along at least one direction can be leveraged to minimize the number of atoms considered and the calculation time. However, such an approach does not lend itself to amorphous or defective materials for which no periodicity exists. In these cases, (much) larger domains containing thousands of atoms might be needed to accurately describe the physics at play, pushing DFT tools to their limit. Here we address this issue by learning and directly predicting the Hamiltonian matrix of large structures through equivariant graph neural networks and so-called augmented partitioning training. We demonstrate the strength of our approach by modeling valence change memory (VCM) cells, achieving a Mean Absolute Error (MAE) of 3.39 to 3.58 meV, as compared to DFT, when predicting the Hamiltonian matrix entries of systems made of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\sim 5,000$</tex> atoms. We then replace the DFT-computed Hamiltonian of these VCMs with the predicted one to compute their energy-resolved transmission function with a quantum transport tool. A qualitatively good agreement between both sets of curves is obtained. Our work provides a path forward to overcome the memory and computational limits of DFT, thus enabling the study of large-scale devices beyond current ab-initio capabilities. Hamiltonian (control theory) ; Quantum ; Valence (chemistry) ; Hamiltonian matrix ; Homogeneous ; Graph ; Topology (electrical circuits) ; Artificial neural network Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Memory and Neural Computing
Authors: Kaba, Sékou-Oumar | Sareen, Kusha | Levy, Daniel | Ravanbakhsh, Siamak
Source: ArXiv.org (article, 2025-11-03)
Field: Materials Science — Materials Chemistry

Energy Loss Functions for Physical Systems Effectively leveraging prior knowledge of a system's physics is crucial for applications of machine learning to scientific domains. Previous approaches mostly focused on incorporating physical insights at the architectural level. In this paper, we propose a framework to leverage physical information directly into the loss function for prediction and generative modeling tasks on systems like molecules and spins. We derive energy loss functions assuming that each data sample is in thermal equilibrium with respect to an approximate energy landscape. By using the reverse KL divergence with a Boltzmann distribution around the data, we obtain the loss as an energy difference between the data and the model predictions. This perspective also recasts traditional objectives like MSE as energy-based, but with a physically meaningless energy. In contrast, our formulation yields physically grounded loss functions with gradients that better align with valid configurations, while being architecture-agnostic and computationally efficient. The energy loss functions also inherently respect physical symmetries. We demonstrate our approach on molecular generation and spin ground-state prediction and report significant improvements over baselines. Physical system ; Leverage (statistics) ; Energy (signal processing) ; Function (biology) ; Divergence (linguistics) ; Entropy (arrow of time) ; Physical science ; Thermal Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Graph Neural Networks
Authors: Kaba, Sékou-Oumar | Sareen, Kusha | Levy, Daniel | Ravanbakhsh, Siamak
Source: arXiv (Cornell University) (preprint, 2025-11-03)
Field: Materials Science — Materials Chemistry

Energy Loss Functions for Physical Systems Effectively leveraging prior knowledge of a system's physics is crucial for applications of machine learning to scientific domains. Previous approaches mostly focused on incorporating physical insights at the architectural level. In this paper, we propose a framework to leverage physical information directly into the loss function for prediction and generative modeling tasks on systems like molecules and spins. We derive energy loss functions assuming that each data sample is in thermal equilibrium with respect to an approximate energy landscape. By using the reverse KL divergence with a Boltzmann distribution around the data, we obtain the loss as an energy difference between the data and the model predictions. This perspective also recasts traditional objectives like MSE as energy-based, but with a physically meaningless energy. In contrast, our formulation yields physically grounded loss functions with gradients that better align with valid configurations, while being architecture-agnostic and computationally efficient. The energy loss functions also inherently respect physical symmetries. We demonstrate our approach on molecular generation and spin ground-state prediction and report significant improvements over baselines. Physical system ; Leverage (statistics) ; Energy (signal processing) ; Function (biology) ; Divergence (linguistics) ; Entropy (arrow of time) ; Physical science ; Thermal Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Graph Neural Networks
Authors: Rifqi Aulia Tanjung | Muhammad Alwi Foury | Fikan Mubarok Rohimsyah
Source: Diffusion and defect data, solid state data. Part B, Solid state phenomena/Solid state phenomena (article, 2025-10-23)
Field: Materials Science — Materials Chemistry

Using Artificial Neural Networks for the Prediction and Optimization of Electrochemical Capacity in Aluminum-Based Sacrificial Anodes Corrosion is a pervasive degradation mechanism that arises when materials interact with their surrounding environment, especially in aggressive conditions like seawater. One effective corrosion countermeasure is the use of sacrificial anodes for cathodic protection, with aluminum anodes becoming increasingly popular for seawater applications. The electrochemical capacity of these sacrificial anodes is pivotal for efficient corrosion prevention. However, optimizing their composition and design to adhere to standards and prolong protection is challenging. This study introduces an Artificial Neural Network (ANN) as a tool to refine the elemental composition of sacrificial anode materials. We devised an ANN model that predicts the electrochemical capacity of aluminum-based sacrificial anodes with impressive accuracy. We also examined the impact of several elements (indium, cadmium, silicon, iron, and copper) on anode capacity using the ANN simulations. The designed ANN models (Al-10) showcased accuracy rates of 92%, endorsing the potential of the ANN approach. Notably, iron was observed to augment anode capacity, while silicon exhibited a negative influence. This research showcases a novel computational approach with ANN to predict and optimize the electrochemical capacity of aluminum-based sacrificial anodes, paving the way for enhanced corrosion prevention in harsh seawater conditions. Anode ; Galvanic anode ; Cathodic protection ; Corrosion ; Electrochemistry ; Artificial seawater Anodic Oxide Films and Nanostructures
Authors: Song Zhang | Heng Zhao | Shiyu Ge | Yao Zhang
Source:  (article, 2025-05-16)
Field: Chemistry — Analytical Chemistry

A method for polarization detection and property identification of fine particulate matter based on LSTM The accurate classification and property identification of fine aerosol particles are of great significance for atmospheric pollution source apportionment, environmental health assessment, and climate model optimization. Polarization measurement technology based on light scattering has become an important means for online dynamic characterization due to its high sensitivity to the microscopic properties of aerosols (particle size, complex refractive index, morphology). This paper combines machine learning to collect aerosol scattering data using a multi-angle polarization vector measurement device, constructs a spatial-polarization correlation matrix (angle × polarization state) as the input for a Long Short-Term Memory (LSTM) network, and uses the LSTM model to learn the temporal characteristics of scattering signals and the spatial-polarization correlation properties. The results show that the LSTM model demonstrates high application potential in the material classification of fine aerosol particles, providing new research ideas and methodological references based on temporal modeling for this field. Particulates ; Polarization (electrochemistry) ; Identification (biology) ; Property (philosophy) ; Computer science ; Artificial intelligence ; Chemistry Spectroscopy and Chemometric Analyses ; Optical Polarization and Ellipsometry ; Remote Sensing and Land Use
Authors: Zamar Daka | Yuqi Zhang | Jieli Li | Liying Yao | Chenxi Luo | Meng Tian | Xi Xia | Gang Qin | Rong Jiang | Min Song | Shunli Ji
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Design of Green Multifunctional Mesoporous Nanosheets for Trace Biomarker Detection, AI-enhanced Pollutant Discrimination, and Synergistic Catalytic Degradation Catalysis ; Mesoporous material ; Adsorption ; Degradation (telecommunications) ; Pollutant ; Electron transfer ; Redox Advanced Nanomaterials in Catalysis ; Nanoplatforms for cancer theranostics ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Chaomin Ding | Yunlong Shao | Ying Kan | Zhengdong Zhang | Ke Li | Xin Zhang | Fan Liu | Dan Song
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Rapid Determination of Methamphetamine Content Using Mid-Infrared Spectroscopy with Chemical Prior Knowledge Partial least squares regression ; Feature selection ; Pattern recognition (psychology) ; Mephedrone ; Wavelet ; Chemometrics ; Noise (video) ; Artificial neural network Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Pharmaceutical Quality and Counterfeiting