jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Min‐Su Kim | Joohwan Ko | Taeyoung Yun | Dinghuai Zhang | Ling Pan | W. M. Kim | Jinkyoo Park | Bengio, Emmanuel | Yoshua Bengio
Source: arXiv (Cornell University) (preprint, 2023-10-04)
Field: Materials Science — Materials Chemistry

Learning to Scale Logits for Temperature-Conditional GFlowNets GFlowNets are probabilistic models that sequentially generate compositional structures through a stochastic policy. Among GFlowNets, temperature-conditional GFlowNets can introduce temperature-based controllability for exploration and exploitation. We propose \textit{Logit-scaling GFlowNets} (Logit-GFN), a novel architectural design that greatly accelerates the training of temperature-conditional GFlowNets. It is based on the idea that previously proposed approaches introduced numerical challenges in the deep network training, since different temperatures may give rise to very different gradient profiles as well as magnitudes of the policy's logits. We find that the challenge is greatly reduced if a learned function of the temperature is used to scale the policy's logits directly. Also, using Logit-GFN, GFlowNets can be improved by having better generalization capabilities in offline learning and mode discovery capabilities in online learning, which is empirically verified in various biological and chemical tasks. Our code is available at \url{https://github.com/dbsxodud-11/logit-gfn} Generalization ; Probabilistic logic ; Logit ; Computer science ; Controllability ; Scale (ratio) ; Scaling ; Code (set theory) ; Machine learning ; Artificial intelligence ; Data mining ; Mathematics ; Applied mathematics Machine Learning in Materials Science ; Metabolomics and Mass Spectrometry Studies ; Computational Drug Discovery Methods
Authors: Ronald Aznavourian | Guillaume Demésy | Sébastien Guenneau | Julien Marot
Source: arXiv (Cornell University) (preprint, 2023-07-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Electromagnetic cloak design with mono-objective and bi-objective optimizers: seeking the best tradeoff between protection and invisibility We revisit the design of cloaks, without resorting to any geometric transform. Cancellation techniques and anomalous resonances have been applied for this purpose. Instead of a deductive reasoning, we propose a novel mono-objective optimization algorithm, namely a ternary grey wolf algorithm, and we adapt a bi-objective optimization algorithm. Firstly, the proposed chaotic ternary grey wolf algorithm searches three-valued spaces for all permittivity values in the cloak while minimizing the summation of a protection criterion and an invisibility criterion. Secondly, a bi-objective genetic algorithm is adapted to find pairs of optimal values of invisibility and protection. Cloak ; Invisibility ; Ternary operation ; Genetic algorithm ; Metamaterial ; Cloaking ; Computer science ; Mathematical optimization ; Permittivity ; Algorithm ; Chaotic ; Mathematics ; Artificial intelligence ; Physics ; Optics ; Dielectric ; Programming language Metamaterials and Metasurfaces Applications
Authors: Nina Vorobkalo | А. С. Байсанов | Yerbolat Makhambetov | Yesmurat Mynzhasar
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — General Materials Science

Technological Research of Process for Producing Titanium Rich Slag and Complex Titanium-Containing Ferroalloy Ferroalloy ; Titanium ; Metallurgy ; Materials science ; Ferrosilicon ; Slag (welding) ; Process (computing) ; Computer science ; Alloy Material Properties and Applications ; Railway Systems and Materials Science ; Engineering and Environmental Studies
Authors: Chengwei Zhang | Yushuang Zhai | Ziyang Gong | Duan, Hongliang | Yuanbin She | Yun‐Fang Yang | An Su
Source: arXiv (Cornell University) (preprint, 2023-11-30)
Field: Materials Science — Materials Chemistry

Transfer Learning across Different Chemical Domains: Virtual Screening of Organic Materials with Deep Learning Models Pretrained on Small Molecule and Chemical Reaction Data Machine learning is becoming a preferred method for the virtual screening of organic materials due to its cost-effectiveness over traditional computationally demanding techniques. However, the scarcity of labeled data for organic materials poses a significant challenge for training advanced machine learning models. This study showcases the potential of utilizing databases of drug-like small molecules and chemical reactions to pretrain the BERT model, enhancing its performance in the virtual screening of organic materials. By fine-tuning the BERT models with data from five virtual screening tasks, the version pretrained with the USPTO-SMILES dataset achieved R2 scores exceeding 0.94 for three tasks and over 0.81 for two others. This performance surpasses that of models pretrained on the small molecule or organic materials databases and outperforms three traditional machine learning models trained directly on virtual screening data. The success of the USPTO-SMILES pretrained BERT model can be attributed to the diverse array of organic building blocks in the USPTO database, offering a broader exploration of the chemical space. The study further suggests that accessing a reaction database with a wider range of reactions than the USPTO could further enhance model performance. Overall, this research validates the feasibility of applying transfer learning across different chemical domains for the efficient virtual screening of organic materials. Virtual screening ; Chemical space ; Chemical database ; Computer science ; Transfer of learning ; Organic molecules ; Training set ; Machine learning ; Artificial intelligence ; Small molecule ; Database ; Drug discovery ; Molecule ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Sensor Technologies
Authors: Vadim Korolev | Artem Mitrofanov
Source: ChemRxiv (preprint, 2023-08-08)
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 ; Benchmarking ; Carbon footprint ; Computer science ; Graphics ; Inference ; Data science ; Artificial intelligence ; Offset (computer science) ; Artificial neural network ; Deep learning ; Environmental economics ; Machine learning ; Business ; Economics ; Marketing ; Ecology Machine Learning in Materials Science ; Green IT and Sustainability ; Environmental Impact and Sustainability
Authors: David Moss
Source:  (preprint, 2023-07-18)
Field: Materials Science — Materials Chemistry

Graphene oxide 2D thin films for electronic and photonic chips 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. Graphene ; Nanotechnology ; Photonics ; Electronics ; Materials science ; Fabrication ; Engineering physics ; Computer science ; Optoelectronics ; Engineering ; Electrical engineering Graphene research and applications ; 2D Materials and Applications ; Nanowire Synthesis and Applications
Authors: Magdalena Wysocka | Oskar Wysocki | Maxime Delmas | Vincent Mutel | André Freitas
Source: arXiv (Cornell University) (preprint, 2023-05-28)
Field: Materials Science — Materials Chemistry

Large Language Models, scientific knowledge and factuality: A framework to streamline human expert evaluation The paper introduces a framework for the evaluation of the encoding of factual scientific knowledge, designed to streamline the manual evaluation process typically conducted by domain experts. Inferring over and extracting information from Large Language Models (LLMs) trained on a large corpus of scientific literature can potentially define a step change in biomedical discovery, reducing the barriers for accessing and integrating existing medical evidence. This work explores the potential of LLMs for dialoguing with biomedical background knowledge, using the context of antibiotic discovery. The framework involves of three evaluation steps, each assessing different aspects sequentially: fluency, prompt alignment, semantic coherence, factual knowledge, and specificity of the generated responses. By splitting these tasks between non-experts and experts, the framework reduces the effort required from the latter. The work provides a systematic assessment on the ability of eleven state-of-the-art models LLMs, including ChatGPT, GPT-4 and Llama 2, in two prompting-based tasks: chemical compound definition generation and chemical compound-fungus relation determination. Although recent models have improved in fluency, factual accuracy is still low and models are biased towards over-represented entities. The ability of LLMs to serve as biomedical knowledge bases is questioned, and the need for additional systematic evaluation frameworks is highlighted. While LLMs are currently not fit for purpose to be used as biomedical factual knowledge bases in a zero-shot setting, there is a promising emerging property in the direction of factuality as the models become domain specialised, scale-up in size and level of human feedback. Context (archaeology) ; Fluency ; Computer science ; Scientific literature ; Systematic review ; Data science ; Sociology of scientific knowledge ; Natural language processing ; Artificial intelligence ; MEDLINE ; Psychology ; Chemistry ; Biology ; Social science ; Sociology Machine Learning in Materials Science ; Biomedical Text Mining and Ontologies
Authors: Daniel Wigh | Joe Arrowsmith | Alexander Pomberger | Kobi Felton | Alexei A. Lapkin
Source: ChemRxiv (preprint, 2023-08-03)
Field: Materials Science — Materials Chemistry

ORDerly: Datasets and benchmarks for chemical reaction data Machine learning has the potential to provide tremendous value to the chemical and material sciences by providing models that promise to save time, energy, and starting material. Model training requires large amounts of clean high-quality data, and the methodology for transforming raw data to machine learning-ready data should be robust, adaptable, and accessible. However, data is often cleaned differently for different projects using proprietary code, making it difficult to compare approaches and creating additional effort for other researchers who want to work with literature-mined data. Herein, we present ORDerly, an open-source Python package with a novel benchmark for reaction data and a highly customizable pipeline for cleaning chemical reaction data stored in accordance with the Open Reaction Database (ORD) schema. ORDerly contains standard cleaning operations, such as frequency filtering and canonicalization checks, in addition to chemically-informed assignment of reaction roles using atom mapping, bespoke name resolution, and reproducible open-source benchmark generation. We use ORDerly to generate a machine learning-ready benchmark dataset for the prediction of reaction conditions, and through extensive analysis, we find the aforementioned cleaning steps to be essential to provide a high quality dataset for machine learning. In particular, we show that datasets missing key cleaning steps can lead to silently overinflated performance metrics. We then demonstrate that ORDerly can be used in an end-to-end pipeline that goes from raw data to a reaction condition prediction model in less than a day. With this customizable open-source solution for cleaning and preparing chemical reaction data, ORDerly is poised to push forward the boundaries of artificial intelligence applications in chemistry by providing a novel benchmark for chemical reaction conditions, and a data pipeline for researchers in the chemical sciences to leverage large reaction datasets. Computer science ; Bespoke ; Raw data ; Python (programming language) ; Benchmark (surveying) ; Pipeline (software) ; Data quality ; Machine learning ; Data mining ; Database ; Artificial intelligence ; Programming language ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Ghazanfar Mehboob | K. Nadeem | Amjad Iqbal | Gohar Mehboob | Shahnawaz Hussain | Mohamed Ragab | Mazhar Iqbal | Sohaib Ajmal | Adel El‐marghany
Source: Acta Mechanica et Automatica (article, 2023-12-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

The Study of Temperature-Dependent Magnetic Properties Variation in CoCr2O4 Nanoparticles with (y = 0.8) and Without Coating Concentration of Non-Magnetic (SiO2)y Abstract The present study investigates the temperature-dependent magnetic (MT) properties of CoCr2O4/(SiO2)y (y = 0 and 0.8) nanoparticles. Nanoparticles were synthesised by using the conventional sol–gel technique. The X-ray diffraction (XRD) method confirmed the normal spinel structure of CoCr2O4 nanoparticles. The main peak analysis of the XRD pattern using Debye–Scherrer’s formula probes the mean crystallite sizes for coated and uncoated nanoparticles, and the sizes based on which the probes have been carried out amount to 19 nm and 28 nm, respectively. The transmission electron microscopy (TEM) image showed the non-spherical shape of these nanoparticles. Field-cooled (FC) and zero field-cooled (ZFC) MT plots were taken by using a superconducting quantum interference device (SQUID) magnetometer. Pure CoCr2O4 nanoparticles showed the ferrimagnetic transition at Curie temperature (Tc = 99 K) on an applied field (H) of 50 Oe. Tc decreased up to 95 K with the increase in 80% SiO2 concentration in CoCr2O4 nanoparticles. For pure samples, conical spiral temperature (TS) and lock-in transition temperature (TL) remain unchanged with increasing magnetic field because of strong spin–lattice coupling. However, for 80% SiO2 impurity, the decrease in Tc was attributed to the reduction in surface disorder with a minor decline in TS and TL. The Ms declined with a decrease in temperature because of the existence of stiffed/strong conical spin-spiral and lock-in states in pure CoCr2O4 nanoparticles, while nanoparticles with 80% coating SiO2 concentration showed abnormal behavior. The coercivity increases with a decrease in temperature due to a decrease in thermal fluctuations at low temperatures for both samples. The fitting of coercivity (Hc) versus temperature plot by using Kneller’s law has given the values of coercivity constant (α) and coercivity at average blocking temperature (TB) for both samples, which are α = 0.54, TB = 75 K and α = 1.59, TB = 81 K, respectively. Hence, the increase in the concentration of SiO2 decreased nanoparticles size and surface disorder in CoCr2O4 nanoparticles while enhancing Ms below spin-spiral state ordering. Materials science ; Ferrimagnetism ; Coercivity ; Nanoparticle ; Analytical Chemistry (journal) ; Crystallite ; High-resolution transmission electron microscopy ; Curie temperature ; Composite material ; Magnetization ; Transmission electron microscopy ; Metallurgy ; Condensed matter physics ; Nanotechnology ; Magnetic field ; Ferromagnetism ; Chemistry ; Chromatography Multiferroics and related materials ; Magnetic Properties and Synthesis of Ferrites ; Advanced Condensed Matter Physics
Authors: Norazalina Saad | Ache Azurahanim Che Abdullah | Nurul Atikah Adila Zainal | Emmellie Laura Albert
Source: Sains Malaysiana (article, 2023-10-31)
Field: Materials Science — Materials Chemistry

Characterization, Antibacterial and Toxicity Evaluation of Biosynthesized Zinc Oxide Nanoparticles Utilizing Eleuthrine Bulbosa Bulb Extract In the current study, Eleuthrine bulbosa bulb extract was utilized to synthesize zinc oxide nanoparticles (ZnO NPs) in a simple, sustainable, and environmentally friendly manner. The bioactive compounds of E. bulbosa extract were identified by gas chromatography-mass spectrometry (GC-MS). Following synthesis of the ZnO NPs via the green method with E. bulbosa bulb extract as the reducing and capping agent, ZnO NPs were characterized using X-Ray Diffraction (XRD), Fourier Transform Infrared Spectroscopy (FTIR), Ultraviolet-Visible Spectroscopy (UV-Vis), and Photoluminescence (PL) further evaluated for antibacterial and cytotoxic activities. GC-MS analysis showed the presence of phytochemical compounds acting as reducing and capping agents. The UV-Vis spectra of ZnO nanoparticles containing E. bulbosaextract showed an optical energy bandgap between 3.12 and 3.89 eV. In addition, XRD showed that the crystalline size of ZnO NPs ranged from 21 to 68 nm with a wurtzite crystal structure. FTIR analysis showed that the plant extract contains identified functional groups including alcohols, phenols, alkene, and flavonoid compounds that influenced the mechanism of bonding with ZnO NPs. Particularly, the peaks of formation of Zn-O stretching vibrations at 470 to 480 cm-1 were successfully shown. In addition, ZnO NPs displayed antibacterial activity, which was greatest against Staphylococcus aureus, and were cytotoxic to MCF-7 and MCF-10A breast cells with IC50 values of 5.540 µg/mL and 15.77 µg/mL, respectively. ZnO NPs were successfully synthesized utilizing a green method, resulting in intriguing biocompatible potential candidates for use in both biomedical and environmental fields due to their eco-friendly synthesis and nontoxic. Nuclear chemistry ; Fourier transform infrared spectroscopy ; Wurtzite crystal structure ; Zinc ; Chemistry ; Nanoparticle ; Antibacterial activity ; Phytochemical ; Organic chemistry ; Materials science ; Nanotechnology ; Chemical engineering ; Biochemistry ; Bacteria Nanoparticles: synthesis and applications ; Diverse Scientific Research Studies
Authors: Mohammed Ehsan Ullah Shareef - | Shahadat Hossain - | Mayeesha Farzana Khan -
Source: International Journal For Multidisciplinary Research (article, 2023-12-19)
Field: Materials Science — Materials Chemistry

Structural and Magnetic Properties of Manganese Zinc Ferrite Nanoparticles Exhibiting Superparamagnetism This thesis report delves into the intriguing realm of nanoscale magnetic materials by investigating the superparamagnetic behavior of MnZn ferrite nanoparticles. These nanoparticles have garnered significant attention due to their unique magnetic properties, which stem from their small size and high surface-to-volume ratio. The study begins with a comprehensive review of the existing literature on superparamagnetism. The obtained results reveal the Superparamagnetic nature of the MnZn ferrite nanoparticles, focusing on their response to external magnetic fields and their potential applications in various fields. The findings suggest that the nanoparticles' magnetic behavior transitions from superparamagnetic to blocked as the temperature decreases, leading to potential applications in data storage, biomedical imaging, and targeted drug delivery. The implications of these findings extend beyond the realm of fundamental research, opening doors for technological advancements. By understanding and harnessing the superparamagnetic behavior of MnZn ferrite nanoparticles, researchers can design novel devices with enhanced functionalities. However, challenges related to size distribution, stability, and surface modification need to be addressed to fully exploit the potential of these nanoparticles. Superparamagnetism ; Materials science ; Nanoparticle ; Nanotechnology ; Magnetic nanoparticles ; Nanoscopic scale ; Ferrite (magnet) ; Magnetic field ; Magnetization ; Composite material ; Physics Magnetic Properties and Synthesis of Ferrites
Authors: Sunny Choudhary | Deepanshi Singh | Mohan K. Bhattarai | Ram S. Katiyar | Poonam Kumari
Source: Journal of Nepal Physical Society (article, 2023-12-31)
Field: Materials Science — Materials Chemistry

Structural and Dielectric Studies of Ni1−xAlxFe2O4 The nanoparticles of Ni1−xAlxFe2O4(x = 0,0.5) were synthesized via using sol-gel route and studied the structural and dielectric effect of aluminium content on Nickel site. The X-ray diffraction (XRD) revealed the single-phase cubic spinel structure of the parent compound. The lattice constant, volume, density, bond lengths, specific surface area and crystallite size were determined from the Rietveld refinement. In the Raman analysis, there are five active modes present in the synthesized samples. FTIR spectroscopy confirmed the formation of spinel ferrite and showed the characteristics absorption bands of metal oxygen (Fe-O) observed around 424-450 and 601-620 cm−1 corresponding to the stretching vibration frequency of the metal-oxygen at the octahedral site and tetrahedral site respectively. Dielectric behaviour showed that the value of dielectric constant (ε') and tangent loss (tanδ) decreases with frequency and confirmed the general behaviour of ferrites. The AC Conductivity (σac) was found lower in Ni0.5Al0.5Fe2O4 compared to NiFe2O4. Dielectric ; Materials science ; Optoelectronics Magnetic Properties and Synthesis of Ferrites ; Pigment Synthesis and Properties ; Electrical and Thermal Properties of Materials