jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Peter Okechukwu Chikelu | S. C. Nwigbo | Raphael Emeka Ozioko | Christian Emeka Okafor | Leo Aganyi Morga
Source: Modelling and Simulation in Materials Science and Engineering (article, 2025-04-30)
Field: Materials Science — Polymers and Plastics

Prediction of mechanical properties of <i>Newboulia laevis</i> particulate composite material using artificial neural network (ANN) Abstract The growing interest in using plant-based materials as substitutes for synthetic ones in the polymer composites industry is primarily driven by the negative health and environmental impacts of synthetic materials. However, the effectiveness and application of these composites are often hindered by poor material design. This research focused on extracting and processing stem fibers from the Newbouldia laevis plant into particles, followed by the creation of composites with varying particulate weight contents (wts). Mechanical testing showed that the composites had a maximum tensile strength of 31.3496 N mm −2 for those containing 10 wt.%, a maximum compression strength of 59.8716 N mm −2 for those with 40 wt.%, and a maximum flexural strength of 36.5808 N mm −2 for those with 10 wt.% particle content. The experimental results were analyzed using artificial neural networks (ANNs) to create a precise predictive model aimed at improving material design. The reliability of the ANN models was assessed using various performance metrics such as mean square error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), normalized root MSE (NRMSE), and the coefficient of determination ( R 2 ). The results showed low error rates for MSE, MAE, MAPE, and NRMSE, and a high R 2 value exceeding 0.9, indicating that the model predictions are highly accurate. These results demonstrate that ANN can be an effective mathematical tool for modeling and predicting the mechanical properties of reinforced polymer composites, ultimately saving both time and resources in material design research. Mean squared error ; Ultimate tensile strength ; Composite number ; Artificial neural network ; Coefficient of determination ; Reliability (semiconductor) ; Compression (physics) ; Flexural strength ; Root mean square Natural Fiber Reinforced Composites ; Material Selection and Properties ; Mechanical Engineering and Vibrations Research
Authors: Yinan Wang | Xiaoyang Wang | Zhenyu Wang | Jing Wu | Jian Lv | Han Wang
Source: ArXiv.org (preprint, 2025-09-12)
Field: Materials Science — Materials Chemistry

OpenCSP: A Deep Learning Framework for Crystal Structure Prediction from Ambient to High Pressure High-pressure crystal structure prediction (CSP) underpins advances in condensed matter physics, planetary science, and materials discovery. Yet, most large atomistic models are trained on near-ambient, equilibrium data, leading to degraded stress accuracy at tens to hundreds of gigapascals and sparse coverage of pressure-stabilized stoichiometries and dense coordination motifs. Here, we introduce OpenCSP, a machine learning framework for CSP tasks spanning ambient to high-pressure conditions. This framework comprises an open-source pressure-resolved dataset alongside a suite of publicly available atomistic models that are jointly optimized for accuracy in energy, force, and stress predictions. The dataset is constructed via randomized high-pressure sampling and iteratively refined through an uncertainty-guided concurrent learning strategy, which enriches underrepresented compression regimes while suppressing redundant DFT labeling. Despite employing a training corpus one to two orders of magnitude smaller than those of leading large models, OpenCSP achieves comparable or superior performance in high-pressure enthalpy ranking and stability prediction. Across benchmark CSP tasks spanning a wide pressure window, our models match or surpass MACE-MPA-0, MatterSim v1 5M, and GRACE-2L-OAM, with the largest gains observed at elevated pressures. These results demonstrate that targeted, pressure-aware data acquisition coupled with scalable architectures enables data-efficient, high-fidelity CSP, paving the way for autonomous materials discovery under ambient and extreme conditions. Suite ; Stability (learning theory) ; Benchmark (surveying) ; Scalability ; Crystal structure prediction ; Ranking (information retrieval) ; Deep learning ; Sampling (signal processing) Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallography and molecular interactions
Authors: Rong Kang | Subhavna Juneja | Alan X. Wang
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Biomaterials

Morphology-Dependent Fluorescence Sensing on Diatom Photonic Crystal: A New Paradigm for Quantitative Machine-Learning Analysis Fluorescence ; Diatom ; Photonic crystal ; Morphology (biology) ; Nanotechnology ; Computer science ; Materials science ; Optics ; Optoelectronics ; Physics ; Geology ; Oceanography Diatoms and Algae Research ; Water Quality Monitoring and Analysis ; Water Quality Monitoring Technologies
Authors: Zhenchuan Ma | Qizhi Teng | Pengcheng Yan | Lindong Li | Kirill M. Gerke | Marina V. Karsanina | Xiaohai He
Source: ArXiv.org (preprint, 2025-11-18)
Field: Materials Science — Materials Chemistry

Statistically controllable microstructure reconstruction framework for heterogeneous materials using sliced-Wasserstein metric and neural networks Heterogeneous porous materials play a crucial role in various engineering systems. Microstructure characterization and reconstruction provide effective means for modeling these materials, which are critical for conducting physical property simulations, structure-property linkage studies, and enhancing their performance across different applications. To achieve superior controllability and applicability with small sample sizes, we propose a statistically controllable microstructure reconstruction framework that integrates neural networks with sliced-Wasserstein metric. Specifically, our approach leverages local pattern distribution for microstructure characterization and employs a controlled sampling strategy to generate target distributions that satisfy given conditional parameters. A neural network-based model establishes the mapping from the input distribution to the target local pattern distribution, enabling microstructure reconstruction. Combinations of sliced-Wasserstein metric and gradient optimization techniques minimize the distance between these distributions, leading to a stable and reliable model. Our method can perform stochastic and controllable reconstruction tasks even with small sample sizes. Additionally, it can generate large-size (e.g. 512 and 1024) 3D microstructures using a chunking strategy. By introducing spatial location masks, our method excels at generating spatially heterogeneous and complex microstructures. We conducted experiments on stochastic reconstruction, controllable reconstruction, heterogeneous reconstruction, and large-size microstructure reconstruction across various materials. Comparative analysis through visualization, statistical measures, and physical property simulations demonstrates the effectiveness, providing new insights and possibilities for research on structure-property linkage and material inverse design. Metric (unit) ; Artificial neural network ; Sample (material) ; Controllability ; Sensitivity (control systems) ; Property (philosophy) ; Inverse problem ; Stochastic optimization ; Microstructure Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Generative Adversarial Networks and Image Synthesis
Authors: Junyun Pan | Ning Shi | Zhihao Zhang | Hongtao Zhang | Yuhong Zhao | Weidong Li | Huadong Fu | Jianxin Xie
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Construction of a Composition-Deformation Mechanism-Property Prediction Model to Simultaneously Improve the Strength and Ductility of Β- Titanium Alloys Via Machine Learning Ductility (Earth science) ; Materials science ; Deformation (meteorology) ; Mechanism (biology) ; Titanium ; Property (philosophy) ; Titanium alloy ; Deformation mechanism ; Composition (language) ; Metallurgy ; Structural engineering ; Composite material ; Engineering ; Microstructure ; Alloy ; Physics Titanium Alloys Microstructure and Properties ; Machine Learning in Materials Science ; Advanced Materials Characterization Techniques
Authors: Wenguang Xiao | Houlin Su | Yawei Ma | Youfu Ma | Wencong Lu | Lisheng Wang | Ge Li | Mingqing Yuan | Lihe Jiang | Lixin Liang | Xu Liu
Source: ChemistrySelect (article, 2025-11-01)
Field: Materials Science — Materials Chemistry

Integration of Machine Learning and Automated Synthesis for Accelerated Drug and Material Research Abstract The challenges posed by global climate change and disease risks have intensified the demand for efficient and practical materials and molecules. Traditional trial‐and‐error approaches are becoming increasingly inefficient and resource‐intensive. The rapid advancement of artificial intelligence (AI) has opened new avenues to accelerate research and shorten development cycles. In fields such as drug discovery, materials innovation, and chemistry, the application of AI has significantly improved research efficiency. As a core component of AI, ML can reduce development time and enhance experimental accuracy and efficiency. In recent years, the integration of ML with automated synthesis technologies has become a major research focus. This combination not only enables more precise chemical synthesis but also drives the digital and intelligent transformation of laboratories. This review provides a concise overview of the fundamental principles of ML and discusses its applications in drug discovery, materials innovation, and automated organic synthesis. It also highlights the latest progress in automated synthesis platforms. Finally, the review summarizes the opportunities and challenges of AI in scientific research and offers a systematic and critical perspective through representative case studies from pharmaceuticals, materials science, and chemistry, presenting a comprehensive view of the current status and future trends of AI‐driven automated synthesis. Applications of artificial intelligence ; Component (thermodynamics) ; Field (mathematics) ; Emerging technologies ; Perspective (graphical) Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Catalysis and Oxidation Reactions
Authors: Xiaoqiang Li | Xiaoyu Hou | X.B. He | Dandan Li | Jinhui Wang | Xingrui Chen | Qichi Le | Dexue Liu | Yunpeng Zhu
Source: Journal of Alloys and Compounds (article, 2025-12-14)
Field: Materials Science — Biomaterials

Research on the microstructure evolution and degradation behavior of biomedical Mg-Al-Mn-La alloys assisted by machine learning Biocompatibility ; Microstructure ; Corrosion ; Ultimate tensile strength ; Alloy ; Dissolution ; Degradation (telecommunications) ; Precipitation Magnesium Alloys: Properties and Applications ; Orthopaedic implants and arthroplasty ; Titanium Alloys Microstructure and Properties
Authors: Junyun Pan | Ning Shi | Zhihao Zhang | Hongtao Zhang | Yuhong Zhao | Weidong Li | Huadong Fu | Jianxin Xie
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Construction of a Composition-Deformation Mechanism-Property Prediction Model to Simultaneously Improve the Strength and Ductility of Β-Titanium Alloys Via Machine Learning Materials science ; Crystal twinning ; Deformation mechanism ; Titanium alloy ; Deformation (meteorology) ; Alloy ; Ultimate tensile strength ; Ductility (Earth science) ; Titanium ; Strain hardening exponent ; Work hardening ; Metallurgy ; Composite material ; Microstructure ; Creep Titanium Alloys Microstructure and Properties ; Metal and Thin Film Mechanics ; Advanced materials and composites
Authors: Matthew Walker | Keith T. Butler
Source: Materials Horizons (article, 2025-12-05)
Field: Materials Science — Materials Chemistry

The carbon cost of materials discovery: Can machine learning really accelerate the discovery of new photovoltaics? Computational screening has become a powerful complement to experimental efforts in the discovery of high-performance photovoltaic (PV) materials. Most workflows rely on density functional theory (DFT) to estimate electronic and optical properties relevant to solar energy conversion. Although more efficient than laboratory-based methods, DFT calculations still entail substantial computational and environmental costs. Machine learning (ML) models have recently gained attention as surrogates for DFT, offering drastic reductions in resource use with competitive predictive performance. In this study, we reproduce a canonical DFT-based workflow to estimate the maximum efficiency limit and progressively replace its components with ML surrogates. By quantifying the CO<sub>2</sub> emissions associated with each computational strategy, we evaluate the trade-offs between predictive efficacy and environmental cost. Our results reveal multiple hybrid ML/DFT strategies that optimize different points along the accuracy-emissions front. We find that direct prediction of scalar quantities, such as maximum efficiency, is significantly more tractable than using predicted absorption spectra as an intermediate step. Interestingly, ML models trained on DFT data can outperform DFT workflows using alternative exchange-correlation functionals in screening applications, highlighting the consistency and utility of data-driven approaches. We also assess strategies to improve ML-driven screening through expanded datasets and improved model architectures tailored to PV-relevant features. This work provides a quantitative framework for building low-emission, high-throughput discovery pipelines. Workflow ; Consistency (knowledge bases) ; Photovoltaic system ; Density functional theory ; Predictive modelling ; Limit (mathematics) ; Scalar (mathematics) ; Work (physics) Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Photovoltaic Systems and Sustainability
Authors: Becker, Florian | Laurent, Sophie | Ricci, Alessandro | Moretti, Elena | Jensen, Lars | Qiqi Su
Source:  (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning-Assisted Investigations Toward Polymer Synthesis: Progress and Outlook Polymer ; Work (physics) ; Process (computing) ; Feature (linguistics) Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization ; Block Copolymer Self-Assembly
Authors: Pelin Sezgin | Feride Neva Yüngül | Beste Naz Karaca | Hasan Can Gülbalkan | Seda Keskın
Source: ACS Materials Au (article, 2025-11-12)
Field: Chemistry — Inorganic Chemistry

Molecular Modeling-Based Machine Learning for Accurate Prediction of Gas Diffusivity and Permeability in Metal–Organic Frameworks Gas diffusion determines the performance of metal-organic frameworks (MOFs) in various practical applications, including membrane-based separations, yet its experimental measurement is challenging. We presented an efficient computational framework that integrates high-fidelity molecular dynamics (MD) simulations with machine learning (ML) to predict the diffusivities of CO<sub>2</sub>, N<sub>2</sub>, O<sub>2</sub>, CH<sub>4</sub>, and H<sub>2</sub> in >18,000 synthesized and hypothetical MOFs. ML models trained on MD data accurately predicted gas diffusivities of any given MOF within minutes using only easily accessible structural and guest-related properties. We provided an interactive, user-friendly web interface for predicting diffusivities of MOFs to facilitate material selection. Leveraging ML-predicted diffusivities, we evaluated membrane-based gas separation performances of all MOFs for seven industrially important separations: CO<sub>2</sub>/N<sub>2</sub>, CO<sub>2</sub>/CH<sub>4</sub>, N<sub>2</sub>/CH<sub>4</sub>, H<sub>2</sub>/CO<sub>2</sub>, H<sub>2</sub>/CH<sub>4</sub>, H<sub>2</sub>/N<sub>2</sub>, and O<sub>2</sub>/N<sub>2</sub>. The best MOF membranes offering high selectivity and permeability were identified and analyzed by using molecular fingerprinting to reveal the critical chemical properties for designing next-generation MOFs. Thermal diffusivity ; Permeability (electromagnetism) ; Molecular dynamics ; Experimental data ; Gas separation ; Diffusion ; Membrane ; Interface (matter) Metal-Organic Frameworks: Synthesis and Applications ; Membrane Separation and Gas Transport ; Catalysts for Methane Reforming
Authors: Yuanrui Li | Yali Lu | Qiang Zhang | Zongjin Hu | Weiju Hao | Yu-Ling Song
Source: ACS Sustainable Chemistry & Engineering (article, 2025-06-13)
Field: Materials Science — Materials Chemistry

Design of Multifunctional Catalysts and Potential-Dependent Inverted Adsorption: A Sweet Marriage of Synergistic “Genome” Investigated by Machine Learning and First-Principles Calculations The pursuit of two-dimensional single-atom catalysts (SACs) holds profound importance in the quest for efficient, stable, and economical alternatives to noble metals in hydrogen evolution (HER), oxygen evolution (OER), and oxygen reduction (ORR) reactions. By harnessing the remarkable tunability of transition metal (TM) active centers and their coordination environments coupled with the diversity of substrate materials, these catalysts present unprecedented opportunities for the design of highly stable and active electrocatalysts. This study offers a comprehensive exploration of the catalytic HER/OER/ORR activity in 207 ((TM-NxHxO4–x)3@g-C12B12N12H12) SACs, utilizing a seamless integration of density functional theory (DFT) and machine learning (ML). Twelve bifunctional OER/ORR catalysts with overpotentials exceeding Pt/IrO2 were identified, with (Rh-N2H2O2-V)3@g-C12B12N12H12 and (Rh-N1H1O3)3@g-C12B12N12H12 monolayers emerging as standout trifunctional electrocatalysts due to their remarkably low HER overpotential. Through the Sure Independence Screening and Sparsifying Operator (SISSO) method, a clear relationship between intrinsic properties and activity was uncovered, with its acceptable prediction accuracy in intermediates adsorption affirmed. The constant-potential method further underscores the essential role of electric double-layer capacitance in modulating the kinetic barrier of the rate-determining step in them. We observed shifts in the Fermi level with changing electrochemical potential altering d-orbital occupation, highlighting the synergy between metal atomic orbitals and the band of substrate. With O2 as the model adsorbate, we reveal the direct impact of these shifts on adsorption energies, uncovering a fascinating inversion of adsorption energies on Rh under varying electrochemical potentials. Such findings are driven by the filling of the dxz orbital, which plays a pivotal role in stabilizing the O2 π orbital and reshaping the electronic structure. Catalysis ; Adsorption ; Chemistry ; Nanotechnology ; Materials science ; Chemical engineering ; Engineering ; Organic chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysis and Hydrodesulfurization Studies
Authors: Marcos Barba Ballarín | Ricardo. López Ruiz
Source: Zaguan (University of Zaragoza Repository) (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

MACHINE LEARNING: MÉTODOS DE ENSAMBLE APLICADOS A LA ESTIMACIÓN DE LA TEMPERATURA CRÍTICA EN MATERIALES SUPERCONDUCTORES El aprendizaje automático, ampliamente conocido por su acepción en inglés machine learning, vive hoy una época dorada, especialmente en el área del deep learning y las inteligencias artificiales generativas. No obstante, sin tanta repercusión mediática y antes de que estos modelos vieran la luz, las últimas décadas han estado lideradas por multitud de modelos predictivos que, sin alcanzar los increíbles resultados de las redes neuronales modernas, siguen siendo la elección preferida por muchos profesionales en ámbitos tan diversos como la investigación científica, la detección de fraude bancario, la predicción de mercados financieros o la toma de decisiones corporativas, entre otros.<br />En la mayoría de los casos, los modelos a los que actualmente nos referimos como machine learning <<tradicional>>, no alcanzan los sorprendentes rendimientos de los modernos algoritmos basados en redes neuronales. Sin embargo, su estabilidad, su menor necesidad de datos e infraestructura computacional y, sobre todo, su explicabilidad, que nos permite interpretar cuál es la estrategia que el algoritmo ha encontrado para obtener las predicciones, los convierten en una opción realmente atractiva.<br />En el presente proyecto tomamos la iniciativa de estudiar algunos de los modelos más potentes de esta área del aprendizaje automático: aquellos algoritmos que, por medio de los conocidos como métodos de ensamble, combinan varios modelos en uno solo para lograr un mayor rendimiento. No sin antes revisar la estructura básica utilizada por todas estas técnicas: el árbol de decisión.<br />El objetivo será no solo acercarnos al funcionamiento de estas técnicas, sino también desarrollar una aplicación práctica, en este caso, en el ámbito de la investigación científica y, en particular, de la física de materiales: predecir la temperatura crítica en compuestos superconductores. Además, discutiremos las posibilidades que, tras el auge del deep learning, quedan para este tipo de modelos.<br /><br /> Machine Learning in Materials Science ; Computational Physics and Python Applications ; E-Learning and Knowledge Management
Authors: Shagun Varshney | Ram Pravesh Pandey | Shivam Mishra | Akhilesh Kumar Maurya | Nidhi Mishra
Source: Nano-Structures & Nano-Objects (article, 2025-12-05)
Field: Materials Science — Materials Chemistry

Biogenic synthesis of silver nanoparticles using Azadirachta indica and Thevetia peruviana leaf extracts: Characterization, antibacterial analysis, machine learning-based toxicity, and In-Silico molecular docking studies for potential biomedical applications Azadirachta ; Silver nanoparticle ; Antibacterial activity ; Nanoparticle ; Fourier transform infrared spectroscopy ; Bacteria ; Docking (animal) Nanoparticles: synthesis and applications ; Graphene and Nanomaterials Applications ; Moringa oleifera research and applications