jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yang Jeong Park | Sung Eun Jerng | Jin‐Sung Park | Choah Kwon | C.-W. Hsu | Zhichu Ren | Sungroh Yoon | Ju Li
Source: arXiv (Cornell University) (preprint, 2023-08-25)
Field: Materials Science — Materials Chemistry

1.5 million materials narratives generated by chatbots The advent of artificial intelligence (AI) has enabled a comprehensive exploration of materials for various applications. However, AI models often prioritize frequently encountered materials in the scientific literature, limiting the selection of suitable candidates based on inherent physical and chemical properties. To address this imbalance, we have generated a dataset of 1,494,017 natural language-material paragraphs based on combined OQMD, Materials Project, JARVIS, COD and AFLOW2 databases, which are dominated by ab initio calculations and tend to be much more evenly distributed on the periodic table. The generated text narratives were then polled and scored by both human experts and ChatGPT-4, based on three rubrics: technical accuracy, language and structure, and relevance and depth of content, showing similar scores but with human-scored depth of content being the most lagging. The merger of multi-modality data sources and large language model (LLM) holds immense potential for AI frameworks to help the exploration and discovery of solid-state materials for specific applications. Rubric ; Computer science ; Relevance (law) ; Lagging ; Narrative ; Consistency (knowledge bases) ; Limiting ; Natural language processing ; Data science ; Artificial intelligence ; Information retrieval ; Engineering ; Psychology ; Linguistics ; Mathematics education ; Mathematics Machine Learning in Materials Science ; Topic Modeling
Authors: Vladimir V. Vinogradov | Julia Razlivina | Andrei Dmitrenko
Source: Research Square (preprint, 2023-11-08)
Field: Materials Science — Materials Chemistry

DiZyme: The Ultimate Resource for Nanozyme Multiple Catalytic Activity Prediction Abstract Nanozymes are unique materials with many valuable properties for applications in biomedicine, biosensing, clinical diagnostics, environmental monitoring and beyond. However, it is usually challenging to find new nanozymes. In this work, we developed a machine learning (ML) approach to search for new nanozymes and deployed a web platform DiZyme, featuring a unique database of nanozymes, catalytic activity prediction, data visualization and DiZyme Assistant interface powered by a large language model (LLM). For the first time, we enable prediction of multiple catalytic activities of nanozymes by training an ensemble learning algorithm achieving R 2 = 0.75 for the Michaelis-Menten constant and R 2 = 0.77 for the maximum velocity. We envision accurate prediction of multi-catalytic activity (peroxidase, oxidase, and catalase) promoting entirely novel applications for a wide range of surface-modified inorganic nanozyme compositions. The DiZyme state-of-the-art database of nanozymes contains 1210 experimental samples with a wide range of compositions and molecular features. The DiZyme Assistant based on the ChatGPT model provides users with supporting information on experimental samples, such as synthesis procedures, measurement protocols, etc. DiZyme (dizyme.aicidlab.itmo.ru) is now openly available worldwide. Computer science ; Benchmark (surveying) ; Visualization ; Resource (disambiguation) ; Training set ; Nanotechnology ; Database ; Chemistry ; Artificial intelligence ; Materials science Advanced Nanomaterials in Catalysis ; Advanced biosensing and bioanalysis techniques ; Nanocluster Synthesis and Applications
Authors: Jaewoong Choi | Byungju Lee
Source: arXiv (Cornell University) (preprint, 2023-08-18)
Field: Materials Science — Materials Chemistry

Accelerated materials language processing enabled by GPT Materials language processing (MLP) is one of the key facilitators of materials science research, as it enables the extraction of structured information from massive materials science literature. Prior works suggested high-performance MLP models for text classification, named entity recognition (NER), and extractive question answering (QA), which require complex model architecture, exhaustive fine-tuning and a large number of human-labelled datasets. In this study, we develop generative pretrained transformer (GPT)-enabled pipelines where the complex architectures of prior MLP models are replaced with strategic designs of prompt engineering. First, we develop a GPT-enabled document classification method for screening relevant documents, achieving comparable accuracy and reliability compared to prior models, with only small dataset. Secondly, for NER task, we design an entity-centric prompts, and learning few-shot of them improved the performance on most of entities in three open datasets. Finally, we develop an GPT-enabled extractive QA model, which provides improved performance and shows the possibility of automatically correcting annotations. While our findings confirm the potential of GPT-enabled MLP models as well as their value in terms of reliability and practicability, our scientific methods and systematic approach are applicable to any materials science domain to accelerate the information extraction of scientific literature. Computer science ; Transformer ; Artificial intelligence ; Reliability (semiconductor) ; Information extraction ; Language model ; Natural language processing ; Task (project management) ; Named-entity recognition ; Machine learning ; Information retrieval ; Systems engineering Machine Learning in Materials Science ; Topic Modeling ; Software Engineering Research
Authors: Vadim Korolev | Artem Mitrofanov
Source: ChemRxiv (preprint, 2023-07-31)
Field: Materials Science — Materials Chemistry

Carbon Footprint of Artificial Intelligence in Materials Science: Should We Be Concerned? While artificial intelligence is promoting remarkable progress in natural sciences, its broader societal implications are mostly disregarded. In this study, we evaluate the environmental impacts of deep learning in materials science through extensive benchmarking. Specifically, a diverse set of neural networks is trained for a particular supervised learning task to assess greenhouse gas (GHG) emissions during training and inference phases. A chronological perspective shows diminishing returns, which are expressed in a 28% decrease in mean absolute error and nearly a 15000% increase in carbon footprint of model training in 2016–2022. By utilizing up-to-date graphics processing units, it is possible to partially offset the immense growth in GHG emissions. Nonetheless, the practice to employ energy-efficient hardware is unaddressed by the materials informatics community, as it follows from the literature analysis in the field. Based on our findings, we encourage researchers to report GHG emissions on par with standard performance metrics. Greenhouse gas ; Carbon footprint ; Benchmarking ; Computer science ; Inference ; Graphics ; Artificial intelligence ; Offset (computer science) ; Artificial neural network ; Data science ; Environmental economics ; Economics ; Management ; Ecology Machine Learning in Materials Science ; Green IT and Sustainability ; Environmental Impact and Sustainability
Authors: Vadim Korolev | Artem Mitrofanov
Source: ChemRxiv (preprint, 2023-12-21)
Field: Materials Science — Materials Chemistry

The carbon footprint of artificial intelligence in materials science While artificial intelligence drives remarkable progress in natural sciences, its broader societal implications are mostly disregarded. In this study, we evaluate environmental impacts of deep learning in materials science through extensive benchmarking. In particular, a set of diverse neural networks is trained for a given supervised learning task to assess greenhouse gas (GHG) emissions during training and inference phases. A chronological perspective showed diminishing returns, manifesting themselves as a 28% decrease in mean absolute error and nearly a 15000% increase in the carbon footprint of model training in 2016–2022. By means of up-to-date graphics processing units, it is possible to partially offset the immense growth of GHG emissions. Nonetheless, the practice of employing energy-efficient hardware is overlooked by the materials informatics community, as follows from a literature analysis in the field. On the basis of our findings, we encourage researchers to report GHG emissions together with standard performance metrics. Greenhouse gas ; Carbon footprint ; Benchmarking ; Computer science ; Inference ; Graphics ; Artificial intelligence ; Artificial neural network ; Offset (computer science) ; Deep learning ; Data science ; Environmental economics ; Machine learning ; Economics ; Management ; Ecology Machine Learning in Materials Science ; Environmental Impact and Sustainability ; Green IT and Sustainability
Authors: David Moss
Source:  (preprint, 2023-01-09)
Field: Materials Science — Materials Chemistry

Graphene oxide for electronic and photonic chips &lt;p&gt;As a derivative of graphene, graphene oxide (GO) was initially developed by chemists to emulate some of the key properties of graphene, but it was soon recognized as a unique material in its own right, addressing an application space that is not accessible to chemical vapor deposition based materials. Over the past decade, GO has emerged as a new frontier material with tremendous advances in its material fabrication and quality control over its properties. These in turn have led to rapid progress in GO based photonics, electronics, and optoelectronics concepts and devices, evoking new science and paving the way for many technological breakthroughs with exceptional performance. Here, we review the unique fundamental characteristics of GO, its thin film fabrication methods, patterning techniques, and mechanisms for tuning its material properties. This latter capability in particular has enabled novel advanced functional photonic, electronic, and optoelectronic devices. Understanding these insights is essential for designing and tailoring GO properties for state-of-the-art applications including solar energy harvesting, energy storage, medical diagnosis, image displays, and optical communications. We conclude by discussing the open challenges and exciting opportunities of this field, together with future prospects for major technological advancements and breakthroughs.&lt;/p&gt; Graphene ; Nanotechnology ; Photonics ; Electronics ; Materials science ; Fabrication ; Engineering physics ; Computer science ; Optoelectronics ; Engineering ; Electrical engineering Graphene research and applications ; Graphene and Nanomaterials Applications
Authors: Tejasvi Bhatia | Anuradha Sandhu
Source:  (review, 2023-05-21)
Field: Materials Science — Polymers and Plastics

2D materials to nanocomposites with innovative properties: A Critical Review With advancements in polymer sciences, the innovation of two-dimensional nanomaterial-based polymer nanocomposites has come to light. These are a blend of nanoscale materials and polymers that give rise to advanced mechanical, morphological, electrical, and structural properties. Generally, two-dimensional nanomaterials, such as nanofilms, nanosheets, nanoribbons, nanofillers, and carbon nanotubes, are fabricated with a wide range of organic, inorganic, and metal polymers to acquire desirable properties. The chemistry of these nanomaterial-based nanocomposites shows that even a small amount of nanomaterial in the polymer matrix can result in enhanced material properties. The dielectric, conductive and energy storage properties of these materials are also of significant importance. These nanomaterial-based polymer nanocomposites are next-generation materials that will be highly utilized for different purposes. The blend of existing polymers and advanced 2d nanocomposites has proven expeditious solutions to many problems in the world. This review provides details about the mechanical, morphological, and electric properties of two-dimensional nanomaterial-based polymer nanocomposites and their respective characterization techniques. Nanomaterials ; Nanocomposite ; Materials science ; Polymer ; Carbon nanotube ; Polymer nanocomposite ; Nanotechnology ; Conductive polymer ; Characterization (materials science) ; Nanoparticle ; Composite material Conducting polymers and applications ; Dielectric materials and actuators ; Advanced Sensor and Energy Harvesting Materials
Authors: Jinlu Zhang | Yin Fang | Ningyu Zhang | Xin Shao | Huajun Chen | Xiaohui Fan
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2023-11-28)
Field: Materials Science — Materials Chemistry

Exploring the Potential of Large Language Models in Molecular Tasks: An Insightful Evaluation with GPT‐4 Abstract In the rapidly changing realm of artificial intelligence, large language models (LLMs) such as GPT-4 are increasingly being explored for their potential to aid and enhance the field of molecular research. This study explores the performance of GPT-4 and GPT-3.5 in molecular research, particularly in generating and optimizing molecular structures. The results highlight GPT-4’s strengths in certain areas of molecular optimization, while also revealing challenges in accurately generating complex molecules. The findings underscore the necessity for integrating these models with domain-specific tools to enhance their application in scientific research, particularly in molecular studies. The study offers insights into the potential of LLMs for advancing molecular research, paving the way for future developments in this rapidly evolving field. Realm ; Field (mathematics) ; Management science ; Computer science ; Nanotechnology ; Data science ; Engineering ethics ; Political science ; Engineering ; Materials science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topic Modeling
Authors: Venkata Rohit Punyapu | Jiazhou Zhu | Paul J. Meza-Morales | Anish Chaluvadi | O. Thompson Mefford | Rachel B. Getman
Source: arXiv (Cornell University) (preprint, 2023-09-18)
Field: Materials Science — Materials Chemistry

Computational Exploration of Magnetic Saturation and Anisotropy Energy for Nonstoichiometric Ferrite Compositions A grand challenge in materials research is identifying the relationship between composition and performance. Herein, we explore this relationship for magnetic properties, specifically magnetic saturation (M$_s$) and magnetic anisotropy energy (MAE) of ferrites. Ferrites are materials derived from magnetite (which has the chemical formulae Fe$_3$O$_4$) that comprise metallic elements in some combination such as Fe, Mn, Ni, Co, Cu and Zn. They are used in a variety of applications such as electromagnetism, magnetic hyperthermia, and magnetic imaging. Experimentally, synthesis and characterization of magnetic materials is time consuming. In order to create insight to help guide synthesis, we compute the relationship between ferrite composition and magnetic properties using density functional theory (DFT). Specifically, we compute M$_s$ and MAE for 571 ferrite structures with the formulae M1$_x$M2$_y$Fe$_{3-x-y}$O$_4$, where M1 and M2 can be Mn, Ni, Co, Cu and/or Zn and 0 $\le$ x $\le$ 1 and y = 1 - x. By varying composition, we were able to vary calculated values of M$_s$ and MAE by up to 9.6$\times$10$^5$ A m$^{-1}$ and 14.1$\times$10$^5$ J m$^{-3}$, respectively. Our results suggest that composition can be used to optimize magnetic properties for applications in heating, imaging, and recording. This is mainly achieved by varying M$_s$, as these applications are more sensitive to variation in M$_s$ than MAE. Ferrite (magnet) ; Magnetite ; Materials science ; Saturation (graph theory) ; Anisotropy ; Anisotropy energy ; Magnetic anisotropy ; Chemical composition ; Condensed matter physics ; Analytical Chemistry (journal) ; Nuclear magnetic resonance ; Magnetic field ; Magnetization ; Thermodynamics ; Chemistry ; Metallurgy ; Physics ; Mathematics ; Composite material ; Optics ; Quantum mechanics Magnetic Properties and Synthesis of Ferrites ; Iron oxide chemistry and applications ; Magnetic Properties and Applications
Authors: Noor Naoom | Emad͏͏͏͏͏͏͏͏͏͏͏͏͏͏͏͏͏͏͏ Yousif | Khalid Zainulabdeen | Israa Salman | Husnun Amalia | Universitas Trisakti | Muhammad Rahimi Yusop | Universiti Kebangsaan Malaysia | Amamer Redwan | Muna Bufaroosha
Source: Journal of Sustainable Materials Processing and Management (article, 2023-05-07)
Field: Chemistry — Organic Chemistry

Synthesis and Applications of Organotin(IV) Compounds: Mini Review Organotin(IV) compounds have seen a marked increase in industry utilization over the years and exhibited enormous economic benefits as well as environmental costs due to their numerous industrial, medical, and agricultural uses and other applications. The present review is a continuation of aseries of reviews on the use of organotin(IV), chemicals, synthesis, characteristics and geometry as well as the industrial and biological applications. Chemistry ; Engineering ; Biochemical engineering Organometallic Compounds Synthesis and Characterization ; Marine Biology and Environmental Chemistry ; Thermal and Kinetic Analysis
Authors: Sudarson Roy Pratihar | Deepesh Pai | M. V. Aditya Nag
Source: arXiv (Cornell University) (preprint, 2023-11-18)
Field: Materials Science — Materials Chemistry

AIMS-EREA -- A framework for AI-accelerated Innovation of Materials for Sustainability -- for Environmental Remediation and Energy Applications Many environmental remediation and energy applications (conversion and storage) for sustainability need design and development of green novel materials. Discovery processes of such novel materials are time taking and cumbersome due to large number of possible combinations and permutations of materials structures. Often theoretical studies based on Density Functional Theory (DFT) and other theories, coupled with Simulations are conducted to narrow down sample space of candidate materials, before conducting laboratory-based synthesis and analytical process. With the emergence of artificial intelligence (AI), AI techniques are being tried in this process too to ease out simulation time and cost. However tremendous values of previously published research from various parts of the world are still left as labor-intensive manual effort and discretion of individual researcher and prone to human omissions. AIMS-EREA is our novel framework to blend best of breed of Material Science theory with power of Generative AI to give best impact and smooth and quickest discovery of material for sustainability. This also helps to eliminate the possibility of production of hazardous residues and bye-products of the reactions. AIMS-EREA uses all available resources -- Predictive and Analytical AI on large collection of chemical databases along with automated intelligent assimilation of deep materials knowledge from previously published research works through Generative AI. We demonstrate use of our own novel framework with an example, how this framework can be successfully applied to achieve desired success in development of thermoelectric material for waste heat conversion. Sustainability ; Computer science ; Process (computing) ; Material efficiency ; Generative grammar ; Artificial intelligence ; Management science ; Risk analysis (engineering) ; Engineering ; Business Machine Learning in Materials Science
Authors: Antonio Cardinale | Alessandro Castrogiovanni | Théophile Gaudin | Joppe Geluykens | Teodoro Laino | Matteo Manica | Daniel Probst | Philippe Schwaller | Aleksandros Sobczyk | Alessandra Toniato | Alain C. Vaucher | Heiko Wolf | Federico Zipoli
Source: CHIMIA International Journal for Chemistry (article, 2023-08-09)
Field: Materials Science — Materials Chemistry

Fuelling the Digital Chemistry Revolution with Language Models The RXN for Chemistry project, initiated by IBM Research Europe - Zurich in 2017, aimed to develop a series of digital assets using machine learning techniques to promote the use of data-driven methodologies in synthetic organic chemistry. This research adopts an innovative concept by treating chemical reaction data as language records, treating the prediction of a synthetic organic chemistry reaction as a translation task between precursor and product languages. Over the years, the IBM Research team has successfully developed language models for various applications including forward reaction prediction, retrosynthesis, reaction classification, atom-mapping, procedure extraction from text, inference of experimental protocols and its use in programming commercial automation hardware to implement an autonomous chemical laboratory. Furthermore, the project has recently incorporated biochemical data in training models for greener and more sustainable chemical reactions. The remarkable ease of constructing prediction models and continually enhancing them through data augmentation with minimal human intervention has led to the widespread adoption of language model technologies, facilitating the digitalization of chemistry in diverse industrial sectors such as pharmaceuticals and chemical manufacturing. This manuscript provides a concise overview of the scientific components that contributed to the prestigious Sandmeyer Award in 2022. IBM ; Automation ; Computer science ; Retrosynthetic analysis ; Artificial intelligence ; Data science ; Chemistry ; Engineering ; Nanotechnology ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Mostafa Hussein | Elsayed Ahmed Elsayed | Ibrahim Ibrahim
Source: International Journal of Materials Technology and Innovation (article, 2023-12-24)
Field: Materials Science — Materials Chemistry

Preparation and characterization of cobalt ferrite CoFe2O4 thin films from alcoholic medium and their application restrictions On various substrates, CoFe2O4 thin films were produced via an electrodeposition-anodization technique using a non-aqueous sulphate solution. At room temperature, the deposited alloy was electrochemically anodized (1M KOH) into the appropriate hydroxide. The cyclic voltammetric technique (CV) was used to investigate the electrodeposition of co-fe alloy to different substrate materials, to optimize the oxidation and reduction processes taking place on the surfaces of different electrodes. The Galvanostatic technique is also used to adjust the formation of the initial deposits. Also, the potentiodynamic polarization method was used to evaluate the corrosion performance of the ferrite on each substrate material and its stability.The crystal structure, crystallite size, microstructure, and magnetic properties of ferrite films were studied using X-ray diffraction (XRD), scanning electron microscopy (SEM), and vibrating sample magnetometer (VSM) techniques.The obtained results revealed that cobalt ferrite nanoparticles deposited on Pt, SS and Cu substrates enhanced the magnetic and electrochemical properties. The corrosion rate results showed that CoFe2O4 thin film can be used safely in high acidic and alkaline atmospheres for Pt and SS substrates (high corrosion resistance), but not for Cu substrates at all pH values except pH = 9 (low corrosion resistance). Cobalt ferrite ; Materials science ; Cobalt ; Ferrite (magnet) ; Characterization (materials science) ; Chemical engineering ; Metallurgy ; Nanotechnology ; Composite material ; Engineering Magnetic Properties and Synthesis of Ferrites