jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Pengxin Wang | Qing Tao | Hongbiao Dong | G.M.A.M. El-Fallah
Source: Computational Materials Science (article, 2025-02-12)
Field: Materials Science — Materials Chemistry

Advanced machine learning analysis of radiation hardening in reduced-activation ferritic/martensitic steels • Analysed 1985–2024 radiation data for RAFM steels, e.g., Eurofer97, F82H. • GBDT achieved top accuracy among GBDT, XGBoost, RF, ResMLP and 1D-CNN. • Ta, W, Cr, temperature, and dose found key to radiation hardening. • Validated GBDT predictions using JLM-0, F82H, Eurofer97, and OPTIFER data. • Radiation hardening peaks at 315 °C and 20–35 dpa, decreases above 375 °C. This study uses advanced machine learning models to investigate the radiation hardening behaviour of reduced activation ferritic martensitic (RAFM) steels. An extensive dataset spanning nearly four decades (1985 to 2024) and covering various steel series, including Eurofer97, F82H, T91, OPTIFER, JLM, JLF, and CLAM, was extensively analysed. Multiple models, including Gradient Boosting Decision Trees (GBDT), XGBoost, Random Forests (RF), ResMLP, and One-Dimensional Convolutional Neural Networks (1D-CNN), were employed with hyperparameter optimisation to maximise predictive accuracy. Among these models, GBDT achieved the highest accuracy (R 2 : 0.87). The findings reveal significant impacts from elements like Ta, W, and Cr, as well as test temperature and irradiation dose. Radiation hardening peaks at 315 °C due to increased dislocation loops and precipitates but declines above 375 °C as these features diminish and martensitic laths recover, softening the steel. The hardening response to radiation dose shows an increase up to 20 dpa, a slight decrease between 20–35 dpa, and stabilising thereafter. Additionally, W and Cr enhance radiation hardening up to 375 °C, with Cr exhibiting a stronger effect, while Ta is observed to mitigate hardening. These insights contribute to a deeper understanding of radiation effects on RAFM steels, offering a predictive framework for material design and optimisation in nuclear environments. This work highlights machine learning as a powerful tool for advancing materials science and enhancing predictive capability for radiation behaviour in steels. Martensite ; Hardening (computing) ; Radiation hardening ; Materials science ; Metallurgy ; Radiation ; Composite material ; Microstructure ; Physics ; Nuclear physics Fusion materials and technologies ; Nuclear Materials and Properties ; Welding Techniques and Residual Stresses
Authors: Henrik Eliasson | Y. Chiang | Thaylan Pinheiro Araújo | Xiansheng Li | Rolf Erni | Sharon Mitchell | Javier Pérez‐Ramírez
Source: Advanced Materials (article, 2025-03-23)
Field: Materials Science — Materials Chemistry

Tracking Dynamics of Supported Indium Oxide Catalysts in CO<sub>2</sub> Hydrogenation to Methanol by In Situ TEM Supported reducible oxides, such as indium oxide on monoclinic zirconia (In<sub>2</sub>O<sub>3</sub>/m-ZrO<sub>2</sub>), are promising catalysts for green methanol synthesis via CO<sub>2</sub> hydrogenation. Growing evidence suggests that dynamic restructuring under reaction conditions plays a crucial but poorly understood role in catalytic performance. To address this, the direct visualization of the state-of-the-art In<sub>2</sub>O<sub>3</sub>/m-ZrO<sub>2</sub> catalyst under CO<sub>2</sub> hydrogenation conditions (T = 553 K, P = 1.9 bar, CO<sub>2</sub>:H<sub>2</sub> = 1:4) is pioneered using in situ scanning transmission electron microscopy (STEM), comparing its behavior to In<sub>2</sub>O<sub>3</sub> on supports with similar (tetragonal, t-ZrO<sub>2</sub> or anatase TiO<sub>2</sub>) or lower (LSm-ZrO<sub>2</sub>) surface areas. Complementary in situ infrared spectroscopy and catalytic tests confirm methanol formation under equivalent conditions. A machine-learning-based difference imaging approach differentiates and ranks restructuring patterns, revealing that partially reduced InO<sub>x</sub> species on m-ZrO<sub>2</sub> undergo cyclic aggregation-redispersion via atomic surface migration, maintaining high active phase dispersion. High-resolution ex situ STEM analysis further shows the epitaxial formation of In<sub>2</sub>O<sub>3</sub> mono- and bilayers on (100) m-ZrO<sub>2</sub> facets, highlighting strong oxide-support interactions. In contrast, sintering prevails on t-ZrO<sub>2</sub>, a-TiO<sub>2</sub>, and low-surface m-ZrO<sub>2</sub>, correlating with lower methanol productivity. This work underscores the pivotal role of oxide-support interfacial interactions in the reaction-induced restructuring of InO<sub>x</sub> species and establishes a framework for tracking nanoscale catalyst dynamics. Materials science ; Catalysis ; Oxide ; Cubic zirconia ; Chemical engineering ; Sintering ; Methanol ; Tetragonal crystal system ; Crystallography ; Crystal structure ; Chemistry ; Composite material ; Ceramic ; Organic chemistry ; Metallurgy Catalytic Processes in Materials Science ; Catalysts for Methane Reforming ; Catalysis and Oxidation Reactions
Authors: Kun Yu | Siyuan Lu | Kaiwen Qiu | Y. ZHANG | Aijun Sun | Shiqi Gong | Kai Wang | Xuzhu Gao | Xiangyu Xu | Hao Wang
Source: Advanced Science (article, 2025-07-06)
Field: Materials Science — Materials Chemistry

Biomimetic Analysis of Neurotransmitters for Disease Diagnosis through Light‐Driven Nanozyme Sensor Array and Machine Learning Neurological diseases, including Alzheimer's disease, Parkinson's disease, and multiple sclerosis, pose a significant global health challenge due to their complex pathogenesis and widespread prevalence. These disorders are often associated with disruptions in neurotransmitter regulation, leading to progressive cognitive and motor impairments. Conventional diagnostic methods are time-consuming and lack the sensitivity required for early-stage detection. Herein, for the first time a novel photoresponsive nanozyme sensor array is presented that integrates metal-organic frameworks (MOFs) and machine learning algorithms for the rapid, sensitive, and multiplexed detection of neurotransmitters. Wherein, Zn(II) meso-Tetra(4-carboxyphenyl)porphine (ZnTCPP) -based MOFs, with their large specific surface area, enhance the interaction between reactant substrates and catalytic active sites within the material, significantly improving response sensitivity. Additionally, light-driven catalysis greatly accelerates the response speed of the nanozyme. Mimicking the mammalian olfactory system, the array responds to various neurotransmitters in a patterned manner, enabling accurate differentiation and quantification within minutes. It maintains high precision even in complex biological samples such as serum and cerebrospinal fluid. The biomimetic sensor can detect neurotransmitter signatures linked to neurological disorders, such as Alzheimer's disease. This platform offers significant potential for early diagnosis and continuous monitoring of neurological conditions. Neuroscience ; Disease ; Multiple sclerosis ; Neurotransmitter ; Computer science ; Nanotechnology ; Medicine ; Biology ; Materials science ; Pathology ; Central nervous system Advanced Nanomaterials in Catalysis ; Molecular Sensors and Ion Detection ; Advanced biosensing and bioanalysis techniques
Authors: Luqi Dong | Xuanlin Zhang | Ziduo Yang | Lei Shen | Yunhao Lu
Source: npj Computational Materials (article, 2025-03-06)
Field: Materials Science — Materials Chemistry

Accurate piezoelectric tensor prediction with equivariant attention tensor graph neural network The piezoelectric materials enable the mutual conversion between mechanical and electrical energy, which drive a multi-billion dollar industry through their applications as sensors, actuators, and energy harvesters. The third-rank piezoelectric tensor is the core matrices for piezoelectric materials and their devices. However, the high costs of obtaining full piezoelectric tensor data through either experimental or computational methods make a significant challenge. Here, we propose an equivariant attention tensor graph neural network (EATGNN) that can identify crystal symmetry and remain independent of the reference frame, ultimately enabling the accurate prediction of the complete third-rank piezoelectric tensor. Especially, we perform an irreducible decomposition of the piezoelectric tensor into four irreducible representations to efficiently reserve the symmetry under group transformation operations. Our results further demonstrate that this model performs well in both bulk and two-dimensional materials. Finally, combining EATGNN with first-principles calculations, we discovered several potential high-performance piezoelectric materials. Tensor (intrinsic definition) ; Equivariant map ; Piezoelectricity ; Graph ; Mathematics ; Computer science ; Pure mathematics ; Physics ; Discrete mathematics ; Acoustics Machine Learning in Materials Science ; Thermal properties of materials ; Advanced Sensor and Energy Harvesting Materials
Authors: Jiaying Li | Lijie Li | H.A. Brink | Giulia Allegri | Saskia Lindhoud
Source: Materials Horizons (review, 2025-01-01)
Field: Materials Science — Polymers and Plastics

Polyelectrolyte complex-based materials for separations: progress, challenges and opportunities Polyelectrolyte complex (PEC) based materials could provide a sustainable alternative to conventional materials, especially for separation applications. However, reproducible production remains a challenge due to the many parameters influencing the polyelectrolyte complexation process, eventually affecting the properties and performance of the final material. Here, we provide an overview of how different parameters affect polyelectrolyte complexation and discuss promising PEC-based materials for separation applications, <i>i.e.</i>, porous membranes, functional and barrier coatings, adhesives, saloplastics, and extraction media. Additionally, we highlight the challenges and opportunities and discuss what is needed to get to the next level. We envision that collaboration between experimentalists and theoreticians can leverage experimental datasets with accurate descriptions of all the parameters for multiscale modelling, machine learning and artificial intelligence approaches that can be used to design PEC materials and predict their properties. Polyelectrolyte ; Nanotechnology ; Materials science ; Sustainable production ; Biochemical engineering ; Process engineering ; Production (economics) ; Engineering ; Polymer ; Composite material Polymer Nanocomposites and Properties ; biodegradable polymer synthesis and properties ; Membrane Separation and Gas Transport
Authors: Chenchen Lu | Zhen Li | Xinxin Sang | Zheyong Fan | Xujun Xu | Yingyan Zhang | Ke Xu | Yanhua Cheng | Junhua Zhao | Jin‐Cheng Zheng | Ning Wei
Source: Small (article, 2025-03-07)
Field: Materials Science — Materials Chemistry

Stress‐Driven Grain Boundary Structural Transition in Diamond by Machine Learning Potential Understanding the structural dynamics of carbon grain boundaries, particularly in diamond, is essential for advancing next-generation device applications. Carbon's diverse allotropes, driven by its versatile chemical bonding, hold immense potential, yet analyzing these boundaries is challenging due to the limitations of experimental techniques and the computational demands of ab initio molecular dynamics simulations. In this study, a machine learning-based molecular dynamics potential, rigorously trained on ab initio data, that accurately predicts structural transitions in incoherent twin boundaries within diamond is introduced. This potential reveals the atomic-scale mechanisms driving these transitions and identifies an 80% reduction in interfacial thermal conductance during the grain boundary transition. These findings provide deep insights into the complex behavior of diamond grain boundaries, uncovering a novel mechanism that regulates thermal properties and paving the way for enhanced thermal management in diamond-based technologies. Grain boundary ; Diamond ; Materials science ; Molecular dynamics ; Ab initio ; Nanotechnology ; Chemical physics ; Atomic units ; Thermal conductivity ; Computational chemistry ; Chemistry ; Microstructure ; Physics ; Metallurgy ; Composite material Diamond and Carbon-based Materials Research ; Machine Learning in Materials Science ; Force Microscopy Techniques and Applications
Authors: Jinjin Liu | Xinyu Chen | Qiaoqiao Diao | Zheng Tang | Xiangheng Niu
Source: Biosensors (review, 2025-05-29)
Field: Materials Science — Materials Chemistry

Machine-Learning-Assisted Nanozyme-Based Sensor Arrays: Construction, Empowerment, and Applications In the past decade, nanozymes have been attracting increasing interest in academia due to their stable performance, low cost, and easy modification. With the catalytic signal amplification feature, nanozymes not only find wide use in traditional "lock-and-key" single-target detection but hold great potential in high-throughput multiobjective analysis via fabricating sensor arrays. In particular, the rise of machine learning in recent years has greatly advanced the design, construction, signal processing, and utilization of sensor arrays. The constructive collaboration of nanozymes, sensor arrays, and machine learning is accelerating the development of biochemical sensors. To highlight the emerging field, in this minireview, we created a concise summary of machine-learning-assisted nanozyme-based sensor arrays. First, the construction of nanozyme-involved sensor arrays is introduced from several aspects, including nanozyme materials and activities, sensing variables, and signal outputs. Then, the roles of machine learning in signal treatment, information extraction, and outcome feedback are emphasized. Afterwards, typical applications of machine-learning-assisted nanozyme-involved sensor arrays in environmental detection, food analysis, and biomedical sensing are discussed. Finally, the promise of machine-learning-assisted nanozyme-based sensor arrays in biochemical sensing is highlighted, and some future trends are also pointed out to attract more interest and effort to promote the emerging field for better practical use. Empowerment ; Computer science ; Nanotechnology ; Materials science ; Political science Advanced Nanomaterials in Catalysis ; Electrochemical sensors and biosensors ; Advanced biosensing and bioanalysis techniques
Authors: Wenying Zhang | Meifang Wang | Ping’an Ma | Jun Lin
Source: Coordination Chemistry Reviews (article, 2025-04-05)
Field: Materials Science — Materials Chemistry

Recent progress of advanced Nanozymes for targeted oncotherapy and synergistic immunotherapy Chemistry ; Immunotherapy ; Nanotechnology ; Biochemical engineering ; Immune system ; Immunology Advanced Nanomaterials in Catalysis ; Nanoplatforms for cancer theranostics ; Advanced biosensing and bioanalysis techniques
Authors: Aaron D. Kaplan | Runze Liu | Ji Qi | Tsz Wai Ko | Bowen Deng | Janosh Riebesell | Gerbrand Ceder | Kristin A. Persson | Shyue Ping Ong
Source: ArXiv.org (preprint, 2025-03-06)
Field: Materials Science — Materials Chemistry

A Foundational Potential Energy Surface Dataset for Materials Accurate potential energy surface (PES) descriptions are essential for atomistic simulations of materials. Universal machine learning interatomic potentials (UMLIPs)$^{1-3}$ offer a computationally efficient alternative to density functional theory (DFT)$^4$ for PES modeling across the periodic table. However, their accuracy today is fundamentally constrained due to a reliance on DFT relaxation data.$^{5,6}$ Here, we introduce MatPES, a foundational PES dataset comprising $\sim 400,000$ structures carefully sampled from 281 million molecular dynamics snapshots that span 16 billion atomic environments. We demonstrate that UMLIPs trained on the modestly sized MatPES dataset can rival, or even outperform, prior models trained on much larger datasets across a broad range of equilibrium, near-equilibrium, and molecular dynamics property benchmarks. We also introduce the first high-fidelity PES dataset based on the revised regularized strongly constrained and appropriately normed (r$^2$SCAN) functional$^7$ with greatly improved descriptions of interatomic bonding. The open source MatPES initiative emphasizes the importance of data quality over quantity in materials science and enables broad community-driven advancements toward more reliable, generalizable, and efficient UMLIPs for large-scale materials discovery and design. Interatomic potential ; Range (aeronautics) ; Property (philosophy) ; Molecular dynamics ; Surface (topology) ; Energy (signal processing) ; Relaxation (psychology) Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Physical and Chemical Molecular Interactions
Authors: Meral Yüce | Nazlı Öncer | Ceren Duru Çınar | Beyza Nur Günaydın | Zeynep İdil Akçora | Hasan Kurt
Source: Biosensors (article, 2025-03-05)
Field: Chemistry — Analytical Chemistry

Comprehensive Raman Fingerprinting and Machine Learning-Based Classification of 14 Pesticides Using a 785 nm Custom Raman Instrument Raman spectroscopy enables fast, label-free, qualitative, and quantitative observation of the physical and chemical properties of various substances. Here, we present a 785 nm custom-built Raman spectroscopy instrument designed for sensing applications in the 400-1700 cm<sup>-1</sup> spectral range. We demonstrate the performance of the instrument by fingerprinting 14 pesticide reference samples with over twenty technical repeats per sample. We present molecular Raman fingerprints of the pesticides comprehensively and distinguish similarities and differences among them using multivariate analysis and machine learning techniques. The same pesticides were additionally investigated using a commercial 532 nm Raman instrument to see the potential variations in peak shifts and intensities. We developed a unique Raman fingerprint library for 14 reference pesticides, which is comprehensively documented in this study for the first time. The comparison shows the importance of selecting an appropriate excitation wavelength based on the target analyte. While 532 nm may be advantageous for certain compounds due to resonance enhancement, 785 nm is generally more effective for reducing fluorescence and achieving clearer Raman spectra. By employing machine learning techniques like the Random Forest Classifier, the study automates the classification of 14 different pesticides, streamlining data interpretation for non-experts. Applying such combined techniques to a wider range of agricultural chemicals, clinical biomarkers, or pollutants could provide an impetus to develop monitoring technologies in food safety, diagnostics, and cross-industry quality control applications. Raman spectroscopy ; Artificial intelligence ; Pesticide ; Fingerprint (computing) ; Analyte ; Computer science ; Chemometrics ; Analytical Chemistry (journal) ; Pattern recognition (psychology) ; Chemistry ; Machine learning ; Environmental chemistry ; Chromatography ; Optics ; Physics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Pesticide Residue Analysis and Safety
Authors: Shan Gao | David Julian McClements | Bing Wang | Arif Rashid | Xiao Geng | Shenglin Sun | Wentao Wang | Hanxue Hou
Source: ACS Sustainable Chemistry & Engineering (article, 2025-10-23)
Field: Materials Science — Biomaterials

Regulation of Mechanical and Barrier Properties of Starch/PBAT Films from Extrusion Perspective: Structural Evolution, Correlation Insights, and Predictive Model Development Starch/poly(butylene adipate-co-terephthalate) (PBAT) composites are promising biodegradable materials that may be used as sustainable packaging materials in the food and other industries. These composite materials are usually fabricated by using blown-extrusion methods. In this study, the structural evolution of starch/PBAT composites during extrusion was elucidated. Starch underwent a transition from its typical A-type crystalline structure to a predominantly amorphous structure, and then recrystallized into a V6-type crystalline structure in the composite pellets and films. These structural changes were accompanied by hydrogen-bond rearrangement (starch–starch to starch–glycerol), double-helix disintegration, and molecular-weight reduction. Phase separation during film blowing contributed to molecular rearrangements and changes in the crystallization state within the starch and PBAT phases. The relationships between the composition and processing parameters used to form the blown-extrusion films and their final physicochemical properties was established. Correlation analysis revealed the maximum effect size (partial eta-square ≥ 0.9) and pronounced correlation (Mantel’s p ≤ 0.001, r ≥ 0.25) of “starch proportion” with the films’ mechanical and barrier properties, alongside significant interactions among the film-forming parameters (P < 0.05). On this basis, an artificial neural network (3 × 88 × 126 × 15) was constructed to predict the mechanical and barrier properties from these film-forming parameters, with high prediction accuracy (R ≥ 0.9) and generalization ability. These findings enhance the understanding of how extrusion regulates the mechanical and barrier properties of starch/PBAT films, which may help in the industrial production of these sustainable materials. Extrusion ; Amorphous solid ; Crystallization ; Composite number ; Fused deposition modeling ; Starch ; Phase (matter) biodegradable polymer synthesis and properties ; Food composition and properties ; Nanocomposite Films for Food Packaging
Authors: Pragalbh Vashishtha | Hitesh Gupta Kattamuri | Nikhil Thawari | Murugaiyan Amirthalingam | Rohit Batra
Source: Nature Machine Intelligence (article, 2025-01-02)
Field: Materials Science — Materials Chemistry

Reusability report: Deep learning-based analysis of images and spectroscopy data with AtomAI Reusability ; Spectroscopy ; Computer science ; Deep learning ; Medicine ; Artificial intelligence ; Physics ; Operating system Machine Learning in Materials Science ; Nuclear Physics and Applications ; Advanced X-ray and CT Imaging
Authors: Alfonso Pedone | Marco Bertani | Matilde Benassi
Source: Journal of Chemical Theory and Computation (article, 2025-04-24)
Field: Materials Science — Ceramics and Composites

Revisiting Machine Learning Potentials for Silicate Glasses: The Missing Role of Dispersion Interactions Machine learning interatomic potentials (MLIPs) offer a promising alternative to traditional force fields and ab initio methods for simulating complex materials such as oxide glasses. In this work, we present the first evaluation of the pretrained MACE (Multi-ACE) model [D.P. Kovács et al., J. Chem. Phys. 159(2023), 044118] for silicate glasses, using sodium silicates as a test case. We compare its performance with a DeePMD-based MLIP specifically trained on sodium silicate compositions [M. Bertani et al., J. Chem. Theory Comput. 20(2024), 1358-1370] and assess their accuracy in reproducing structural and dynamical properties. Additionally, we investigate the role of dispersion interactions by incorporating the D3(BJ) correction in both models. Our results show that while MACE accurately reproduces neutron structure factors, pair distribution functions, and Si[Q<sup>n</sup>] speciation, it performs slightly worst for elastic properties calculations. However, it is suitable for the simulations of sodium silicate glasses. The inclusion of dispersion interactions significantly improves the reproduction of density and elastic properties for both MLIPs, highlighting their critical role in glass modeling. These findings provide insight into the transferability of general MLIPs to disordered systems and emphasize the need for dispersion-aware training data sets in developing accurate force fields for oxide glasses. Silicate glass ; Silicate ; Dispersion (optics) ; Computer science ; Data science ; Chemical physics ; Physics ; Chemistry ; Mineralogy ; Optics ; Chemical engineering ; Engineering Glass properties and applications ; Geological and Geochemical Analysis ; Geochemistry and Geologic Mapping