jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yong Zhao | Edirisuriya M. Dilanga Siriwardane | Zhenyao Wu | Nihang Fu | Mohammed Al‐Fahdi | Ming Hu | Jianjun Hu
Source: npj Computational Materials (article, 2023-03-18)
Field: Materials Science — Materials Chemistry

Physics guided deep learning for generative design of crystal materials with symmetry constraints Abstract Discovering new materials is a challenging task in materials science crucial to the progress of human society. Conventional approaches based on experiments and simulations are labor-intensive or costly with success heavily depending on experts’ heuristic knowledge. Here, we propose a deep learning based Physics Guided Crystal Generative Model (PGCGM) for efficient crystal material design with high structural diversity and symmetry. Our model increases the generation validity by more than 700% compared to FTCP, one of the latest structure generators and by more than 45% compared to our previous CubicGAN model. Density Functional Theory (DFT) calculations are used to validate the generated structures with 1869 materials out of 2000 are successfully optimized and deposited into the Carolina Materials Database www.carolinamatdb.org , of which 39.6% have negative formation energy and 5.3% have energy-above-hull less than 0.25 eV/atom, indicating their thermodynamic stability and potential synthesizability. Generative grammar ; Symmetry (geometry) ; Heuristic ; Task (project management) ; Density functional theory ; Stability (learning theory) ; Crystal (programming language) ; Atom (system on chip) ; Computer science ; Generative model ; Artificial intelligence ; Materials science ; Theoretical physics ; Physics ; Machine learning ; Mathematics ; Engineering ; Geometry ; Quantum mechanics ; Systems engineering Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electronic and Structural Properties of Oxides
Authors: Yuetian Jia | Chao Qian | Zhixiang Fan | Tong Cai | Er‐Ping Li | Hongsheng Chen
Source: Light Science & Applications (article, 2023-03-30)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A knowledge-inherited learning for intelligent metasurface design and assembly Recent breakthroughs in deep learning have ushered in an essential tool for optics and photonics, recurring in various applications of material design, system optimization, and automation control. Deep learning-enabled on-demand metasurface design has been the subject of extensive expansion, as it can alleviate the time-consuming, low-efficiency, and experience-orientated shortcomings in conventional numerical simulations and physics-based methods. However, collecting samples and training neural networks are fundamentally confined to predefined individual metamaterials and tend to fail for large problem sizes. Inspired by object-oriented C++ programming, we propose a knowledge-inherited paradigm for multi-object and shape-unbound metasurface inverse design. Each inherited neural network carries knowledge from the "parent" metasurface and then is freely assembled to construct the "offspring" metasurface; such a process is as simple as building a container-type house. We benchmark the paradigm by the free design of aperiodic and periodic metasurfaces, with accuracies that reach 86.7%. Furthermore, we present an intelligent origami metasurface to facilitate compatible and lightweight satellite communication facilities. Our work opens up a new avenue for automatic metasurface design and leverages the assemblability to broaden the adaptability of intelligent metadevices. Computer science ; Metamaterial ; Photonics ; Aperiodic graph ; Deep learning ; Construct (python library) ; Adaptability ; Artificial intelligence ; Artificial neural network ; Object (grammar) ; Human–computer interaction ; Computer architecture Metamaterials and Metasurfaces Applications ; Advanced Materials and Mechanics ; Polydiacetylene-based materials and applications
Authors: Joshua A. Homer | Long Xu | Namitharan Kayambu | Qinheng Zheng | Eun Joung Choi | Byeong Moon Kim | K. Barry Sharpless | Han Zuilhof | Jiajia Dong | John E. Moses
Source: Nature Reviews Methods Primers (article, 2023-08-03)
Field: Chemistry — Organic Chemistry

Sulfur fluoride exchange Click chemistry ; Chemistry ; Sulfur ; Fluoride ; Catalysis ; Combinatorial chemistry ; Computer science ; Primer (cosmetics) ; Nanotechnology ; Organic chemistry ; Materials science ; Inorganic chemistry Click Chemistry and Applications ; Chemical Synthesis and Analysis ; Monoclonal and Polyclonal Antibodies Research
Authors: Diksha Garg | Kandi Sridhar | Baskaran Stephen Inbaraj | Prince Chawla | Manikant Tripathi | Minaxi Sharma
Source: Bioengineering (review, 2023-08-25)
Field: Materials Science — Materials Chemistry

Nano-Biofertilizer Formulations for Agriculture: A Systematic Review on Recent Advances and Prospective Applications In the twenty-first century, nanotechnology has emerged as a potentially game-changing innovation. Essential minerals are mostly unavailable in modern cropping systems without the application of synthetic fertilizers, which have a serious negative impact on the ecosystem. This review focuses on the coupling of nanoparticles with biofertilizers to function as nano-biofertilizers (NBFs), which may ensure world food security in the face of the rising population. The inoculation of plants with NBFs improves plant development and resistance to stress. Metallic nanoparticles as well as organic components comprising polysaccharide and chitosan may be encapsulated, utilizing microbe-based green synthesis to make NBFs, which circumvents the limitations of conventional chemical fertilizers. The application of NBFs is just getting started, and shows more promise than other approaches for changing conventional farming into high-tech "smart" farming. This study used bibliographic analysis using Web of Science to find relevant papers on "nano biofertilizers", "plants", and "agriculture". These subjects have received a lot of attention in the literature, as shown by the co-citation patterns of these publications. The novel use of nanotechnology in agriculture is explored in this research work, which makes use of the unique characteristics of nanoscale materials to address urgent concerns including nutrient delivery, crop protection, and sustainable farming methods. This study attempts to fill in some of the gaps in our knowledge by discussing the formulation, fabrication, and characterization of NBFs, as well as elucidating the mechanisms by which NBFs interact with plants and how this benefits the ability of the plant to withstand biotic and abiotic stress brought about by climate change. This review also addresses recent developments and future directions in farming using NBF formulations in the field. Biofertilizer ; Agriculture ; Nanotechnology ; Computer science ; Biochemical engineering ; Biotechnology ; Engineering ; Materials science ; Ecology ; Biology ; Agronomy Nanoparticles: synthesis and applications
Authors: Rachid Hsissou | Khadija Dahmani | Anouar El Magri | Abdelfettah Hmada | Zaki Safi | Nadia Dkhireche | M. Galai | Nuha Wazzan | Авни Бериша
Source: Polymers (article, 2023-04-21)
Field: Materials Science — Materials Chemistry

A Combined Experimental and Computational (DFT, RDF, MC and MD) Investigation of Epoxy Resin as a Potential Corrosion Inhibitor for Mild Steel in a 0.5 M H2SO4 Environment In this work, a tetrafunctional epoxy resin entitled 2,3,4,5-tetraglycidyloxy pentanal (TGP) was tested and investigated as a potential corrosion inhibitor for mild steel (MS) in 0.5 M H<sub>2</sub>SO<sub>4</sub> solution. The corrosion inhibition process for mild steel was employed alongside various techniques, such as potentiodynamic polarization (PDP), electrochemical impedance spectroscopy (EIS), temperature effect (TE), scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS) and theoretical approaches (DFT, MC, RDF and MD). Further, the corrosion efficacies obtained at the optimum concentration (10<sup>-3</sup> M of the TGP) were 85.5% (EIS) and 88.6% (PDP), respectively. The PDP results indicated that the TGP tetrafunctional epoxy resin acted the same as an anodic inhibitor type in 0.5 M H<sub>2</sub>SO<sub>4</sub> solution. SEM and EDS analyses found that the protective layer formed on the MS electrode surface in the presence of TGP could prevent the attack of the sulfur ions. The DFT calculation provided more information regarding the reactivity, geometric properties and the active centers of the corrosion inhibitory efficiency of the tested epoxy resin. RDF, MC and MD simulations showed that the investigated inhibitory resin have a maximum inhibition efficiency in 0.5 M H<sub>2</sub>SO<sub>4</sub> solution. Epoxy ; Dielectric spectroscopy ; Corrosion ; Materials science ; Scanning electron microscope ; Polarization (electrochemistry) ; Corrosion inhibitor ; Nuclear chemistry ; Anode ; Electrochemistry ; Composite material ; Chemical engineering ; Electrode ; Chemistry ; Physical chemistry Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Andrew Dickson White | Glen M. Hocky | Heta A. Gandhi | Mehrad Ansari | Sam Cox | Geemi P. Wellawatte | Subarna Sasmal | Ziyue Yang | Kangxin Liu | Yuvraj Singh | Willmor J. Peña Ccoa
Source: Digital Discovery (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Assessment of chemistry knowledge in large language models that generate code In this work, we investigate the question: do code-generating large language models know chemistry? Our results indicate, mostly yes. To evaluate this, we introduce an expandable framework for evaluating chemistry knowledge in these models, through prompting models to solve chemistry problems posed as coding tasks. To do so, we produce a benchmark set of problems, and evaluate these models based on correctness of code by automated testing and evaluation by experts. We find that recent LLMs are able to write correct code across a variety of topics in chemistry and their accuracy can be increased by 30 percentage points <i>via</i> prompt engineering strategies, like putting copyright notices at the top of files. Our dataset and evaluation tools are open source which can be contributed to or built upon by future researchers, and will serve as a community resource for evaluating the performance of new models as they emerge. We also describe some good practices for employing LLMs in chemistry. The general success of these models demonstrates that their impact on chemistry teaching and research is poised to be enormous. Computer science ; Code (set theory) ; Programming language Machine Learning in Materials Science ; Topic Modeling ; Chemical Synthesis and Analysis
Authors: Martyna Durko‐Maciag | Gilles Ulrich | Denis Jacquemin | Jarosław Myśliwiec | Julien Massue
Source: Physical Chemistry Chemical Physics (review, 2023-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Solid-state emitters presenting a modular excited-state proton transfer (ESIPT) process: recent advances in dual-state emission and lasing applications This review aims at providing a broad readership of material and physical chemists, as well as those interested in <i>ab initio</i> calculations, about recent advances in the fields of dual solution-solid emitters and lasing applications based on organic dyes displaying an excited-state intramolecular proton transfer (ESIPT) process. ESIPT is known to be highly sensitive to the immediate environment leading to the engineering of a wide range of stimuli-responsive fluorescent dyes. With the structural diversity of ESIPT-capable fluorophores being large, many applications have been targeted over the years in the fields of optoelectronics, biology and luminescent displays. This review wishes to point out two emerging applications concerning ESIPT fluorophores, which are the answer for the quest for emitters fluorescing both in solution and in the solid state, and those capable of light amplification. Lasing threshold ; Excited state ; Materials science ; Modular design ; Fluorescence ; Nanotechnology ; Luminescence ; Optoelectronics ; Computer science ; Physics ; Optics ; Wavelength ; Atomic physics Photochemistry and Electron Transfer Studies ; Luminescence and Fluorescent Materials ; Organic Light-Emitting Diodes Research
Authors: Markus J. Buehler
Source: Applied Mechanics Reviews (article, 2023-10-19)
Field: Materials Science — Materials Chemistry

MechGPT, a Language-Based Strategy for Mechanics and Materials Modeling That Connects Knowledge Across Scales, Disciplines, and Modalities Abstract For centuries, researchers have sought out ways to connect disparate areas of knowledge. While early scholars (Galileo, da Vinci, etc.) were experts across fields, specialization took hold later. With the advent of Artificial Intelligence, we can now explore relationships across areas (e.g., mechanics-biology) or disparate domains (e.g., failure mechanics-art). To achieve this, we use a fine-tuned large language model (LLM), here for a subset of knowledge in multiscale materials failure. The approach includes the use of a general-purpose LLM to distill question-answer pairs from raw sources followed by LLM fine-tuning. The resulting MechGPT LLM foundation model is used in a series of computational experiments to explore its capacity for knowledge retrieval, various language tasks, hypothesis generation, and connecting knowledge across disparate areas. While the model has some ability to recall knowledge from training, we find that LLMs are particularly useful for extracting structural insights through Ontological Knowledge Graphs. These interpretable graph structures provide explanatory insights, frameworks for new research questions, and visual representations of knowledge that also can be used in retrieval-augmented generation. Three versions of MechGPT are discussed, featuring different sizes from 13 × 109 to 70 × 109 parameters, and reaching context lengths of more than 10,000 tokens. This provides ample capacity for sophisticated retrieval augmented strategies, as well as agent-based modeling where multiple LLMs interact collaboratively and/or adversarially, the incorporation of new data from the literature or web searches, as well as multimodality. Computer science ; Context (archaeology) ; Data science ; Modalities ; Text corpus ; Artificial intelligence ; Sociology Machine Learning in Materials Science ; Topic Modeling ; Software Engineering Research
Authors: Elizabeth Champa-Bujaico | Ana M. Díez‐Pascual | Alba Lomas Redondo | Pilar García-Díaz
Source: Composites Part B Engineering (article, 2023-11-16)
Field: Materials Science — Polymers and Plastics

Optimization of mechanical properties of multiscale hybrid polymer nanocomposites: A combination of experimental and machine learning techniques Machine learning (ML) models provide fast and accurate predictions of material properties at a low computational cost. Herein, the mechanical properties of multiscale poly(3-hydroxybutyrate) (P3HB)-based nanocomposites reinforced with different concentrations of multiwalled carbon nanotubes (MWCNTs), WS2 nanosheets and sepiolite (SEP) nanoclay have been predicted. The nanocomposites were prepared via solution casting. SEM images revealed that the three nanofillers were homogenously and randomly dispersed into the matrix. A synergistic reinforcement effect was attained, resulting in an unprecedented stiffness improvement of 132% upon addition of 1:2:2 wt% SEP:MWCNTs:WS2. Conversely, the increments in strength were only moderates (up to 13.4%). A beneficial effect in the matrix ductility was also found due to the presence of both nanofillers. Four ML approaches, Recurrent Neural Network (RNN), RNN with Levenberg's algorithm (RNN-LV), decision tree (DT) and Random Forest (RF), were applied. The correlation coefficient (R2), mean absolute error (MAE) and mean square error (MSE) were used as statistical indicators to compare their performance. The best-performing model for the Young's modulus was RNN-LV with 3 hidden layers and 50 neurons in each layer, while for the tensile strength was the RF model using a combination of 100 estimators and a maximum depth of 100. An RNN model with 3 hidden layers was the most suitable to predict the elongation at break and impact strength, with 90 and 50 neurons in each layer, respectively. The highest correlation (R2 of 1 and 0.9203 for the training and test set, respectively) and the smallest errors (MSE of 0.13 and MAE of 0.31) were obtained for the prediction of the elongation at break. The developed models represent a powerful tool for the optimization of the mechanical properties in multiscale hybrid polymer nanocomposites, saving time and resources in the experimental characterization process. Materials science ; Composite material ; Ultimate tensile strength ; Nanocomposite ; Casting ; Carbon nanotube ; Mean squared error ; Ductility (Earth science) ; Random forest ; Recurrent neural network ; Stiffness ; Artificial neural network ; Machine learning ; Computer science ; Mathematics ; Creep Polymer Nanocomposites and Properties ; Conducting polymers and applications ; Advanced Sensor and Energy Harvesting Materials
Authors: Nathan C. Frey | Ryan Soklaski | Simon Axelrod | Siddharth Samsi | Rafael Gómez‐Bombarelli | Connor W. Coley | Vijay Gadepally
Source: Nature Machine Intelligence (article, 2023-10-23)
Field: Materials Science — Materials Chemistry

Neural scaling of deep chemical models Abstract Massive scale, in terms of both data availability and computation, enables important breakthroughs in key application areas of deep learning such as natural language processing and computer vision. There is emerging evidence that scale may be a key ingredient in scientific deep learning, but the importance of physical priors in scientific domains makes the strategies and benefits of scaling uncertain. Here we investigate neural-scaling behaviour in large chemical models by varying model and dataset sizes over many orders of magnitude, studying models with over one billion parameters, pre-trained on datasets of up to ten million datapoints. We consider large language models for generative chemistry and graph neural networks for machine-learned interatomic potentials. We investigate the interplay between physical priors and scale and discover empirical neural-scaling relations for language models in chemistry with a scaling exponent of 0.17 for the largest dataset size considered, and a scaling exponent of 0.26 for equivariant graph neural network interatomic potentials. Scaling ; Computer science ; Artificial neural network ; Artificial intelligence ; Computation ; Key (lock) ; Generative grammar ; Exponent ; Graph ; Deep learning ; Machine learning ; Prior probability ; Scale (ratio) ; Statistical physics ; Theoretical computer science ; Data science ; Algorithm ; Mathematics ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Ashwini Kumar | Sunny Shah | Titilope John Jayeoye | Akash Kumar | Akshay Parihar | Bhupendra G. Prajapati | Sudarshan Singh | Devesh U. Kapoor
Source: Frontiers in Nanotechnology (article, 2023-05-02)
Field: Materials Science — Materials Chemistry

Biogenic metallic nanoparticles: biomedical, analytical, food preservation, and applications in other consumable products Biogenic metallic nanoparticles (BMNPs) are nanostructure materials synthesized through biological processes that have gained significant attention due to their small size and high surface area-to-volume ratio. BMNPs have several advantages over chemically synthesized ones due to their eco-friendly synthesis regimen, sustainability, biocompatibility, and diverse multifarious biomedical applications. Moreover, the superior cytocompatibility and stability due to the capping layer over metallic nanoparticles (MNPs), reduces the like hood of toxicity and side effects, making them a safer alternative to traditional drug delivery methods. Among several promising applications of BMNPs, their antibacterial activity, analytical sensing of heavy metals, and their roles in food preservations have been widely explored. In addition, to drug delivery and imaging, BMNPs have also been investigated for therapeutic activity such as antimicrobial efficacy against the skin and soft tissue nosocomial pathogens and targeting cancer cells in cancer therapy. The present review bestows several characterization techniques involved with MNPs and compressive aspects of the biogenic synthesis of MNPs using agricultural and biological materials, which reduces the cost of synthesis and minimizes the use of hazardous chemicals. The review also focuses on the multifold applications of BMNPs including biomedical, analytical, preservation of food, and in other consumable goods with toxicological aspects. Nanotechnology ; Biocompatibility ; Materials science ; Drug delivery ; Nanoparticle ; Environmentally friendly ; Metallurgy Nanoparticles: synthesis and applications
Authors: Yan Li | Xiaojin Huang | Shuxin Liu | Haowen Liang | Yuye Ling | Yikai Su
Source: Opto-Electronic Science (article, 2023-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Metasurfaces for near-eye display applications Virtual reality (VR) and augmented reality (AR) are revolutionizing our lives. Near-eye displays are crucial technologies for VR and AR. Despite the rapid advances in near-eye display technologies, there are still challenges such as large field of view, high resolution, high image quality, natural free 3D effect, and compact form factor. Great efforts have been devoted to striking a balance between visual performance and device compactness. While traditional optics are nearing their limitations in addressing these challenges, ultra-thin metasurface optics, with their high light-modulating capabilities, may present a promising solution. In this review, we first introduce VR and AR near-eye displays, and then briefly explain the working principles of light-modulating metasurfaces, review recent developments in metasurface devices geared toward near-eye display applications, delved into several advanced natural 3D near-eye display technologies based on metasurfaces, and finally discuss about the remaining challenges and future perspectives associated with metasurfaces for near-eye display applications. Computer science ; Virtual reality ; Augmented reality ; Stereo display ; Holographic display ; Display device ; Computer graphics (images) ; Human–computer interaction ; Optics ; Physics ; Holography Metamaterials and Metasurfaces Applications ; Advanced Optical Imaging Technologies ; Photonic Crystals and Applications