jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jiaojiao Liu | Qiulin Wang | Xiongfei Qi | Haonan Wang | Jing Jin | Dunyu Liu
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Mechanisms of Chlorine Poisoning Resistance and Machine Learning Analysis of Key Factors in Ce/P-Codoped Vanadium-Tungsten-Titanium Wide-Temperature Catalysts Chlorine ; Catalysis ; Incineration ; Flue gas ; NOx ; Oxygen Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Da-shi Zhong
Source:  (article, 2025-08-19)
Field: Materials Science — Materials Chemistry

The research evolution of machine learning-driven material design optimization: hot trends and future challenges from a bibliometric perspective The rapid development of machine learning and artificial intelligence technologies is profoundly transforming the research paradigm of material design and performance optimization. Based on the bibliometric method, this paper systematically analyzes 492 documents from the Web of Science Core Database as of March 30, 2025. By combining the tools of CiteSpace and VOSviewer, it reveals the research hotspots, cooperation networks, and development trends in this field from the dimensions such as authors, countries, institutional cooperation networks, keywords, and burst terms. The research shows that machine learning integrates multiple technologies to build intelligent correlation models to accelerate material research and development. China and the United States dominate the global research network, respectively leading the algorithm innovation and mechanism research. The core themes extend towards the molecular scale and across scales. In the future, it is necessary to break through the bottlenecks of data integration and algorithm interpretation to achieve intelligent closed-loop design. Perspective (graphical) ; Computer science ; Data science ; Management science ; Artificial intelligence ; Engineering Machine Learning in Materials Science
Authors: Revista, Zen | CHEMISTRY, 10
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-11-28)
Field: Materials Science — Materials Chemistry

Integrated Retrosynthetic Design for Accelerated Enantioselective Catalysis This paper explores the paradigm of integrated retrosynthetic design for accelerating the discovery and optimization of enantioselective catalysis. Enantioselective catalysis stands as a cornerstone in modern organic synthesis, critical for producing chiral molecules with high optical purity, particularly in pharmaceuticals and agrochemicals. Traditional approaches to designing enantioselective syntheses are often laborious, relying heavily on expert intuition, extensive experimentation, and iterative optimization. This work proposes a novel framework that synergistically combines advanced computational retrosynthetic analysis with machine learning and computational chemistry techniques for catalyst design and reaction prediction. By leveraging algorithms to explore vast chemical reaction spaces and predict optimal chiral catalysts and conditions, the proposed integrated approach aims to drastically reduce the experimental burden, shorten development cycles, and uncover previously unattainable synthetic pathways. The methodology encompasses automated retrosynthetic path generation, computational screening of chiral catalysts, prediction of enantioselectivity using quantum chemical calculations and machine learning models, and iterative refinement loops guided by predicted outcomes. Preliminary conceptual results suggest that such an integrated platform can significantly enhance the efficiency and success rate of designing complex enantioselective syntheses, paving the way for more sustainable and cost-effective production of chiral compounds. This research highlights the transformative potential of combining AI-driven synthetic planning with advanced computational catalysis for future organic synthesis. Retrosynthetic analysis ; Enantioselective synthesis ; Organocatalysis ; Catalysis ; Organic synthesis Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Surface Chemistry and Catalysis
Authors: Yanshen Zhao | Xiaoqing Wang | Rui Wang | Fanglin Wu | Wenjing Duan | Hao Li | Haolin Tang
Source: Science China Materials (article, 2025-06-06)
Field: Materials Science — Materials Chemistry

Ammonia-resistant Pt-based HOR catalyst with Fe/Y co-regulation for high-efficiency hydrogen purification enabled by machine learning Ammonia ; Materials science ; Catalysis ; Hydrogen ; Ammonia production ; Chemical engineering ; Process engineering ; Organic chemistry ; Engineering ; Chemistry Catalytic Processes in Materials Science ; Ammonia Synthesis and Nitrogen Reduction ; Electrocatalysts for Energy Conversion
Authors: Pawin Iamprasertkun
Source: Precision Chemistry (article, 2025-10-16)
Field: Materials Science — Materials Chemistry

How to Efficiently Design 2D Materials for Electrochemical Applications Using Machine Learning Two dimensional (2D) materials have transitioned from lab findings to potential applications. Starting with the isolation of graphene, the field has rapidly expanded to encompass a broad spectrum of materials, including transition metal dichalcogenides, MXenes, and so on. Each of them offers unique structural, electronic, optical, and electrochemical properties. These materials have been recognized as candidates for applications in energy storage and conversion including electrocatalysts. As we approach the limits of traditional "trial-and-error" methods, the integration of statistical analysis, machine learning (ML), live (real-time) electrochemistry, and generative AI presents a compelling path forward. These tools are no longer aspirational; they are becoming essential to navigating the vast and complex design space of 2D materials for electrochemical applications in the future. Field (mathematics) ; Generative grammar ; Electrochemical energy storage ; Isolation (microbiology) ; Deep learning ; Energy (signal processing) ; Space (punctuation) Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Fuel Cells and Related Materials
Authors: Ke Wang (82395) | Ming Lei (161082) | Xing-Gui Zhou (1489381) | De Chen (119865) | Yi-An Zhu (1489387)
Source: Figshare (article, 2025-05-16)
Field: Materials Science — Materials Chemistry

Rational Design of Perovskite Anode Materials in SolidOxide Electrolysis Cells: Machine Learning-Assisted Prediction ofThermal Expansion Coefficients Perovskite oxides have been recognized as promising electrode materials for solid oxide electrolysis cells. In this work, the phonon dispersion and thermal expansion coefficients (TECs) of LaBO<sub>3</sub> (B = Sc–Cu), LaFe<sub>0.5</sub>B<sub>0.5</sub>O<sub>3</sub> (B = Cr, Mn, Co, Ni), and LaCrO<sub>3−δ</sub> and LaFeO<sub>3−δ</sub> (δ = 0, 0.25, 0.5) have been studied by performing density functional perturbation theory calculations under the quasi-harmonic approximation. The calculated TECs agree well with experimental data, and LaFe<sub>0.5</sub>Co<sub>0.5</sub>O<sub>3</sub> and LaFe<sub>0.5</sub>Ni<sub>0.5</sub>O<sub>3</sub> have TECs close to those of the electrolyte yttria-stabilized zirconia (YSZ). A machine learning model is then developed to enable high-throughput screening of potential perovskite anode materials, where the data set is established based on our calculated TECs and experimentally reported values. The SHAP analysis indicates dominant factors governing the TEC include the cation radius and Mulliken electronegativity of the B-site element and the crystal gamma angle. Finally, 507 candidates compatible with YSZ are identified from 13,095 perovskite oxides. Perovskite (structure) ; Thermal expansion ; Oxide ; Electrolysis ; Yttria-stabilized zirconia ; Electrolyte ; Electronegativity ; Anode Machine Learning in Materials Science ; Advancements in Solid Oxide Fuel Cells ; Thermal Expansion and Ionic Conductivity
Authors: Feicheng Huan | Feng Shi | Gaoyang Luo | Xiang Pan | Jingnan Zheng | Jianguo Wang
Source: ACS Applied Materials & Interfaces (article, 2025-09-26)
Field: Materials Science — Materials Chemistry

Surface-Dependent Interfacial Concentration of Oxygen Confined within Pd Interlayers: Molecular Dynamics with a Neural Network Potential Obtaining a detailed understanding of the interfacial dynamics of oxygen on palladium surfaces is crucial for industrial applications. However, it remains challenging to develop reaction-transport coupling mechanisms to enhance the activity and stability of Pd-based catalysts in confined environments. Herein, by integrating the established global neural network (G-NN) potential and molecular dynamics (MD) simulations, the interfacial concentrations of confined O<sub>2</sub> molecules within Pd interlayers were investigated systematically under various conditions. The developed reactive NN potential, rigorously validated against DFT benchmarks with an average error of 0.026 eV/atom, demonstrated precise structural discrimination capabilities among three Pd surfaces and subsequently produced reasonable catalytic structures. The Pd(100) surface exhibited the highest reactivity, followed by Pd(211), with the lowest on Pd(111). These differences show strong correlations with a reduced interlayer distance (approximately 1 nm) and the degree of surface reconstruction patterns through a comprehensive analysis of mean square displacement and reaction rate. Density distribution, in conjunction with radial distribution function analyses, further demonstrates how the interlayer confinement effect, as well as surface-specific atomic arrangements, remarkably regulate the interfacial concentration of oxygen. This work provides universal guidance for elucidating the macroscopic mechanism linking the bulk and interfacial concentrations in confined systems through large-scale simulations. Molecular dynamics ; Catalysis ; Mean squared displacement ; Work (physics) ; Molecule ; Palladium ; Coupling (piping) ; Oxygen Catalytic Processes in Materials Science ; Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Jiayi Tang | Liang Cao | Guanghui Xu | Mingdong Dong | J Xia | Ming Li
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Accelerating the Discovery of Binary Interconnect Materials: A Graph Neural Network and Diversity Ranking Workflow Workflow ; Reliability (semiconductor) ; Interconnection ; Ranking (information retrieval) ; Stability (learning theory) ; Artificial neural network ; Binary number Machine Learning in Materials Science ; Graphene research and applications ; Copper Interconnects and Reliability
Authors: Tong Xu | Yuzi He | Yanxun Lan | Huaijun Xie | Fangfang Ma | Lihao Su | Jiansheng Cui | Deming Xia | Jingwen Chen
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Chemistry — Spectroscopy

Prediction of Hydrolysis Pathways and Kinetics of Sulfamethoxazole a Machine-Learning-Based Molecular Dynamics and Experimental Study Kinetics ; Hydrolysis ; Chemistry ; Molecular dynamics ; Dynamics (music) ; Biochemical engineering ; Computational chemistry ; Organic chemistry ; Physics ; Engineering ; Psychology ; Pedagogy Analytical Chemistry and Chromatography ; Various Chemistry Research Topics
Authors: Xinmei Zhang | Xingxing Wang | Wei Wang | Zhipeng Yuan | Jin Peng | J M Shi | Peng He | Yunfeng Chang
Source: Journal of Alloys and Compounds (article, 2025-12-20)
Field: Materials Science — Materials Chemistry

Machine learning–driven thermoelectric materials: Review on prediction, optimization, and discovery Interpretability ; Key (lock) ; Thermoelectric effect ; Thermoelectric materials ; Big data ; Coupling (piping) ; Energy (signal processing) ; Thermal conductivity Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; MXene and MAX Phase Materials
Authors: Cong Fu | Yuchao Lin | Zachary Krueger | Haiyang Yu | Maho Nakata | Jianwen Xie | Emine Küçükbenli | Xiaofeng Qian | Shuiwang Ji
Source: PubMed (preprint, 2025-07-01)
Field: Chemistry — Physical and Theoretical Chemistry

Augmenting Molecular Graphs with Geometries via Machine Learning Interatomic Potentials. Accurate molecular property predictions require 3D geometries, which are typically obtained using expensive methods such as density functional theory (DFT). Here, we attempt to obtain molecular geometries by relying solely on machine learning interatomic potential (MLIP) models. To this end, we first curate a large-scale molecular relaxation dataset comprising 3.5 million molecules and 300 million snapshots. Then MLIP foundation models are trained with supervised learning to predict energy and forces given 3D molecular structures. Once trained, we show that the foundation models can be used in different ways to obtain geometries either explicitly or implicitly. First, it can be used to obtain low-energy 3D geometries via geometry optimization, providing relaxed 3D geometries for downstream molecular property predictions. To mitigate potential biases and enhance downstream predictions, we introduce geometry fine-tuning based on the relaxed 3D geometries. Second, the foundation models can be directly fine-tuned for property prediction when ground truth 3D geometries are available. Our results demonstrate that MLIP foundation models trained on relaxation data can provide valuable molecular geometries that benefit property predictions. Property (philosophy) ; Foundation (evidence) ; Ground truth ; Relaxation (psychology) ; Energy (signal processing) ; Molecular graph ; Molecular dynamics ; Topology (electrical circuits) History and advancements in chemistry ; Computational Drug Discovery Methods ; Genetics, Bioinformatics, and Biomedical Research
Authors: Raúl Duque | Arman Duha | Mario F. Borunda
Source: Physical Review Materials (article, 2025-08-29)
Field: Materials Science — Ceramics and Composites

Machine learning-driven analytical models for threshold displacement energy prediction in materials Understanding the behavior of materials under irradiation is crucial for the design and safety of nuclear reactors, spacecraft, and other radiation environments. The threshold displacement energy (${E}_{d}$) is a critical parameter for understanding radiation damage in materials, yet its determination often relies on costly experiments or simulations. This work leverages the machine learning-based sure independence screening and sparsifying operator (SISSO) method to derive accurate, analytical models for predicting ${E}_{d}$ using fundamental material properties. The models outperform traditional approaches for monoatomic materials, capturing key trends with high accuracy. While predictions for polyatomic materials highlight challenges due to dataset complexity, they reveal opportunities for improvement with expanded data. This study identifies cohesive energy and melting temperature as key factors influencing ${E}_{d}$, offering a robust framework for efficient, data-driven predictions of radiation damage in diverse materials. Materials science ; Displacement (psychology) ; Energy (signal processing) ; Composite material ; Engineering physics ; Mechanical engineering ; Engineering Advanced ceramic materials synthesis ; Machine Learning in Materials Science ; Nuclear Materials and Properties
Authors: Steve Dave Wansi Wendji | Rémi Piotrowski | Antonio Familiari | Carlo Massobrio | Mauro Boero | Christine Tugène | Firas Shuaib | David Hamani | Pierre‐Marie Geffroy | Philippe Thomas | Alfonso Pedone | Assil Bouzid | Olivier Masson | Gaëlle Delaizir | Guido Ori
Source: ChemRxiv (preprint, 2025-01-27)
Field: Materials Science — Polymers and Plastics

Structure, bonding and ionic mobility in Na-V-P-O glasses for energy storage applications through experiments, first-principles and machine learning-accelerated molecular dynamics Na-V-P-O glasses are promising cathode materials for sodium ion batteries, and yet a thorough understanding of their atomic scale behavior has so far been elusive. In this work we integrate structural and electrochemical experiments with first-principles and large-scale machine learning-accelerated molecular dynamics to elucidate quantitatively the interplay among structure, bonding, and ion mobility on space and time scales of unprecedented extensions. We unravel the existence of a broad V coordination distribution together with heterogeneous Na-ion mobility featuring percolation channels. Our results are instrumental in the search of NVP glasses optimization for electrochemical applications. Ion ; Electrochemistry ; Ionic bonding ; Atomic units ; Molecular dynamics ; Chemical physics ; Materials science ; Percolation (cognitive psychology) ; Cathode ; Scale (ratio) ; Work (physics) ; Energy storage ; Nanotechnology ; Physics ; Chemistry ; Computational chemistry ; Physical chemistry ; Thermodynamics ; Electrode ; Power (physics) Transition Metal Oxide Nanomaterials ; Advancements in Battery Materials ; Advanced Battery Materials and Technologies
Authors: Renata Andrade | Júlia Gonçalves Aguiar | Magda Bergmann | Liliane Lavoura Bueno Sachs | Sérgio Henrique Godinho Silva | Alberto Vasconcellos Inda | Marco Aurélio Carbone Carneiro | Nilton Curi
Source: Ciência e Agrotecnologia (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Machine learning allied to green tech analysis: Pioneering approaches for remineralizers and silicate fertilizers chemical composition assessment ABSTRACT Traditional methods for the chemical characterization of agrominerals remain costly and generate chemical waste, especially for large-scale applications. Portable X-ray fluorescence (pXRF) spectrometry has recently emerged as a tool for rapid material analysis. However, pXRF applications to remineralizers (REM), silicate fertilizers (SiF), and other agrominerals remain unexplored. This study aimed to: i) apply pXRF data to characterize agrominerals, and ii) assess six machine learning algorithms [projection pursuit regression (PPR), partial least squares (PLS), random forest (RF), support vector machine (SVM), extreme gradient boosting (XGB), and cubist regression (CR)] for agrominerals chemical composition assessment. 53 REM and SiF samples underwent traditional wet-chemical analysis (ICM 40B method) and triplicate pXRF scanning (60 s). The dataset was randomly separated into modeling (70%) and validation (30%) sub-datasets, and the machine learning algorithms were trained using k-fold cross-validation. Results indicated highly accurate predictions. Among the 49 predicted elements, 41 showed high accuracy (Al, Ba, Be, Bi, Ca, Ce, Co, Cr, Cs, Cu, Fe, Ga, Ge, Hf, In, K, La, Li, Lu, Mg, Mn, Mo, Na, Nb, Ni, P, Pb, Rb, S, Sc, Sr, Tb, Th, Ti, Tl, U, V, Y, Yb, Zn, and Zr) with R2 above 0.80, and RPD values ranging from 1.31 to 23.0. The presence of nutrients such as Ca, Mg, K, S, Fe, Mn, Zn, and P stood out. CR outperformed the other algorithms. The study demonstrates the potential of using pXRF combined with machine learning algorithms for accurate, rapid, cheap and environmentally friendly prediction of the chemical composition of agrominerals. Random forest ; Support vector machine ; Partial least squares regression ; Chemical composition ; Boosting (machine learning) ; Gradient boosting ; Regression Spectroscopy and Chemometric Analyses ; Soil Geostatistics and Mapping ; X-ray Spectroscopy and Fluorescence Analysis
Authors: Yan-Yu Xie | Xiao-Dong Li | Xiuying Liu | Jing-Xin Yu | Junfei Wang
Source: International Journal of Hydrogen Energy (article, 2025-04-30)
Field: Chemistry — Inorganic Chemistry

Integrating molecular simulations with multilayer perceptron neural networks to predict the CH4/H2 adsorption and separation of MTV-MOFs Adsorption ; Artificial neural network ; Multilayer perceptron ; Perceptron ; Hydrogen molecule ; Computer science ; Materials science ; Biological system ; Artificial intelligence ; Chemistry ; Hydrogen ; Physical chemistry ; Biology Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Industrial Gas Emission Control
Authors: Shirish Chandrakar | Swati Mahato | Soundarya Kose | Poorwa Gore | N.P. Gurao
Source: Modelling and Simulation in Materials Science and Engineering (article, 2025-07-21)
Field: Materials Science — Metals and Alloys

Elucidating the influence of alloying elements on hydrogen embrittlement in steels through machine learning-aided property prediction Abstract A data-driven machine learning strategy was employed to predict the ultimate tensile strength and ductility for different classes of steel in the presence of hydrogen. Data collected from the literature was trained and tested in different machine learning models such as linear regression (LR), gradient boosting machine regression (GBM), decision tree regressor (DT), least absolute shrinkage and selection operator (LASSO), elastic net (EN), random forest regression (RFR). The EN model shows an R 2 score of 0.90 and 0.87 to predict the UTS and ductility, respectively, whereas the RFR model shows an R 2 score of 0.87 and 0.90.The hydrogen embrittlement index, defined as the relative loss in ductility due to hydrogen charging, was used as a quantitative measure of hydrogen embrittlement susceptibility. Further insights into the contribution of individual input feature space in altering the tensile properties of steels in the presence of hydrogen were obtained from the feature importance score, Pearson correlation matrix, and SHapley Additive exPlanations (SHAP) analysis. The systematic study provides insights into the effect of alloying elements on the hydrogen embrittlement of steels. The data-driven machine learning approach established in the current study can be employed for life prediction of in-service components and for devising alloy design strategies for improved resistance to hydrogen embrittlement.&amp;#xD; Materials science ; Hydrogen embrittlement ; Embrittlement ; Metallurgy ; Property (philosophy) ; Corrosion Hydrogen embrittlement and corrosion behaviors in metals ; Welding Techniques and Residual Stresses ; Material Properties and Failure Mechanisms
Authors: LIM KYUNGJUN (21983285) | Freederia AI Researcher (21983315)
Source: Figshare (article, 2025-08-20)
Field: Materials Science — Polymers and Plastics

Enhanced Polypropylene Degradation Prediction using Machine Learning and Data-Driven Material Fatigue Modeling **Abstract:** Traditional methods for predicting polypropylene (PP) degradation and lifespan rely on empirical testing and simplified models, often overlooking complex interplay of environmental factors and material microstructure. This paper introduces a novel framework, the Polypropylene Degradation Prediction Engine (PDPPE), leveraging machine learning (ML) and data-driven material fatigue modeling to significantly improve the accuracy of degradation prediction. PDPPE incorporates a multi-modal data ingestion layer, semantic decomposition, a layered evaluation pipeline incorporating advanced theorem proving and finite element simulation, and a meta-self-evaluation loop for continual learning. Results demonstrate a 30-45% improvement in lifespan prediction compared to conventional Arrhenius-based models, with immediate implications for polymer lifecycle management, waste reduction, and sustainable product design, particularly in applications such as automotive components and packaging. --- *This document is a part of the Freederia Research Archive. Explore our complete collection of advanced research at [en.freederia.com](https://en.freederia.com), or visit our main portal at [freederia.com](https://freederia.com) to learn more about our mission and other initiatives.* Polypropylene ; Pipeline (software) ; Degradation (telecommunications) ; Product (mathematics) ; Finite element method ; Automotive industry ; Predictive modelling Polymer crystallization and properties ; Machine Learning in Materials Science ; Polymer Nanocomposites and Properties