jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yonghwan Kwon | Seokju Lee | Junkyu Kim | Jinwon Jun | Woojin Jeon | Young Joo Park | Hyun‐Joong Kim | Johannes Gierschner | Jaesang Lee | Youngdo Kim | Min Sang Kwon
Source: Nature Communications (article, 2024-04-02)
Field: Chemistry — Organic Chemistry

Ultraviolet light blocking optically clear adhesives for foldable displays via highly efficient visible-light curing In developing an organic light-emitting diode (OLED) panel for a foldable smartphone (specifically, a color filter on encapsulation) aimed at reducing power consumption, the use of a new optically clear adhesive (OCA) that blocks UV light was crucial. However, the incorporation of a UV-blocking agent within the OCA presented a challenge, as it restricted the traditional UV-curing methods commonly used in the manufacturing process. Although a visible-light curing technique for producing UV-blocking OCA was proposed, its slow curing speed posed a barrier to commercialization. Our study introduces a highly efficient photo-initiating system (PIS) for the rapid production of UV-blocking OCAs utilizing visible light. We have carefully selected the photocatalyst (PC) to minimize electron and energy transfer to UV-blocking agents and have chosen co-initiators that allow for faster electron transfer and more rapid PC regeneration compared to previously established amine-based co-initiators. This advancement enabled a tenfold increase in the production speed of UV-blocking OCAs, while maintaining their essential protective, transparent, and flexible properties. When applied to OLED devices, this OCA demonstrated UV protection, suggesting its potential for broader application in the safeguarding of various smart devices. Materials science ; OLED ; Visible spectrum ; Ultraviolet ; Optoelectronics ; UV filter ; Adhesive ; Ultraviolet light ; Curing (chemistry) ; Blocking (statistics) ; Nanotechnology ; Computer science ; Optics ; Composite material Photopolymerization techniques and applications ; Green IT and Sustainability ; Organic Light-Emitting Diodes Research
Authors: Qinhua Zhang | Jingyuan Zhou | Haonan Zhang | Chong Qi | Qiang Zhou | Runze Guo | Hao Yang | Tao Xing | Mingqing Wang | Mingbo Wu | Wenting Wu
Source: Advanced Functional Materials (article, 2024-03-25)
Field: Materials Science — Materials Chemistry

Terphenyl Functionalized Covalent Triazine Polymer as Metal‐Free Photocatalyst for Superior Hydrogen Peroxide Production Abstract Artificial photosynthesis of H 2 O 2 from H 2 O and O 2 by visible light is a promising approach, but the efficiency is still unsatisfactory because of the inefficient O 2 adsorption and proton supply. Here, terphenyl functionalized covalent triazine polymer (CTP‐TD) is prepared for efficient H 2 O 2 production. Experimental and theoretical results show that terphenyl structure can promote O 2 adsorption by regulating the oxygen evolution (O 2 → 1 O 2 ) and improve proton supply by accelerating water oxidation. The terphenyl structure significantly promotes the singlet oxygen ( 1 O 2 ) production via the intersystem crossing (ISC) process. The electrophilic 1 O 2 is more easily adsorbed on the catalyst surface than O 2 . Simultaneously, the accumulated holes at the terphenyl structure lower water oxidation energy barriers and accelerate the water dehydrogenation process. This work provides a valuable reference and new inspiration for the polymer design of efficient H 2 O 2 generation from the perspective of reactant supply. Materials science ; Terphenyl ; Hydrogen peroxide ; Triazine ; Covalent bond ; Photocatalysis ; Polymer ; Metal ; Photochemistry ; Nanotechnology ; Polymer chemistry ; Organic chemistry ; Catalysis ; Composite material ; Chemistry ; Metallurgy Covalent Organic Framework Applications ; Advanced Photocatalysis Techniques ; Caching and Content Delivery
Authors: Laura Libnan Haidar | Marcela Bilek | Behnam Akhavan
Source: Small (review, 2024-02-23)
Field: Materials Science — Surfaces, Coatings and Films

Surface Bio‐engineered Polymeric Nanoparticles Surface bio-engineering of polymeric nanoparticles (PNPs) has emerged as a cornerstone in contemporary biomedical research, presenting a transformative avenue that can revolutionize diagnostics, therapies, and drug delivery systems. The approach involves integrating bioactive elements on the surfaces of PNPs, aiming to provide them with functionalities to enable precise, targeted, and favorable interactions with biological components within cellular environments. However, the full potential of surface bio-engineered PNPs in biomedicine is hampered by obstacles, including precise control over surface modifications, stability in biological environments, and lasting targeted interactions with cells or tissues. Concerns like scalability, reproducibility, and long-term safety also impede translation to clinical practice. In this review, these challenges in the context of recent breakthroughs in developing surface-biofunctionalized PNPs for various applications, from biosensing and bioimaging to targeted delivery of therapeutics are discussed. Particular attention is given to bonding mechanisms that underlie the attachment of bioactive moieties to PNP surfaces. The stability and efficacy of surface-bioengineered PNPs are critically reviewed in disease detection, diagnostics, and treatment, both in vitro and in vivo settings. Insights into existing challenges and limitations impeding progress are provided, and a forward-looking discussion on the field's future is presented. The paper concludes with recommendations to accelerate the clinical translation of surface bio-engineered PNPs. Nanotechnology ; Context (archaeology) ; Biomedicine ; Computer science ; Drug delivery ; Materials science ; Bioinformatics ; Biology Polymer Surface Interaction Studies ; Bone Tissue Engineering Materials ; Nanoplatforms for cancer theranostics
Authors: Afaf Aldahish | S. Nirenjen | Rajalakshimi Vasudevan | Taha Alqahtani | Saud Alqahtani | Ahmad Mohammad Asiri | D Praveen | Tamilanban Thamaraikani | Chitra Vellapandian | J. Narayanan
Source: Pharmaceuticals (review, 2024-09-30)
Field: Materials Science — Biomaterials

Silk Fibroin Nanofibers: Advancements in Bioactive Dressings through Electrospinning Technology for Diabetic Wound Healing Non-healing diabetic wounds present a significant healthcare burden globally, necessitating innovative therapeutic strategies. Bioactive dressings and polymeric nanofibers, particularly silk-based formulations fabricated through electrospinning, offer promising avenues for enhancing diabetic wound healing. Further research is warranted to optimize formulation parameters and validate efficacy in larger clinical trials. Fibroin ; Electrospinning ; Nanofiber ; Wound healing ; Wound dressing ; Diabetic ulcers ; Medicine ; SILK ; Biomedical engineering ; Materials science ; Nanotechnology ; Surgery ; Diabetic foot ; Diabetes mellitus ; Composite material Silk-based biomaterials and applications ; Electrospun Nanofibers in Biomedical Applications ; Bone Tissue Engineering Materials
Authors: Matteo Orfano | Jacopo Perego | Charl X. Bezuidenhout | Irène Villa | Roberto Lorenzi | Benoît Sabot | Sylvie Pierre | Silvia Bracco | Sergio Piva | Angiolina Comotti | Angelo Monguzzi
Source: Advanced Functional Materials (article, 2024-05-27)
Field: Chemistry — Inorganic Chemistry

Reabsorption‐Free Scintillating Hetero‐Ligand MOF Crystals Activated by Ultrafast Energy Transfer Abstract Fast photoluminescence and scintillation with a Stokes shift larger than 1 eV is achieved in hetero‐ligand metal–organic framework (MOF) crystals comprising inorganic linking nodes and fluorescent conjugated ligands. By finely engineering the MOF composition with the use of ligands with strictly complementary emission and absorption properties and highly delocalized molecular electronic orbitals, the singlet excitons diffusion is enhanced through the ligand framework, fully exploiting both Förster and Dexter energy transfer mechanisms. This allows for the sensitization of energy acceptor ligand fluorescence by ultrafast non‐radiative energy transfer with a rate up to the THz range. This efficient antenna effect instantly activates the MOF scintillation with a Stokes shift as large as 1.3 eV in the blue spectral range, matching the highest sensitivity spectral window of the best photodetector available. This is obtained using a minimal doping level of the energy acceptor species, with a consequent elimination of emission re‐absorption that allows the achievement of a 500% increment of the MOFs scintillation efficiency and the detection of the radioactive krypton isotope 85 Kr from the gas phase with an improved sensitivity compared with the reference material. Materials science ; Delocalized electron ; Photoluminescence ; Absorption (acoustics) ; Acceptor ; Scintillation ; Exciton ; Ligand (biochemistry) ; Luminescence ; Photochemistry ; Optoelectronics ; Optics ; Chemistry ; Physics Metal-Organic Frameworks: Synthesis and Applications ; Lanthanide and Transition Metal Complexes ; Luminescence Properties of Advanced Materials
Authors: Shuan Chen | Sunggi An | Ramil Babazade | Yousung Jung
Source: Nature Communications (article, 2024-03-13)
Field: Materials Science — Materials Chemistry

Precise atom-to-atom mapping for organic reactions via human-in-the-loop machine learning Atom-to-atom mapping (AAM) is a task of identifying the position of each atom in the molecules before and after a chemical reaction, which is important for understanding the reaction mechanism. As more machine learning (ML) models were developed for retrosynthesis and reaction outcome prediction recently, the quality of these models is highly dependent on the quality of the AAM in reaction datasets. Although there are algorithms using graph theory or unsupervised learning to label the AAM for reaction datasets, existing methods map the atoms based on substructure alignments instead of chemistry knowledge. Here, we present LocalMapper, an ML model that learns correct AAM from chemist-labeled reactions via human-in-the-loop machine learning. We show that LocalMapper can predict the AAM for 50 K reactions with 98.5% calibrated accuracy by learning from only 2% of the human-labeled reactions from the entire dataset. More importantly, the confident predictions given by LocalMapper, which cover 97% of 50 K reactions, show 100% accuracy for 3,000 randomly sampled reactions. In an out-of-distribution experiment, LocalMapper shows favorable performance over other existing methods. We expect LocalMapper can be used to generate more precise reaction AAM and improve the quality of future ML-based reaction prediction models. Computer science ; Artificial intelligence ; Atom (system on chip) ; Substructure ; Graph ; Machine learning ; Theoretical computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Kong Meng | Kun Bai | Shaorui Sun
Source: Journal of Energy Storage (article, 2024-10-08)
Field: Materials Science — Materials Chemistry

Artificial intelligence driven design of cathode materials for sodium-ion batteries using graph deep learning method Cathode ; Graph ; Ion ; Computer science ; Sodium ; Artificial intelligence ; Materials science ; Engineering ; Electrical engineering ; Chemistry ; Theoretical computer science ; Metallurgy Machine Learning in Materials Science ; Advancements in Battery Materials ; Advanced Battery Technologies Research
Authors: Yuhang Zhou | Jianlong Wen | Yijing Nie
Source: Macromolecules (article, 2024-03-22)
Field: Materials Science — Polymers and Plastics

Microscopic Mechanisms of Self-Healing in Polymers Revealed by Molecular Simulations and Machine Learning At present, there is still a lack of in-depth research into the relationship between structural changes during the healing process and self-healing efficiency of self-healing polymers. In the current work, molecular dynamics (MD) simulations and machine learning methods were applied to investigate the microscopic mechanisms of self-healing in polymers. Based on MD simulations, it was found that chain segments and the entire chains diffuse into the crack region during healing, together with the reformation of reversible interactions. Segment diffusion is closely related to the reconstruction of reversible interactions, and chain diffusion can lead to the interpenetration of chains. Based on machine learning methods, it was demonstrated that chain diffusion (the interpenetration of chains) plays the most crucial role in influencing the self-healing efficiency of polymers. Furthermore, based on the Random Forest method, a quantitative relationship between the structural changes in the healing process and the self-healing efficiency can be established. Self-healing ; Polymer ; Self-healing material ; Molecular dynamics ; Polymer science ; Materials science ; Chemistry ; Nanotechnology ; Chemical physics ; Computational chemistry ; Composite material ; Medicine Polymer composites and self-healing ; Polymer crystallization and properties ; Advanced Polymer Synthesis and Characterization
Authors: Leon Schlosser | Debanjan Rana | Philipp M. Pflüger | Felix Katzenburg | Frank Glorius
Source: Journal of the American Chemical Society (article, 2024-05-02)
Field: Materials Science — Materials Chemistry

EnTdecker − A Machine Learning-Based Platform for Guiding Substrate Discovery in Energy Transfer Catalysis Due to the magnitude of chemical space, the discovery of novel substrates in energy transfer (EnT) catalysis remains a daunting task. Experimental and computational strategies to identify compounds that successfully undergo EnT-mediated reactions are limited by their time and cost efficiency. To accelerate the discovery process in EnT catalysis, we herein present the EnTdecker platform, which facilitates the large-scale virtual screening of potential substrates using machine-learning (ML) based predictions of their excited state properties. To achieve this, a data set is created containing more than 34,000 molecules aiming to cover a vast fraction of synthetically relevant compound space for EnT catalysis. Using this data predictive models are trained, and their aptitude for an in-lab application is demonstrated by rediscovering successful substrates from literature as well as experimental validation through luminescence-based screening. By reducing the computational effort needed to obtain excited state properties, the EnTdecker platform represents a tool to efficiently guide substrate selection and increase the experimental success rate for EnT catalysis. Moreover, through an easy-to-use web application, EnTdecker is made publicly accessible under entdecker.uni-muenster.de. Chemistry ; Chemical space ; Substrate (aquarium) ; Virtual screening ; Transfer of learning ; Catalysis ; Scale (ratio) ; Biochemical engineering ; Task (project management) ; Nanotechnology ; Drug discovery ; Combinatorial chemistry ; Machine learning ; Computer science ; Systems engineering Machine Learning in Materials Science ; Radical Photochemical Reactions ; CO2 Reduction Techniques and Catalysts
Authors: David Dalmau | Juan V. Alegre‐Requena
Source: Wiley Interdisciplinary Reviews Computational Molecular Science (article, 2024-09-01)
Field: Materials Science — Materials Chemistry

<scp>ROBERT</scp> : Bridging the Gap Between Machine Learning and Chemistry ABSTRACT Beyond addressing technological demands, the integration of machine learning (ML) into human societies has also promoted sustainability through the adoption of digitalized protocols. Despite these advantages and the abundance of available toolkits, a substantial implementation gap is preventing the widespread incorporation of ML protocols into the computational and experimental chemistry communities. In this work, we introduce ROBERT, a software carefully crafted to make ML more accessible to chemists of all programming skill levels, while achieving results comparable to those of field experts. We conducted benchmarking using six recent ML studies in chemistry containing from 18 to 4149 entries. Furthermore, we demonstrated the program's ability to initiate workflows directly from SMILES strings, which simplifies the generation of ML predictors for common chemistry problems. To assess ROBERT's practicality in real‐life scenarios, we employed it to discover new luminescent Pd complexes with a modest dataset of 23 points, a frequently encountered scenario in experimental studies. Bridging (networking) ; Computer science ; Chemistry ; Computer network Machine Learning in Materials Science ; Various Chemistry Research Topics ; Computational Drug Discovery Methods
Authors: Yingying Ma | Pengcheng Xu | Minjie Li | Xiaobo Ji | Wenyue Zhao | Wencong Lu
Source: npj Computational Materials (article, 2024-07-02)
Field: Materials Science — Materials Chemistry

The mastery of details in the workflow of materials machine learning Abstract As machine learning (ML) continues to advance in the field of materials science, the variation in strategies for the same steps of the ML workflow becomes increasingly significant. These details can have a substantial impact on results, yet they have not received the deserved attention. This review aims to explore the various strategies available for each detail within the general workflow of materials ML. Firstly, the general workflow of materials ML will be introduced to help readers gain an understanding of potential details. Subsequently, different strategies of details within each step of the workflow will be presented through state-of-the-art case studies. The potential outcomes associated with choosing different strategies to details will be explored. Following this, suitable strategies for details will be recommended based on distinct application scenarios. Finally, directions for the future development of materials ML concerning details will be proposed. Through these discussions, we aspire to offer a comprehensive understanding of the nuances in details within materials ML. This will serve as valuable reference and guidance for researchers in both materials science and ML. Workflow ; Computer science ; Mathematics education ; Psychology ; Database Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Mineral Processing and Grinding
Authors: Mingfeng Liu | Jian-Tao Wang | Junwei Hu | Peitao Liu | Haiyang Niu | Xuexi Yan | Jiangxu Li | Haile Yan | Bo Yang | Yan Sun | Chunlin Chen | Georg Kresse | Liang Zuo | Xing‐Qiu Chen
Source: Nature Communications (article, 2024-04-09)
Field: Materials Science — Materials Chemistry

Layer-by-layer phase transformation in Ti3O5 revealed by machine-learning molecular dynamics simulations Reconstructive phase transitions involving breaking and reconstruction of primary chemical bonds are ubiquitous and important for many technological applications. In contrast to displacive phase transitions, the dynamics of reconstructive phase transitions are usually slow due to the large energy barrier. Nevertheless, the reconstructive phase transformation from β- to λ-Ti<sub>3</sub>O<sub>5</sub> exhibits an ultrafast and reversible behavior. Despite extensive studies, the underlying microscopic mechanism remains unclear. Here, we discover a kinetically favorable in-plane nucleated layer-by-layer transformation mechanism through metadynamics and large-scale molecular dynamics simulations. This is enabled by developing an efficient machine learning potential with near first-principles accuracy through an on-the-fly active learning method and an advanced sampling technique. Our results reveal that the β-λ phase transformation initiates with the formation of two-dimensional nuclei in the ab-plane and then proceeds layer-by-layer through a multistep barrier-lowering kinetic process via intermediate metastable phases. Our work not only provides important insight into the ultrafast and reversible nature of the β-λ transition, but also presents useful strategies and methods for tackling other complex structural phase transitions. Metadynamics ; Metastability ; Chemical physics ; Molecular dynamics ; Phase (matter) ; Phase transition ; Materials science ; Kinetic energy ; Transformation (genetics) ; Chemistry ; Computational chemistry ; Physics ; Condensed matter physics ; Classical mechanics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Titanium Alloys Microstructure and Properties
Authors: Yi Wang | Sa Ma | Jianbao Gao | Jing Zhong | Tianchuang Gao | Shenglan Yang | Lijun Zhang | Qian Li
Source: Journal of Material Science and Technology (article, 2024-09-06)
Field: Materials Science — Materials Chemistry

A novel atomic mobility model for alloys under pressure and its application in high pressure heat treatment Al-Si alloys by integrating CALPHAD and machine learning CALPHAD ; Materials science ; High pressure ; Thermodynamics ; Metallurgy ; Phase diagram ; Chemistry ; Phase (matter) ; Physics Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Nuclear Materials and Properties
Authors: Hisham Alghamdi | Chika Maduabuchi | Kingsley Okoli | Mohammad Alobaid | Mohammed A. Alghassab | Ahmed S. Alsafran | Emad Makki | Mohammad Alkhedher
Source: International Journal of Energy Research (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Latest Advancements in Solar Photovoltaic‐Thermoelectric Conversion Technologies: Thermal Energy Storage Using Phase Change Materials, Machine Learning, and 4E Analyses In recent times, the significance of renewable energy generation has increased and photovoltaic‐thermoelectric (PV‐TE) technologies have emerged as a promising solution. However, the incorporation of these technologies still faces difficulties in energy storage and optimization. This review paper addresses these challenges by providing a comprehensive overview of the latest advancements in PV‐TE technologies. The paper emphasizes the integration of phase change materials (PCMs) for thermal energy storage, also buttressing the use of encapsulated PCM for thermal storage and efficiency, and the use of hybrid PCM to enhance overall performance. Furthermore, reviews on the use of machine learning techniques for efficient optimization and the integration of thermoelectric modules into tandem perovskite silicon solar cells have been comprehensively analyzed. The advancements in photovoltaic‐thermoelectric systems, as reviewed in this article, signify significant progress in attaining sustainable and effective energy production and storage. This review comprehensively addresses the 4Es, underlining their importance. It not only consolidates recent developments but also charts a path for future research in the field of PV‐TE technologies, offering precise insights to guide upcoming studies and innovations. Photovoltaic system ; Renewable energy ; Photovoltaics ; Thermoelectric effect ; Process engineering ; Thermal energy storage ; Energy storage ; Solar energy ; Phase-change material ; Thermoelectric materials ; Systems engineering ; Engineering physics ; Computer science ; Materials science ; Environmental science ; Phase change ; Engineering ; Electrical engineering ; Physics Advanced Thermoelectric Materials and Devices ; Phase Change Materials Research ; Solar Thermal and Photovoltaic Systems