jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yang Yu | Chang Wang | Faan-Fung Hung | Long Jiang | Chi‐Ming Che | Junzhi Liu
Source: Angewandte Chemie International Edition (article, 2025-02-20)
Field: Chemistry — Organic Chemistry

π‐Extended Heli(aminoborane)s with Highly Bright Circularly Polarized Luminescence and Narrowband Emission Helical molecular carbons (HMCs) possess high absorption/luminescence dissymmetry factors (g<sub>abs</sub>/g<sub>lum</sub>) and significant luminescence quantum yield (Φ<sub>lum</sub>), resulting in a high circularly polarized luminescence (CPL) brightness (B<sub>CPL</sub>), which is essential for the development of CPL materials for practical applications. Herein, we designed and synthesized a series of boron-nitrogen (BN)-doped HMCs, named π-extended heli(aminoborane)s (E[10]HAB-A, E[10]HAB-B and E[10]HAB-C), consisting of laterally π-extended [10]helicene skeleton with alternating N and B atoms at the inner rim. The aromaticity, electronic structures, and photophysical properties of E[10]HAB-A/B/C were systematically investigated through experiments and theoretical calculations. E[10]HAB-A/B/C displayed remarkable photophysical properties, including high molar extinction coefficient and bright narrowband emission. The isolated enantiomers of E[10]HAB-A/B/C exhibited intense circular dichroism (CD) and CPL, in which E[10]HAB-A shows g<sub>abs</sub> and g<sub>lum</sub> values up to 0.024 and 0.017, simultaneously with high Φ<sub>lum</sub> of 82 % and a narrow full width at half maximum of 16 nm. Accordingly, E[10]HAB-A exhibits a B<sub>CPL</sub> as high as 583 M<sup>-1</sup> cm<sup>-1</sup>, which is the largest value among the reported BN-doped HMCs. Our study indicates that inner rim BN-doping and π-extension are effective strategies to achieve high Φ<sub>lum</sub> and balanced g<sub>lum</sub> values in HMCs. Luminescence ; Circular dichroism ; Crystallography ; Doping ; Enantiomer ; Chemistry ; Materials science ; Stereochemistry ; Optoelectronics Synthesis and Properties of Aromatic Compounds ; Luminescence and Fluorescent Materials ; Organoboron and organosilicon chemistry
Authors: Siyao Cheng | Qinglin Zhou | Daohu Sheng | Wei Dong | Jin‐Hu Dou | Yuan‐Biao Huang | Rong Cao | Aming Xie | Roland A. Fischer | Soumya Mukherjee | Weijin Li
Source: Advanced Science (article, 2025-06-10)
Field: Chemistry — Inorganic Chemistry

Cu─X Bonds Regulated Conduction and Polarization Loss in Conductive Metal‐Organic Framework Under Electromagnetic Field Conduction and polarization are known to profoundly impact conductive metal-organic frameworks (c-MOFs) for their applications in electromagnetic wave (EMW) absorption. Albeit a few advances along c-MOF platforms in enhancing their EMW absorption performances, reticular modulation-led inter/intra-layer conduction and polarization loss remains an unmet challenge. To address this, a ligand substitution-guided bottom-up structural control strategy is introduced to study the depth of reticular modulation-led inter/intra-layer conduction and polarization loss in c-MOFs under an electromagnetic (EM) field. A family of triphenylene-X ligands (X = -NH<sub>2</sub>, -OH, and -SH) is harnessed to afford an isoreticular family of three Cu-based c-MOFs. Thanks to the distinct Cu─X bonds, such a platform allowed to systematically study the synergistic features of conduction and polarization loss in EMW absorption enhancement. One of the trio, Cu<sub>3</sub>(HITP)<sub>2</sub> (X = -NH<sub>2</sub>; HITP, 2,3,6,7,10,11-hexahydroxytriphenylene) is identified with an optimal EM loss capacity under the EM field, achieving a record-high reflection loss of -63.03 dB in the effective absorption range of 3-18 GHz band. Setting up a new benchmark for EM loss among c-MOFs, this study introduces a way to leverage control in the charge mobility characteristics of Cu─X bonds relative to the dielectric losses at both molecular and atomic scales. Electrical conductor ; Polarization (electrochemistry) ; Metal ; Electromagnetic field ; Materials science ; Thermal conduction ; Field (mathematics) ; Chemical engineering ; Optoelectronics ; Chemistry ; Composite material ; Physical chemistry ; Metallurgy ; Physics ; Engineering Metal-Organic Frameworks: Synthesis and Applications ; Advanced Memory and Neural Computing ; Conducting polymers and applications
Authors: Mouad Hachhach | Sanae Bayou | Achraf El Kasmi | Mohamed Zoubair Saidi | Hanane Akram | Mounir Hanafi | Ouafae Achak | Chaouki El Moujahid | Tarik Chafik
Source: Eng—Advances in Engineering (article, 2025-07-01)
Field: Materials Science — Materials Chemistry

Towards Sustainable Scaling-Up of Nanomaterials Fabrication: Current Situation, Challenges, and Future Perspectives Nanomaterials are present everywhere today and represent the new industrial revolution. Depending on the application, there are many ways to synthesize nanomaterials with different properties. The industrial production of nanomaterials faces various challenges at different stages, going from conception and design to implementation and scaling-up of the production process, which can limit the growth of practical application at a large-scale scope, such as due to the lack of reproducibility, safety, and environmental impact. Here, we discuss current advances achieved for nanomaterial production at a large scale, encompassing a range of synthetic strategies and post-treatment modifications used to enhance the nanomaterials’ performance. A particular interest is devoted to highlighting the progress of MoS2 nanomaterials’ application. Thus, overcoming those discussed challenges becomes a new prospect for the future perspectives of industrial nanomaterials and nanotechnologies. Current (fluid) ; Scaling ; Nanotechnology ; Nanomaterials ; Risk analysis (engineering) ; Computer science ; Materials science ; Engineering ; Business ; Electrical engineering ; Mathematics Nanoparticles: synthesis and applications ; TiO2 Photocatalysis and Solar Cells ; Nanotechnology research and applications
Authors: Gerhard Jung | Rinske M. Alkemade | Victor Bapst | Daniele Coslovich | Laura Filion | François P. Landes | Andrea J. Liu | Francesco Saverio Pezzicoli | Hayato Shiba | Giovanni Volpe | Francesco Zamponi | Ludovic Berthier | Giulio Biroli
Source: Nature Reviews Physics (review, 2025-01-06)
Field: Materials Science — Materials Chemistry

Roadmap on machine learning glassy dynamics Interpretability ; Computer science ; Benchmarking ; Artificial intelligence ; Transferability ; Machine learning ; Data science ; Underpinning ; Key (lock) ; Deep learning ; Dynamics (music) ; Human–computer interaction ; Engineering ; Physics Material Dynamics and Properties ; Phase-change materials and chalcogenides ; Theoretical and Computational Physics
Authors: Ivan Malashin | В С Тынченко | Andrei Gantimurov | Vladimir Nelyub | А. С. Бородулин
Source: Polymers (review, 2025-04-19)
Field: Materials Science — Materials Chemistry

Physics-Informed Neural Networks in Polymers: A Review The modeling and simulation of polymer systems present unique challenges due to their intrinsic complexity and multi-scale behavior. Traditional computational methods, while effective, often struggle to balance accuracy with computational efficiency, especially when bridging the atomistic to macroscopic scales. Recently, physics-informed neural networks (PINNs) have emerged as a promising tool that integrates data-driven learning with the governing physical laws of the system. This review discusses the development and application of PINNs in the context of polymer science. It summarizes the recent advances, outlines the key methodologies, and analyzes the benefits and limitations of using PINNs for polymer property prediction, structural design, and process optimization. Finally, it identifies the current challenges and future research directions to further leverage PINNs for advanced polymer modeling. Leverage (statistics) ; Bridging (networking) ; Computer science ; Management science ; Computational model ; Data science ; Context (archaeology) ; Systems engineering ; Process (computing) ; Complex system ; Nanotechnology ; Biochemical engineering ; Artificial intelligence ; Materials science ; Engineering Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Fuel Cells and Related Materials
Authors: Dong-Duan Liu | Qiao Li | Yujie Zhu | Ruojie Cheng | Tan Zeng | Hongxiao Yang | Jun Ma | Jinliang He | Qi Li | Chao Yuan
Source: Applied Physics Letters (article, 2025-02-03)
Field: Materials Science — Materials Chemistry

Physics-informed neural networks for phase-field simulation in designing high energy storage performance polymer nanocomposites Dielectric polymers for electrostatic energy storage are used in modern electronic and electrical systems, and their performance can be significantly enhanced through doping with ultralow content nanofillers to improve energy storage performance. Understanding the underlying physical mechanisms of polymer nanocomposites is essential for designing high-performance dielectric polymers. This paper presents a conduction model that integrates Richardson–Schottky emission and hopping conduction to describe charge injection and transport in polymer composites. Phase-field simulations, incorporating electrical, thermal, and mechanical breakdown mechanisms, investigate the influence of nanofiller volume fraction, size, and dielectric constant on the dielectric response and breakdown behaviors under high temperature and electric fields. We propose the Physics-Informed Neural Networks for phase-field simulation that integrates the physical rules of charge transport, phase evolution, and boundary conditions. By embedding phase field models within the Physics-Informed Neural Networks' structure, this method demonstrates the ability to predict the breakdown strength and energy density of polymer nanocomposites. This work provides crucial guidelines for designing high-performance dielectric energy storage capacitors under extreme conditions. Nanocomposite ; Field (mathematics) ; Materials science ; Polymer ; Energy storage ; Nanotechnology ; Phase (matter) ; Energy (signal processing) ; Engineering physics ; Physics ; Composite material ; Thermodynamics ; Quantum mechanics Ferroelectric and Piezoelectric Materials ; Dielectric materials and actuators ; Multiferroics and related materials
Authors: Sasmita Mishra | Bikash Kumar Jena
Source: Energy & Fuels (article, 2025-01-13)
Field: Materials Science — Materials Chemistry

Review and Perspectives on Multifunctional Applications of Hexagonal Boron Nitride Nanosheets and Quantum Dots in Energy Conversions Hexagonal boron nitride (h-BN), a unique two-dimensional (2D) material, has emerged as a promising catalyst in various industrial applications. This review paper delves into the revolutionary strides made in the sphere of catalysis by harnessing the properties of mainly h-BN nanosheets (h-BNNSs) and h-BN Quantum dots (h-BNQDs), achieved through innovative synthesis methods and their diverse applications. The paper begins by elucidating the distinctive characteristics of h-BN, such as its high thermal stability, excellent chemical inertness, and remarkable electronic properties. These intrinsic features make h-BN an ideal candidate for catalytic applications, prompting researchers to focus on developing novel synthesis techniques. The synthesis section reviews cutting-edge methodologies, including chemical vapor deposition (CVD), microwave-assisted methods, liquid-phase exfoliation (LPE), and hydrothermal, highlighting each approach’s advantages and limitations. The pursuit of scalable and cost-effective synthesis methods is underscored to enable the widespread utilization of h-BN in catalysis. The review explores the catalytic power of h-BN in diverse applications in the energy conversion domain, Moving beyond synthesis. h-BN’s catalytic activity in hydrogen evolution reactions (HER), oxygen reduction reactions (ORR), oxygen evolution reactions (OER), nitrogen reduction reactions (NRR), and others are thoroughly examined, showcasing its potential in clean energy technologies and other energy conversion processes. The electronic structure and surface properties of h-BN are examined carefully to comprehend the underlying principles guiding its catalytic behavior. The synergistic effects of combining h-BNNSs and h-BNQDs with catalytic materials are also explored, opening avenues for designing multifunctional catalytic systems. This review highlights the untapped potential of h-BNQDs for catalytic applications. The review concludes by outlining the current challenges and prospects in the field. Strategies for overcoming material scalability, long-term stability, and enhanced catalytic performance are proposed. The potential incorporation of h-BN with emerging technologies, such as artificial intelligence and machine learning, is also discussed, envisioning a future where catalysis is revolutionized through intelligent material design. Hexagonal boron nitride ; Quantum dot ; Boron nitride ; Nanotechnology ; Materials science ; Hexagonal crystal system ; Boron ; Nitride ; Engineering physics ; Chemical engineering ; Chemistry ; Graphene ; Physics ; Crystallography ; Engineering ; Organic chemistry ; Layer (electronics) 2D Materials and Applications ; MXene and MAX Phase Materials ; Graphene research and applications
Authors: Marco Gibaldi | Anna Kapeliukha | Andrew J. P. White | Jun Luo | Rebeca Mayo | Jake Burner | Tom K. Woo
Source: Chemical Science (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

MOSAEC-DB: a comprehensive database of experimental metal–organic frameworks with verified chemical accuracy suitable for molecular simulations Ongoing developments in computational databases seek to improve the accessibility and breadth of high-throughput screening and materials discovery efforts. Their reliance on experimental crystal structures necessitates significant processing prior to computation in order to resolve any crystallographic disorder or partial occupancies and remove any residual solvent molecules in the case of activated porous materials. Contemporary investigations revealed that deficiencies in the experimental characterization and computational preprocessing methods generated considerable occurrence of structural errors in metal-organic framework (MOF) databases. The MOSAEC MOF database (MOSAEC-DB) tackles these structural reliability concerns through utilization of innovative preprocessing and error analysis protocols applying the concepts of oxidation state and formal charge to exclude erroneous crystal structures. Comprising more than 124k crystal structures, this work maintains the largest and most accurate dataset of experimental MOFs ready for immediate deployment in molecular simulations. The databases' comparative diversity is demonstrated through its enhanced coverage of the periodic table, expansive quantity of structures, and balance of chemical properties relative to existing MOF databases. Chemical and geometric descriptors, as well as DFT electrostatic potential-fitted charges, are included to facilitate subsequent atomistic simulation and machine-learning (ML) studies. Curated subsets-sampled according to their chemical properties and structural uniqueness-are also provided to further enable ML studies in recognition of the strict demand for duplicate structure elimination and dataset diversity in such applications. Computer science ; Metal-organic framework ; Chemical database ; Database ; Metal ; Materials science ; Data mining ; Chemistry ; Physical chemistry ; Organic chemistry ; Metallurgy Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; X-ray Diffraction in Crystallography
Authors: Martin Uhrin | Austin Zadoks | Luca Binci | Nicola Marzari | Iurii Timrov
Source: npj Computational Materials (article, 2025-01-25)
Field: Materials Science — Materials Chemistry

Machine learning Hubbard parameters with equivariant neural networks Density-functional theory with extended Hubbard functionals (DFT + <i>U</i> + <i>V</i>) provides a robust framework to accurately describe complex materials containing transition-metal or rare-earth elements. It does so by mitigating self-interaction errors inherent to semi-local functionals which are particularly pronounced in systems with partially-filled d and f electronic states. However, achieving accuracy in this approach hinges upon the accurate determination of the on-site <i>U</i> and inter-site <i>V</i> Hubbard parameters. In practice, these are obtained either by semi-empirical tuning, requiring prior knowledge, or, more correctly, by using predictive but expensive first-principles calculations. Here, we present a machine learning model based on equivariant neural networks which uses atomic occupation matrices as descriptors, directly capturing the electronic structure, local chemical environment, and oxidation states of the system at hand. We target here the prediction of Hubbard parameters computed self-consistently with iterative linear-response calculations, as implemented in density-functional perturbation theory (DFPT), and structural relaxations. Remarkably, when trained on data from 12 materials spanning various crystal structures and compositions, our model achieves mean absolute relative errors of 3% and 5% for Hubbard <i>U</i> and <i>V</i> parameters, respectively. By circumventing computationally expensive DFT or DFPT self-consistent protocols, our model significantly expedites the prediction of Hubbard parameters with negligible computational overhead, while approaching the accuracy of DFPT. Moreover, owing to its robust transferability, the model facilitates accelerated materials discovery and design via high-throughput calculations, with relevance for various technological applications. Hubbard model ; Perturbation theory (quantum mechanics) ; Density functional theory ; Computer science ; Artificial neural network ; Electronic structure ; Overhead (engineering) ; Statistical physics ; Algorithm ; Artificial intelligence ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Electronic and Structural Properties of Oxides
Authors: Andrew J. P. White | Marco Gibaldi | Jake Burner | Rebeca Mayo | Tom K. Woo
Source: Journal of the American Chemical Society (article, 2025-05-16)
Field: Chemistry — Inorganic Chemistry

High Structural Error Rates in “Computation-Ready” MOF Databases Discovered by Checking Metal Oxidation States "Computation-ready" metal-organic framework (MOF) databases provide essential raw data for high-throughput computational screening (HTS) and machine-learning approaches to materials discovery. However, the structural fidelity of these databases remains largely unquantified. We introduce MOSAEC, an algorithm that detects chemically invalid structures based on metal oxidation states. MOSAEC was manually validated against 14,796 MOF structures from the popular CoRE database and found to flag erroneous structures with 96% accuracy. Examination of 14 leading experimental and hypothetical MOF databases containing >1.9 million structures reveals structural error rates exceeding 40% in most cases. Analysis of 8 recent HTS studies which highlighted top-performing candidates shows that 52% of these structures were chemically invalid. Chemistry ; Computation ; Database ; Metal ; Algorithm ; Organic chemistry ; Computer science Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Advanced Nanomaterials in Catalysis
Authors: Nagarjuna Prakash Dalbanjan | Karuna Korgaonkar | Manjunath P. Eelager | Basavaraj Neelappa Gonal | Arihant Jayawant Kadapure | Suresh B. Arakera | S. K. Praveen Kumar
Source: Nano TransMed (article, 2025-07-15)
Field: Materials Science — Biomaterials

In-silico strategies in nano-drug design: Bridging nanomaterials and pharmacological applications Rapid advancements in nanotechnology have transformed drug design and delivery systems, allowing for precise and efficient therapeutic interventions. This review examines the transformative role of in-silico approaches in nano-drug design, focusing on their ability to predict, optimize, and refine nanomaterial properties for pharmacological applications. Key computational tools such as molecular modelling, machine learning, computational fluid dynamics, and bioinformatics are thoroughly investigated, with a focus on their contributions to understanding drug loading, toxicity, targeting strategies, and nano-bio interactions. Furthermore, the incorporation of emerging technologies like digital twins and quantum computing shows the potential to overcome current limitations in accuracy, scalability, and personalization. Despite significant progress, challenges remain, particularly in closing the gap between computational predictions and experimental validations, dealing with data quality issues, and navigating regulatory frameworks. This review emphasizes the importance of interdisciplinary collaboration and innovation in realizing the full potential of in-silico methods for advancing nanotherapeutics. Addressing these challenges positions the field to accelerate the development of safe, effective, and personalized medicines. • Integrates in-silico approaches for nano-drug design and pharmacology. • Highlights molecular modelling for predicting nanoparticle mechanisms. • Explores AI and ML in optimizing nano-bio interactions. • Reviews CFD and bioinformatics in enhancing drug delivery systems. • Discusses challenges bridging in-silico predictions with experimental validations. Bridging (networking) ; In silico ; Nanomaterials ; Nanotechnology ; Drug ; Nano- ; Computer science ; Materials science ; Pharmacology ; Medicine ; Chemistry Nanoparticle-Based Drug Delivery ; Advanced Drug Delivery Systems ; Advanced biosensing and bioanalysis techniques
Authors: Chuan Mu | Zhihe Liu | Qiaofeng Yao | Qian He | Jianping Xie
Source: SmartMat (article, 2025-01-09)
Field: Materials Science — Materials Chemistry

Engineering Metal Nanoclusters at the Atomic Level for Effective Electrocatalysis ABSTRACT With the advances of nanochemistry in the past several decades, a diverse set of nanomaterials has been developed as electrocatalysts with enhanced activity, selectivity, and durability for electrocatalytic reactions. However, it has remained as a long challenge to systematically understand the mechanism of electrocatalytic reactions, which involves multiple protons‐coupled electron transfer processes and varied products at the atomic level, intrinsically because of the complexity and polydispersity of the traditional nanomaterials. By sharp contrast, ligand‐protected metal nanoclusters (NCs) possess atomically precise structures and abundant active sites, facilitating their applications as effective model electrocatalysts for revealing the mechanism of electrocatalytic reactions. This review summarizes recent progress in atom‐level engineering of metal NCs as model catalysts for electrocatalytic reactions. Specifically, we first discuss the effects of metal composition engineering, including doping and size effects, on the electrocatalytic performance of metal NCs. Then similar electrocatalytic discussion extends to ligand effects of metal NCs, where ligand type and coverage engineering are deciphered. Moreover, we discuss how the overall charge and morphology of NCs modify their electrocatalytic performance. The fundamental and methodological insights summarized in this review should serve as useful references guiding the future development of effective metal electrocatalysts in diverse sectors of industry. Nanoclusters ; Electrocatalyst ; Nanotechnology ; Materials science ; Chemistry ; Physical chemistry Nanocluster Synthesis and Applications ; Electrocatalysts for Energy Conversion ; Machine Learning in Materials Science