jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: J. Dhakshayani | B. Surendiran | J. Jyothsna | A. S. Syed Shahul Hameed | Narendran Rajagopalan
Source: International Journal of Computational Science and Engineering (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

A deep learning based automated phenotyping for identification of overuse of synthetic fertilisers in Amaranthus crop Amaranth (Amaranthus spp.) is a significant leafy vegetable and cereal crop with high nutrient benefits that is widely consumed worldwide. To maximise its yield, farmers massively rely upon synthetic fertilisers to enhance the quality of the crop. However, this obsessive usage of inorganic fertiliser leads to severe ecosystem damage. For agricultural and ecological sustainability, it is essential to comprehend the process underlying this environmental degradation. This paper analyses the effect of inorganic fertilisers on the growth and yield of Amaranthus. By identifying Amaranthus's productivity and adaptability in different chemically treated soil conditions and automatically phenotyping its traits using image-based deep learning models, this study aims to determine the overuse of synthetic fertilisers. A comparative evaluation of different state-of-art CNN models was carried out, and the experimental result proves that DenseNet-121 could be a more appropriate learning algorithm for the proposed system with 84% accuracy. It is believed that the proposed deep learning based automated phenotyping framework could greatly assist farmers in understanding the actual requirement of soil, thus avoiding the residual impact of fertiliser abuse in the environment. Identification (biology) ; Crop ; Artificial intelligence ; Deep learning ; Agronomy ; Mathematics ; Biology ; Computer science ; Botany Spectroscopy and Chemometric Analyses ; Seed and Plant Biochemistry
Authors: Bartosz Barzdajn | C.P. Race
Source: Modelling and Simulation in Materials Science and Engineering (article, 2024-12-17)
Field: Materials Science — Materials Chemistry

Optimal design of experiments in the context of machine-learning inter-atomic potentials: improving the efficiency and transferability of kernel based methods Abstract Data-driven machine learning (ML) models of atomistic interactions are often based on flexible and non-physical functions that can relate nuanced aspects of atomic arrangements to predictions of energies and forces. As a result, these potentials are only as good as the training data (usually the results of so-called ab initio simulations), and we need to ensure that we have enough information to make a model sufficiently accurate, reliable and transferable. The main challenge stems from the fact that descriptors of chemical environments are often sparse, high-dimensional objects without a well-defined continuous metric. Therefore, it is rather unlikely that any ad hoc method for selecting training examples will be indiscriminate, and it is easy to fall into the trap of confirmation bias, where the same narrow and biased sampling is used to generate training and test sets. We will show that an approach derived from classical concepts of statistical planning of experiments and optimal design can help to mitigate such problems at a relatively low computational cost. The key feature of the method we will investigate is that it allows us to assess the quality of the data without obtaining reference energies and forces—a so-called offline approach. In other words, we are focusing on an approach that is easy to implement and does not require sophisticated frameworks that involve automated access to high performance computing. Transferability ; Materials science ; Context (archaeology) ; Kernel (algebra) ; Machine learning ; Kernel method ; Artificial intelligence ; Computer science ; Support vector machine ; Mathematics ; Combinatorics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Fault Detection and Control Systems
Authors: Mengze Li | Jiaqi Cai | Yang Yang | Li Deng | Xiaopeng Li | Francesca Iacopi
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Deep Learning Inverse Design of Broadband Dual-Frequency Metasurfaces Using Additive Manufacturing Broadband ; Dual (grammatical number) ; Inverse ; Computer science ; Materials science ; Mathematics ; Telecommunications ; Art Metamaterials and Metasurfaces Applications ; Antenna Design and Analysis ; Advanced Antenna and Metasurface Technologies
Authors: H.P.G.P.A. Sewwandi | P.G.M.P.B. Pallegedara | S.D. Varathaseelan | Isuranga Nipun Kumara | Chamishka Ishani Rupasiri | Umal Nanumura
Source:  (article, 2024-01-01)
Field: Chemistry — Inorganic Chemistry

Automated data-driven system for natural rubber coagulation using coconut water as a coagulant The natural rubber industry covers a wide range of industries, from automotive to healthcare; the efficient and sustainable production of rubber sheets is therefore indispensable. Conventionally, coagulation of rubber involves the use of chemical coagulants like formic acid, which, though effective in their operation, pose serious health risks to workers, including respiratory and skin irritations, especially upon long-term exposure [1]. Besides that, such chemicals contribute to environmental degradation, and shifting toward safer and more sustainable alternatives should be the immediate concern. Coconut water has emerged as a promising natural coagulant due to its inherent acidity, developed during fermentation, which facilitates the coagulation of rubber. It has been reported that the quality of the rubber sheets produced using coconut water is comparable to those produced using chemical agents [2]. In addition, the use of coconut water reduces the health risks for workers and moves toward the global drive to have environmentally friendly industrial practices. However, natural coagulants like coconut water have their own challenges in the application process: variations in environmental conditions and the need for precise monitoring generally create a lack of consistency in production. This paper presents a novel automated system with integration of IoT sensors, machine learning algorithms, and blockchain technology to address some of the issues identified above [3]. IoT sensors monitor, in real-time, critical parameters such as pH, temperature, and coagulation time to maintain optimum conditions along the process. Machine learning models using historical and real-time data predict the quality of rubber sheets to enable the detection of anomalies early on and proactively make adjustments. Blockchain further enhances this with an immutable record of production to ensure traceability and consumer trust [4]. Coagulation ; Natural rubber ; Pulp and paper industry ; Natural (archaeology) ; Computer science ; Process engineering ; Waste management ; Environmental science ; Materials science ; Composite material ; Engineering ; Geology Coconut Research and Applications ; Fisheries and Aquaculture Studies ; Agricultural and Environmental Management
Authors: Mohammad Fakhratul Ridwan Zulkifli | S. Wahab | Samsuri Abdullah | Suriani Binti Mat Jusoh | W.B. Wan Nik
Source: AIP conference proceedings (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Corrosion current density prediction of 3C steel under different seawater environment via artificial neural network The harsh seawater environment provides a corrosive medium to all structures built from metallic materials that operate within this environment. Due to its mechanical strength, ease of manufacture, formability, and low cost, 3C steel is the most widely used structural material in various sectors. However, the corrosion problem cannot be avoided due to its interaction with seawater. Furthermore, due to the extensive range of parameters regulating the rate, predicting the corrosion rate of 3C steel structures in the marine environment is challenging. Hence, corrosion rate modelling improves better understanding in terms of corrosion behaviour. In this study, the corrosion rate prediction of 3C steel in seawater was conducted via an artificial neural network (ANN). This study aims to establish a corrosion current density prediction of 3C steel influenced by different seawater environment parameters. The data came from previous studies that looked at dissolved oxygen, salinity, pH value, temperature, and oxidation-reduction potential to see how these affected the corrosion rate of 3C steel in seawater. Three training algorithms were proposed in Multi-Layer Perceptron (MLP), which are Levenberg Marquardt (LM), Bayesian Regularization (BR), and Scaled Conjugate Gradient (SCG). The results show that the LM algorithm is the best training algorithm in this study according to its performance indicator represented by high regression, R, and low mean square error (MSE) value. Seawater ; Corrosion ; Artificial neural network ; Artificial seawater ; Materials science ; Multilayer perceptron ; Mean squared error ; Metallurgy ; Computer science ; Machine learning ; Mathematics ; Geology ; Statistics Corrosion Behavior and Inhibition ; Non-Destructive Testing Techniques ; Concrete Corrosion and Durability
Authors: Max Huisman | Axel Huerre | Saikat Saha | John C. Crocker | Valeria Garbin
Source: Soft Matter (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Linking local microstructure to fracture location in a two-dimensional amorphous solid under isotropic strain Brittle fracturing of materials is common in natural and industrial processes over a variety of length scales. Knowledge of individual particle dynamics is vital to obtain deeper insight into the atomistic processes governing crack propagation in such materials, yet it is challenging to obtain these details in experiments. We propose an experimental approach where isotropic dilational strain is applied to a densely packed monolayer of attractive colloidal microspheres, resulting in fracture. Using brightfield microscopy and particle tracking, we examine the microstructural evolution of the monolayer during fracturing. Furthermore, we propose and test a parameter termed Weakness that estimates the likelihood for particles to be on a crack line, based on a quantified representation of the microstructure in combination with a machine learning algorithm. Regions that are more prone to fracture exhibit an increased Weakness value, however the exact location of a crack depends on the nucleation site, which cannot be predicted <i>a priori</i>. An analysis of the microstructural features that most contribute to increased Weakness values suggests that local density is more important than orientational order. Our methodology and results provide a basis for further research on microscopic processes during the fracturing process. Microstructure ; Isotropy ; Amorphous solid ; Fracture (geology) ; Strain (injury) ; Materials science ; Composite material ; Crystallography ; Chemistry ; Physics ; Anatomy ; Optics ; Medicine Material Dynamics and Properties ; Metallic Glasses and Amorphous Alloys ; Theoretical and Computational Physics
Authors: Suryamukhi Kuchibhotla | Manish Singh
Source:  (article, 2024-01-01)
Field: Chemistry — Electrochemistry

TpT-ADE: Transformer Based Two-Phase ADE Extraction Extracting adverse reactions to medications or treatments is a crucial activity in the biomedical domain.The task involves identifying mentions of drugs and their adverse effects/events in raw text, which is challenging due to the unstructured nature of clinical narratives.In this paper, we propose TpT-ADE, a novel joint twophase transformer model combined with natural language processing (NLP) techniques, to identify adverse events (AEs) caused by drugs.In the first phase of TpT-ADE, entities are extracted and are grounded with their standard terms using the Unified Medical Language System (UMLS) knowledge base.In the second phase, entity and relation classification is performed to determine the presence of a relationship between the drug and AE pairs.TpT-ADE also identifies the intensity of AE entities by constructing a parts-of-speech (POS) embedding model.Unlike previous approaches that use complex classifiers, TpT-ADE employs a shallow neural network and yet outperforms the state-of-the-art methods on the standard ADE corpus. Transformer ; Computer science ; Extraction (chemistry) ; Materials science ; Electrical engineering ; Engineering ; Voltage ; Chromatography ; Chemistry Electrochemical Analysis and Applications ; Analytical Chemistry and Sensors
Authors: Desheng Ma | Steven E. Zeltmann | Chenyu Zhang | Zhaslan Baraissov | Yu‐Tsun Shao | Cameron Duncan | Jared Maxson | Auralee Edelen | David A. Muller
Source: arXiv (Cornell University) (preprint, 2024-12-29)
Field: Materials Science — Surfaces, Coatings and Films

Emittance Minimization for Aberration Correction I: Aberration correction of an electron microscope without knowing the aberration coefficients Precise alignment of the electron beam is critical for successful application of scanning transmission electron microscopes (STEM) to understanding materials at atomic level. Despite the success of aberration correctors, aberration correction is still a complex process. Here we approach aberration correction from the perspective of accelerator physics and show it is equivalent to minimizing the emittance growth of the beam, the span of the phase space distribution of the probe. We train a deep learning model to predict emittance growth from experimentally accessible Ronchigrams. Both simulation and experimental results show the model can capture the emittance variation with aberration coefficients accurately. We further demonstrate the model can act as a fast-executing function for the global optimization of the lens parameters. Our approach enables new ways to quickly quantify and automate aberration correction that takes advantage of the rapid measurements possible with high-speed electron cameras. In part II of the paper, we demonstrate how the emittance metric enables rapid online tuning of the aberration corrector using Bayesian optimization. Thermal emittance ; Spherical aberration ; Optics ; Contrast transfer function ; Electron microscope ; Physics ; Optical aberration ; Computer science ; Lens (geology) ; Wavefront ; Beam (structure) Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Advancements in Photolithography Techniques
Authors: Stacy M. Copp
Source:  (article, 2024-03-13)
Field: Materials Science — Materials Chemistry

Developing near-infrared emissive DNA-stabilized silver nanoclusters for biomedical imaging in the tissue transparency window The increased transparency of biological tissues and fluids to near-infrared (NIR) light motivates the development of NIR fluorescence imaging. We are developing a promising new class of NIR emitters for fluorescence imaging, DNA-stabilized silver nanoclusters (Ag-DNAs), for bioimaging and sensing. By developing chemically-informed machine learning models, we map the sequence of the templating DNA oligomer onto silver nanocluster color, allowing us to discover new NIR-emissive Ag-DNAs. We also determine how nanocluster geometry influences photophysical and chiroptical properties. Finally, we report that Ag-DNAs can retain their chemical stability in biologically relevant solutions when protected by additional halide ligands. Together, this work has led to the development of new NIR-emissive Ag-DNAs for potential applications as fluorescent contrast agents in the NIR tissue transparency window. Nanoclusters ; Fluorescence ; Materials science ; Near-infrared spectroscopy ; Transparency (behavior) ; Nanotechnology ; Infrared ; DNA ; Optical transparency ; Optoelectronics ; Chemistry ; Optics ; Computer science Nanocluster Synthesis and Applications ; Gold and Silver Nanoparticles Synthesis and Applications ; Advanced biosensing and bioanalysis techniques
Authors: JingLong Gao | Lei Chang | Haitham A. Mahmoud
Source: Aerospace Science and Technology (article, 2024-11-24)
Field: Materials Science — Materials Chemistry

A multi-physical coupling NURBS-based isogeometric analysis for nonlinear electrodynamics of three-directional poroelastic functionally graded circular nanoplate: Introducing deep neural network algorithm for nonlinear electrodynamics problems Nonlinear system ; Isogeometric analysis ; Coupling (piping) ; Poromechanics ; Artificial neural network ; Physics ; Topology (electrical circuits) ; Mathematical analysis ; Classical mechanics ; Mathematics ; Computer science ; Engineering ; Finite element method ; Mechanical engineering ; Electrical engineering ; Artificial intelligence ; Quantum mechanics Nonlocal and gradient elasticity in micro/nano structures ; Composite Structure Analysis and Optimization ; Numerical methods in engineering
Authors: Saibo She | Xuan Pang | Jun Liu | Xinnan Zheng | Kuohai Yu | Xun Zou | Ruoxuan Zhu | Rui Guo | Wuliang Yin
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Fast Inversion of Parameters on Jiles Atherton Hysteresis Model Based on Physics-Guided Deep Learning Network Inversion (geology) ; Hysteresis ; Physics ; Statistical physics ; Artificial intelligence ; Computer science ; Condensed matter physics ; Geology ; Seismology Magnetic Properties and Applications
Authors: Alexander J. Hoffman | Mingrou Xie | Rafael Gómez‐Bombarelli
Source: ChemRxiv (preprint, 2024-10-07)
Field: Chemistry — Inorganic Chemistry

Learning descriptors to predict organic structure-directing agent applicability in zeolite synthesis Zeolite synthesis frequently relies on organic structure-directing agents (OS- DAs), but the process of identifying the best OSDA to synthesize a given zeolite remains difficult. We use previously gathered binding energy data, in additional to the formation energies of the siliceous zeolite frameworks and approximate binding entropies of OSDAs to develop new descriptors to improve predictions based on known OSDA-zeolite pairs in the litera- ture. Our earlier work used templating energy (ET ) to rank the most likely OSDA-zeolite pairs to be produced from synthesis. Using literature recall area-under-the-curve (AUC) as a performance metric, we find that comput- ing energies associated with the net transformation that occurs during zeo- lite synthesis (the sum of the formation energy of the zeolite framework and the OSDA binding energy) provides a modest improvement over ET when predicting the zeolite phase that a given OSDA produces, from 67.1% aver- age literature recall to 69.1%, but slightly worsens predictions for the best OSDA for a given zeolite framework, from 67.7% to 64.9%. We then use machine learning symbolic regression to develop a new descriptor, which we call αT , that slightly improves upon ET for predicting an OSDA for a given framework, with an average literature recall of 68.2%. While zeolite synthesis remains difficult to predict a priori, the approaches used in this work provide one option for improving these predictions. Zeolite ; Computer science ; Artificial intelligence ; Chemistry ; Organic chemistry ; Catalysis Zeolite Catalysis and Synthesis ; Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Alexander J. Hoffman | Mingrou Xie | Rafael Gómez‐Bombarelli
Source: ChemRxiv (preprint, 2024-12-06)
Field: Materials Science — Materials Chemistry

Learning descriptors to predict organic structure-directing agent applicability in zeolite synthesis Zeolite synthesis frequently relies on organic structure-directing agents (OSDAs), but the process of identifying the best OSDA to synthesize a given zeolite remains difficult. We use previously gathered binding energy data, in additional to the formation energies of the siliceous zeolite frameworks and approximate binding entropies of OSDAs to develop new descriptors to improve predictions based on known OSDA-zeolite pairs in the literature. Our earlier work used templating energy (Eij,T) to rank the most likely OSDA-zeolite pairs to be produced from synthesis. Using literature recall area-under-the-curve (AUC) as a performance metric, we find that computing energies associated with the net transformation that occurs during zeolite synthesis (the sum of the formation energy of the zeolite framework and the OSDA binding energy) provides a modest improvement over Eij,T when predicting the zeolite phase that a given OSDA produces, from 67.5% average literature recall to 72.3%, but negligibly improves predictions for the best OSDA for a given zeolite framework, from 68.3% to 68.8%. We then use machine learning symbolic regression to develop a new descriptor, which we call αij,T, that slightly improves upon Eij,T for predicting an OSDA for a given framework, with an average literature recall of 71.8%. While zeolite synthesis remains difficult to predict a priori, the approaches used in this work provide one option for improving these predictions. Zeolite ; Materials science ; Metric (unit) ; Energy (signal processing) ; Work (physics) ; A priori and a posteriori ; Algorithm ; Nanotechnology ; Mechanical engineering ; Computer science ; Mathematics ; Engineering ; Chemistry ; Statistics ; Organic chemistry Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis ; Computational Drug Discovery Methods
Authors: Zhen Yue | Jun Li | Lisheng Liu | Hai Mei
Source: Applied Physics A (article, 2024-02-20)
Field: Materials Science — Materials Chemistry

Atomistic explanation of compression-induced deformation mechanisms in boron carbide Boron carbide ; Deformation (meteorology) ; Compression (physics) ; Materials science ; Deformation mechanism ; Carbide ; Boron ; Composite material ; Crystallography ; Chemistry ; Microstructure Boron and Carbon Nanomaterials Research ; Advanced materials and composites ; Diamond and Carbon-based Materials Research