jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Roland G. St. Michel | Ryan J. Jang | Aaron Garrison | Ilia Kevlishvili | Heather J. Kulik
Source: arXiv (Cornell University) (preprint, 2026-04-07)
Field: Materials Science — Materials Chemistry

The BOS-Lig Dataset: Accurate Ligand Charges from a Consensus Approach for 66,810 Experimentally Synthesized Ligands Understanding ligand properties is essential for computational high-throughput screening of transition metal complexes. However, ligand properties such as net charge and other information such as their application area are often absent or inconsistently recorded in crystallographic datasets. Here, we construct a ligand dataset from 126,985 mononuclear transition metal complexes curated from the Cambridge Structural Database. Using an iterative charge-balancing workflow that combines complex charges, metal oxidation states, and consensus across crystallographic observations, we confidently assign net charges to 66,810 ligands among 94,581 identified unique ligand structures to curate the Boston Open-Shell Ligand (BOS-Lig) dataset. The workflow assigns ligand charges in homoleptic complexes first and then iteratively propagates these assignments across heteroleptic environments, allowing charges to be inferred even when direct charge information is unavailable. We analyze cases where simple heuristics such as the octet rule would have failed and introduce a purity metric to identify when our charge assignments may be incorrect. Each ligand is also classified in terms of its metal coordinating atoms and whether there are multiple variants (i.e., hemilability). We then link complexes to their associated journal abstracts and apply a topic-modeling workflow to link 25,146 ligands with functional application areas spanning reactivity, redox chemistry, biological chemistry, and photophysical chemistry. Together, we provide an experimentally grounded dataset of ligand chemical space that connects charge and functional application as a foundation for computational screening and data-driven ligand design. Ligand (biochemistry) ; Homoleptic ; Charge (physics) ; Metric (unit) ; Heuristics ; Workflow ; Metal Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metal-Catalyzed Oxygenation Mechanisms
Authors: Roland G. St. Michel | Ryan J. Jang | Aaron Garrison | Ilia Kevlishvili | Heather J. Kulik
Source: arXiv (Cornell University) (article, 2026-04-07)
Field: Materials Science — Materials Chemistry

The BOS-Lig Dataset: Accurate Ligand Charges from a Consensus Approach for 66,810 Experimentally Synthesized Ligands Understanding ligand properties is essential for computational high-throughput screening of transition metal complexes. However, ligand properties such as net charge and other information such as their application area are often absent or inconsistently recorded in crystallographic datasets. Here, we construct a ligand dataset from 126,985 mononuclear transition metal complexes curated from the Cambridge Structural Database. Using an iterative charge-balancing workflow that combines complex charges, metal oxidation states, and consensus across crystallographic observations, we confidently assign net charges to 66,810 ligands among 94,581 identified unique ligand structures to curate the Boston Open-Shell Ligand (BOS-Lig) dataset. The workflow assigns ligand charges in homoleptic complexes first and then iteratively propagates these assignments across heteroleptic environments, allowing charges to be inferred even when direct charge information is unavailable. We analyze cases where simple heuristics such as the octet rule would have failed and introduce a purity metric to identify when our charge assignments may be incorrect. Each ligand is also classified in terms of its metal coordinating atoms and whether there are multiple variants (i.e., hemilability). We then link complexes to their associated journal abstracts and apply a topic-modeling workflow to link 25,146 ligands with functional application areas spanning reactivity, redox chemistry, biological chemistry, and photophysical chemistry. Together, we provide an experimentally grounded dataset of ligand chemical space that connects charge and functional application as a foundation for computational screening and data-driven ligand design. Ligand (biochemistry) ; Homoleptic ; Charge (physics) ; Metric (unit) ; Heuristics ; Workflow ; Metal Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metal-Catalyzed Oxygenation Mechanisms
Authors: Eric Han Wang | Weijia Yan | Ruihong Huang
Source: MRS Communications (article, 2026-01-12)
Field: Materials Science — Materials Chemistry

Thermal analysis of 3D GPU-memory architectures with boron nitride interposer Interposer ; Thermal management of electronic devices and systems ; Boron nitride ; Thermal ; Silicon nitride ; Thermal conductivity ; Electrical conductor ; Nitride ; Thermal analysis Thermal properties of materials ; 3D IC and TSV technologies ; Silicon and Solar Cell Technologies
Authors: Angzhen Li | Yindong Zhang | Fei Yan | Xiaosong Lu
Source: Journal of Physics D Applied Physics (article, 2026-01-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Spontaneous emission control in light-emitting metasurfaces Abstract Light-emitting metasurfaces are of great significance in many cutting-edge fields such as topological photonics, singularity optics, and quantum optics due to their excellent control over light. In recent years, devices based on light-emitting metasurfaces, like communication antennas, holographic imaging, and stealth devices have been continuously developed. Herein, we summarize recent progress of light-emitting metasurfaces from the perspective of spontaneous emission control in terms of intensity, direction, and phase. We also discuss promising research directions of light-emitting metasurfaces, including novel structures and mechanisms, the integration of various light-emitting materials, and their combination with artificial intelligence. Holography ; Singularity ; Perspective (graphical) ; Spontaneous emission ; Quantum ; Control (management) Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Thermal Radiation and Cooling Technologies
Authors: Siu Wun Cheung | Youngsoo Choi | Jean‐Luc Fattebert | Jonas L. Kaufman | Daniel Osei-Kuffuor
Source: arXiv (Cornell University) (article, 2026-02-25)
Field: Materials Science — Materials Chemistry

A Reduced Order Model approach for First-Principles Molecular Dynamics Computations To leverage the redundancy between the electronic structure computed at each step of first-principles molecular dynamics, we present a data-driven modeling framework for Kohn-Sham Density Functional Theory that bypasses the explicit optimization of electronic wavefunctions. We sample a priori representative atomic configurations and construct a low-dimensional basis that efficiently approximates the electronic structure subspace. Subsequently, we employ this reduced basis in a direct solver for the electronic single particle density matrix, thereby enabling the efficient determination of ground state without iterative wavefunction optimization. We demonstrate the efficacy of our approach in a Born-Oppenheimer molecular dynamics of a water molecule, showing that the resulting simulations accurately reproduce key structural properties, such as bond lengths and bond angle, obtained from full first-principles molecular dynamics. This work highlights the potential of data-driven approaches to develop efficient electronic structure solvers for first-principles simulations. Electronic structure ; Molecular dynamics ; A priori and a posteriori ; Solver ; Leverage (statistics) ; Computation ; Basis function ; Basis (linear algebra) Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Quantum many-body systems
Authors: Inês Gomes | V.H. Carneiro | jose miguel de almeida outeiro | H. Puga
Source: The International Journal of Advanced Manufacturing Technology (article, 2026-04-11)
Field: Materials Science — Materials Chemistry

Unlocking the potential of ultrasonic consolidation: a critical review of process modelling and simulation frameworks Ultrasonic Consolidation (UC) is a solid‑state additive manufacturing technology that enables layer‑by‑layer bonding of metallic foils at temperatures substantially below their melting point. This low-temperature processing window preserves the bulk material properties while allowing multimaterial integration and embedding of sensitive components. Despite its significant industrial potential, the underlying physical and mechanical mechanisms of UC remain only partially understood, and process optimisation is still largely guided by empirical approaches rather than theoretical models. In this context, numerical modelling has become a key tool for exploring the coupled phenomena governing UC, including frictional heating, plastic deformation, thermomechanical softening, and microstructural evolution. Nevertheless, existing modelling efforts remain fragmented in terms of their physical scope, spatial and temporal scales, and validation methodologies. This review presents a critical assessment of the numerical frameworks developed to date, categorising them into thermomechanical, crystal plasticity, energy‑based, and system‑level approaches. Particular attention is given to the representation of frictional behaviour, constitutive material response, heat generation, and softening mechanisms. Furthermore, as a major limitation, model validation remains a challenge, as most studies rely primarily on macroscopic observations, such as thermal evolution or geometric variation, whereas advanced characterisation techniques, including scanning electron microscopy, in situ strain mapping, and high-resolution microstructural analysis, are comparatively underexploited. By clarifying methodological assumptions, pinpointing mechanistic ambiguities, and outlining unresolved knowledge gaps, this review proposes a structured framework to guide next-generation UC models toward multiscale, multiphysics, and experimentally anchored predictive tools. Such integration is essential for computationally efficient, scalable simulations and for supporting digital twin and AI-driven industrial implementation of UC. Process (computing) ; Ultrasonic sensor ; Process modeling ; Work (physics) ; Physical modelling Ultrasound and Cavitation Phenomena ; Rheology and Fluid Dynamics Studies ; Granular flow and fluidized beds
Authors: Siu Wun Cheung | Youngsoo Choi | Jean‐Luc Fattebert | Jonas L. Kaufman | Daniel Osei-Kuffuor
Source: Open MIND (preprint, 2026-02-25)
Field: Materials Science — Materials Chemistry

A Reduced Order Model approach for First-Principles Molecular Dynamics Computations To leverage the redundancy between the electronic structure computed at each step of first-principles molecular dynamics, we present a data-driven modeling framework for Kohn-Sham Density Functional Theory that bypasses the explicit optimization of electronic wavefunctions. We sample a priori representative atomic configurations and construct a low-dimensional basis that efficiently approximates the electronic structure subspace. Subsequently, we employ this reduced basis in a direct solver for the electronic single particle density matrix, thereby enabling the efficient determination of ground state without iterative wavefunction optimization. We demonstrate the efficacy of our approach in a Born-Oppenheimer molecular dynamics of a water molecule, showing that the resulting simulations accurately reproduce key structural properties, such as bond lengths and bond angle, obtained from full first-principles molecular dynamics. This work highlights the potential of data-driven approaches to develop efficient electronic structure solvers for first-principles simulations. Electronic structure ; Molecular dynamics ; A priori and a posteriori ; Solver ; Leverage (statistics) ; Computation ; Basis function ; Basis (linear algebra) Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Quantum many-body systems
Authors: Siu  Wun Cheung | Youngsoo Choi | Jean-Luc Fattebert | Jonas Kaufman | Daniel Osei-Kuffuor
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

A Reduced Order Model approach for First-Principles Molecular Dynamics Computations Electronic structure ; Molecular dynamics ; A priori and a posteriori ; Solver ; Leverage (statistics) ; Computation ; Basis function ; Basis (linear algebra) Machine Learning in Materials Science ; Quantum many-body systems ; Model Reduction and Neural Networks
Authors: Maria-Chrysanthi Kafentzi | Grigorios Papageorgiou | Kalliopi Ladomenou
Source: Inorganics (article, 2026-01-23)
Field: Materials Science — Materials Chemistry

From Porphyrinic MOFs and COFs to Hybrid Architectures: Design Principles for Photocatalytic H2 Evolution Solar-driven hydrogen production via photocatalytic water splitting represents a promising route toward sustainable and low-carbon energy systems. Among emerging photocatalysts, porphyrin-based framework materials, specifically porphyrinic metal–organic frameworks (PMOFs) and porphyrinic covalent organic frameworks (PCOFs), have attracted increasing attention owing to their strong visible-light absorption, tunable electronic structures, permanent porosity, and well-defined catalytic architectures. In these systems, porphyrins function as versatile photosensitizers whose photophysical properties can be precisely tailored through metalation, peripheral functionalization, and integration into ordered frameworks. This review provides a comprehensive, design-oriented overview of recent advances in PMOFs, PCOFs, and hybrid porphyrinic architectures for photocatalytic H2 evolution. We discuss key structure–activity relationships governing light harvesting, charge separation, and hydrogen evolution kinetics, with particular emphasis on the roles of porphyrin metal centers, secondary building units, linker functionalization, framework morphology, and cocatalyst integration. Furthermore, we highlight how heterojunction engineering through coupling porphyrinic frameworks with inorganic semiconductors, metal sulfides, or single-atom catalytic sites can overcome intrinsic limitations related to charge recombination and limited spectral response. Current challenges, including long-term stability, reliance on noble metals, and scalability, are critically assessed. Finally, future perspectives are outlined, emphasizing rational molecular design, earth-abundant catalytic motifs, advanced hybrid architectures, and data-driven approaches as key directions for translating porphyrinic frameworks into practical photocatalytic hydrogen-generation technologies. Photocatalysis ; Metal-organic framework ; Rational design ; Design elements and principles ; Catalysis ; Heterojunction ; Hydrogen production Covalent Organic Framework Applications ; Advanced Photocatalysis Techniques ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Ahmad ElShiekh
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2026-03-12)
Field: Materials Science — Materials Chemistry

AI Compute at the Thermodynamic Limit: Energy Scarcity, GPU Heat, and Functional Quantum Materials Artificial intelligence (AI) compute infrastructure is approaching a hard thermodynamic boundary. Modern GPU-centric data centres convert upwards of 95% of consumed electrical energy into waste heat, and projections indicate that AI-related electricity demand will exceed 1,000 TWh annually before 2030. Conventional mitigation strategies—improved Power Usage Effectiveness (PUE), advanced cooling topologies, and chip-level voltage scaling—are necessary but insufficient; they reduce the rate of approach to the thermodynamic ceiling without raising it. This paper reframes the AI energy crisis as a closed-loop thermodynamic systems problem and proposes functional quantum materials as the enabling technology for partial waste-heat recovery. We develop a rigorous energy-balance model for a thermoelectric generator (TEG) integrated into a GPU server rack, derive the conditions under which net energy recirculation is thermodynamically feasible, and quantify expected performance bounds using Bornite (Cu5FeS4) as the active material. Electricity ; Thermoelectric generator ; Generator (circuit theory) ; Energy (signal processing) ; Thermoelectric materials ; Electricity generation ; Efficient energy use ; Non-equilibrium thermodynamics ; Power (physics) Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Advanced Thermodynamics and Statistical Mechanics
Authors: Wei Wu | Zhuojun Liu | Guixin Qiu | Bo Chen | Jin Liu
Source: Journal of Optics (article, 2026-02-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Quasi-bound states in the continuum for nonlinear applications in asymmetry metasurfaces Abstract Asymmetry metasurfaces supporting quasi-bound states in the continuum (BIC) have recently emerged as a powerful platform for enhancing nonlinear optical processes by combining ultrahigh quality resonances with strong electromagnetic field confinement. Leveraging these unique properties, asymmetry metasurfaces have enabled significant advances in a broad range of nonlinear phenomena, including second- and third-harmonic generation, nonlinear chiral effects, high-harmonic generation, and spontaneous parametric down-conversion. Insights are also provided into several emerging research frontiers by asymmetry metasurfaces, such as adopting high-refractive-index materials, exploring novel quasi-BIC regulating mechanisms, and exploiting artificial intelligence–assisted inverse design for on-demand optimization. Looking ahead, broadband resonances control and novel material integration are anticipated to transform asymmetry metasurfaces into versatile, high-efficiency building blocks for compact and multifunctional nonlinear photonic devices. Asymmetry ; Nonlinear system ; Broadband ; Parametric statistics ; Photonics ; Nonlinear optics ; Inverse ; Metamaterial Metamaterials and Metasurfaces Applications ; Acoustic Wave Phenomena Research ; Plasmonic and Surface Plasmon Research
Authors: Xuan Zhao | Yukun Liu | Kai Wang | Fangli Gao | Xiangjun Bai | Zhanfei Li | Yuchang Wang
Source: Advanced Healthcare Materials (article, 2026-03-28)
Field: Materials Science — Materials Chemistry

Carbon Dots‐Based Nanomaterials for Chronic Wound Healing: From Multifunctional Mechanisms to Smart Dressings and Tissue Regeneration Chronic wounds represent a major clinical challenge due to persistent infection, excessive inflammation, impaired angiogenesis, and defective tissue remodeling. Carbon dots (CDs)-based nanomaterials are promising candidates for addressing these issues, owing to their unique physicochemical properties, tunable surface functionalities, and excellent enzyme-mimetic activities. This review presents a comprehensive overview of recent advances in CDs-based nanomaterials for chronic wound repair under a framework from fundamental principles to translational applications. We first outline the pathophysiological features of chronic wounds and summarize the classification, synthesis, and functionalization strategies of CDs. We then elaborate on the rational design of CDs-based nanomaterials, including stimuli-responsive systems, nanozymes, and multifunctional composites. The multilevel therapeutic mechanisms are dissected, covering infection control, selective ROS scavenging, inflammatory modulation, angiogenesis, and extracellular matrix remodeling. We further highlight applications in real-time wound monitoring, dynamic phase-adaptive therapy, and smart dressings. Key challenges remain in scalable synthesis, long-term biosafety, and clinical translation. Future efforts should focus on green synthesis, engineering optimization, and AI-assisted development to link laboratory research with clinical practice. This review provides a systematic roadmap for the design and translation of next-generation CDs-based wound therapies. Regeneration (biology) ; Chronic wound ; Extracellular matrix ; Nanomaterials ; Surface modification ; Wound healing ; Tissue repair ; Tissue engineering Carbon and Quantum Dots Applications ; Advanced Nanomaterials in Catalysis ; Nanoplatforms for cancer theranostics
Authors: B.L. Dargaville | Thayanithi Ayyachi | D.W. Hutmacher
Source: Materials Today (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Advancing analysis of differential scanning calorimetry data – Converging DSC and AI Differential scanning calorimetry (DSC) is a prominent analytical technique in materials science, offering detailed insights into the thermal properties and behavior of materials. DSC provides valuable information for understanding material composition, structure, and performance by measuring the heat flow associated with thermal events. Interpreting DSC curves is a complex process that requires substantial expertise, and misinterpretation can lead to inaccurate conclusions about material properties. Integrating artificial intelligence (AI) into DSC presents a transformative opportunity to significantly enhance the accuracy, precision, and reliability of thermal analysis. By employing advanced AI algorithms, researchers can analyze DSC data in real time, allowing immediate insights into thermal transitions, such as glass transition, crystallization, and melting. This review outlines how the convergence of DSC and AI can not only expedite the research process but also standardize data interpretation, minimize human error, and reduce reliance on specialized operator expertise, thereby empowering non-experts to interpret DSC data with greater confidence and accuracy. This integration enhances the accessibility, reproducibility, and credibility of thermal data derived from advanced thermal analysis techniques across various scientific and industrial sectors. Differential scanning calorimetry ; Process (computing) ; Thermal analysis ; Thermal ; Reliability (semiconductor) ; Credibility Thermal and Kinetic Analysis ; Machine Learning in Materials Science ; Chemical Thermodynamics and Molecular Structure