jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Arafat Rahman | Md Sojib Hossain | Abdullah Siddique
Source: Journal of Materials Science (article, 2025-07-17)
Field: Materials Science — Materials Chemistry

Review: machine learning approaches for diverse alloy systems Abstract The integration of machine learning (ML) into alloy design has revolutionized the discovery and optimization of advanced materials by enabling high-throughput, data-driven methodologies. This review systematically examines recent advancements in ML applications across diverse alloy systems, including steels, aluminum alloys, magnesium alloys, nickel-based superalloys, high-entropy alloys (HEAs), shape memory alloys, and metallic glasses. We categorize ML approaches into supervised, unsupervised, and reinforcement learning paradigms, detailing their specific implementations for property prediction, phase stability analysis, and composition optimization. Advanced techniques, such as inverse design frameworks and physics-informed ML models, have demonstrated substantial improvements in predictive accuracy and interpretability by integrating domain knowledge with data-driven approaches. The review further explores the synergy between ML and traditional computational methods, including CALPHAD-based thermodynamic modeling and density functional theory (DFT), enhancing the reliability of property predictions. We highlight case studies where ML-driven strategies have successfully accelerated alloy discovery, optimized mechanical properties, and identified novel compositions with tailored performance metrics. Additionally, we address key challenges in ML-driven alloy design, including data scarcity, feature selection, model interpretability, and the necessity for standardized benchmarking datasets. By providing a comprehensive evaluation of current methodologies and emerging trends, this review underscores the transformative role of ML in advancing next-generation alloy design and manufacturing, ultimately enabling the rapid development of high-performance materials for aerospace, energy, biomedical, and structural applications. Materials science ; Solid mechanics ; Alloy ; Metallurgy ; Polymer science ; Composite material Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Shanhui Diao | Qiong Wu | Shimei Li | Gang Xu | Xiangling Ren | Longfei Tan | Guihua Jiang | Peng Song | Xianwei Meng
Source: Materials Horizons (review, 2025-01-01)
Field: Materials Science — Materials Chemistry

From synthesis to properties: expanding the horizons of machine learning in nanomaterials research Nanotechnology involves the manipulation of matter at dimensions ranging from 1 to 100 nanometers. Prediction of synthesis parameters, structure, properties and applications is a cascade process in nanomaterials research, each of these stages being interconnected and having a correlative influence on one another. Traditionally, the "trial and error" approach in nanomaterial research has several limitations, including time-consuming, laborious and resource-intensive. With the rise and vigorous development of artificial intelligence technology as the fourth paradigm of materials research, machine learning offers a significant research prospect for the accelerated new materials design, synthesis optimization and property prediction. In this review, the three key elements of machine learning including data, descriptors and machine learning methods for nanomaterial research are discussed. An overview of the applications of machine learning in nanomaterial research is provided, particularly focusing on various synthesis methods of single nanomaterials and property prediction of nanocomposites, through the framework of synthesis-structure-property-application relationships. Finally, the potential of this fast-growing field is highlighted, as well as the formidable challenges it faces. Nanomaterials ; Property (philosophy) ; New horizons ; Nanotechnology ; Materials science ; Engineering ; Aerospace engineering ; Philosophy ; Epistemology Machine Learning in Materials Science
Authors: Sandeep Jain | Ayan Bhowmik | Jaichan Lee
Source: Science and Technology of Advanced Materials (article, 2025-01-31)
Field: Materials Science — Biomaterials

Machine learning approaches for predicting and validating mechanical properties of Mg rare earth alloys for light weight applications In this work, we have attempted to predict the mechanical behaviour of light weight Mg-based rare earth alloys fabricated through different mechanical and thermal processes. Our approach involves machine learning techniques across a range of different thermomechanical processes such as solution treatment, homogenization, extrusion and aging behaviour. The effectiveness of machine learning models is evaluated using performance metrics, including Coefficient of determination (R<sup>2</sup>), Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). After modeling and selection of best model, the mechanical behaviour of new alloys was predicted in terms of ultimate tensile strength, yield strength and total elongation. The predicted results highlight the superior predictive accuracy of the K-Nearest Neighbors (KNN) machine learning model, demonstrating its better performance metrics compared with other machine learning approaches. This model has been found to predict the material properties with an effective evaluation matrix (R<sup>2</sup> = 0.955, MAE = 3.4% and RMSE = 4.5%). Rare earth ; Materials science ; Nanotechnology ; Metallurgy Magnesium Alloys: Properties and Applications ; Aluminum Alloys Composites Properties ; Aluminum Alloy Microstructure Properties
Authors: Payam Edalati | Gaspar Andrade | Renato Belli Strozi | Shivam Dangwal | Kaveh Edalati | Ricardo Floriano
Source: Journal of Alloys and Compounds (article, 2025-03-28)
Field: Materials Science — Materials Chemistry

Room temperature hydrogen storage properties of Ti-Zr-Mn-Fe-Co high-entropy alloys designed by semi-empirical descriptors, thermodynamic calculations and machine learning High entropy alloys ; Hydrogen storage ; Thermodynamics ; Materials science ; Hydrogen ; Entropy (arrow of time) ; Metallurgy ; Chemistry ; Alloy ; Physics Hydrogen Storage and Materials ; High Entropy Alloys Studies ; Nuclear Materials and Properties
Authors: Yue Liu | Zhengwei Yang | Xinxin Zou | Yuxiao Lin | Shuchang Ma | Wei Zuo | Zheyi Zou | Hong Wang | Maxim Avdeev | Siqi Shi
Source: Materials Science and Engineering R Reports (article, 2025-06-18)
Field: Materials Science — Materials Chemistry

A general framework to govern machine learning oriented materials data quality Computer science ; Quality (philosophy) ; Machine learning ; Data quality ; Artificial intelligence ; Data science ; Engineering ; Epistemology ; Operations management Machine Learning in Materials Science ; Mineral Processing and Grinding ; Machine Learning and Algorithms
Authors: Lucía Morán‐González | Arron L. Burnage | Ainara Nova | David Balcells
Source: ACS Catalysis (review, 2025-05-14)
Field: Materials Science — Materials Chemistry

AI Approaches to Homogeneous Catalysis with Transition Metal Complexes Artificial intelligence (AI) is transforming research in chemistry, including homogeneous catalysis with transition metals. Over the past 15 years, the number of publications combining AI with catalysis has increased exponentially, reflecting the interest and strength of this strategy in the field. Since this is a broad emerging discipline, it is essential to establish guidelines that clarify the diverse approaches already available. The complexity of the tasks that can be carried out with AI tools is directly linked to the nature of their components, including datasets, representations, algorithms, and high-throughput experimental and computational facilities. In parallel to the evolution of these tools, applications to catalysis have also advanced. Initially, models were developed to predict key aspects of the reaction mechanism, aiming at screening catalyst candidates. Subsequent studies have incorporated experimental data to optimize reaction conditions and yields. More recently, generative AI based on deep learning methods has enabled the inverse design of novel catalysts with predefined target properties. While most studies rely on computational data, recent advancements have improved the acquisition of experimental data, enabling AI-driven automated workflows. This Perspective gives a critical overview on selected studies that reflect the state of the art in the application of AI to homogeneous metal-catalyzed reactions, also highlighting future opportunities and challenges. Catalysis ; Transition metal ; Homogeneous ; Homogeneous catalysis ; Chemistry ; Combinatorial chemistry ; Organic chemistry ; Thermodynamics ; Physics Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Asymmetric Hydrogenation and Catalysis
Authors: Yuhang Song | Jiakai Li | Dongzhi Chi | Zhengtao Xu | Jie Liu | Mingxi Chen | Ziyu Wang
Source: Chemical Communications (review, 2025-01-01)
Field: Chemistry — Inorganic Chemistry

AI-driven advances in metal–organic frameworks: from data to design and applications Metal-organic frameworks (MOFs) are a versatile class of porous materials with unprecedented structural tunability, surface area, and application potential in areas such as gas storage, carbon capture, and biomedicine. However, their immense chemical design space poses significant challenges for conventional discovery and optimization methods. Recent advances in artificial intelligence (AI) and machine learning (ML) have introduced transformative capabilities to this field, enabling accurate property prediction, automated structure generation, and synthesis planning at scale. This review provides a comprehensive overview of AI-driven strategies for accelerating MOF research. It discusses key databases, deep learning architectures, generative models, and hybrid AI-simulation frameworks that have reshaped the design and screening of high-performance MOFs. Techniques such as graph neural networks and AL have enabled breakthroughs in structure-property prediction, while integration with robotics is advancing autonomous laboratories. Despite these advancements, challenges remain in data quality, model interpretability, and experimental validation. Future directions include physics-informed ML models, standardized data protocols, and deeper integration of AI with chemical robotics. By highlighting both opportunities and current limitations, this review aims to provide a roadmap for the next generation of AI-accelerated MOF innovation. Transformative learning ; Deep learning ; Robotics ; Generative grammar ; Chemical space ; Key (lock) ; Graph ; Data integration Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Seong-Min Kim | Joshua Schrier | Yousung Jung
Source: Angewandte Chemie International Edition (article, 2025-02-13)
Field: Materials Science — Materials Chemistry

Explainable Synthesizability Prediction of Inorganic Crystal Polymorphs Using Large Language Models We evaluate the ability of machine learning to predict whether a hypothetical crystal structure can be synthesized and explain those predictions to scientists. Fine-tuned large language models (LLMs) trained on a human-readable text description of the target crystal structure perform comparably to previous bespoke convolutional graph neural network methods, but better prediction quality can be achieved by training a positive-unlabeled learning model on a text-embedding representation of the structure. An LLM-based workflow can then be used to generate human-readable explanations for the types of factors governing synthesizability, extract the underlying physical rules, and assess the veracity of those rules. These explanations can guide chemists in modifying or optimizing non-synthesizable hypothetical structures to make them more feasible for materials design. Bespoke ; Workflow ; Computer science ; Embedding ; Convolutional neural network ; Representation (politics) ; Artificial intelligence ; Artificial neural network ; Graph ; Crystal (programming language) ; Machine learning ; Natural language processing ; Theoretical computer science ; Programming language Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Gaobo Lin | Teng Guo | Wenwen Lin | Haoan Fan | Lei Guo | Shengbai Zhang | Bolong Li | Jianghao Wang | Huiping Ji | Weiyu Song | Jie Fu
Source: ACS Catalysis (article, 2025-07-22)
Field: Materials Science — Materials Chemistry

Machine Learning Accelerated Screening Advanced Single-Atom Anchored MXenes Electrocatalyst for Nitrogen Fixation Electrochemical nitrogen reduction reaction (NRR) has garnered significant attention as an alternative to the energy-intensive Haber–Bosch process. However, the vast search space for electrocatalysts and unclear structure–activity relationships limit the rational design of electrocatalysts for NRR. Herein, we present a machine learning-driven catalyst screening process to achieve high-throughput screening of two-dimensional material (MXene) supported single-atom catalysts (MXene-SACs) targeting NRR performance. Utilizing a database from density functional theory calculations as input, we identified four top-performing catalysts from 3146 MXene-SACs and recognized an effective intrinsic descriptor to accelerate high-throughput screening without additional computations, which was further validated experimentally by 10 synthesized MXene-SACs. In-depth study of the descriptor revealed a NRR mechanism: electron transfer from the single atom to coordinating atoms, causing crystal field splitting into eg and t2g bands. The interaction between the empty eg band of the single atom and the N2H π* orbital, activated by electrons from the t2g occupied orbital, facilitates the N≡N bond, promoting a smooth reaction under weak N2 adsorption. Impressively, the screened Mo2CO2–Zr, with strong atomic interactions, achieved a superior Faradaic efficiency of 31.7%. This work can not only provide a deeper understanding of the catalytic processes but also offer a foundation for future catalyst design and synthesis. MXenes ; Electrocatalyst ; Nitrogen fixation ; Catalysis ; Nitrogen atom ; Nitrogen ; Chemistry ; Materials science ; Nanotechnology ; Electrochemistry ; Physical chemistry ; Electrode ; Biochemistry ; Organic chemistry MXene and MAX Phase Materials ; Ammonia Synthesis and Nitrogen Reduction ; Advanced Photocatalysis Techniques
Authors: Yu Sun | Shi-Rong Qin | Yuncang Li | Naimul Hasan | Yan Vivian Li | Jiangguo Liu
Source: Scientific Reports (article, 2025-02-04)
Field: Materials Science — Biomaterials

Machine learning integrated with in vitro experiments for study of drug release from PLGA nanoparticles This paper investigates delivery of encapsulated drug from poly lactic-co-glycolic micro-/nano-particles. Experimental data collected from about 50 papers are analyzed by machine learning algorithms including linear regression, principal component analysis, Gaussian process regression, and artificial neural networks. The focus is to understand the effect of drug solubility, drug molecular weight, particle size, and pH-value of the release matrix/environment on drug release profiles. The results obtained from machine learning is then used as guidelines for designing new in vitro experiments to examine dependence of drug release profiles on those four factors. It is interesting to see that indeed the results of the new in vitro experiments are in basic agreement with the results obtained from machine learning. PLGA ; Nanoparticle ; In vitro ; Drug ; Computer science ; Pharmacology ; Chemistry ; Nanotechnology ; Medicine ; Materials science ; Biochemistry Nanoparticle-Based Drug Delivery ; Advanced Drug Delivery Systems ; Graphene and Nanomaterials Applications
Authors: Qijun Yu | Ninggui Ma | Chihon Leung | Han Liu | Y. Ren | Zhanhua Wei
Source: Journal of Materials Informatics (review, 2025-02-12)
Field: Materials Science — Materials Chemistry

AI in single-atom catalysts: a review of design and applications Single-atom catalysts (SACs) have emerged as a research frontier in catalytic materials, distinguished by their unique atom-level dispersion, which significantly enhances catalytic activity, selectivity, and stability. SACs demonstrate substantial promise in electrocatalysis applications, such as fuel cells, CO2 reduction, and hydrogen production, due to their ability to maximize utilization of active sites. However, the development of efficient and stable SACs involves intricate design and screening processes. In this work, artificial intelligence (AI), particularly machine learning (ML) and neural networks (NNs), offers powerful tools for accelerating the discovery and optimization of SACs. This review systematically discusses the application of AI technologies in SACs development through four key stages: (1) Density functional theory (DFT) and ab initio molecular dynamics (AIMD) simulations: DFT and AIMD are used to investigate catalytic mechanisms, with high-throughput applications significantly expanding accessible datasets; (2) Regression models: ML regression models identify key features that influence catalytic performance, streamlining the selection of promising materials; (3) NNs: NNs expedite the screening of known structural models, facilitating rapid assessment of catalytic potential; (4) Generative adversarial networks (GANs): GANs enable the prediction and design of novel high-performance catalysts tailored to specific requirements. This work provides a comprehensive overview of the current status of AI applications in SACs and offers insights and recommendations for future advancements in the field. Atom (system on chip) ; Catalysis ; Computer science ; Nanotechnology ; Materials science ; Chemistry ; Embedded system ; Organic chemistry Catalytic Processes in Materials Science ; Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Jacob T. Sivak | Saeed S. I. Almishal | Mary Kathleen Caucci | Yueze Tan | Dhiya Srikanth | Joseph Petruska | Matthew Furst | Long‐Qing Chen | Christina M. Rost | Jon‐Paul Maria | Susan B. Sinnott
Source: Physical Review Letters (article, 2025-05-28)
Field: Materials Science — Materials Chemistry

Discovering High-Entropy Oxides with a Machine-Learning Interatomic Potential High-entropy materials shift the traditional materials discovery paradigm to one that leverages disorder, enabling access to unique chemistries unreachable through enthalpy alone. We present a self-consistent approach integrating computation and experiment to understand and explore single-phase rocksalt high-entropy oxides. By leveraging a machine-learning interatomic potential, we rapidly and accurately map high-entropy composition space using our two descriptors: bond length distribution and mixing enthalpy. The single-phase stabilities for all experimentally stabilized rocksalt compositions are correctly resolved, with dozens more compositions awaiting discovery. Enthalpy ; Computation ; Entropy (arrow of time) ; Statistical physics ; Computer science ; Interatomic potential ; Materials science ; Thermodynamics ; Physics ; Molecular dynamics ; Algorithm ; Quantum mechanics Machine Learning in Materials Science ; High Entropy Alloys Studies ; Electronic and Structural Properties of Oxides
Authors: Yunsung Lim | Hyunsoo Park | Aron Walsh | Jihan Kim
Source: Matter (article, 2025-06-12)
Field: Chemistry — Inorganic Chemistry

Accelerating CO2 direct air capture screening for metal-organic frameworks with a transferable machine learning force field Field (mathematics) ; Metal-organic framework ; Computer science ; Materials science ; Nanotechnology ; Chemistry Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Phase Equilibria and Thermodynamics
Authors: Deep Mondal | Sujoy Datta | Debnarayan Jana
Source: Physical Chemistry Chemical Physics (review, 2025-01-01)
Field: Materials Science — Materials Chemistry

Navigating the evolution of two-dimensional carbon nitride research: integrating machine learning into conventional approaches Carbon nitride research has reached a promising point in today's research endeavours with diverse applications including photocatalysis, energy storage, and sensing due to their unique electronic and structural properties. Recent advances in machine learning (ML) have opened new avenues for exploring and optimizing the potential of these materials. This study presents a comprehensive review of the integration of ML techniques in carbon nitride research with an introduction to CN classifications and recent advancements. We discuss the methodologies employed, such as supervised learning, unsupervised learning, and reinforcement learning, in predicting material properties, optimizing synthesis conditions, and enhancing performance metrics. Key findings indicate that ML algorithms can significantly reduce experimental trial-and-error, accelerate discovery processes, and provide deeper insights into the structure-property relationships of carbon nitride. The synergistic effect of combining ML with traditional experimental approaches is highlighted, showcasing studies where ML driven models have successfully predicted novel carbon nitride compositions with enhanced functional properties. Future directions in this field are also proposed, emphasizing the need for high-quality datasets, advanced ML models, and interdisciplinary collaborations to fully realize the potential of carbon nitride materials in next-generation technologies. Carbon nitride ; Nitride ; Nanotechnology ; Carbon fibers ; Materials science ; Computer science ; Chemistry ; Photocatalysis ; Composite number ; Organic chemistry Machine Learning in Materials Science ; Advanced Photocatalysis Techniques ; Electronic and Structural Properties of Oxides
Authors: Timothy K. Mulenga | Sanjay Mavinkere Rangappa | Suchart Siengchin
Source: Archives of Computational Methods in Engineering (review, 2025-03-17)
Field: Materials Science — Polymers and Plastics

Natural Fiber Composites: A Comprehensive Review on Machine Learning Methods Natural (archaeology) ; Fiber ; Natural fiber ; Computer science ; Composite material ; Materials science ; Geology Natural Fiber Reinforced Composites ; Additive Manufacturing and 3D Printing Technologies ; Textile materials and evaluations