jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jing Wang | Xin Wang | Dingguo Xu
Source: Acta Biomaterialia (article, 2025-02-18)
Field: Materials Science — Biomaterials

Machine learning insights into calcium phosphate nucleation and aggregation In this study, we utilized machine learning interatomic potentials (MLIPs) to investigate the nucleation mechanisms of calcium phosphate, a critical component of bone and teeth. Our analysis encompassed the process from pre-nucleation stage to the growth of amorphous calcium phosphate (ACP) in solution. We observed fluctuations in free calcium ion concentration and tracked the formation of uniform clusters in the early nucleation phases, confirming the existence of pre-nucleation clusters (PNCs). The PNCs are characterized by the composition Ca<sub>2</sub>[(PO<sub>4</sub>)<sub>1.6</sub>(HPO<sub>4</sub>)(H<sub>2</sub>PO<sub>4</sub>)<sub>0.4</sub>] and predominantly exhibit a triangular structure formed by phosphate groups. This structure is not only the core of the short-range ordered units in ACP but also exhibits the structural characteristics of the fundamental building blocks of HAP. Importantly, these clusters interact dynamically with water molecules through hydrogen bonding and proton exchange, which is essential for their stability and growth. The gradual growth of these clusters occurs via ion attachment and cluster adsorption. This work provides insights into calcium phosphate mineralization, with implications for materials science and biomedical engineering, particularly in biomaterial synthesis. The application of MLIPs demonstrates a high-accuracy, efficient approach for simulating complex systems may advance our understanding of crystallization and biomineralization processes. STATEMENT OF SIGNIFICANCE: Calcium phosphate nucleation is crucial in biological mineralization and the synthesis of biomaterials, serving as a key aspect in the design of hydroxyapatite (HAP)-based biomaterials. However, the mechanisms of early nucleation remain unclear due to the complex ion-water interactions, which lead to rapid nucleation rates and small cluster sizes. This study combines MLIP with MD simulations to explore the nucleation process of calcium phosphate, revealing the transition from pre-nucleation to the formation of ACP. It clarifies the relationship between PNCs and the crystalline structure of HAP. This work addresses the knowledge gap regarding early-stage calcium phosphate nucleation and highlights the potential of MLIP in simulating complex ionic solutions, laying a solid foundation for AI-guided research in biological and biomedical materials. Nucleation ; Materials science ; Calcium ; Phosphate ; Composite material ; Chemical engineering ; Metallurgy ; Thermodynamics ; Engineering ; Chemistry ; Biochemistry Calcium Carbonate Crystallization and Inhibition ; Crystallization and Solubility Studies ; X-ray Diffraction in Crystallography
Authors: Yifan Zeng | Jun Wang | Fengwang Li | Tongliang Liu | Aoni Xu
Source: ACS Materials Au (article, 2025-10-18)
Field: Materials Science — Materials Chemistry

AI-Accelerated Discovery of Electrocatalyst Materials The rational exploration and design of high-performance, stable electrocatalysts are crucial for efficient renewable energy storage, conversion, and utilization. Artificial intelligence (AI) is revolutionizing this field by significantly reducing the time and cost associated with conventional trial-and-error experimentation and density functional theory (DFT) calculations. Advancements in data quality, computing power, and algorithms have positioned AI as a key enabler in understanding electrocatalytic mechanisms, designing advanced materials, analyzing structures, and predicting performance. This review highlights the pivotal role of AI in electrocatalyst discovery, focusing on the critical aspects of data, descriptors, and machine learning models. We discuss various AI approaches, including their applications in accelerating DFT calculations, exploring reaction mechanisms, designing electrocatalysts, and predicting performance, providing a comprehensive overview of the current state-of-the-art. We also address the challenges and opportunities in leveraging AI for electrocatalyst development, emphasizing the importance of data quality, model selection, and collaborative research. This review aims to guide researchers in effectively utilizing AI to accelerate the discovery and optimization of electrocatalysts for a renewable energy future. Electrocatalyst ; Enabling ; Key (lock) ; Field (mathematics) ; Renewable energy ; Applications of artificial intelligence Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Fuel Cells and Related Materials
Authors: Yunjia Bao | Tao Chen | Zhuo Miao | Weidong Zheng | Puqing Jiang | Kunfeng Chen | Ruiqiang Guo | Dongfeng Xue
Source: Advanced Electronic Materials (article, 2025-04-28)
Field: Materials Science — Materials Chemistry

Machine‐Learning‐Assisted Understanding of Depth‐Dependent Thermal Conductivity in Lithium Niobate Induced by Point Defects Abstract Lithium niobate (LiNbO 3 , LN) has unique electro‐optic and piezoelectric properties, making it widely used in optical devices, telecommunications, sensors, and acoustic systems. Thermal conductivity κ is a critical property influencing the performance and reliability of these applications. Point defects commonly exist in LN and can significantly reduce its κ . However, the effects of point defects on thermal transport in LN remain poorly understood. In this work, LN crystals are prepared through thermal reduction at 600–800 °C, inducing a depth‐dependent distribution of oxygen vacancies (V O ) that increases in concentration with increasing reduction temperature. Time‐domain thermoreflectance and square‐pulsed source measurements reveal a significant suppression and a notable gradient in κ , attributed to the depth‐dependent distribution of V O . A machine learning potential with ab initio accuracy is developed to simulate the impact of typical point defects on thermal transport in LN, demonstrating that V O predominantly suppresses κ by affecting the transport of low‐frequency phonons below 6 THz. Notably, niobium vacancies and antisite defects exhibit similar effects, whereas lithium vacancies show minimal impact. This work highlights the dominant role of V O in modulating κ and provides insights into defect engineering for advanced LN‐based devices and similar ferroelectric crystals. Lithium niobate ; Materials science ; Thermal conductivity ; Lithium (medication) ; Conductivity ; Point (geometry) ; Thermal ; Composite material ; Optoelectronics ; Thermodynamics ; Psychology ; Physical chemistry Ferroelectric and Piezoelectric Materials ; Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science
Authors: Mahmoud Thabet | Eman M. Abd El-Monaem | Mohamed Mouhafid | Mohamed A. Abdel‐Lateef | Tarek A. Seaf Elnasr | Yousef M. A. Alruwaili | Ahmed Hamad Alanazi | Hazim M. Ali | Abdul Rahman Mohamed | Mohamed F. Cheira | Hassanien Gomaa
Source: Environmental Processes (article, 2025-03-25)
Field: Chemistry — Organic Chemistry

A Hybrid Mesoporous Sulphur-doped CoMn Oxide @ Cobalt Alginate-derived Carbon Spongy-like Beads for Methylene Blue Decolorization: Isotherm, Kinetic, and Machine Learning Modeling Methylene blue ; Cobalt ; Mesoporous material ; Sulfur ; Materials science ; Oxide ; Carbon fibers ; Cobalt oxide ; Chemical engineering ; Chemistry ; Catalysis ; Inorganic chemistry ; Organic chemistry ; Composite material ; Composite number Nanomaterials for catalytic reactions ; Catalytic Processes in Materials Science ; Adsorption and biosorption for pollutant removal
Authors: Shihan Deng | Hongyu Yu | Junyi Ji | Changsong Xu | Hongjun Xiang
Source: Physical review. B./Physical review. B (article, 2025-05-09)
Field: Materials Science — Materials Chemistry

Deterministic and efficient switching of sliding ferroelectrics Recent studies highlight the scientific importance and broad application prospects of two-dimensional sliding ferroelectrics, which prevalently exhibit vertical polarization with suitable stackings. It is crucial to understand the mechanisms of sliding ferroelectricity and to deterministically and efficiently switch the polarization with optimized electric fields. Here, applying our dream-Allegro multitask equivariant neural network, which simultaneously predicts structure-dependent interatomic potentials and Born effective charges, we construct a comprehensive model for the boron nitride $(\mathrm{BN})$ bilayer. The molecular dynamics simulations reveal a remarkably high Curie temperature of up to 1400 K, facilitated by robust intralayer chemical bonds and delicate interlayer van der Waals interactions. More importantly, it is found that, compared to the out-of-plane electric field, the inclined field not only leads to deterministic switching of electric polarization, but also largely lowers the critical strength of the field, due to the presence of the in-plane polarization in the transition state. This strategy of an inclined field is demonstrated to be universal for other sliding ferroelectric systems with monolayer structures belonging to the symmetry group $p\overline{6}m2$, such as transition metal dichalcogenides. Materials science ; Control theory (sociology) ; Computer science ; Artificial intelligence ; Control (management) Ferroelectric and Piezoelectric Materials ; Ferroelectric and Negative Capacitance Devices ; Acoustic Wave Resonator Technologies
Authors: Mouyang Cheng | Chu-Liang Fu | Ryotaro Okabe | Abhijatmedhi Chotrattanapituk | Artittaya Boonkird | Hung Tuan Nguyen | Mingda Li
Source: ArXiv.org (preprint, 2025-02-05)
Field: Materials Science — Materials Chemistry

AI-driven materials design: a mini-review Materials design is an important component of modern science and technology, yet traditional approaches rely heavily on trial-and-error and can be inefficient. Computational techniques, enhanced by modern artificial intelligence (AI), have greatly accelerated the design of new materials. Among these approaches, inverse design has shown great promise in designing materials that meet specific property requirements. In this mini-review, we summarize key computational advancements for materials design over the past few decades. We follow the evolution of relevant materials design techniques, from high-throughput forward machine learning (ML) methods and evolutionary algorithms, to advanced AI strategies like reinforcement learning (RL) and deep generative models. We highlight the paradigm shift from conventional screening approaches to inverse generation driven by deep generative models. Finally, we discuss current challenges and future perspectives of materials inverse design. This review may serve as a brief guide to the approaches, progress, and outlook of designing future functional materials with technological relevance. Materials science Machine Learning in Materials Science ; BIM and Construction Integration ; Additive Manufacturing and 3D Printing Technologies
Authors: Kashif Faheem | Sagheer A. Onaizi | Muhammad S. Vohra
Source: Applied Sciences (article, 2025-03-25)
Field: Chemistry — Inorganic Chemistry

Novel ZIF-67@Bentonite (ZIF-67@BNT) Nanocomposite for Aqueous Methyl Orange Removal The indiscriminate discharge of synthetic dyes into wastewater streams poses a severe threat to the environment as well as to human well-being. Among all these dyes, methyl orange (MO) attracts attention due to its widespread use and persistence in industrial effluents. This study investigated the use of zeolitic imidazolate framework and bentonite (ZIF-67@BNT) nanocomposite material for the removal of MO from the aqueous phase. Various characterization techniques were employed such as FTIR, XRD, and TGA to verify the successful synthesis of the ZIF-67@BNT adsorbent, which was subsequently utilized to investigate the adsorption of MO. Batch adsorption studies demonstrated a high MO adsorption capacity of 187 mg/g. A response surface methodology (RSM)-based modeling exercise was used to optimize the adsorption process. While assessing the impact of various operational factors, the initial MO concentration followed by ZIF-67@BNT dose were noted to be important. Adsorption kinetics and isotherm studies were also completed. The ZIF-67@BNT nanocomposite after adsorption analysis indicated multiple mechanisms facilitating MO uptake. Additionally, various machine learning (ML) models such ANN, SVR, RF, and GPR were also utilized to predict MO adsorption onto ZIF-67@BNT nanocomposite under a varying set of conditions. Bentonite ; Nanocomposite ; Materials science ; Aqueous solution ; Methyl orange ; Chemical engineering ; Nuclear chemistry ; Composite material ; Chemistry ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Water Quality Monitoring and Analysis ; Adsorption and biosorption for pollutant removal
Authors: Ryotaro Okabe | Mouyang Cheng | Abhijatmedhi Chotrattanapituk | Manasi Mandal | K. L. Mak | Denisse Córdova Carrizales | Nguyen Tuan Hung | Xiang Fu | Bowen Han | Yao Wang | Weiwei Xie | Robert J. Cava | Tommi Jaakkola | Yongqiang Cheng | Mingda Li
Source: Nature Materials (article, 2025-09-22)
Field: Materials Science — Materials Chemistry

Structural constraint integration in a generative model for the discovery of quantum materials Quantum ; Scalability ; Generative grammar ; Constraint (computer-aided design) ; Density functional theory ; Structural stability ; Stability (learning theory) ; Lattice (music) ; Path (computing) Machine Learning in Materials Science ; Advanced Condensed Matter Physics ; Catalysis and Oxidation Reactions
Authors: Soumya Sivalingam | Kisa Fatima | S. Lekshmi | S. Govindan Namboothiri | P.K. Krishnapriya | Varsha Arun Shreya | V. Harikrishnan | A. Chithra Mohan | Hyunjin Joh | J. R. Rani | Lokesh Singh Panwar | K. Sreedhar | P. Jayakumar | Shibnath Samanta | Ji Young Jo | Gopinathan Anoop
Source: Journal of Alloys and Compounds (article, 2025-05-01)
Field: Materials Science — Materials Chemistry

Advancements in thermoelectric materials: Emerging trends in organic, inorganic systems, and material informatics Materials informatics ; Thermoelectric materials ; Electronic materials ; Thermoelectric effect ; Materials science ; Informatics ; Nanotechnology ; Engineering physics ; Health informatics ; Physics ; Engineering ; Political science ; Thermodynamics ; Electrical engineering Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Chalcogenide Semiconductor Thin Films
Authors: João Morado | Kirill Zinovjev | Lester O. Hedges | Daniel J. Cole | Julien Michel
Source: Journal of Chemical Theory and Computation (article, 2025-11-10)
Field: Materials Science — Materials Chemistry

Enhancing Electrostatic Embedding for ML/MM Free Energy Calculations Hybrid ML/MM approaches that combine machine learning (ML) potentials with molecular mechanics (MM) potentials offer a promising balance between computational cost and accuracy. Most ML/MM simulations reported to date employ mechanical embedding schemes, and rely on Lennard-Jones and Coulomb potentials to model intermolecular interactions between the ML and MM regions. A promising approach to improving ML/MM schemes is to use electrostatic embedding, where polarization effects on the ML region by the MM region are explicitly incorporated. The electrostatic machine learning embedding (EMLE) method has been developed for this purpose. Here, we compute absolute hydration free energies for a set of small organic molecules to derive robust methodologies for training EMLE models using quantum mechanical data. We establish protocols for fine-tuning the static and induced components of electrostatic interactions and evaluate the accuracy limits of fitting these components to first-principles calculations. We also introduce an empirical adjustment to enhance agreement with experimental results, strengthening the competitiveness of ML/MM simulations relative to state-of-the-art methods. Overall, our findings provide valuable insights into the challenges and opportunities of electrostatic embedding ML/MM simulations, and offer strategies for achieving robust modeling of classes of drug-like molecules where the accuracy of conventional MM force fields fall short. Embedding ; Electrostatics ; Coulomb ; Intermolecular force ; Electric potential energy ; Quantum ; Set (abstract data type) ; Polarization (electrochemistry) Machine Learning in Materials Science ; Protein Structure and Dynamics ; Advanced Chemical Physics Studies
Authors: Hüseyin Beytüt | Kerim Özbeyaz | Şemsettın Temiz
Source: Applied Sciences (article, 2025-01-02)
Field: Materials Science — Materials Chemistry

A Novel Hybrid Die Design for Enhanced Grain Refinement: Vortex Extrusion–Equal-Channel Angular Pressing (Vo-CAP) A novel hybrid Severe Plastic Deformation (SPD) method called Vortex Extrusion–Equal-Channel Angular Pressing (Vo-CAP) was developed and applied to AA6082 workpieces in this study. Before experimental application, a comprehensive optimization of the die design was performed considering effective strain, strain inhomogeneity, and pressing load parameters. The optimization process utilized an integrated approach combining Finite Element Analysis (FEA), artificial neural networks (ANNs), and the non-dominated sorting genetic algorithm II (NSGA-II). The optimized die successfully achieved a balance between maximizing effective strain while minimizing pressing load and strain inhomogeneity. The Vo-CAP process incorporates a unique conical die design that enables assembly without traditional fasteners. Moreover, this novel die combines VE and ECAP advantages in a single-pass operation. While VE has been previously studied, experimental work was limited to specific configurations, and its integration with ECAP had not been explored. Through the development of Vo-CAP, this research gap has been addressed. The results showed substantial enhancements in hardness values, ultimate tensile strength, and strain homogeneity. These findings demonstrate that Vo-CAP represents a significant advancement in SPD, offering an efficient single-pass process for improving the mechanical properties of aluminum alloys through grain refinement. Pressing ; Extrusion ; Die (integrated circuit) ; Materials science ; Vortex ; Composite material ; Nanotechnology ; Mechanics ; Physics Microstructure and mechanical properties ; Metallurgy and Material Forming ; Metal Alloys Wear and Properties
Authors: Jiaxin Zhu | Yong Pan | Feihong Yang
Source: Materials Today Chemistry (article, 2025-08-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Influence of intrinsic defects on the structural, electronic and optical properties of 2D- β-Ga2O3 with Varying surfaces Materials science ; Chemical physics ; Nanotechnology ; Crystallography ; Chemistry Ga2O3 and related materials ; Advanced Photocatalysis Techniques ; ZnO doping and properties
Authors: Olajesu Favor Olanrewaju | Isiaka Oluwole Oladele | Samson Oluwagbenga Adelani
Source: Hybrid Advances (review, 2025-04-08)
Field: Materials Science — Polymers and Plastics

A Comprehensive Review on plant and animal fiber reinforced composites: Experimental and theoretical approaches to interfacial strength optimization and potential applications Natural fiber-reinforced composites (NFRCs) have become vital in various engineering applications due to their exceptional properties and ease of manufacturing. Properties such as lightweight, sustainability, design flexibility, microstructure, durability, and advanced fabrication techniques have expanded their use across industries. Also, NFRCs are preferred because the extensive reliance on synthetic fibers presents significant challenges in recycling and waste management. Despite their excellent properties, NFRCs have three main challenges: fiber degradation, water degradation, and weak interfacial strength (incompatibility of fibers with the matrix). Consequently, research efforts have been directed at combating these challenges using different surface treatment techniques. However, research has been skewed towards experimental approaches for improving the interfacial strength in plant fiber polymer matrix composites (PMCs). Hence, there exists a dearth of information on the computational approaches for optimizing the interfacial properties of NFRCs. Hence, this review provides experimental and computational approaches (machine learning) for comprehensive optimizing strategies for different natural fibers (plant, animal, and microorganism) and matrices (polymers, metals, and ceramics). This review also highlights the importance of theoretical approaches and numerical modeling in analyzing and optimizing NFRCs. Finally, the review highlights recent advancements in NFRCs, their mechanical properties, potential applications, and future directions. Composite material ; Materials science ; Fiber Natural Fiber Reinforced Composites ; Additive Manufacturing and 3D Printing Technologies ; Advanced Cellulose Research Studies
Authors: Jan‐Hendrik Pöhls | Chun-Wan Timothy Lo | Marissa MacIver | Yu‐Chih Tseng | Yurij Mozharivskyj
Source: Chemistry of Materials (article, 2025-03-12)
Field: Materials Science — Materials Chemistry

Driving Thermoelectric Optimization in AgSbTe<sub>2</sub> via Design of Experiments and Machine Learning Systemic optimization of thermoelectric materials is arduous due to their conflicting electrical and thermal properties. A strategy based on Design of Experiments and machine learning is developed to optimize the thermoelectric efficiency of AgSb1+xTe2+y, an established thermoelectric. From eight experiments, high thermoelectric performance in AgSb1.021Te2.04 is revealed with a peak and average thermoelectric figure of merit of 1.61 ± 0.24 at 600 K and 1.18 ± 0.18 (300–623 K), respectively, which is >30% higher than the best literature values for AgSb1+xTe2+y. Ag deficiency and suppression of secondary phases in AgSb1.021Te2.04 improve the electrical properties and reduce the thermal conductivity (∼0.4 W m–1 K–1). Our strategy is implemented into an open-source graphical user interface, and it can be used to optimize the methodologies, properties, and processes across different scientific fields. Thermoelectric effect ; Computer science ; Machine design ; Materials science ; Nanotechnology ; Artificial intelligence ; Engineering ; Mechanical engineering ; Physics Advanced Thermoelectric Materials and Devices ; Chalcogenide Semiconductor Thin Films ; Machine Learning in Materials Science