jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ehsan Alibagheri | Mohammad Khazaei | Mehdi Estili | Samad Moslehi | Kaoru Ohno | S. Mehdi Vaez Allaei
Source: Applied Materials Today (article, 2025-08-19)
Field: Materials Science — Materials Chemistry

Data-driven discovery of novel synthesizable MAX phase materials Computer science ; Phase (matter) ; Algorithm ; Physics MXene and MAX Phase Materials ; 2D Materials and Applications ; Ferroelectric and Negative Capacitance Devices
Authors: D. D. Liang | Zheng Pan | Siyuan Zhang | Zhaoyang Zhang | Ruyi Song | Rundong Zhao
Source: npj Computational Materials (article, 2025-11-25)
Field: Materials Science — Materials Chemistry

Prediction of frontier band spin splitting in 2D perovskites via deep neural networks Two-dimensional (2D) hybrid organic-inorganic perovskites (HOIPs) have strong potential for optoelectronic applications due to their polarized photon absorption and emission properties. These macroscopic behaviors are intrinsically linked to microscopic symmetry breaking, particularly the emergence of momentum-dependent, non–centrosymmetric spin splitting in frontier electronic bands. To efficiently identify such spin-related phenomena, we combine first-principles calculations and deep learning models to explore the correlation between in-plane bond distortions and spin-orbit splitting. Our model achieves 100% accuracy in qualitatively identifying systems with observable spin splitting, and over 80% quantitative accuracy in predicting its magnitude and location, confirming that in-plane structural distortions are key descriptors of spin splitting. The trained model can be readily extended to real 2D HOIP systems and is expected to benefit experimentalists by enabling rapid screening and discovery of functional materials, especially in cases where ab initio calculations are not feasible due to computational cost. Observable ; Spin (aerodynamics) ; Symmetry (geometry) ; Ab initio ; Artificial neural network ; Photon ; Absorption (acoustics) ; Ab initio quantum chemistry methods Machine Learning in Materials Science ; Perovskite Materials and Applications ; 2D Materials and Applications
Authors: Somayyeh Nikkhah | Abbas Azarpour | Sohrab Zendehboudi | Noori M. Cata Saady
Source: Energy & Fuels (article, 2025-08-25)
Field: Chemistry — Inorganic Chemistry

Predictive Modeling of CO<sub>2</sub> Adsorption in Metal–Organic Frameworks Using Hybrid Machine Learning Approaches Carbon dioxide (CO2) emissions from industrial and energy sources lead to global warming and drive climate change. To address the critical challenge of rising atmospheric CO2 levels, strategies such as CO2 capture are essential for limiting emissions and mitigating environmental impact. Among various materials, metal–organic frameworks (MOFs) have emerged as highly promising and effective adsorbents for CO2 capture, offering high selectivity and capacity. This study evaluates the performance of various machine learning (ML) models, including artificial neural network-particle swarm optimization (ANN-PSO), coupled simulated annealing-least squares support vector machine (CSA-LSSVM), and adaptive neuro-fuzzy inference system (ANFIS), in predicting the CO2 capture capacity of MOFs. Additionally, gene expression programming (GEP) is utilized to find a mathematical correlation between CO2 capture capacity and key operating variables such as pressure and surface area. The model development considered the input variables: temperature, pressure, surface area, pore volume, and enthalpy. Performance metrics such as mean square error (MSE) and coefficient of determination (R2) are calculated for the training and testing phases to assess the model accuracy and reliability. Among the evaluated models, CSA-LSSVM exhibits the best performance, achieving R2 values of 0.971 and 0.915 and MSE values of 0.0025 and 0.0034 for the training and testing phases, respectively. Sensitivity analysis conducted on the CSA-LSSVM model reveals that pressure significantly influences CO2 capture capacity, followed by surface area. The results demonstrate the potential of ML models, particularly CSA-LSSVM, as powerful tools for predicting and optimizing the performance of MOFs in CO2 capture, considering cost, energy efficiency, and environmental sustainability. Adsorption ; Metal-organic framework ; Computer science ; Artificial intelligence ; Materials science ; Chemistry ; Machine learning ; Process engineering ; Environmental science ; Organic chemistry ; Engineering Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Phase Equilibria and Thermodynamics
Authors: Wei‐Guang Xiong | Miaomiao Song | D. C. Zhang | Shibin Wang | Ai‐Zheng Chen
Source: Colloids and Surfaces B Biointerfaces (review, 2025-06-23)
Field: Materials Science — Materials Chemistry

High-entropy nanozyme biosensors: Machine learning-assisted design and stimulus-responsive applications Computer science ; Stimulus (psychology) ; Biosensor ; Artificial intelligence ; Nanotechnology ; Psychology ; Materials science ; Cognitive psychology Advanced Nanomaterials in Catalysis ; Nanocluster Synthesis and Applications ; Nanoplatforms for cancer theranostics
Authors: Alessandro Campari | Florian Konert | Nima Razavi | Oded Sobol | Antonio Alvaro
Source: Computational Materials Science (article, 2025-11-07)
Field: Materials Science — Metals and Alloys

Hydrogen-assisted cracking: A deep learning approach for fractographic analysis Hydrogen handling equipment suffers from interaction with their operating environment, which degrades the mechanical properties and compromises component integrity. Hydrogen-assisted cracking is responsible for several industrial failures with potentially severe consequences. A thorough failure analysis can determine the failure mechanism, locate its origin, and identify possible root causes to avoid similar events in the future. Post-mortem fractographic analysis can classify the fracture mode and determine whether the hydrogen-metal interaction contributed to the component's breakdown. Experts in fracture classification identify characteristic marks and textural features by visual inspection to determine the failure mechanism. Although widely adopted, this process is time-consuming and influenced by subjective judgment and individual expertise. This study aims to automate fractographic analysis through advanced computer vision techniques. Different materials were tested in hydrogen atmospheres and inert environments, and their fracture surfaces were analyzed by scanning electron microscopy to create an extensive image dataset. A pre-trained Convolutional Neural Network was fine-tuned to accurately classify brittle and ductile fractures. In addition, Grad-CAM interpretability method was adopted to identify the image regions most influential in the model's prediction and compare the saliency maps with expert annotations. This approach offered a reliable data-driven alternative to conventional fractographic analysis. • Deep learning automates fractographic analysis of steels exposed to hydrogen gas. • CNN model based on ResNet-50 can classify fractures with 99.10 % accuracy. • Length scale and resolution are crucial for correctly classifying fracture modes. • Grad-CAM identifies the image regions most influential in the model's prediction. • The share of brittle fracture mode correlates with the embrittlement index. Interpretability ; Fracture (geology) ; Convolutional neural network ; Brittleness ; Deep learning ; Hydrogen embrittlement ; Fractography ; Failure mode and effects analysis ; Visualization Hydrogen embrittlement and corrosion behaviors in metals ; Material Properties and Failure Mechanisms ; Fatigue and fracture mechanics
Authors: Fengjuan Ding | Tengjiao Hong | Fulong Dong | Dong Huang
Source: Crystals (article, 2025-07-31)
Field: Materials Science — Materials Chemistry

Research Advancements in High-Temperature Constitutive Models of Metallic Materials The constitutive model is widely employed to characterize the rheological properties of metallic materials under high-temperature conditions. It is typically derived from a series of high-temperature tests conducted at varying deformation temperatures, strain rates, and strains, including hot stretching, hot compression, separated Hopkinson pressure bar testing, and hot torsion. The original experimental data used for establishing the constitutive model serves as the foundation for developing phenomenological models such as Arrhenius and Johnson–Cook models, as well as physical-based models like Zerilli–Armstrong or machine learning-based constitutive models. The resulting constitutive equations are integrated into finite element analysis software such as Abaqus, Ansys, and Deform to create custom programs that predict the distributions of stress, strain rate, and temperature in materials during processes such as cutting, stamping, forging, and others. By adhering to these methodologies, we can optimize parameters related to metal processing technology; this helps to prevent forming defects while minimizing the waste of consumables and reducing costs. This study provides a comprehensive overview of commonly utilized experimental equipment and methods for developing constitutive models. It discusses various types of constitutive models along with their modifications and applications. Additionally, it reviews recent research advancements in this field while anticipating future trends concerning the development of constitutive models for high-temperature deformation processes involving metallic materials. Constitutive equation ; Materials science ; Engineering ; Structural engineering ; Finite element method High-Velocity Impact and Material Behavior ; Metallurgy and Material Forming ; Microstructure and mechanical properties
Authors: Frederick Nii Ofei Bruce | Xin Wang | Xin Bai | Wen Minjie | Dongping Chen | Song Cheng | Henry J. Curran | Yang Li
Source: Fuel (article, 2025-09-16)
Field: Materials Science — Materials Chemistry

Unraveling the thermal decomposition kinetics of triethyl phosphite ReaxFF ; Bond cleavage ; Homolysis ; Bond-dissociation energy ; Dissociation (chemistry) ; Thermal decomposition ; Radical ; Ab initio ; Kinetic energy Thermal and Kinetic Analysis ; Zeolite Catalysis and Synthesis ; Flame retardant materials and properties
Authors: Dongmei Bi | Shuangshuang Yan | Yue Kang | Chengxizi Zhang | Shanjian Liu | Yinjiao Liu
Source: Journal of Industrial and Engineering Chemistry (article, 2025-07-02)
Field: Materials Science — Materials Chemistry

Low-temperature NH3-SCR NOx reduction and SO2 resistance of Cu-modified N/S co-doped carbon-based catalysts NOx ; Selective catalytic reduction ; Catalysis ; Doping ; Carbon fibers ; Reduction (mathematics) ; Chemistry ; Inorganic chemistry ; Chemical engineering ; Materials science ; Organic chemistry ; Composite number ; Optoelectronics ; Composite material ; Combustion Catalytic Processes in Materials Science ; Ammonia Synthesis and Nitrogen Reduction ; Caching and Content Delivery
Authors: Zhifeng Lei | Xinxuan Wang | Zhongtao Lu | Xiege Huang | Wenjuan Li | Xiaobin Feng | Gang Chen | Pengcheng Zhai | Bo Duan | Guodong Li | Qingjie Zhang
Source: Acta Materialia (article, 2025-08-08)
Field: Materials Science — Materials Chemistry

Atomic-scale insights into the failure origins of polycrystalline thermoelectric materials: CoSb3, Mg2Si, and SnSe Materials science ; Thermoelectric effect ; Crystallite ; Atomic units ; Thermoelectric materials ; Scale (ratio) ; Engineering physics ; Nanotechnology ; Metallurgy ; Composite material ; Thermal conductivity ; Thermodynamics Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Chalcogenide Semiconductor Thin Films
Authors: Noah Oyeniran | Oyshee Chowdhury | Chongze Hu
Source: Applied Physics Letters (article, 2025-03-01)
Field: Materials Science — Materials Chemistry

First-principles and Monte Carlo simulations of high-entropy MXenes In this work, we developed a computational framework that integrates first-principles density functional theory (DFT) calculations with Monte Carlo (MC) algorithm to search for the most stable configuration of high-entropy (HE) MXenes. This framework can predict the minimum energy configurations of HE MXenes with interlayer segregation. For instance, DFT/MC simulation indicates that (Ti0.5Cr0.5)4C3 MXenes exhibit interlayer segregation, where Cr atoms are favorable to segregate into the outermost top and bottom layers, forming out-of-plane MXene (o-MXenes). Such an o-MXene structure was also found in (Nb0.5Mo0.5)4C3, (Cr0.5Mo0.5)4C3, and (Ti0.33Cr0.33Mo0.34)4C3 MXenes, which is in good agreement with prior studies. The classical molecular dynamics (MD)/MC simulations using machine learning interatomic potentials further validate the interlayer segregation-induced o-MXene observed in the DFT/MC simulations. This DFT/MC framework can be easily extended to predict the stable phases for other material systems, suggesting its broad applicability and impact. Monte Carlo method ; Statistical physics ; MXenes ; Monte Carlo molecular modeling ; Monte Carlo method in statistical physics ; Kinetic Monte Carlo ; Physics ; Hybrid Monte Carlo ; Materials science ; Markov chain Monte Carlo ; Nanotechnology ; Mathematics ; Statistics MXene and MAX Phase Materials ; 2D Materials and Applications ; Ferroelectric and Negative Capacitance Devices
Authors: Muhammad Zeeshan Farooq | Yiyong Wu | M.Y. Zheng | Liang‐Xing Lu
Source: Journal of Magnesium and Alloys (article, 2025-02-01)
Field: Materials Science — Materials Chemistry

Ignition-proof magnesium alloys with rare earth elements: A novel framework to predict combustion phases, surface morphologies, and hidden features using heating rates • ML framework predicted combustion phases and hidden surface behaviors. • Ignition-proof alloys with RE elements were successfully developed. • RE-rich alloys showed superior performance for aerospace and defense uses. • Image processing captured self-healing and crack-resistant oxide layers. • Heating rate simulations provided insights into ignition mechanisms. This study presents a proposed interdisciplinary framework for developing ignition-resistant magnesium alloys and analyzing their combustion behavior. It focuses on both commercial AZ31, AZ91, WE43 and formulated Mg-Gd-Y-Zn-Zr alloys with various rare earth elements (REEs) contents. The research integrates experimental methods, heating rate simulations, advanced image processing, and machine learning (ML) techniques to identify key mechanisms that enhance ignition resistance, particularly for aerospace and other industrial applications. A novel alloy composition, Mg-8Gd-6Y-0.6Zn-0.6Zr, demonstrated exceptional non-combustibility in air. The study is systematically to classifies the combustion process into distinct phases and surface morphologies by leveraging supervised and unsupervised learning models based on unseen heating rate features. Advanced image processing techniques reveal dynamic surface morphology changes, including thermal deformation, melting spots, gas bubble formation, and transformations during saturation and post-melting phases, while unsupervised ML models also validate these outstanding predictions of surface morphology features. Additionally, the research highlights the synergistic effects of REEs in forming dense, protective oxide layers, refining microstructures, and delaying ignition. This phase-based analysis provides the combustion behavior of magnesium alloys, which is crucial for evaluating their performance in industrial fire scenarios. Materials science ; Magnesium ; Ignition system ; Combustion ; Rare earth ; Autoignition temperature ; Surface (topology) ; Metallurgy ; Thermodynamics ; Physical chemistry Nuclear Materials and Properties ; Machine Learning in Materials Science ; Energetic Materials and Combustion
Authors: Tianyu Zhang | Jin Yan | Ke Jin | Peng Zhao | Yuexin Zhou | Yifan Zhou | Sheng Ye | Yao Xu | Baopeng Ma | Shanghao Chen | Jinxuan Cheng | Jiahui Chen | Zhaoyue Yao | Jin Zhang | Feng Cao | Lijun Zhang | Jun Mao | Yuhao Fu | Qian Zhang
Source: Proceedings of the National Academy of Sciences (article, 2025-11-19)
Field: Materials Science — Materials Chemistry

Substantial planar plastic anisotropy in inorganic Mg <sub>3</sub> Bi <sub>2</sub> single crystals Elucidating the fundamental microscopic mechanisms governing plastic deformation is crucial for the rational design of functional materials with tailored mechanical properties. Recent advances in Mg<sub>3</sub>Bi<sub>2</sub>-based thermoelectric materials have revealed exceptional room-temperature ductility in these compounds. However, the origin of their plastic behavior remains elusive. Herein, we report a pronounced in-plane plastic anisotropy in single-crystalline Mg<sub>3</sub>Bi<sub>2</sub>. Micropillar compression reveals that the observed anisotropy is critically dependent on the activation of single versus double slip systems, and superior plastic deformability can be achieved once the double slip system is activated. The interatomic potential for Mg<sub>3</sub>Bi<sub>2</sub> was developed via the machine learning approach, and molecular dynamics simulations establish that the crystallographic orientation-dependent activation of competing slip systems constitutes the fundamental origin of the plastic anisotropy in Mg<sub>3</sub>Bi<sub>2</sub>. Additionally, our study demonstrates that pyramidal [Formula: see text] dislocations play a crucial role in the plasticity of Mg<sub>3</sub>Bi<sub>2</sub>. Anisotropy ; Plasticity ; Slip (aerodynamics) ; Planar ; Ductility (Earth science) ; Deformation (meteorology) ; Nanocrystal Advanced Thermoelectric Materials and Devices ; Heusler alloys: electronic and magnetic properties ; Thermal Expansion and Ionic Conductivity
Authors: Kehan Guo | Yili Shen | Gisela Abigail Gonzalez-Montiel | Yi Huang | Yujun Zhou | Mihir Surve | Zhichun Guo | Payel Das | Nitesh V. Chawla | Olaf Wiest | Xiangliang Zhang
Source:  (article, 2025-09-01)
Field: Chemistry — Physical and Theoretical Chemistry

Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction To Generation and Beyond The rapid advent of machine learning (ML) and artificial intelligence (AI) has catalyzed major transformations in chemistry, yet the application of these methods to spectroscopic and spectrometric data—termed Spectroscopy Machine Learning (SpectraML)—remains relatively underexplored. Modern spectroscopic techniques (MS, NMR, IR, Raman, UV-Vis) generate an ever-growing volume of high-dimensional data, creating a pressing need for automated and intelligent analysis beyond traditional expert-based workflows. In this survey, we provide a unified review of SpectraML, systematically examining state-of-the-art approaches for both forward tasks (molecule-to-spectrum prediction) and inverse tasks (spectrum-to-molecule inference). We trace the historical evolution of ML in spectroscopy—from early pattern recognition to the latest foundation models capable of advanced reasoning—and offer a taxonomy of representative neural architectures, including graph-based and transformer-based methods. Addressing key challenges such as data quality, multimodal integration, and computational scalability, we highlight emerging directions like synthetic data generation, large-scale pretraining, and few- or zero-shot learning. To foster reproducible research, we release an open-source repository containing curated datasets and code implementations. Our survey serves as a roadmap for researchers, guiding advancements at the intersection of spectroscopy and AI. Intersection (aeronautics) ; Key (lock) ; TRACE (psycholinguistics) ; Taxonomy (biology) ; Applications of artificial intelligence Various Chemistry Research Topics ; Metabolomics and Mass Spectrometry Studies ; Water Quality Monitoring and Analysis
Authors: Edgar Mendoza | Pietro Dall’Olio | L. S. S. Coelho | Antonio Peregrín | Samuel López-Domínguez | Floris F. S. van der Tak | M. Carvajal
Source: Astronomy and Astrophysics (article, 2025-05-15)
Field: Chemistry — Spectroscopy

Unmasking the physical information inherent to interstellar spectral line profiles with machine learning Context . Physical and chemical properties, such as kinetic temperature, volume density, and molecular composition of interstellar clouds are inherent in their line spectra at submillimeter wavelengths. Therefore, the spectral line profiles could be used to estimate the physical conditions of a given source. Aims . We present a new bottom-up approach, based on machine learning (ML) algorithms, to extract the physical conditions in a straightforward way from the line profiles without using radiative transfer equations. Methods . We simulated, for the typical physical conditions of dense molecular clouds and star-forming regions, the emission in spectral lines of the two isomers HCN and HNC, from J = 1–0 to J = 5–4 between 30 and 500 GHz, which are commonly observed in dense molecular clouds and star forming regions. The generated data cloud distribution has been parametrised using the line intensities and widths to enable a new way to analyse the spectral line profiles and to infer the physical conditions of the region. The line profile parameters have been charted to the HNC/HCN ratio and the excitation temperature of the molecule(s). Three ML algorithms have been trained, tested, and compared aiming to unravel the excitation conditions of HCN and HNC and their abundance ratio. Results. Machine learning results obtained with two spectral lines, one for each isomer HCN and HNC, have been compared with the local thermodynamic equilibrium (LTE) analysis for the cold source R CrA IRS 7B. The estimate of the excitation temperature and of the abundance ratio, in this case considering the two spectral lines, is in agreement with our LTE analysis. The complete optimisation procedure of the algorithms (training, testing, and prediction of the target quantities) have the potential to predict interstellar cloud properties from line profile inputs at lower computational cost than before. Conclusions . It is the first time that the spectral line profiles are mapped according to the physical conditions charting the ratio of two isomers and the excitation temperature of the molecules. In addition, a bottom-up approach starting from a set of simulated spectral data at different physical conditions is proposed to interpret line observations of interstellar regions and to estimate their physical conditions. This new approach presents the potential relevance to unravel hidden interstellar conditions with the use of ML methods. Physics ; Astrophysics ; Line (geometry) ; Interstellar medium ; Spectral line ; Astronomy ; Galaxy Spectroscopy and Laser Applications ; Sensor Technology and Measurement Systems ; Atmospheric Ozone and Climate