jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tomasz Chudziak | Verónica Montes‐García | Włodzimierz Czepa | Dawid Pakulski | A. Musiał | Cataldo Valentini | Michał Bielejewski | Michela Carlin | Aurelia Tubaro | Marco Pelin | Paolo Samorı́ | Artur Ciesielski
Source: Nanoscale (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

A comparative investigation of the chemical reduction of graphene oxide for electrical engineering applications The presence of oxygen-containing functional groups on the basal plane and at the edges endows graphene oxide (GO) with an insulating nature, which makes it rather unsuitable for electronic applications. Fortunately, the reduction process makes it possible to restore the sp<sup>2</sup> conjugation. Among various protocols, chemical reduction is appealing because of its compatibility with large-scale production. Nevertheless, despite the vast number of reported chemical protocols, their comparative assessment has not yet been the subject of an in-depth investigation, rendering the establishment of a structure-performance relationship impossible. We report a systematic study on the chemical reduction of GO by exploring different reducing agents (hydrazine hydrate, sodium borohydride, ascorbic acid (AA), and sodium dithionite) and reaction times (2 or 12 hours) in order to boost the performance of chemically reduced GO (CrGO) in electronics and in electrochemical applications. In this work, we provide evidence that the optimal reduction conditions should vary depending on the chosen application, whether it is for electrical or electrochemical purposes. CrGO exhibiting a good electrical conductivity (>1800 S m<sup>-1</sup>) can be obtained by using AA (12 hours of reaction), Na<sub>2</sub>S<sub>2</sub>O<sub>4</sub> and N<sub>2</sub>H<sub>4</sub> (independent of the reaction time). Conversely, CrGO displaying a superior electrochemical performance (specific capacitance of 211 F g<sup>-1</sup>, and capacitance retention >99.5% after 2000 cycles) can be obtained by using NaBH<sub>4</sub> (12 hours of reaction). Finally, the compatibility of the different CrGOs with wearable and flexible electronics is also demonstrated using skin irritation tests. The strategy described represents a significant advancement towards the development of environmentally friendly CrGOs with <i>ad hoc</i> properties for advanced applications in electronics and energy storage. Graphene ; Materials science ; Oxide ; Reduction (mathematics) ; Nanotechnology ; Chemical reduction ; Chemical engineering ; Chemistry ; Metallurgy ; Engineering ; Electrochemistry ; Physical chemistry ; Electrode Graphene research and applications ; Advancements in Battery Materials ; Graphene and Nanomaterials Applications
Authors: Sergio Ramírez‐Barroso | Fernando Romeo‐Gella | Jesús M. Fernández‐García | Siyang Feng | Lara Martínez‐Fernández | David García‐Fresnadillo | Inés Corral | Nazario Martı́n | Reinhold Wannemacher
Source: Advanced Materials (article, 2023-04-24)
Field: Materials Science — Materials Chemistry

Curved Nanographenes: Multiple Emission, Thermally Activated Delayed Fluorescence, and Non‐Radiative Decay The intriguing and rich photophysical properties of three curved nanographenes (CNG 6, 7, and 8) are investigated by time-resolved and temperature-dependent photoluminescence (PL) spectroscopy. CNG 7 and 8 exhibit dual fluorescence, as well as dual phosphorescence at low temperature in the main PL bands. In addition, hot bands are detected in fluorescence as well as phosphorescence, and, in the narrow temperature range of 100-140 K, thermally activated delayed fluorescence (TADF) with lifetimes on the millisecond time-scale is observed. These findings are rationalized by quantum-chemical simulations, which predict a single minimum of the S<sub>1</sub> potential of CNG 6, but two S<sub>1</sub> minima for CNG 7 and CNG 8, with considerable geometric reorganization between them, in agreement with the experimental findings. Additionally, a higher-lying S<sub>2</sub> minimum close to S<sub>1</sub> is optimized for the three CNG, from where emission is also possible due to thermal activation and, hence, non-Kasha behavior. The presence of higher-lying dark triplet states close to the S<sub>1</sub> minima provides mechanistic evidence for the TADF phenomena observed. Non-radiative decay of the T<sub>1</sub> state appears to be thermally activated with activation energies of roughly 100 meV and leads to disappearance of phosphorescence and TADF at T > 140 K. Phosphorescence ; Materials science ; Photoluminescence ; Fluorescence ; Millisecond ; Photochemistry ; Luminescence ; Radiative transfer ; Atmospheric temperature range ; Quantum efficiency ; Molecular physics ; Atomic physics ; Optoelectronics ; Chemistry ; Physics ; Optics ; Thermodynamics Luminescence and Fluorescent Materials ; Carbon and Quantum Dots Applications ; Organic Light-Emitting Diodes Research
Authors: Ilaria Arduino | R. Fonte | Mattia Tiboni | Letizia Porcelli | Simona Serratì | Dafina Fondaj | Tania Rafaschieri | Annalisa Cutrignelli | Gabriella Guida | Luca Casettari | Amalia Azzariti | Angela Lopedota | Nunzio Denora | Rosa Maria Iacobazzi
Source: International Journal of Pharmaceutics (article, 2023-12-09)
Field: Materials Science — Biomaterials

Microfluidic development and biological evaluation of targeted therapy-loaded biomimetic nano system to improve the metastatic melanoma treatment Optimizing current therapies is among next steps in metastatic melanoma (MM) treatment landscape. The innovation of this study is the design of production process by microfluidics of cell membrane (CM)-modified nanoparticles (NPs), as an emerging biomimetic platform that allows for reduced immune clearance, long blood circulation time and improved specific tumor targeting. To achieve melanoma selectivity, direct membrane fusion between synthetic liposomes and CMs extracted from MM cell line was performed by microfluidic sonication approach, then the hybrid liposomes were loaded with cobimetinib (Cob) or lenvatinib (Lenva) targeting agents and challenged against MM cell lines and liver cancer cell line to evaluate homotypic targeting and antitumor efficacy. Characterization studies demonstrated the effective fusion of CM with liposome and the high encapsulation efficiency of both drugs, showing the proficiency of microfluidic-based production. By studying the targeting of melanoma cells by hybrid liposomes versus liposomes, we found that both NPs entered cells through endocytosis, whereas the former showed higher selectivity for MM cells from which CM was extracted, with 8-fold higher cellular uptake than liposomes. Hybrid liposome formulation of Cob and Lenva reduced melanoma cells viability to a greater extent than liposomes and free drug and, notably, showed negligible toxicity as demonstrated by bona fide haemolysis test. The CM-modified NPs presented here have the potential to broaden the choice of therapeutic options in MM treatment. Liposome ; Melanoma ; Endocytosis ; Chemistry ; Viability assay ; Cancer research ; Nanotechnology ; Materials science ; Cell ; Pharmacology ; Medicine ; Biochemistry Nanoparticle-Based Drug Delivery ; 3D Printing in Biomedical Research ; Microfluidic and Bio-sensing Technologies
Authors: Matthijs L. A. Hakkennes | Michael S. Meijer | Jan Paul Menzel | Anne-Charlotte Goetz | Roy Van Duijn | Maxime A. Siegler | Francesco Buda | Sylvestre Bonnet
Source: Journal of the American Chemical Society (article, 2023-06-09)
Field: Chemistry — Physical and Theoretical Chemistry

Ligand Rigidity Steers the Selectivity and Efficiency of the Photosubstitution Reaction of Strained Ruthenium Polypyridyl Complexes While photosubstitution reactions in metal complexes are usually thought of as dissociative processes poorly dependent on the environment, they are, in fact, very sensitive to solvent effects. Therefore, it is crucial to explicitly consider solvent molecules in theoretical models of these reactions. Here, we experimentally and computationally investigated the selectivity of the photosubstitution of diimine chelates in a series of sterically strained ruthenium(II) polypyridyl complexes in water and acetonitrile. The complexes differ essentially by the rigidity of the chelates, which strongly influenced the observed selectivity of the photosubstitution. As the ratio between the different photoproducts was also influenced by the solvent, we developed a full density functional theory modeling of the reaction mechanism that included explicit solvent molecules. Three reaction pathways leading to photodissociation were identified on the triplet hypersurface, each characterized by either one or two energy barriers. Photodissociation in water was promoted by a proton transfer in the triplet state, which was facilitated by the dissociated pyridine ring acting as a pendent base. We show that the temperature variation of the photosubstitution quantum yield is an excellent tool to compare theory with experiments. An unusual phenomenon was observed for one of the compounds in acetonitrile, for which an increase in temperature led to a surprising decrease in the photosubstitution reaction rate. We interpret this experimental observation based on complete mapping of the triplet hypersurface of this complex, revealing thermal deactivation to the singlet ground state through intersystem crossing. Chemistry ; Intersystem crossing ; Photochemistry ; Ruthenium ; Quantum yield ; Selectivity ; Pyridine ; Triplet state ; Density functional theory ; Solvent effects ; Ground state ; Acetonitrile ; Photodissociation ; Molecule ; Singlet state ; Solvent ; Computational chemistry ; Excited state ; Organic chemistry ; Catalysis Photochemistry and Electron Transfer Studies ; Metal complexes synthesis and properties ; Electrocatalysts for Energy Conversion
Authors: Xiting Peng | Xiaonan Wang
Source: MRS Bulletin (review, 2023-02-01)
Field: Materials Science — Materials Chemistry

Next-generation intelligent laboratories for materials design and manufacturing Automation ; Computer science ; Process (computing) ; Systems engineering ; Laboratory automation ; Nanotechnology ; Engineering ; Mechanical engineering Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advanced X-ray and CT Imaging
Authors: Riley J. Hickman | Malcolm Sim | Sergio Pablo‐García | Ivan Woolhouse | Han Hao | Zeqing Bao | Pauric Bannigan | Christine Allen | Matteo Aldeghi | Alán Aspuru‐Guzik
Source: ChemRxiv (preprint, 2023-09-05)
Field: Materials Science — Materials Chemistry

Atlas: A Brain for Self-driving Laboratories Self-driving laboratories (SDLs) are next-generation research and development platforms for closed-loop, autonomous experimentation that combine ideas from artificial intelligence, robotics, and high-performance computing. A critical component of SDLs is the decision-making algorithm used to prioritize experiments to be performed. This SDL “brain” often relies on optimization strategies that are guided by machine learning models, such as Bayesian optimization. However, the diversity of hardware constraints and scientific questions being tackled by SDLs require the availability of a set of flexible algorithms that have yet to be implemented in a single software tool. Here, we report Atlas, an application-agnostic Python library for Bayesian optimization that is specifically tailored to the needs of SDLs. Atlas provides facile access to state-of-the-art, model-based optimization algorithms—including mixed-parameter, multi-objective, constrained, robust, multi-fidelity, meta-learning, and molecular optimization—as an all-in-one tool that is expected to suit the majority of specialized SDL needs. After a brief description of its core capabilities, we demonstrate Atlas’ utility by optimizing the oxidation potential of metal complexes with an autonomous electrochemical experimentation platform. We expect Atlas to expand the breadth of design and discovery problems in the natural sciences that are immediately addressable with SDLs. Computer science ; Atlas (anatomy) ; Artificial intelligence ; Bayesian optimization ; Python (programming language) ; Robotics ; Machine learning ; Software ; Bayesian probability ; Programming language ; Robot Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning and Data Classification
Authors: Wilton J. M. Kort-Kamp | Magali Ferrandon | Xiaoping Wang | Jae Hyung Park | Rajesh K. Malla | Towfiq Ahmed | Edward F. Holby | Deborah J. Myers | Piotr Zelenay
Source: Journal of Power Sources (article, 2023-01-13)
Field: Materials Science — Materials Chemistry

Adaptive learning-driven high-throughput synthesis of oxygen reduction reaction Fe–N–C electrocatalysts Benchmark (surveying) ; Throughput ; Catalysis ; Computer science ; Reduction (mathematics) ; Nanotechnology ; Process engineering ; Chemistry ; Materials science ; Engineering ; Mathematics ; Organic chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Fuel Cells and Related Materials
Authors: Vishu Gupta | Wei‐keng Liao | Alok Choudhary | Ankit Agrawal
Source: MRS Communications (article, 2023-08-16)
Field: Materials Science — Materials Chemistry

Evolution of artificial intelligence for application in contemporary materials science Abstract Contemporary materials science has seen an increasing application of various artificial intelligence techniques in an attempt to accelerate the materials discovery process using forward modeling for predictive analysis and inverse modeling for optimization and design. Over the last decade or so, the increasing availability of computational power and large materials datasets has led to a continuous evolution in the complexity of the techniques used to advance the frontier. In this Review, we provide a high-level overview of the evolution of artificial intelligence in contemporary materials science for the task of materials property prediction in forward modeling. Each stage of evolution is accompanied by an outline of some of the commonly used methodologies and applications. We conclude the work by providing potential future ideas for further development of artificial intelligence in materials science to facilitate the discovery, design, and deployment workflow. Graphical abstract Workflow ; Computer science ; Artificial intelligence ; Task (project management) ; Software deployment ; Process (computing) ; Systems engineering ; Software engineering ; Engineering Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Mengjiao Yang | KwangHwan Cho | Amil Merchant | Pieter Abbeel | Dale Schuurmans | Igor Mordatch | Ekin D. Cubuk
Source: arXiv (Cornell University) (preprint, 2023-10-18)
Field: Materials Science — Materials Chemistry

Scalable Diffusion for Materials Generation Generative models trained on internet-scale data are capable of generating novel and realistic texts, images, and videos. A natural next question is whether these models can advance science, for example by generating novel stable materials. Traditionally, models with explicit structures (e.g., graphs) have been used in modeling structural relationships in scientific data (e.g., atoms and bonds in crystals), but generating structures can be difficult to scale to large and complex systems. Another challenge in generating materials is the mismatch between standard generative modeling metrics and downstream applications. For instance, common metrics such as the reconstruction error do not correlate well with the downstream goal of discovering stable materials. In this work, we tackle the scalability challenge by developing a unified crystal representation that can represent any crystal structure (UniMat), followed by training a diffusion probabilistic model on these UniMat representations. Our empirical results suggest that despite the lack of explicit structure modeling, UniMat can generate high fidelity crystal structures from larger and more complex chemical systems, outperforming previous graph-based approaches under various generative modeling metrics. To better connect the generation quality of materials to downstream applications, such as discovering novel stable materials, we propose additional metrics for evaluating generative models of materials, including per-composition formation energy and stability with respect to convex hulls through decomposition energy from Density Function Theory (DFT). Lastly, we show that conditional generation with UniMat can scale to previously established crystal datasets with up to millions of crystals structures, outperforming random structure search (the current leading method for structure discovery) in discovering new stable materials. Computer science ; Scalability ; Generative model ; Generative grammar ; Probabilistic logic ; Crystal structure prediction ; Theoretical computer science ; Representation (politics) ; Stability (learning theory) ; Scale (ratio) ; Artificial intelligence ; Machine learning ; Algorithm ; Crystal structure Machine Learning in Materials Science ; Topic Modeling
Authors: Hannes H. Loeffler | Jiazhen He | Alessandro Tibo | Jon Paul Janet | Alexey Voronov | Lewis Mervin | Ola Engkvist
Source: ChemRxiv (preprint, 2023-11-06)
Field: Materials Science — Materials Chemistry

REINVENT4: Modern AI–Driven Generative Molecule Design REINVENT4 is a modern open–source generative AI framework for the design of small molecules. The software utilizes recurrent neural networks and transformer architectures to drive molecule generation. These generators are seamlessly embedded within the general machine learning optimization algorithms transfer learning, reinforcement learning and curriculum learning. REINVENT4 enables and facilitates de novo design, R-group replacement, library design, linker design, scaffold hopping and molecule optimization. This contribution gives an overview of the software and describes its design. Algorithms and their applications are discussed in detail. REINVENT4 is a command line tool which reads a user configuration in either TOML or JSON format. The aim of this release is to provide reference implementations for some of the most common algorithms in AI based molecule generation. An additional goal with the release is to create a framework for education and future innovation in AI based molecular design. The software is available from https://github.com/ MolecularAI/REINVENT4 and released under the permissive Apache 2.0 license. Computer science ; Generative grammar ; Software ; Reinforcement learning ; Artificial intelligence ; Software design ; Computer architecture ; Transformer ; Software engineering ; Programming language ; Software development ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Zhengqu Zhu | Jiaqi Liu | Huan Zhao | Jing Pang | Pu Wang | Jiaquan Zhang
Source: Materials (article, 2023-02-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Study of the Soft Magnetic Properties of FeSiAl Magnetic Powder Cores by Compounding with Different Content of Epoxy Resin FeSiAl is a commonly used soft magnetic material because of its high resistivity, low core loss, and low cost. In order to systematically study the effect of epoxy resin (EP) on the insulated coating and pressing effect of FeSiAl magnetic powders, six groups of composite powders and their corresponding soft magnetic powder cores (SMPCs) were prepared by changing the content of EP, and the soft magnetic properties of the powders and SMPCs were characterized. The results showed that FeSiAl powders exhibited good sphericity and morphology. The <i>M</i><sub>s</sub> of FeSiAl/EP composite powders was between 117.4-124.8 emu·g<sup>-1</sup> after adding (0.3, 0.5, 0.7, 1, 1.5, and 2 wt. %) EP. The permeability <i>μ<sub>e</sub></i> of SMPCs increased first and then decreased with the increase in EP content. Among them, when the EP content was 1 wt. %, the corresponding SMPCs had the highest <i>μ<sub>e</sub></i> and excellent DC bias performance (63%, 100 Oe). In the whole test frequency range (50~1000 kHz), SMPCs with 1 wt. % EP content had the lowest core loss (1733.9 mW·cm<sup>-3</sup> at 20 mT and 1000 kHz). After that, the loss separation study in the low-frequency range (50~250 kHz) was conducted, and the hysteresis loss and eddy current loss of SMPCs with 1 wt. % EP content were also the lowest. In addition, SMPCs also exhibited the best overall performance when the EP content was 1 wt. %. The results of this study can guide the design of composite insulation coating schemes and promote the development of soft magnetic materials for medium and high frequency applications. Materials science ; Epoxy ; Composite material ; Coating ; Composite number ; Compounding ; Core (optical fiber) Magnetic Properties of Alloys ; Electromagnetic wave absorption materials ; Metallic Glasses and Amorphous Alloys
Authors: Eleonora G. Hochreiner | Bas G. P. van Ravensteijn
Source: Journal of Polymer Science (article, 2023-10-27)
Field: Chemistry — Organic Chemistry

Polymerization‐induced self‐assembly for drug delivery: A critical appraisal Abstract Polymerization‐induced self‐assembly ( PISA ) with (in situ) encapsulation of (therapeutic) cargo has proven to be an efficient preparation method for loaded polymeric micelles and polymersomes, thereby presenting significant opportunities in the field of drug delivery. However, despite extensive research efforts, no significant advances toward systematic in vivo studies or clinical applications have been achieved to date. In this Review, we outline the current state‐of‐the‐art of cargo encapsulation via PISA with a specific focus on developments achieved in the past 5 years. Considering the general requirements for functional drug delivery systems, we identify the major hurdles that still need to be overcome in order to push PISA ‐derived systems from a promising academic exercise to viable candidates for clinical translation. Polymersome ; Drug delivery ; Nanotechnology ; Encapsulation (networking) ; Computer science ; Chemistry ; Materials science ; Copolymer ; Computer security ; Polymer Advanced Polymer Synthesis and Characterization ; Polymer Surface Interaction Studies ; Nanoparticle-Based Drug Delivery
Authors: Aubrey Toland | Tran Doan Huan | Lihua Chen | Yinghao Li | Chao Zhang | Will R. Gutekunst | Rampi Ramprasad
Source: The Journal of Physical Chemistry A (article, 2023-12-06)
Field: Materials Science — Materials Chemistry

Accelerated Scheme to Predict Ring-Opening Polymerization Enthalpy: Simulation-Experimental Data Fusion and Multitask Machine Learning Ring-opening enthalpy (Δ<i>H</i><sup>ROP</sup>) is a fundamental thermodynamic quantity controlling the polymerization and depolymerization of an important class of recyclable polymers, namely, those created from ring-opening polymerization (ROP). Highly accurate first-principles-based computational methods to compute Δ<i>H</i><sup>ROP</sup> are computationally too demanding to efficiently guide the design of depolymerizable polymers. In this work, we develop a generalizable machine-learning model that was trained on experimental measurements and reliably computed simulation results of Δ<i>H</i><sup>ROP</sup> (the latter provides a pathway to systematically increase the chemical diversity of the data). Predictions of Δ<i>H</i><sup>ROP</sup> using this machine-learning model require essentially no time while the prediction accuracy is about ∼8 kJ/mol, approaching the well-known chemical accuracy. We hope that this effort will contribute to the future development of new depolymerizable polymers. Polymerization ; Enthalpy ; Depolymerization ; Polymer ; Computer science ; Work (physics) ; Scheme (mathematics) ; Ring (chemistry) ; Fusion ; Artificial intelligence ; Machine learning ; Materials science ; Chemistry ; Thermodynamics ; Mathematics ; Physics ; Polymer chemistry ; Organic chemistry Machine Learning in Materials Science ; Polymer crystallization and properties ; Advanced Polymer Synthesis and Characterization