jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Matt Ball | Dragos Horvath | Thierry Kogej | Mikhail Kabeshov | Alexandre Varnek
Source: Chemical Science (review, 2025-01-01)
Field: Materials Science — Materials Chemistry

Predicting reaction conditions: a data-driven perspective The selection of optimal reaction conditions is a critical challenge in synthetic chemistry, influencing the efficiency, sustainability, and scalability of chemical processes. While machine learning (ML) has emerged as a promising tool for predicting reaction conditions in computer-aided synthesis planning (CASP), existing approaches face many significant challenges, including data quality, sparsity, choice of reaction representation and method evaluation. Recent studies have suggested that these models may fail to surpass literature-derived popularity baselines, underscoring these problems. In this work, we provide a critical review of state-of-the-art ML techniques, identifying innovations which have addressed the key challenges facing researchers when modelling conditions. To illustrate how relevant reaction representations can improve existing models, we perform a case study of heteroaromatic Suzuki-Miyaura reactions, derived from US patent data (USPTO). Using Condensed Graph of Reaction-based inputs, we demonstrate how this alternative representation can enhance the predictive power of a model beyond popularity baselines. Finally, we propose future directions for the field beyond improving data quality, suggesting potential options to mitigate data issues prevalent in existing literature data. This perspective aims to guide researchers in understanding and overcoming current limitations in computational reaction condition prediction. Perspective (graphical) ; Data science ; Computer science ; Artificial intelligence Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Sandeep Kumar Yadav | Vishwa Prakash Jha | Durga Prasad Pabba | Arun Thirumurugan
Source: Sustainable Energy & Fuels (article, 2025-12-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Development of core–shell nanofibers in magnetoelectric sensors, fuel cells, and drug-delivery applications: a review Core–shell nanofibers have diverse applications, including drug delivery, water purification, tissue engineering, and sensor technologies, highlighting their multifunctional potential in both biomedical and environmental fields. Nanofiber ; Fuel cells ; Electrospinning ; Drug delivery Supercapacitor Materials and Fabrication ; Electrospun Nanofibers in Biomedical Applications ; Conducting polymers and applications
Authors: Yu‐Hang Yuan | Yuchen Gao | Xiang Chen | Qiang Zhang
Source: National Science Open (article, 2025-10-13)
Field: Materials Science — Materials Chemistry

Overcoming data scarcity challenges in AI-driven energy chemistry research Artificial Intelligence (AI) has become an increasingly important propellant for energy materials and energy chemistry research, such as accelerating advanced energy materials discovery, analyzing vast amounts of data from both experiments and computations, process optimization for materials syntheses, management and monitoring of energy storage devices such as lithium batteries, and algorithm-optimized grid load forecasting. Looking back at recent pioneering works of AI-driven energy chemistry research, constructing a dataset with both high quality and large quantity is almost the first step and largely determines the following success of training AI models and figuring out corresponding scientific issues. Scarcity ; Process (computing) ; Figuring ; Energy (signal processing) ; Quality (philosophy) ; Data quality ; Energy engineering Machine Learning in Materials Science
Authors: Martin Hoffmann Petersen | Jin Hyun Chang | Arghya Bhowmik | Juan Maria García Lastra
Source: Scientific Data (article, 2025-08-16)
Field: Materials Science — Materials Chemistry

Dataset exploring the atomic scale structure and ionic dynamics of polyanion sodium cathode materials Polyanionic sodium cathode materials exhibit promising electrochemical properties and high stability, making this chemical space worth exploring to enhance the performance of sodium-ion batteries. Given the vast chemical space, fast and efficient computational methods are needed, highlighting the need for machine learning (ML)-guided approaches. To support the ML-driven discovery, we have developed a comprehensive theoretical dataset comprising 113532 DFT-calculated structures with atomic charges and 184612 structures without atomic charges for four polyanion sodium-ion cathode materials: NaTMPO<sub>4</sub> (olivine), NaTMPO<sub>4</sub> (maricite), Na<sub>2</sub>TMSiO<sub>4</sub>, and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub><mml:mrow><mml:mi>N</mml:mi> <mml:mi>a</mml:mi></mml:mrow> <mml:mrow><mml:mn>2</mml:mn> <mml:mo>.</mml:mo> <mml:mn>56</mml:mn></mml:mrow> </mml:msub> <mml:msub><mml:mrow><mml:mi>T</mml:mi> <mml:mi>M</mml:mi></mml:mrow> <mml:mrow><mml:mn>1</mml:mn> <mml:mo>.</mml:mo> <mml:mn>72</mml:mn></mml:mrow> </mml:msub> <mml:msub><mml:mrow><mml:mo>(</mml:mo> <mml:msub><mml:mrow><mml:mi>S</mml:mi> <mml:mi>O</mml:mi></mml:mrow> <mml:mrow><mml:mn>4</mml:mn></mml:mrow> </mml:msub> <mml:mo>)</mml:mo></mml:mrow> <mml:mrow><mml:mn>3</mml:mn></mml:mrow> </mml:msub> </mml:math> , with transition metals TM ∈ {Fe, Mn, Co, Ni}. This dataset includes optimizations of structures, ab initio molecular dynamics simulation trajectories sampled at 1000 K, and structures generated from ML-driven molecular dynamics simulation at 1000 K using an active learning algorithm. The dataset contains both single and multiple-TM compositions, providing a diverse representation of cathode materials. Using cathode specific subsets of this dataset to train ML models, we demonstrate that we can create ML interatomic potentials that reproduces the DFT results of the polyanion sodium cathode materials. Cathode ; Molecular dynamics ; Atomic units ; Ion ; Electrochemistry ; Ionic bonding ; Chemical physics ; Representation (politics) ; Sodium ; Materials science ; Chemical space ; Chemistry ; Computer science ; Computational chemistry ; Physical chemistry ; Physics Machine Learning in Materials Science ; Advancements in Battery Materials ; X-ray Diffraction in Crystallography
Authors: Leena Regi Saleth | Mehak Gupta | Gautam Sharma | Elika Verma | Sanjiv Dhingra
Source: Communications Materials (article, 2025-12-17)
Field: Materials Science — Materials Chemistry

Meta-analyses of the evolution of MXene synthesis for bioengineering and artificial intelligence-driven applications The year 2011 marked a breakthrough in material science innovation with the discovery of MXenes, an emerging family of two-dimensional transition metal-based nanomaterials. Owing to their distinctive properties, MXenes have rapidly surfaced as transformative materials, particularly in energy-storage and nanomedicine. In this review, we systematically explore the evolution of MXene synthesis, from its discovery to current advancements, focussing on their bioengineering applications, through a meta-analytic and bibliometric lens. We discuss synthesis methods, ranging from hydrofluoric acid (HF)-based etching to non-HF approaches, along with post-synthesis processes like intercalation, delamination and surface functionalization, that tailor MXene properties for biomedical therapeutics. We also overview key microscopy, spectroscopy and diffraction-based characterization methods, to understand their structure and functionality. Additionally, discussion on artificial intelligence (AI)-driven innovations highlights the significant shift in material science. By connecting synthesis methods with resulting characteristics and meta-analyses trends, this review emphasizes MXenes' transformative potential in regenerative therapeutics and diagnostics. MXenes ; Transformative learning ; Characterization (materials science) ; Key (lock) ; Etching (microfabrication) MXene and MAX Phase Materials ; 2D Materials and Applications ; Advanced Sensor and Energy Harvesting Materials
Authors: Linjing Hao | Jinghe Yang | Guosheng Li | Peng Li | Yijun Cao
Source: Reviews in Inorganic Chemistry (article, 2025-04-26)
Field: Materials Science — Materials Chemistry

Development and challenges of coal-based graphene family nanomaterials Abstract Coal, a natural non-homogeneous polymer material, boasts a complex three-dimensional cross-linked network structure. Developing low-carbon processing methods and achieving clean utilization of coal are crucial challenges in today’s research landscape. In recent years, graphene and its derivatives, collectively known as graphene family nanomaterials (GFNs), have emerged as revolutionary multifunctional carbon nanomaterials due to their exceptional properties and vast application potential. The unique molecular characteristics of coal and its derivatives present a significant opportunity for the synthesis of GFNs. Therefore, it is necessary to summarize the research progress made on coal-based GFNs to clarify their future development trajectory. This paper reviews the synthesis methods employed for coal-based GFNs and discusses the factors influencing this process. Moreover, the applications of coal-based GFNs in the fields of biomedicine, sensing, photo/electrocatalysis and energy storage are summarized and the challenges of coal-based GFNs are presented. Hopefully, this review can spark new ideas for clean production of coal and the design of coal-based functional materials. Graphene ; Chemistry ; Nanomaterials ; Nanotechnology ; Coal ; Organic chemistry ; Materials science Graphene research and applications ; Graphene and Nanomaterials Applications ; Supercapacitor Materials and Fabrication
Authors: Jiayu Peng
Source: ChemRxiv (preprint, 2025-05-27)
Field: Materials Science — Materials Chemistry

Toward Data-Driven Predictive Modeling of Electrocatalyst Stability and Surface Reconstruction Catalyst dissolution and surface restructuring are ubiquitous in electrocatalysis, often leading to formidable activity–stability trade-offs and obscure electrochemically-induced surface species that severely hinder the understanding and optimization of electrocatalysts under diverse harsh operating conditions. As even state-of-the-art characterization techniques lack the resolution and efficiency for the unambiguous elucidation of decomposition kinetics and reconstruction dynamics at electrocatalytic interfaces, many atomistic modeling approaches—following the recent advances in physics-driven machine learning—have been widely used to facilitate the atom-by-atom understanding and rational engineering of electrocatalyst stability and dynamics. This Perspective systematically assesses classical and data-driven approaches in theoretical surface science and computational catalysis, recognizing their achievements and highlighting their limitations in throughput, efficiency, accuracy, bias, transferability, and scalability toward enabling realistic and predictive modeling of electrocatalyst degradation and reconstruction. By examining different methods spanning first-principles simulations, surface sampling, neural network interatomic potentials, and generative deep learning models, it is underscored how such data-driven computational techniques help elucidate the precise nature of various key interfacial atomistic processes to address existing technical challenges in surface modeling and provide a new paradigm to optimize dissolution kinetics and restructuring dynamics for electrocatalyst design. Stability (learning theory) ; Electrocatalyst ; Surface (topology) ; Computer science ; Machine learning ; Mathematics ; Chemistry ; Geometry Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Electrocatalysts for Energy Conversion
Authors: Tobias Henkes | Shubham Sharma | Alexandre Tkatchenko | Mariana Rossi | Igor Poltavskyi
Source: Journal of Chemical Information and Modeling (article, 2025-10-24)
Field: Materials Science — Materials Chemistry

aims-PAX: Parallel Active Exploration Enables Expedited Construction of Machine Learning Force Fields for Molecules and Materials Recent advances in machinelearning force fields (MLFFs) have significantly extended the reach of atomistic simulations. Continuous progress in this field requires reliable reference data sets, accurate MLFF architectures, and efficient active learning strategies to enable robust modeling of complex molecular and material systems. Here we introduce aims-PAX, an expedited, multitrajectory active learning framework that streamlines the development of stable and accurate MLFFs. Designed for a wide range of researchers, aims-PAX offers a modular, high-performance workflow that couples diversified sampling with scalable training across CPU and GPU architectures. Integrated with the widely used <i>ab initio</i> code FHI-aims, the framework supports state-of-the-art ML models and data set generation using general-purpose (or "foundational") force-fields for rapid deployment in diverse systems. We demonstrate the capabilities of aims-PAX in various challenging tasks: creating data sets and models for highly flexible peptides, multiple organic molecules at once, explicitly solvated molecules, and for efficiently handling computationally demanding systems such as the CsPbI<sub>3</sub> perovskite. We show that aims-PAX achieves a reduction of up to 3 orders of magnitude in the number of required reference calculations, automatically selects challenging systems within a given chemical space, facilitates simulation of solvated molecules with more than thousand atoms, while enabling a 10-fold speedup in active-learning time through optimized resource utilization. This positions aims-PAX as a powerful and versatile platform for next-generation atomistic simulations in both academic and industrial settings. Workflow ; Scalability ; Field (mathematics) ; Speedup ; Force field (fiction) ; Software deployment ; Petascale computing ; Streamlines, streaklines, and pathlines Machine Learning in Materials Science
Authors: Fatima Ahmad Muhammad | Yashika Bajaj | Ikhwan Mohd Noor | Muhd Zu Azhan Yahya | Tarun Yadav | Rohit Kumar | Pramod K. Singh
Source: Zastita materijala (article, 2025-06-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Poly (vinylidene fluoride) polymer electrolyte-based supercapacitors PVDF, or polyvinylidenefluoride, is a popular polymer in the fluoropolymer family because of its superior mechanical strength, thermal stability, and piezoelectric qualities. It is chemically resistant to various substances, including diverse acids, bases, organic solvents, oil, and fat, and it is also easily processed. Electrochemical devices with superior energy storage effectiveness are necessary because of the increasing electricity consumption in the modern world. Because of its great power density, extended lifespan, remarkable charge/discharge cycle stability, and inexpensive cost, supercapacitors are regarded as amazing energy-storing devices. Supercapacitors' electrochemical performance is entirely dependent on the selection of their basic components and their manufacturing process. Materials made from carbon with improved thermophysical characteristics and deformation strength are currently of great interest. These materials have various applications in numerous sectors due to their distinctive physical and chemical properties. The abundance of research on the "structure-property," manufacturing, application, and ecology of composite polymers based on polyvinylidene fluoride (PVDF) can be attributed to the many opportunities for their use in science and technology. It is feasible to achieve a high degree of multidisciplinarity and integration of polymer science by using innovative technologies to build an expanded conceptual picture regarding polymeric materials. This leads to the formation of fundamental problems in polymer science, the solution of which affects a significant improvement to the natural scientific picture of the modern world.PVDF is arguably the most sought-after polymer nowadays due to its ability to self-polarize in the presence of an electric field. Researchers and experts involved in the development of energy harvesting and storage devices such as self-charging supercapacitors are drawn to the piezoelectric, ferroelectric, and pyroelectric capabilities of PVDF. The advanced properties of PVDF and its potential applications in various polymer forms are explained. Supercapacitor ; Fluoride ; Electrolyte ; Polymer ; Materials science ; Polymer electrolytes ; Chemical engineering ; Polymer chemistry ; Chemistry ; Inorganic chemistry ; Electrode ; Composite material ; Capacitance ; Ionic conductivity ; Engineering ; Physical chemistry Supercapacitor Materials and Fabrication ; Advanced Sensor and Energy Harvesting Materials ; Dielectric materials and actuators
Authors: Alaa El-Sisi | S. Abdel-Monsef | Hani Salim
Source: Journal of Composites Science (article, 2025-12-01)
Field: Materials Science — Materials Chemistry

Artificial Intelligence in the Design and Optimization of Laminated FRP Composites: A Review of Methodologies and Applications The growing adoption of laminated fiber-reinforced polymer (FRP) composites in aerospace, automotive, and civil engineering demands advanced design methodologies capable of navigating their complex anisotropic behavior. While traditional design approaches rely heavily on iterative simulations and classical optimization, recent advances in artificial intelligence (AI) offer a transformative alternative. This review systematically examines the expanding role of AI in composite design and optimization—highlighting a critical transition from physics-based modeling to data-driven, intelligent frameworks. This paper emphasizes emerging AI paradigms not yet widely covered in the composite literature, including Explainable AI (XAI) for interpretable decision-making and Large Language Models (LLMs) for automating design synthesis and knowledge retrieval. Key findings demonstrate AI’s capacity to efficiently optimize stacking sequences, ply orientations, and manufacturing parameters while satisfying multi-objective constraints such as weight, stiffness, and damage tolerance. Furthermore, we explore AI’s integration across the composite lifecycle—from surrogate-assisted finite element analysis and uncertainty-aware design allowables to in-service structural health monitoring. By bridging the gap between computational intelligence and industrial practicability, this review underscores AI’s potential not as a supplementary tool, but as a foundational technology poised to redefine next-generation composite engineering. Bridging (networking) ; Transformative learning ; Robustness (evolution) ; Key (lock) ; Intelligent decision support system ; Engineering design process Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Advanced Multi-Objective Optimization Algorithms
Authors: Hashim Hamood Jabbar Al-Gburi | S.A. Hassanzadeh-Tabrizi | Saeid Jabbarzare
Source: International Journal of Biomaterials (article, 2025-01-01)
Field: Materials Science — Biomaterials

Production of Cu <sub>0.5</sub> Zn <sub>0.5</sub> Fe <sub>2</sub> O <sub>4</sub> Nanostructures as a Hyperthermia Agent for Cancer Healing Cancer is a pervasive and devastating disease affecting various parts of the body, posing significant challenges to human societies. Recently, the development of novel magnetic and biocompatible nanoparticles has emerged as a promising approach for magnetic hyperthermia in cancer treatment, complementing existing therapeutic methods. In the present work, Cu<sub>0.5</sub>Zn<sub>0.5</sub>Fe<sub>2</sub>O<sub>4</sub> mixed spinel nanoparticles were produced via a sol-gel combustion route. The produced magnetic nanopowders were studied via FTIR, SEM, XRD, and VSM techniques. XRD results confirmed the formation of the spinel structure of ferrites. Microstructural investigations showed that the synthesized nanoparticles have a particle size ranging from 20 to 200 nm. The VSM results displayed that the saturation magnetization and coercivity of Cu<sub>0.5</sub>Zn<sub>0.5</sub>Fe<sub>2</sub>O<sub>4</sub> nanoparticles were 57 emu/g and 24 Oe, respectively. Saturation magnetization for the Cu<sub>0.5</sub>Zn<sub>0.5</sub>Fe<sub>2</sub>O<sub>4</sub> specimens improved with increasing heat treatment temperature. In order to examine the samples' heating effectiveness for magnetic hyperthermia therapy, various magnetic fields were used. The temperature of the Cu<sub>0.5</sub>Zn<sub>0.5</sub>Fe<sub>2</sub>O<sub>4</sub> powders increased from 37°C to 47°C in 10 min when exposed to a 400-Oe magnetic field and 200-kHz frequency. Results showed that the fabricated products have the potential to be used as hyperthermia agents for cancer therapy. The novelty of this study focuses on the use of Cu<sub>0.5</sub>Zn<sub>0.5</sub>Fe<sub>2</sub>O<sub>4</sub> mixed spinel as a new hyperthermia agent with more biocompatible constituent elements. Production (economics) ; Nanostructure ; Materials science ; Nanotechnology Nanoparticle-Based Drug Delivery ; Magnetic Properties and Synthesis of Ferrites ; Characterization and Applications of Magnetic Nanoparticles
Authors: Olga Miryuk
Source: Material and Mechanical Engineering Technology (article, 2025-09-30)
Field: Materials Science — Materials Chemistry

Study of Magnesite Binders’ Resistance in Liquid Aggressive Environments The study presents the results of operational properties of magnesia binders. The research objective is to determine the nature of aggressive liquid media impact on magnesia binders of various compositions. Comparative tests of strength properties of caustic magnesite and a combined magnesia-slag binder were carried out. Diffractometric analysis was used to study binders’ composition. Binders’ microstructure was studied by electron microscopy. The nature of impact of water, chloride and sulfate solutions on binders’ strength was estimated by a resistance coefficient. Effectiveness of combination of caustic magnesite and metallurgical slag was proven. Activating effect of the magnesia binder allows latent compounds of metallurgical slag to be involved in the hydrate formation processes. Expediency of using a complex grout containing solutions of magnesium chloride and sulfate for magnesia binders is shown. Aggressive effect of water and salt solutions on the strength properties of magnesite binders was studied. Information on the change in strength of binders after various periods of exposure to aggressive liquids was provided. The causes of destructive processes occurring in the magnesite binders were identified. Magnesium pentahydroxychloride’s decisive role in the formation of durability of magnesite stone was confirmed. The nature of impact of aggressive salt environments on durability of binders of various compositions was established. Technological methods for increasing durability of magnesite binders in the liquid environments were proposed, consisting in the rational minimization of magnesium pentahydroxychloride due to the use of a combined binder and (or) complex salt grouting fluids Magnesite ; Durability ; Magnesium ; Gypsum ; Chloride ; Sulfate ; Microstructure Magnesium Oxide Properties and Applications ; Layered Double Hydroxides Synthesis and Applications ; Concrete and Cement Materials Research