jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Gaurav Jhaa (8267121) | Mithu Kumari (22826558) | Priyakumari Chakkingal Parambil (4025087)
Source: Figshare (article, 2025-12-11)
Field: Materials Science — Materials Chemistry

Deciphering theBonding in Metavalent Materials forNext-Generation Memory Phase change materials (PCMs) have garnered significant attention due to their applications in phase change memory (PCM) technology, which has the potential to surpass the von Neumann bottleneck via in-memory computing. This is particularly useful in artificial intelligence applications requiring rapid data transfer. Therefore, identifying the bonding mechanism in such materials is crucial, as it will aid in designing better materials for PCM applications. In this context, a new bonding mechanism named metavalency, indicating a chemical bond in between metallic and covalent, was introduced recently to describe the bonding in phase change materials. Metavalent materials are characterized by nearly half bond order, large Born effective charge (BEC), low band gap, moderate electrical conductivity, and large optical dielectric constant. However, their bonding features overlap significantly with those of the well-known hypervalent and partial σ-bonded systems, raising fundamental questions about whether metavalency constitutes a truly distinct bonding mechanism. Here, we employ molecular orbital theory and DFT calculations to show that the known metavalent compounds are electron-sufficient/rich hypercoordinate systems with partial σ bonds formed mainly by p–p overlap and have low ionicity, low band gap, and consequently large BEC. This implies that they belong to a distinct class of compounds, though the bonding mechanism is not new. This refined understanding of metavalent bonding provides a foundational framework for the rational design of novel PCMs, paving the way for advancements in phase change memory technology. Chemical bond ; Hypervalent molecule ; Phase (matter) ; Mechanism (biology) ; Band gap ; Anodic bonding ; Characterization (materials science) Phase-change materials and chalcogenides ; Advanced Memory and Neural Computing ; Polymer composites and self-healing
Authors: Małgorzata J. Zimoń | Fausto Martelli
Source: ArXiv.org (preprint, 2025-06-26)
Field: Materials Science — Materials Chemistry

Predicting Thermodynamics of Liquid Water from Time Series Analysis Thermodynamics, introduced over two centuries ago, remains foundational to our understanding of physical, chemical, biological, and engineering systems. Its principles are traditionally grounded in the statistical mechanics framework, which explains macroscopic behavior from microscopic states. In this work, we propose an alternative approach that interprets thermodynamic behavior through the lenses of time series analysis, an approach commonly used in other fields, including finance, climate, and signal processing. We perform classical molecular dynamics simulations of liquid water, the most complex, anomalous, and important substance known, over a wide range of its phase diagram. By examining the temporal evolution of the hydrogen bond network (HBN) topology, we demonstrate that the dynamics of microscopic topological motifs populating the HBN encode the system's macroscopic thermodynamic behavior. Furthermore, our approach enables the prediction of thermodynamic properties in regions beyond those directly sampled in our simulations. We achieve this result by leveraging artificial intelligence to uncover patterns in temporally resolved data that are often lost through conventional averaging. This work offers new insights into the fundamental behavior of water and network-forming materials more broadly, establishing a new paradigm for understanding material properties beyond the classical confines of statistical mechanics. Liquid water ; Statistical mechanics ; Series (stratigraphy) ; Range (aeronautics) ; Work (physics) ; Molecular dynamics ; ENCODE ; Artificial neural network ; Material properties Machine Learning in Materials Science ; Topological and Geometric Data Analysis ; Neural Networks and Reservoir Computing
Authors: Gaurav Jhaa | Mithu Kumari | Priyakumari Chakkingal Parambil
Source: The Journal of Physical Chemistry C (article, 2025-12-11)
Field: Materials Science — Materials Chemistry

Deciphering the Bonding in Metavalent Materials for Next-Generation Memory Phase change materials (PCMs) have garnered significant attention due to their applications in phase change memory (PCM) technology, which has the potential to surpass the von Neumann bottleneck via in-memory computing. This is particularly useful in artificial intelligence applications requiring rapid data transfer. Therefore, identifying the bonding mechanism in such materials is crucial, as it will aid in designing better materials for PCM applications. In this context, a new bonding mechanism named metavalency, indicating a chemical bond in between metallic and covalent, was introduced recently to describe the bonding in phase change materials. Metavalent materials are characterized by nearly half bond order, large Born effective charge (BEC), low band gap, moderate electrical conductivity, and large optical dielectric constant. However, their bonding features overlap significantly with those of the well-known hypervalent and partial σ-bonded systems, raising fundamental questions about whether metavalency constitutes a truly distinct bonding mechanism. Here, we employ molecular orbital theory and DFT calculations to show that the known metavalent compounds are electron-sufficient/rich hypercoordinate systems with partial σ bonds formed mainly by p–p overlap and have low ionicity, low band gap, and consequently large BEC. This implies that they belong to a distinct class of compounds, though the bonding mechanism is not new. This refined understanding of metavalent bonding provides a foundational framework for the rational design of novel PCMs, paving the way for advancements in phase change memory technology. Chemical bond ; Hypervalent molecule ; Phase (matter) ; Mechanism (biology) ; Band gap ; Anodic bonding ; Characterization (materials science) Phase-change materials and chalcogenides ; Advanced Memory and Neural Computing ; Polymer composites and self-healing
Authors: Paul Hager | Xiaodong Zou
Source: Structural Dynamics (article, 2025-03-01)
Field: Materials Science — Materials Chemistry

Automated serial (rotation) electron diffraction for high-throughput structure determination and phase analysis of complex polycrystalline samples Crystals, too small for single crystal X-ray diffraction and phase mixtures, too complex for powder X-ray diffraction have long presented a significant challenge in the fields of materials science and pharmaceutical industry. During the past decade, three- dimensional electron diffraction (3D ED) or microcrystal electron diffraction (MicroED), has made large breakthroughs in structure determination of nano- and microcrystals, from inorganic porous materials such as zeolites and metal-organic frameworks, to organic pharmaceuticals and proteins. Electron diffraction has also a unique advantage in phase analysis of complex polycrystalline powder and can detect minor phases, invisible for powder X-ray diffraction. However, manual selection of crystals may be user-biased with a risk of missing some phases. It is also challenging to collect high resolution 3D ED/MicroED data from very beam-sensitive crystals. In this talk, I will present automated and high-speed serial electron diffraction (SerialED) developed by our group to tackle these obstacles. SerialED uses a snapshot data acquisition strategy to collect diffraction patterns from thousands of individual crystals within minutes, with one pattern per crystal. I will show that SerialED data resolution is greatly improved for beam-sensitive samples, which allowed ab initio structure solution of a beam-sensitive zeolite ZSM-25 by merging SerialED data from hundreds of crystals. We also developed serial rotation electron diffraction (SerialRED) combining SerialED and 3D ED/MicroED for crystal screening and structure determination, which enabled phase analysis and detection of minor phases. The integration of an automated data processing pipeline opens the possibility for high-throughput screening, paving the way for an application in data-driven automated synthesis in the future. Crystallite ; Rotation (mathematics) ; Phase (matter) ; Diffraction ; Throughput ; Electron diffraction ; Materials science ; Computer science ; Phase retrieval ; X-ray crystallography ; Crystallography ; Physics ; Chemistry ; Optics ; Artificial intelligence ; Fourier transform X-ray Diffraction in Crystallography
Authors: Hyojung Kim
Source: Electronic Materials Letters (article, 2025-10-04)
Field: Materials Science — Materials Chemistry

Two Dimensional MXenes Drive Multifunctional Resistive Memory, Gas Sensing, and Gasistor Devices: A Perspective MXenes ; Neuromorphic engineering ; Resistive touchscreen ; Perspective (graphical) ; Function (biology) ; Transition metal ; Carbide MXene and MAX Phase Materials ; Advanced Memory and Neural Computing ; Ferroelectric and Negative Capacitance Devices
Authors: François Berthiaume
Source: Nano LIFE (article, 2025-04-22)
Field: Materials Science — Biomaterials

(Invited) Brief Review: Nanoparticle-based Drug Delivery — A Historical Overview This brief historical review depicts recent advancements in nanoparticle-based drug delivery systems, as they have been reported in the journal Nano LIFE for the past 15 years. We begin by summarizing the types of materials used to create nanoparticles, including liposomes, polymers, metals, and carbon-based structures. We then describe their passive targeting properties and various functionalization strategies used to provide active targeting, thus improving their selectivity in vivo. We also explain the emerging use of nanoparticle drug delivery systems for applications beyond cancer therapy, such as in immune modulation and orthopedic areas. Some of the future challenges highlighted include large-scale production and regulatory hurdles, and future areas of growth include artificial intelligence-driven optimization and biogenic manufacturing. Drug delivery ; Drug ; Nanotechnology ; Nanoparticle ; Computer science ; Medicine ; Data science ; Pharmacology ; Materials science Nanoparticle-Based Drug Delivery
Authors: Shanshan Wang | Jin Zhang
Source: National Science Open (article, 2025-11-01)
Field: Materials Science — Materials Chemistry

Artificial intelligence and energy revolution we organize a special topic on “Artificial Intelligence and Energy Revolution”, which includes 4 high-quality papers covering the latest research, reviews, and perspectives of AI-empowered energy material investigations. Energy (signal processing) ; Applications of artificial intelligence ; Industrial Revolution ; Expert system ; Energy consumption Machine Learning in Materials Science ; Artificial Intelligence Applications ; Advanced Technologies and Applied Computing
Authors: Yong Liu | Kangjie Zhou | Shu Wang | Zhen Guo
Source: Advanced Engineering Materials (article, 2025-12-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Green Electromagnetic Shielding Materials from Biomass: Mechanisms, Design Strategies, and Future Perspectives The rapid advancement of microelectronic technologies and smart devices has intensified the need for electromagnetic interference (EMI) shielding materials that are not only efficient and multifunctional, but also sustainable and environmentally friendly. Biomass‐based materials, with their unique hierarchical architectures, diverse chemical functionalities, and inherent renewability, have emerged as promising candidates in this domain. This review provides a systematic overview of the structural characteristics and functional advantages of biomass‐derived EMI shielding composite materials, with an emphasis on shielding mechanisms and performance optimization strategies. Particular attention is given to the synergistic integration of various conductive components (e.g., MXene, carbon, metals, and conductive polymers) with rational structural designs (such as film, porous frameworks, and bicontinuous architectures), which enable the development of materials with enhanced shielding efficiency and structural stability. Finally, the outlook outlines key future directions, including sustainable filler development, intelligent shielding architectures, green scalable fabrication, and data‐driven materials design. This work offers comprehensive insights to guide the innovation of next‐generation high‐performance, ecofriendly EMI shielding materials for advanced electronic and communication applications. Electromagnetic shielding ; EMI ; Electromagnetic interference ; Multiphysics ; Microelectronics ; Electrical conductor ; MXenes ; Structural material ; Electromagnetic compatibility Electromagnetic wave absorption materials ; MXene and MAX Phase Materials ; Electromagnetic Fields and Biological Effects
Authors: Hirofumi Tsuruta | Masaya Kumagai
Source: ArXiv.org (preprint, 2025-09-01)
Field: Materials Science — Materials Chemistry

MatPROV: A Provenance Graph Dataset of Material Synthesis Extracted from Scientific Literature Synthesis procedures play a critical role in materials research, as they directly affect material properties. With data-driven approaches increasingly accelerating materials discovery, there is growing interest in extracting synthesis procedures from scientific literature as structured data. However, existing studies often rely on rigid, domain-specific schemas with predefined fields for structuring synthesis procedures or assume that synthesis procedures are linear sequences of operations, which limits their ability to capture the structural complexity of real-world procedures. To address these limitations, we adopt PROV-DM, an international standard for provenance information, which supports flexible, graph-based modeling of procedures. We present MatPROV, a dataset of PROV-DM-compliant synthesis procedures extracted from scientific literature using large language models. MatPROV captures structural complexities and causal relationships among materials, operations, and conditions through visually intuitive directed graphs. This representation enables machine-interpretable synthesis knowledge, opening opportunities for future research such as automated synthesis planning and optimization. Structuring ; Representation (politics) ; Scientific literature ; Graph ; Identification (biology) ; Image synthesis Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Scientific Computing and Data Management
Authors: Yi Hu | Linfang Zhong | Pengqi Wang | Jiamian Zhan | Wenhui Yang | Xiaozhong Qiu | Honghao Hou
Source: Journal of Advanced Research (article, 2025-12-01)
Field: Materials Science — Biomaterials

Assembly delivery of bioactive matters: Advances, challenges, and prospects The core principle of ADS lies in the synergistic integration of molecular self-assembly and stimulus-responsive smart materials, enabling the rational design of carriers with precise structure-function relationships for the intelligent delivery of bioactive agents. Key advantages of ADS include precise targeting through multi-level strategies, intelligent responsiveness, multimodal synergy, enhanced biocompatibility, and flexible engineering. Representative breakthroughs have been achieved in drug delivery, tissue engineering, and gene editing. Furthermore, the convergence of ADS with emerging technologies, such as artificial intelligence and microfluidics, offers a strategic pathway toward next-generation systems that are efficient, intelligent, and highly precise, establishing new paradigms for biomedical applications. Key (lock) ; Drug delivery ; Gene delivery ; Core (optical fiber) ; Targeted drug delivery ; Rational design Supramolecular Self-Assembly in Materials ; Graphene and Nanomaterials Applications ; Hydrogels: synthesis, properties, applications
Authors: Kyle D. Crans (11649336) | Zhaohong Sun (14093662) | Ariel A. Nehoray (20386691) | Richard L. Brutchey (1284846)
Source: Figshare (article, 2025-05-15)
Field: Materials Science — Materials Chemistry

Data-DrivenMapping of the Cesium Cadmium BromidePhase Space Utilizing a Soft-Chemistry Approach Soft-chemistry techniques provide a versatile approach to synthesizing inorganic materials under mild conditions, enabling access to compositions and structures that are challenging to achieve through traditional thermodynamically driven solid-state methods. However, these solution-based routes often result in phase competition, requiring precise control over reaction conditions to achieve selective product formation. While one-variable-at-a-time (OVAT) approaches have traditionally been used for phase selection, data-driven strategies are emerging as more efficient methods for navigating complex synthetic spaces. Ternary metal halides, such as cesium cadmium bromides (Cs–Cd–Br), are of growing interest due to their potential in wide and ultrawide band gap applications. Unlike the well-studied cesium lead halide phases, the compositional diversity and solution-based synthesis of ternary Cs–Cd–Br phases remain largely unexplored. This study systematically investigates the synthetic phase space of the Cs–Cd–Br system by constructing a data-driven phase map. Using a common set of precursors and a standardized experimental procedure, we successfully synthesize all four known Cs–Cd–Br phasesCsCdBr<sub>3</sub>, Cs<sub>2</sub>CdBr<sub>4</sub>, Cs<sub>3</sub>CdBr<sub>5</sub>, and Cs<sub>7</sub>Cd<sub>3</sub>Br<sub>13</sub>each exhibiting distinct structures, morphologies, and optical properties. Our findings highlight the potential of soft-chemistry methods for expanding the library of ternary metal halides and provide key insights into the thermodynamic and kinetic factors governing phase formation. Ternary operation ; Halide ; Caesium ; Phase (matter) ; Metal ; Phase diagram ; Cadmium ; Chemical space ; Key (lock) Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Perovskite Materials and Applications
Authors: Rajiv Kumar | Roop Lal | Bhavna Singh
Source:  (article, 2025-10-11)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Sustainable Nanocomposites for Energy Storage and Intelligent Computing in Eco-Efficient Systems The further development of intelligent computing, as well as sustainable energy systems, demands new materials that should be high-performance and environmentally friendly. This is because this research is dedicated to the production and study of the properties of the graphene-zinc oxide-silica (GZS) nanocomposites as eco-friendly materials in the next-generation energy storage applications and low-power intelligent electronic devices. It consisted of a distinctive synthesis strategy to maximize electrical conductivity and charge storage capacity and showed a great prospect of application in smart sensors, embedded systems of the internet of things, and energy-efficient devices with artificial intelligence. Structural stability and electrochemical behavior were elucidated using advanced material characterization methods such as XRD, SEM and TEM. Optimized nanocomposite especially sample Rk-6 had the lowest impedance value, which implies that it could be applicable in energy storage devices and power flow control. This contribution is to scalable, sustainable technologies encompassing intelligent computing and environmental responsibility - essential in future smart energy systems, and smart mobility. Energy storage ; Electrochemical energy storage ; Smart material ; Nanocomposite ; Characterization (materials science) ; Renewable energy ; Computer data storage ; Electronics Supercapacitor Materials and Fabrication ; Graphene research and applications ; Advancements in Battery Materials
Authors: Xiaohui Li (73588) | Lihong Su (467332) | Jing Ru (420034) | Liyang Zhou (6414557) | Shuhua Wang (1305378) | Jinghui Wang (620961) | Shiping Zhu (193679)
Source: Figshare (article, 2025-09-24)
Field: Chemistry — Organic Chemistry

Digital Strategies to Improve the Product Qualityand Production Efficiency of Fluorinated Polymers: 3. AI-AssistedModeling of Industrial Dispersion Polymerization of TFE/PPVE/HFP Modeling and simulation have provided a platform for virtual industrial production process optimization with reduced cost and time. Artificial intelligence (AI), combined with modeling and simulation, could help accelerate model development and improve model prediction capability. This work first reports kinetic modeling of tetrafluoroethylene (TFE) with perfluoropropyl Vinyl Ether (PPVE), and hexafluoropropylene (HFP) in a dispersion polymerization system at an industrial scale. Furthermore, AI tools with reasoning functions are utilized for model development. The model parameters, such as critical chain lengths, partition coefficients, and Henry coefficients, are estimated by AI, which significantly reduces unknown parameters for model validation with industrial data and improves model prediction capability. The model can provide deep insight into the production process and predict the key product properties such as monomer conversion, composition, and molecular weight. Process (computing) ; Dispersion (optics) ; Industrial production ; Product (mathematics) ; Polymerization ; Modeling and simulation ; Production (economics) ; Tetrafluoroethylene ; Work (physics) Advanced Polymer Synthesis and Characterization ; Process Optimization and Integration ; Advanced Control Systems Optimization
Authors: Saranya Balasubramaniyam | Thirumalaikumaran Rathinam | M. Srinivasan | Aswini Rajendran | Sharmila Sakthisivanandhan
Source: Journal of Holistic Integrative Pharmacy (article, 2025-12-01)
Field: Materials Science — Materials Chemistry

Chitosan–2D nanocomposites: A convergence of nature, nanotech, and sustainability The integration of natural biopolymers with two-dimensional (2D) nanomaterials has opened new avenues for designing multifunctional, sustainable, and high-performance materials. Chitosan, a biodegradable and biocompatible polysaccharide, holds significant promise in biomedical and environmental applications but is limited by weak mechanical strength, poor electrical conductivity, and restricted functional versatility. To overcome these challenges, recent studies have focused on chitosan–2D nanocomposites incorporating materials such as graphene, MoS 2 , and MXenes. These hybrids demonstrate remarkable enhancements in structural, thermal, electrical, and biological properties through strong interfacial interactions. This review systematically summarizes the state-of-the-art fabrication strategies—including solution casting, electrospinning, and layer-by-layer assembly—and highlights their impact on material performance. The synergistic properties of these composites enable advanced applications in tissue engineering, drug delivery, antimicrobial coatings, and environmental remediation. Furthermore, we discuss the role of interfacial chemistry and nanostructure organization in dictating functional outcomes. Finally, key challenges related to scalability, regulatory approval, and biosafety are examined, along with emerging directions such as stimuli-responsive systems and AI-assisted material design. This review collectively highlights the potential of chitosan–2D nanocomposites as next-generation sustainable platforms at the nexus of nanotechnology, biotechnology, and green innovation. Nexus (standard) ; Biocompatible material ; Sustainability ; Biosafety ; Convergence (economics) ; Nanomaterials ; Nanocomposite ; Key (lock) MXene and MAX Phase Materials ; Graphene and Nanomaterials Applications ; Supercapacitor Materials and Fabrication