jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Gayatri Rajput | Vijayalakshmi Gosu | Vikas Kumar Sangal | Ram B. Gupta | Verraboina Subbaramaiah
Source: Chemical Engineering Science (article, 2025-02-22)
Field: Chemistry — Organic Chemistry

Machine learning enabled catalytic wet peroxidation of levofloxacin bearing wastewater using Cu/MCM-41 Wastewater ; Bearing (navigation) ; MCM-41 ; Chemistry ; Catalysis ; Nuclear chemistry ; Pulp and paper industry ; Chemical engineering ; Waste management ; Engineering ; Computer science ; Organic chemistry ; Artificial intelligence Nanomaterials for catalytic reactions ; Catalytic Processes in Materials Science ; Advanced Photocatalysis Techniques
Authors: Haoran Lin | Ming Wu | Zihang Zhao | Fengyi Zhang | Chunze Zhou | Disheng Yang | Yikai Fu | Keying Feng | Lijun Liang
Source: ACS Applied Materials & Interfaces (article, 2025-08-21)
Field: Materials Science — Materials Chemistry

Design of High-Performance MXene-Based 2D Membranes for Desalination via Machine Learning and Hybrid Optimization Algorithms The global scarcity of freshwater resources has intensified the demand for efficient seawater desalination technologies. However, conventional approaches, such as reverse osmosis and nanofiltration, often suffer from high energy consumption and limited membrane performance. MXene, a promising two-dimensional (2D) material, offers unique structural and surface chemical properties that can enhance membrane-based separations. This study presents an integrated inverse design framework that combines machine learning (ML), optimization algorithms, and molecular dynamics (MD) simulations to accelerate the development of high-performance MXene membranes. A dual-target predictive model based on XGBoost was constructed to estimate the water flux and salt rejection, identifying surface charge density and pore area as key governing parameters. A range of intelligent optimization strategies was used to design the MXene membrane with a high desalination performance. Especially, the hybrid optimization methods showed clear advantages over baseline algorithms, delivering faster convergence and obtaining an optimized MXene membrane. MD simulations validated the predicted performance of optimized structures, showing good agreement with the prediction results of the ML model. The optimal configuration of Ti<sub>3</sub>C<sub>2</sub>O<sub>2</sub>─with a charge coefficient of 1.1 and a pore area of 82.72 Å<sup>2</sup>─achieved an excellent desalination performance with water permeability and salt rejection. This work demonstrates the power of integrating artificial intelligence and molecular modeling for the rational design of desalination membranes. The proposed framework provides a generalizable and scalable approach for advancing the intelligent development of 2D materials in water treatment applications. Materials science ; Desalination ; Membrane ; Optimization algorithm ; Nanotechnology ; Mathematical optimization MXene and MAX Phase Materials ; Energy Harvesting in Wireless Networks ; Membrane Separation Technologies
Authors: Shiva Mudide | Nick Keller | G. A. Antonelli | Gustavo A. Santa Cruz | Julia Hart | Alexander R. Bruccoleri | Ralf K. Heilmann | Mark L. Schattenburg
Source: Journal of Vacuum Science & Technology B Nanotechnology and Microelectronics Materials Processing Measurement and Phenomena (article, 2025-01-01)
Field: Materials Science — Surfaces, Coatings and Films

Machine learning driven measurement of high-aspect-ratio nanostructures using Mueller matrix spectroscopic ellipsometry Accurate fabrication of high-aspect ratio (HAR) structures in applications from semiconductor devices to x-ray observatories is essential for their optimal performance because their performance directly depends on their structure. High-efficiency critical-angle transmission (CAT) gratings enable high-resolution x-ray spectroscopy in astrophysics, but their performance is only ideal when certain performance-critical parameters, like the bar tilts introduced during deep reactive-ion etching, are tuned to precise values. Traditional measurement methods like small-angle x-ray scattering (SAXS) are accurate, but limit the development of robust control algorithms to nudge performance-critical parameters toward favorable values because they are slow and often destructive. We present a fast, accurate, nondestructive measurement method using Mueller matrix spectroscopic ellipsometry and machine learning. Given a HAR structure, we train on rigorous coupled-wave analysis simulation data to predict Mueller matrix spectra from input performance-critical parameter values. We then invert this forward problem by freezing our network weights, measuring experimental Mueller matrix spectra, and vanilla gradient descending on performance-critical parameters to values that correspond to the input Mueller matrix spectra. Introducing machine learning to invert the forward problem reduces computation time, and experimental results demonstrate close agreement between our method’s determined tilt and SAXS measurements. Our accurate, fast measurement method paves the way for the development of robust control algorithms that adjust fabrication parameters in response to measurement, ensuring optimal performance in not only CAT gratings but also HAR structures embedded in applications from semiconductor to microelectromechanical systems fabrication. Mueller calculus ; Materials science ; Fabrication ; Optics ; Ellipsometry ; Scattering ; Matrix (chemical analysis) ; Physics ; Nanotechnology ; Thin film ; Polarimetry Optical Coatings and Gratings ; Near-Field Optical Microscopy ; Calibration and Measurement Techniques
Authors: Abdullah Nayeem | Hossein Bonakdari | Seddik Khalloufi | Mohammed Aïder
Source: International Dairy Journal (article, 2025-05-09)
Field: Chemistry — Analytical Chemistry

Application of multivariate adaptive regression splines (MARS) to study the colorization occurring in the process of lactulose production following lactose electro-activation Lactulose is widely used in the food and pharmaceutical industries and is obtained from the chemical and enzymatic isomerization processes of lactose. Recently, electro-activation, a chemical-free process, has been effectively used for lactulose production at ambient temperature. However, the electro-activated (EA) isomerization process produces colored lactose solutions like the conventional chemical isomerization processes, which becomes a concerning issue. In this context, artificial intelligence-based machine learning tools can be pivotal. The current study observed the color development due to the EA isomerization process and established a model using the multivariate adaptive regression spline (MARS). The color development was expressed in terms of total color difference (ΔE ab ) using the seven input parameters, such as reaction type, reaction time, relaxation time, relaxation temperature, L* , a* , and b* . A total of 127 configurations resulting from the combination of input numbers (1-7) were employed to understand the color development using MARS. The K-fold (K=10) cross-validation technique was applied to select the best-fit combination for each input configuration that underscored the non-linear and complex nature of the color development, offering insights about the input parameters. The reaction type, reaction time, a *, and b* were identified as the key parameters of the color development. The corresponding basis functions of the best-fit configurations reflected the interactions and connected the nonlinearities across the input parameters to understand their impact on the color development using the MARS algorithm. Lactulose ; Multivariate adaptive regression splines ; Lactose ; Multivariate statistics ; Mars Exploration Program ; Process (computing) ; Regression ; Bayesian multivariate linear regression ; Regression analysis ; Environmental science ; Computer science ; Statistics ; Chemistry ; Food science ; Mathematics ; Biology ; Biochemistry Spectroscopy and Chemometric Analyses ; Advanced Scientific Research Methods ; Meat and Animal Product Quality
Authors: Bingqin Wang | Yadi Zhao | Xuequn Cheng | Xin Zhang | Luntao Wang | Zhong Li | Chao Liu | Dawei Zhang | Xiaogang Li
Source: Corrosion Science (article, 2025-06-20)
Field: Materials Science — Metals and Alloys

Corrosion big-data driven continuous observation of low alloy steel rust layer evolution and mining of influence rules of atmospheric environment interaction Corrosion ; Rust (programming language) ; Alloy ; Layer (electronics) ; Materials science ; Metallurgy ; Alloy steel ; Composite material ; Computer science Hydrogen embrittlement and corrosion behaviors in metals ; Non-Destructive Testing Techniques ; Corrosion Behavior and Inhibition
Authors: Shanmin Gao | Qinyuan Huang | Chen Huang | Li Cheng | Kaihao Liu | Baisheng Sa | Yadong Yu | Dezhen Xue | Zhe Liu | Mengyan Dai
Source: Scientific Reports (article, 2025-07-02)
Field: Materials Science — Materials Chemistry

Deep generative model for the inverse design of Van der Waals heterostructures This study proposes ConditionCDVAE+, a crystal diffusion variational autoencoder (CDVAE) based deep generative model for inverse design of van der Waals (vdW) heterostructures. To address the challenges of traditional experimental methods relying on trial-and-error and existing models struggling to incorporate target property constraints, this work achieves breakthroughs through three innovative stages: (1) introduce the SE(3)-equivariant graph neural network EquiformerV2 as the encoder-decoder within the CDVAE framework to enhance the generation quality of the model; (2) design a module integrating Low-rank Multimodal Fusion and Generative Adversarial Networks to map properties and structures into a joint latent space; and (3) for the first time propose a generative model for the vdW heterostructures, by conducting experimental validation on the dataset constructed from Janus III-VI vdW heterostructures. Experiments demonstrate that ConditionCDVAE+ achieves optimal root mean square error for crystal reconstruction, with improved generation quality. Density Functional Theory calculations confirms 99.51% of generated samples converge to energy minima, indicating superior ground-state convergence. The effectiveness of the model under conditional guidance has also been extensively validated. This framework provides an efficient solution for target-oriented design of vdW heterostructures and holds promise for accelerating the development of novel optoelectronic devices. Inverse ; van der Waals force ; Generative grammar ; Computer science ; Generative model ; Heterojunction ; Van der Waals surface ; Artificial intelligence ; Physics ; Van der Waals radius ; Mathematics ; Condensed matter physics ; Quantum mechanics ; Geometry ; Molecule Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices ; Thermal properties of materials
Authors: Hao Wu | Mingxuan Chen | Hao Cheng | Tong Yang | Minggang Zeng | Ming Yang
Source: Journal of Materials Informatics (article, 2025-02-26)
Field: Materials Science — Materials Chemistry

Interpretable physics-informed machine learning approaches to accelerate electrocatalyst development Identifying exceptional electrocatalysts from the vast materials space remains a formidable challenge. Machine learning (ML) has emerged as a powerful tool to address this challenge, offering high efficiency while maintaining good accuracy in predictions. From this perspective, we provide a brief overview of recent advancements in ML for electrocatalyst discoveries. We emphasize the applications of physics-informed ML (PIML) models and explainable artificial intelligence (XAI) to electrocatalyst development, through which valuable physical and chemical insights can be distilled. Additionally, we delve into the challenges faced by PIML approaches, explore future directions, and discuss potential breakthroughs that could revolutionize the field of electrocatalyst development. Electrocatalyst ; Development (topology) ; Psychology ; Computer science ; Artificial intelligence ; Machine learning ; Physics ; Mathematics ; Electrode ; Quantum mechanics ; Electrochemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Fuel Cells and Related Materials
Authors: Unnikrishna Menon | Brajesh Dubey | Amit Kumar
Source: Biomass and Bioenergy (review, 2025-04-10)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Engineering biomass-derived hard carbon for secondary batteries and supercapacitors. Are we there yet? A comprehensive review Supercapacitor ; Biomass (ecology) ; Carbon fibers ; Environmental science ; Nanotechnology ; Engineering physics ; Materials science ; Engineering ; Capacitance ; Physics ; Composite material ; Ecology ; Biology ; Electrode Supercapacitor Materials and Fabrication ; Advancements in Battery Materials ; Catalysis for Biomass Conversion
Authors: Chengzhou Zhang | Xiaoqian Fu | Honggang Wang
Source: Materials Today Communications (article, 2025-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Unveiling p-type doping strategies in β-Ga2O3: Insights from machine learning and first-principles calculations Materials science ; Doping ; Type (biology) ; Engineering physics ; Nanotechnology ; Condensed matter physics ; Optoelectronics ; Physics ; Biology Ga2O3 and related materials ; Electronic and Structural Properties of Oxides ; Advanced Photocatalysis Techniques
Authors: Shah Faisal | Melanie Po‐Leen Ooi | Sanush Abeysekera | Ye Chow Kuang | Dale Fletcher
Source: IEEE Instrumentation & Measurement Magazine (article, 2025-02-01)
Field: Chemistry — Analytical Chemistry

Uncertainty Quantification and Visualization for Optimal Decision-Making in Hyperspectral Imaging-Based Precision Agriculture [Roadmap for Measurement and Applications] Precision agriculture is rapidly transforming the production workflow for crop monitoring and food quality control through the use of state-of-the-art instrumentation and measurement (I&M) methods that gather information about the crop rapidly and non-destructively. Specifically, the use of hyperspectral imaging (HSI) systems provides the capability to acquire not only spatial but also spectral details. It is no surprise therefore that HSI has become a notable instrument in many areas such as remote sensing and agriculture [1]. Yet despite the usefulness of the instrument, extracting relevant or useful information from HSI data is not an easy task, particularly in uncontrolled lighting conditions which affect the measurement of spectral responses of the object-under-test. In recent years, advancements in Machine Learning (ML) and Deep Learning (DL) have shown success in extracting useful features and performing complex pattern recognition across a range of problems. When used in combination with HSI, it has the potential to extract spectral responses related to plant phenotypes or chemical compounds, thus linking its spectral measurements to crop traits such as ripeness, onset of disease, and nutrient/water deprivation. Fig. 1 illustrates the four key computational steps involved in the use of HSI measurement systems: imaging, data processing, data analysis, and decision-making. Hyperspectral imaging ; Precision agriculture ; Visualization ; Computer science ; Remote sensing ; Artificial intelligence ; Agriculture ; Data mining ; Geography Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Hao Bi | Zhiruo Zhou | Qian Liu | Ran Zhao | Fangyuan Chen | Jun Zhang | Zhurui Shen
Source: Journal of Hazardous Materials (article, 2025-10-10)
Field: Materials Science — Materials Chemistry

Machine learning decoding of electronic thresholds in prediction of oriented generation of solar-induced oxidative reactive species and antibiotics degradation Degradation (telecommunications) ; Electronegativity ; Catalysis ; Quantitative structure–activity relationship ; Metal ; Decoding methods Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Sensor Technologies
Authors: Tetsuro Nagai | Nobuaki Kikkawa | Ryosuke Jinnouchi | Masayuki Kimura | Susumu Okazaki
Source: Journal of Chemical Theory and Computation (article, 2025-02-24)
Field: Materials Science — Materials Chemistry

Machine Learning Model to Predict Free-Energy Landscape and Position-Dependent Diffusion Constant to Extend the Scale of Dynamic Monte Carlo Simulations We present a method to predict mass transport properties of large-scale heterogeneous media via coarse-grained dynamics using a dynamic Monte Carlo (MC) simulation aided by a machine learning (ML) surrogate model. The ML model was constructed to reproduce the free-energy landscape and local diffusion constant calculated by all-atom molecular dynamics (MD) simulations, aiming to efficiently evaluate these two local properties necessary for dynamic MC simulations. In this study, the ML model was built using kernel functions of descriptors representing local elemental distribution functions. The method was applied to the molecular diffusion of hydrogen in perfluorinated ionomer membranes for fuel cells, demonstrating that dynamic MC simulation using the ML model accurately reproduced the global diffusion constant across a wide range of humidity conditions, with a relative error of only 3% as compared with the original MC with the explicit position-dependent free energy and diffusion constant. On the basis of the learning curve of the ML model, even a relatively small training data set can reach a relative error of 5%. This approach is expected to be a valuable tool for elucidating mass transport mechanisms in various heterogeneous systems such as fuel cells, batteries, and biological systems. Monte Carlo method ; Statistical physics ; Scale (ratio) ; Energy landscape ; Constant (computer programming) ; Position (finance) ; Computer science ; Energy (signal processing) ; Diffusion ; Physics ; Mathematics ; Thermodynamics ; Statistics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Electrocatalysts for Energy Conversion
Authors: Chen Shen | Siamak Attarian | Yixuan Zhang | Hongbin Zhang | Mark Asta | Izabela Szlufarska | Dane Morgan
Source: Nature Communications (article, 2025-08-07)
Field: Materials Science — Materials Chemistry

SuperSalt: equivariant neural network force fields for multicomponent molten salts system Molten salts are crucial for clean energy applications, yet exploring their thermophysical properties across diverse chemical space remains challenging. We present the development of a machine learning interatomic potential (MLIP) called SuperSalt, which targets 11-cation chloride melts and captures the essential physics of molten salts with near-DFT accuracy. Using an efficient workflow that integrates systems of one, two, and 11 components, the SuperSalt potential can accurately predict thermophysical properties such as density, bulk modulus, thermal expansion, and heat capacity. Our model is validated across a broad chemical space, demonstrating excellent transferability. We further illustrate how Bayesian optimization combined with SuperSalt can accelerate the discovery of optimal salt compositions with desired properties. This work provides a foundation for future studies that allows easy extensions to more complex systems, such as those containing additional elements. Transferability ; Computer science ; Work (physics) ; Molten salt ; Chemical space ; Workflow ; Space (punctuation) ; Artificial neural network ; Materials science ; Chemistry ; Thermodynamics ; Physics ; Artificial intelligence ; Machine learning Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; X-ray Diffraction in Crystallography
Authors: Francesco Belli | Eva Zurek
Source: npj Computational Materials (article, 2025-04-02)
Field: Materials Science — Materials Chemistry

Efficient modelling of anharmonicity and quantum effects in PdCuH2 with machine learning potentials Quantum nuclear effects and anharmonicity impact a wide range of functional materials and their properties. One of the most powerful techniques to model these effects is the Stochastic Self-Consistent Harmonic Approximation (SSCHA). Unfortunately, the SSCHA is extremely computationally expensive, prohibiting its routine use. We propose a protocol that pairs machine learning interatomic potentials, which can be tailored for the system at hand via active learning, with the SSCHA. Our method leverages an upscaling procedure that allows for the treatment of supercells of up to thousands of atoms with practically minimal computational effort. The protocol is applied to PdCuHx (x = 0−2) compounds, chosen because previous experimental studies have reported superconducting critical temperatures, Tcs, as high as 17 K at ambient pressures in an unknown hydrogenated PdCu phase. We identify a P4/mmm PdCuH2 structure, which is shown to be dynamically stable only upon the inclusion of quantum fluctuations, as being a key contributor to the measured superconductivity. For this system, our methodology is able to reduce the computational expense for the SSCHA calculations by ~96%. The proposed protocol opens the door towards the routine inclusion of quantum nuclear motion and anharmonicity in materials discovery. Anharmonicity ; Quantum ; Quantum machine learning ; Computer science ; Artificial intelligence ; Physics ; Quantum mechanics ; Quantum computer Machine Learning in Materials Science ; Nuclear Materials and Properties ; High-pressure geophysics and materials
Authors: Yujie Liu | Xiaoying Wang | Yuzhou Hao | Xuejie Li | Jun Sun | Turab Lookman | Xiangdong Ding | Zhibin Gao
Source: Journal of Materials Informatics (article, 2025-02-20)
Field: Materials Science — Materials Chemistry

PINK: physical-informed machine learning for lattice thermal conductivity Lattice thermal conductivity (κL ) is crucial for eﬀicient thermal management in electronics and energy conversion technologies. Traditional methods for predicting κL are often computationally expensive, limiting their scalability for large-scale material screening. Empirical models, such as the Slack model, offer faster alternatives but require time-consuming calculations for key parameters such as sound velocity and the Grüneisen parameter. This work presents a high-throughput framework, physical-informed kappa (PINK), which combines the predictive power of crystal graph convolutional neural networks (CGCNNs) with the physical interpretability of the Slack model to predict κL directly from crystallographic information files (CIFs). Unlike previous approaches, PINK enables rapid, batch predictions by extracting material properties such as bulk and shear modulus from CIFs using a well-trained CGCNN model. These properties are then used to compute the necessary parameters for κL calculation through a simplified physical formula. PINK was applied to a dataset of 377,221 stable materials, enabling the eﬀicient identification of promising candidates with ultralow κL values, such as Ag3Te4W and Ag3Te4Ta. The platform, accessible via a user-friendly interface, offers an unprecedented combination of speed, accuracy, and scalability, significantly accelerating material discovery for thermal management and energy conversion applications. Thermal conductivity ; Lattice (music) ; Condensed matter physics ; Materials science ; Artificial intelligence ; Computer science ; Physics ; Composite material Machine Learning in Materials Science ; Thermal properties of materials ; Advanced Thermoelectric Materials and Devices