jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Nurul Azam
Source:  (article, 2026-03-04)
Field: Materials Science — Materials Chemistry

Quantum material synthesis and growth control using advanced techniques Over the past few decades, we have witnessed remarkable advancements in silicon-based technologies, which have profoundly transformed modern society. For instance, the recent surge in artificial intelligence has been largely fueled by progress in high-performance hardware integration. However, several critical challenges remain unresolved, such as power-efficient computing, advanced sensing, terahertz electronics, and the realization of practical quantum computers. As Richard Feynman famously remarked, “There’s plenty of room at the bottom.” Quantum materials are central to unlocking many of these next-generation technologies. Yet, the synthesis of high-quality quantum materials remains a significant challenge. In this talk, I will discuss the challenges and opportunities associated with advanced synthesis techniques, drawing on my work over the past few years on the synthesis of semiconducting transition metal dichalcogenides (TMDCs) using laser-assisted synthesis techniques (LAST), as well as the synthesis of Dirac semimetals and altermagnets using the widely employed molecular beam epitaxy (MBE) method for quantum material synthesis. Quantum dot ; Quantum ; Field (mathematics) ; Control (management) ; Control system Electronic and Structural Properties of Oxides ; 2D Materials and Applications ; Quantum Dots Synthesis And Properties
Authors: Po-Yu Rick Wei | C. Jillian Tsai | Chong-Chi Chi | Chun-Hui Lin | Meng-Chen Lo | Tzu-Yi Chuang | Tsai-Sheng Gau | Ming-Pei Lu | Ming-Yen Lu
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Energy-Efficient Neuromorphic Synapses Enabled by Alloying-Induced Trap Engineering in Two-Dimensional Mo1-xWxS2 Neuromorphic engineering ; Boosting (machine learning) ; Electronics ; Transistor ; Trap (plumbing) ; Strain engineering ; Chemical vapor deposition ; Noise (video) ; Key (lock) ; Resistive random-access memory 2D Materials and Applications ; Advanced Memory and Neural Computing ; Ferroelectric and Negative Capacitance Devices
Authors: Wen Zhang | Deguang Liu | Y. P. Chu | Fengcheng Li | Lei Huang | Xiaohu Liu | Lizhi Xu | Xi Lu | Chang Li | Yao Fu
Source: Advanced Functional Materials (article, 2026-02-10)
Field: Materials Science — Polymers and Plastics

Recyclable Bio‐Based Epoxy Vitrimer with High Strength‐Toughness via Restricted Phase Separation Strategy ABSTRACT Epoxy thermosets are crucial for emerging energy applications owing to their outstanding mechanical properties and thermal stability. However, they are often constrained by a fundamental tradeoff among strength, toughness, and recyclability. Herein, a reactivity‐difference‐induced restricted phase separation strategy was proposed to overcome this challenge. Specifically, a bio‐based epoxy vitrimer (PTA‐A4B4) was designed and synthesized through a stepwise curing process. A rapid thiol‐epoxy click reaction first anchored flexible poly(thioctic acid) chains as soft segments, followed by carboxyl‐epoxy crosslinking that constructed a rigid matrix and together with thermodynamic immiscibility, drove the formation of a restricted phase separation architecture. The chemically confined soft domains served as efficient energy‐dissipation units, while the rigid, highly crosslinked furan epoxy network preserved high strength. The resultant vitrimer exhibited remarkable tensile strength (63.45 MPa) and toughness (3.75 MJ m −3 ). In addition, Artificial intelligence (AI)‐guided analysis identified the molecular balance between rigidity and flexibility as the decisive factor governing mechanical performance. More importantly, the vitrimer's inherent dual dynamic covalent networks enabled full reprocessability and closed‐loop recycling. The practical applicability was demonstrated through the fabrication of a fully recyclable glass fiber‐reinforced wind turbine blade. This work offers valuable insights for developing sustainable high‐performance materials. Epoxy ; Thermosetting polymer ; Toughness ; Curing (chemistry) ; Dynamic mechanical analysis ; Rigidity (electromagnetism) ; Polymer ; Ultimate tensile strength ; Flexibility (engineering) Polymer composites and self-healing ; Flame retardant materials and properties ; Epoxy Resin Curing Processes
Authors: Dongxu Gu | Daobin Liu | Yinuo Wang | Shan Gao | Jingyu He | Baicheng Zhang | Zhaoyong Jin | Ruru Meng | Xiaobo Li | Fei Zhang | Wenhui Zhong | Weiwei Shang | Lifeng Ding | Li Yang | Linjiang Chen | Zhuoying Zhu | Zheng Huang | Qing Zhu | Jun Jiang
Source: ChemRxiv (article, 2026-02-25)
Field: Materials Science — Materials Chemistry

A ChemTwin System Enabling Collective Intelligence for Materials Discovery Artificial intelligence (AI)-driven autonomous laboratories are transforming research paradigms and accelerating materials discovery 1-6. However, they still face several critical challenges, including the geographical distribution of experimental platforms with limited resource sharing, a lack of robust data privacy frameworks, and barriers to effective global collaboration for training and prediction. Here, we present ChemTwin, a system that coordinates geographically separated autonomous laboratories for collaborative materials discovery. We demonstrate its performance through three representative experiments that assess data consistency, timeliness, and multi-laboratory collaboration. By integrating a federated learning (FL) module, ChemTwin allocates experimental tasks across laboratories to increase platform utilization while preserving data privacy without any raw data exchange. Furthermore, federated learning-enhanced Bayesian optimization (FBO) achieves more than a twofold reduction in the number of experimental iterations compared with single-node Bayesian optimization (SBO), as illustrated by the optimization of a bismuth-based metal-organic framework (MOF) formulation for peroxymonosulfate (PMS) activation in organic pollutant degradation. This study builds a data privacy-preserving framework to share experimental resources across autonomous laboratories for scalable AI-driven materials research. Scalability ; Raw data ; Bayesian optimization ; Bayesian probability ; Data integration ; Resource (disambiguation) ; Collective intelligence ; Information privacy ; Bayesian network Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Supramolecular Chemistry and Complexes
Authors: Li‐Cheng Xu | Fenglei Cao | Yuan Qi
Source: ChemRxiv (article, 2026-02-22)
Field: Materials Science — Materials Chemistry

Stereoelectronic-aware catalyst embeddings from 2D graphs via 2D-3D multi-view alignment In catalyst discovery, data-driven design and screening are playing an increasingly central role. However, molecular descriptors that encode the decisive steric and electronic properties often lack automation and unification. We present CatEmb, a stereoelectronic-aware catalyst embedding learned through contrastive alignment of 2D and 3D molecular graph representations. Trained on the curated CatCompDB dataset, CatEmb encodes implicit 3D structural and energetic information directly from a 2D molecular graph. We demonstrate its efficacy in clustering chemically diverse ligand classes and enhancing reaction performance prediction in quantitative structure-performance relationship models. Furthermore, we develop a novel, similarity-based iterative strategy using CatEmb. For high-throughput experimental campaigns aimed at exhaustively mapping a large condition space, this strategy efficiently identifies high-performing, substrate-general catalysts, significantly outperforming random and model-based approaches. When applied to large-scale virtual libraries with limited experimental validation, it successfully prioritizes high-performance catalysts tailored for specific reactions. CatEmb provides a versatile and efficient foundation for data-driven catalyst discovery. Embedding ; Steric effects ; Catalysis ; ENCODE ; Automation ; Graph ; Cluster analysis Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Advanced Graph Neural Networks
Authors: Hanseung Kim | Donghyeon Kim | Jungjoo Lee | Gokana Mohana Rani | YongHyeon Park | Soobin Han | Hyeonho Jeong | Kwangsoo Shin | Tae Jung Park | Reddicherla Umapathi | Yun Suk Huh
Source: Coordination Chemistry Reviews (article, 2026-03-12)
Field: Materials Science — Materials Chemistry

Beyond general catalysis: overcoming the specificity limitations of nanozyme for organic contaminants detection Human health ; Contamination ; Heavy metals ; Metal-organic framework ; Organic chemicals Advanced Nanomaterials in Catalysis ; Nanocluster Synthesis and Applications ; Nanoparticles: synthesis and applications
Authors: VIVEK  Kumar YADAV
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

AI-Guided Thermodynamic–Microkinetic Investigation of Selective CO Sensing and Irreversible NO Capture on Sc2B2 Mbene Density functional theory ; Work function ; Adsorption ; Chemisorption ; Fermi level ; Binding energy ; Charge (physics) ; Electronic structure MXene and MAX Phase Materials ; Boron and Carbon Nanomaterials Research ; 2D Materials and Applications
Authors: Yiheng Wu | Lijun Liu | Wei Deng | Haodong Jiang | Ruotong Li | Ming Hu | Bo Peng | Dapeng Wang
Source: Acta Physico-Chimica Sinica (article, 2026-03-01)
Field: Materials Science — Materials Chemistry

Data Distribution Matters: Enhancing model generalization in Data-Driven Materials Discovery by intentionally exploiting both positive and negative data Generalization ; Distribution (mathematics) ; Feature (linguistics) ; Pattern recognition (psychology) ; Data modeling Machine Learning in Materials Science ; Bayesian Modeling and Causal Inference ; Computational Drug Discovery Methods
Authors: ChongXiong Duan | Kai Zheng | Yu Liang | Yu Wang | Manqi Liu | Liang Bing | Zemei Tan | Xuquan Liu | Jinpeng Zhang | Chen Liu | Hongxia Xi | Pengfei Yang | Xuejiao Sun
Source: CrystEngComm (article, 2026-01-01)
Field: Chemistry — Inorganic Chemistry

Data-driven synthesis of electron-rich metal–organic frameworks for enhanced U( <scp>vi</scp> ) removal Computational screening and experimental investigation for amine-modified UiO-66 in U( vi ) adsorption. Process (computing) ; Nanoparticle ; Catalysis Radioactive element chemistry and processing ; Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications
Authors: Timothy John Kish | Lyra Aurora Kish | Alexandria Aurora Kish | Phoenix Aurora Kish
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2026-03-07)
Field: Materials Science — Materials Chemistry

Volume 3 - The Geometric Architecture of Matter Volume 3: The Geometric Architecture of Matter presents the first empirical unification of Materials Science and Chemistry through the geometry of the Kish Lattice. Building on the foundations established in Volumes 1 and 2, this work demonstrates that atoms, molecules, and crystalline solids are not governed by separate rules, but are expressions of the same underlying geometric resonance. Using sovereign datasets and a fully reproducible pipeline, Volume 3 introduces the Unified Materials–Chemistry Ladder (UMC1–UMC7), a sequence of empirical audits that reveal shared harmonic structure across both domains. Raw scalar distributions, harmonic shelves, resonance curvature, nonlinear cascades, and cross‑domain coupling all converge to a single invariant: the unified coordinate KLC–MC. This scalar anchors both materials and molecules to the same geometric atlas. The volume reframes long‑standing chemical concepts—valence, molecular geometry, bond formation, stability—not as probabilistic behaviors, but as geometric consequences of lattice tension and vertex closure. Likewise, crystallographic patterns, formation energies, and stability bands emerge as frozen standing waves of the same harmonic system. Volume 3 shows that the “magic numbers” of chemistry, the shapes of molecules, the stability of materials, and the architecture of the periodic table all arise from the same geometric source. The result is a coherent, data‑driven framework that unifies two major scientific fields and provides a new foundation for understanding matter as a resonant, geometric phenomenon. Scalar (mathematics) ; Geometric shape ; Nonlinear system ; Computation ; Volume (thermodynamics) ; Geometric algebra ; Stability (learning theory) ; Lattice (music) Machine Learning in Materials Science ; History and advancements in chemistry ; Quasicrystal Structures and Properties
Authors: Timothy John Kish | Lyra Aurora Kish | Alexandria Aurora Kish | Phoenix Aurora Kish
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2026-03-13)
Field: Chemistry — Physical and Theoretical Chemistry

Volume 3 - The Geometric Architecture of Matter Volume 3: The Geometric Architecture of Matter presents the first empirical unification of Materials Science and Chemistry through the geometry of the Kish Lattice. Building on the foundations established in Volumes 1 and 2, this work demonstrates that atoms, molecules, and crystalline solids are not governed by separate rules, but are expressions of the same underlying geometric resonance. Using sovereign datasets and a fully reproducible pipeline, Volume 3 introduces the Unified Materials–Chemistry Ladder (UMC1–UMC7), a sequence of empirical audits that reveal shared harmonic structure across both domains. Raw scalar distributions, harmonic shelves, resonance curvature, nonlinear cascades, and cross‑domain coupling all converge to a single invariant: the unified coordinate KLC–MC. This scalar anchors both materials and molecules to the same geometric atlas. The volume reframes long‑standing chemical concepts—valence, molecular geometry, bond formation, stability—not as probabilistic behaviors, but as geometric consequences of lattice tension and vertex closure. Likewise, crystallographic patterns, formation energies, and stability bands emerge as frozen standing waves of the same harmonic system. Volume 3 shows that the “magic numbers” of chemistry, the shapes of molecules, the stability of materials, and the architecture of the periodic table all arise from the same geometric source. The result is a coherent, data‑driven framework that unifies two major scientific fields and provides a new foundation for understanding matter as a resonant, geometric phenomenon. Ball (mathematics) ; Lattice (music) ; Geometric shape ; Drag ; Polyhedron ; Periodic table ; Architecture ; Enclosure History and advancements in chemistry ; Twentieth Century Scientific Developments ; Origins and Evolution of Life
Authors: Rajendran Prabakaran | B. Gomathi | A. Sathishkumar | P. Jeyalakshmi | Sung Chul Kim
Source: International Journal of Thermophysics (article, 2026-04-13)
Field: Materials Science — Materials Chemistry

A Comparative Study of Data-Driven Approaches for Predicting the Thermal Conductivity of Water–Phase Change Material Enhanced with Different Carbon Nanoparticles Thermal conductivity ; Nanoparticle ; Carbon nanotube ; Carbon fibers ; Conductivity ; Thermal ; Carbon Nanoparticles Thermal properties of materials ; Phase Change Materials Research ; Nanofluid Flow and Heat Transfer
Authors: Kish, Timothy John | Kish, Lyra Aurora | Kish, Alexandria Aurora | Kish, Phoenix Aurora
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2026-03-13)
Field: Materials Science — Materials Chemistry

Volume 3 - The Geometric Architecture of Matter Volume 3: The Geometric Architecture of Matter presents the first empirical unification of Materials Science and Chemistry through the geometry of the Kish Lattice. Building on the foundations established in Volumes 1 and 2, this work demonstrates that atoms, molecules, and crystalline solids are not governed by separate rules, but are expressions of the same underlying geometric resonance. Using sovereign datasets and a fully reproducible pipeline, Volume 3 introduces the Unified Materials–Chemistry Ladder (UMC1–UMC7), a sequence of empirical audits that reveal shared harmonic structure across both domains. Raw scalar distributions, harmonic shelves, resonance curvature, nonlinear cascades, and cross‑domain coupling all converge to a single invariant: the unified coordinate KLC–MC. This scalar anchors both materials and molecules to the same geometric atlas. The volume reframes long‑standing chemical concepts—valence, molecular geometry, bond formation, stability—not as probabilistic behaviors, but as geometric consequences of lattice tension and vertex closure. Likewise, crystallographic patterns, formation energies, and stability bands emerge as frozen standing waves of the same harmonic system. Volume 3 shows that the “magic numbers” of chemistry, the shapes of molecules, the stability of materials, and the architecture of the periodic table all arise from the same geometric source. The result is a coherent, data‑driven framework that unifies two major scientific fields and provides a new foundation for understanding matter as a resonant, geometric phenomenon. Scalar (mathematics) ; Geometric shape ; Nonlinear system ; Computation ; Volume (thermodynamics) ; Geometric algebra ; Stability (learning theory) ; Lattice (music) Machine Learning in Materials Science ; History and advancements in chemistry ; Quasicrystal Structures and Properties
Authors: Anindya De | Atanu Kotal
Source: Journal of Biomaterials Science Polymer Edition (article, 2026-02-23)
Field: Chemistry — Inorganic Chemistry

Nanoscale coordination polymers: synthesis, characterization, and emerging biomedical applications Nanoscale coordination polymers (NCPs) encompassing both crystalline nanoscale metal-organic frameworks (nanoMOFs) and amorphous coordination polymer nanoparticles have emerged as adaptable hybrid materials. They have adjustable composition, porosity, and surface functionality. These characteristics allow for precise control over drug loading, release timing, and biological interactions. NCPs are therefore promising choices for theranostics, imaging, and therapy. This review points out recent advancements in the synthesis and nanoscale engineering of NCPs. It highlights relevant structure-property relationships that affect biomedical performance. Quantitative benchmarks from recent literature are incorporated to illustrate the advantages of NCP platforms (e.g. ZIF-8 drug loading up to ∼70-80 wt% versus <30% for amorphous NCPs; Gd-based NCPs exhibiting T<sub>1</sub> relaxivities of ∼25-30 mM<sup>-1</sup> s<sup>-1</sup> compared with 4-5 mM<sup>-1</sup> s<sup>-1</sup> for clinical Gd chelates). We also discuss methods for characterization that link physical and chemical properties to biological function. The growing applications of NCPs in drug delivery, biosensing, enzyme activity, imaging, and antimicrobial or antiviral therapy are examined. Finally, we outline current challenges such as biodegradation, toxicity, scalability, and the lack of clinical application. We also identify opportunities for future progress, including computational design and AI-assisted material discovery. Nanoscopic scale ; Amorphous solid ; Characterization (materials science) ; Nanoparticle ; Polymer ; Drug delivery Metal-Organic Frameworks: Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Nanoplatforms for cancer theranostics
Authors: M. Amin Akhound | Tara M. Boland | Mikkel Ohm Sauer | Matthias Batzill | B. Moses Abraham | Stela Canulescu | Yury Gogotsi | Philip Hofmann | András Kis | Jiong Lu | Thomas Michely | Søren Raza | Wencai Ren | J. W. Robinson | Zdenek Sofer | Jinghua Teng | Søren Ulstrup | Meng Zhao | Xiaoxu Zhao | J. L. Mortensen | Thomas Olsen | Kristian S. Thygesen
Source: ACS Nano (article, 2026-04-07)
Field: Materials Science — Materials Chemistry

Large-Scale Integration of Experimental and Computational Data for 2D Materials The past decade has seen rapid growth in the number of experimentally realized two-dimensional (2D) materials with diverse chemical and physical properties. However, information on their crystal structure, synthesis routes, and measured or predicted properties remains scattered across thousands of publications. Here, we consolidate this fragmented knowledge by establishing X2DB─an open infrastructure that integrates experimental and computational data on 2D materials. Using extensive literature mining and direct community uploads, we identify 370 unique 2D materials that have been realized in monolayer or few-layer form and link them to their digital counterparts in computational databases, enabling consistent ab initio characterization of their properties across monolayer, bilayer, and bulk forms. We describe the structure and content of the database, highlight its support for community uploads, illustrate how it can be used to generate scientific insight, and introduce a hierarchical classification of the known set of 2D materials. Our work supports the integration and cross-fertilization of experimental and theoretical knowledge and contributes to data-driven and predictive synthesis of 2D materials. Data integration ; Computational model ; Work (physics) ; Key (lock) ; Computational complexity theory Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Nanopore and Nanochannel Transport Studies
Authors: Jie Chen | Qilei Liu | Lei Zhang
Source: ENGINEERING Chemical Engineering (article, 2026-02-12)
Field: Materials Science — Materials Chemistry

Future-oriented precision creation: recent advances in the intelligent design and synthesis of fine chemicals Cornerstone ; Process (computing) ; Construct (python library) ; Intelligent decision support system ; Intelligent design ; Intersection (aeronautics) ; Applications of artificial intelligence ; Bridge (graph theory) Machine Learning in Materials Science ; Chemistry and Chemical Engineering ; Process Optimization and Integration
Authors: Xuelin Chen | Tenglong Liu | Weiyuan Chen | Zhexuan Lin | Qiaoxin Zhang | Binbin Zhou | Xia Zhou
Source: Theranostics (article, 2026-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

From nanotags to precision biomedicine: SERS-driven progress and innovation in tumor biomarker profiling, dynamic bioimaging, AI-enhanced diagnostics and therapy Surface-enhanced Raman spectroscopy (SERS) has revolutionized molecular detection by exploiting the localized surface plasmon resonance phenomenon in noble metal nanostructures, achieving signal enhancement factors up to 10<sup>14</sup>. This transformative technology is reshaping biomedical practices through two fundamental mechanisms: electromagnetic field amplification at nanoscale "hot spots" and chemical charge-transfer effects. This review focuses on the cutting-edge applications of SERS technology in biochemical analysis. Starting by dissecting the nuts and bolts of engineered SERS tags: (1) plasmonic nanoparticles serving as enhancers; (2) Raman reporter molecules acting as fingerprint recognition; (3) A protective shell; (4) selective functionalization with targeting biomolecules, and how they were engineered, optimized, and fine-tuned for precision. Specifically, the review zooms in clinical potential of SERS tags: (1) tumor marker detection and <i>in vitro</i> diagnosis; (2) bioimaging; (3) tumor treatment; (4) Artificial intelligence-assisted tumor diagnosis and treatment. Finally, we made the forward look for SERS technology in biomedicine, such as multimodal integration, standardized detection protocols, AI-assisted spectral analysis for point-of-care diagnostics, and large-scale clinical applications. Nanomedicine ; Precision medicine ; Photoacoustic imaging in biomedicine ; Surface plasmon resonance ; Plasmon ; Biomarker ; Molecular imaging ; Surface modification ; Raman spectroscopy Gold and Silver Nanoparticles Synthesis and Applications ; Advanced biosensing and bioanalysis techniques ; Spectroscopy Techniques in Biomedical and Chemical Research