jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Pieter-Jan Hoedt | Günter Klambauer
Source: arXiv (Cornell University) (preprint, 2023-12-19)
Field: Materials Science — Materials Chemistry

Principled Weight Initialisation for Input-Convex Neural Networks Input-Convex Neural Networks (ICNNs) are networks that guarantee convexity in their input-output mapping. These networks have been successfully applied for energy-based modelling, optimal transport problems and learning invariances. The convexity of ICNNs is achieved by using non-decreasing convex activation functions and non-negative weights. Because of these peculiarities, previous initialisation strategies, which implicitly assume centred weights, are not effective for ICNNs. By studying signal propagation through layers with non-negative weights, we are able to derive a principled weight initialisation for ICNNs. Concretely, we generalise signal propagation theory by removing the assumption that weights are sampled from a centred distribution. In a set of experiments, we demonstrate that our principled initialisation effectively accelerates learning in ICNNs and leads to better generalisation. Moreover, we find that, in contrast to common belief, ICNNs can be trained without skip-connections when initialised correctly. Finally, we apply ICNNs to a real-world drug discovery task and show that they allow for more effective molecular latent space exploration. Convexity ; Computer science ; Regular polygon ; Artificial neural network ; Set (abstract data type) ; SIGNAL (programming language) ; Space (punctuation) ; Task (project management) ; Artificial intelligence ; Algorithm ; Mathematical optimization ; Mathematics Machine Learning in Materials Science ; Adversarial Robustness in Machine Learning ; Computational Drug Discovery Methods
Authors: Shoukun Sun | Fei Xu | Lu Cai | Daniele Salvato | Fidelma Dilemma | Luca Capriotti | Min Xian | Tiankai Yao
Source: arXiv (Cornell University) (preprint, 2023-02-08)
Field: Materials Science — Materials Chemistry

An Efficient Instance Segmentation Approach for Extracting Fission Gas Bubbles on U-10Zr Annular Fuel U-10Zr-based nuclear fuel is pursued as a primary candidate for next-generation sodium-cooled fast reactors. However, more advanced characterization and analysis are needed to form a fundamental understating of the fuel performance, and make U-10Zr fuel qualify for commercial use. The movement of lanthanides across the fuel section from the hot fuel center to the cool cladding surface is one of the key factors to affect fuel performance. In the advanced annular U-10Zr fuel, the lanthanides present as fission gas bubbles. Due to a lack of annotated data, existing literature utilized a multiple-threshold method to separate the bubbles and calculate bubble statistics on an annular fuel. However, the multiple-threshold method cannot achieve robust performance on images with different qualities and contrasts, and cannot distinguish different bubbles. This paper proposes a hybrid framework for efficient bubble segmentation. We develop a bubble annotation tool and generate the first fission gas bubble dataset with more than 3000 bubbles from 24 images. A multi-task deep learning network integrating U-Net and ResNet is designed to accomplish instance-level bubble segmentation. Combining the segmentation results and image processing step achieves the best recall ratio of more than 90% with very limited annotated data. Our model shows outstanding improvement by comparing the previously proposed thresholding method. The proposed method has promising to generate a more accurate quantitative analysis of fission gas bubbles on U-10Zr annular fuels. The results will contribute to identifying the bubbles with lanthanides and finally build the relationship between the thermal gradation and lanthanides movements of U-10Zr annular fuels. Mover, the deep learning model is applicable to other similar material micro-structure segmentation tasks. Segmentation ; Bubble ; Computer science ; Fission ; Thresholding ; Artificial intelligence ; Physics ; Nuclear physics ; Image (mathematics) Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Radioactive element chemistry and processing
Authors: Wenyu Sun | Xiaoyi Wang
Source: Highlights in Science Engineering and Technology (article, 2023-03-29)
Field: Chemistry — Inorganic Chemistry

Composition analysis and identification of ancient glass products Based on the proportion of various components of ancient glass products and the classification method, the composition, change and relation of different types of cultural relics under different weathering conditions are analyzed in detail in this paper. The statistical rules and the basis for the classification of cultural relics are given, and on this basis, the unknown types of glass cultural relics are identified. Firstly, a statistical model is established, and Chi-square test is used to analyze the factors that affect the weathering of cultural relics. It is concluded that the weathering of cultural relics is related to the type of glass and the type of decoration, and the type of glass has a greater influence on the weathering. The chemical composition of the weathered cultural relics before weathering is predicted. Then, after the classification rule of glass is preliminarily explored, the grey correlation model is established to calculate the correlation degree between each chemical component and the two kinds of glass, and then the K-means clustering model is used to divide the glass into subclasses. Thirdly, BP neural network was used to analyze the chemical composition of glass relics of unknown category, identify their types, and analyze the sensitivity of classification results. Finally, according to the idea of discrete Frechet distance algorithm, the cultural relics are grouped according to the type of glass, and the discrete Frechet distance of the broken line of the change rate of the content of different chemical components between cultural relics is used as the basis to judge the correlation relationship, and the correlation relationship between the two substances is analyzed. Weathering ; Chemical composition ; Type (biology) ; Composition (language) ; Statistical analysis ; Basis (linear algebra) ; Mineralogy ; Degree (music) ; Relation (database) ; Mathematics ; Geology ; Materials science ; Statistics ; Computer science ; Geochemistry ; Art ; Physics ; Geometry ; Thermodynamics ; Paleontology ; Literature Pigment Synthesis and Properties ; Advanced Technology in Applications ; Digital Media and Visual Art
Authors: Baobin Xie | Shenyou Peng | Jia Li | Qihong Fang | Peter K. Liaw
Source: Advanced Engineering Materials (article, 2023-06-28)
Field: Materials Science — Materials Chemistry

Physics‐Informed Multistage Machine Learning Strategy for the Nanomachining‐Induced Plastic Deformation Behavior The evolution of the dislocation density induced by the nanomachining process dominates the plastic deformation behaviors of materials, thus affecting the mechanical properties significantly. However, a challenging topic related to how to establish an accurate model for predicting the dislocation density based on the limited simulations and experiments arises due to the complicated thermal–mechanical coupling mechanism during the machining process. Herein, a multistage method integrating machine learning, physics, and high‐throughput atomic simulation is proposed to investigate the effect of cutting speed on the dislocation behavior in polycrystal copper. Compared with the traditional one‐step machine learning method, the constraint of physical features effectively improves the accuracy and generalization ability of the model. The results indicate that the dislocation behaviors depend on the competition between the cutting force and temperature. In the low‐cutting speed, the predominated role of the cutting temperature leads to a rapid decline of the dislocation density. In contrast, the dislocation density tends to be stable under a high‐speed cutting process due to the dynamic balance between the effects of the cutting force and temperature. Notably, the proposed strategy provides a new and universal framework to design the machining parameters to obtain high‐quality products. Dislocation ; Machining ; Materials science ; Deformation (meteorology) ; Plasticity ; Mechanical engineering ; Process (computing) ; Thermal ; Mechanics ; Computer science ; Composite material ; Physics ; Metallurgy ; Engineering Microstructure and mechanical properties ; Metal and Thin Film Mechanics ; Force Microscopy Techniques and Applications
Authors: Fleming Kretschmer | Eva-Maria Harrieder | Martin Hoffmann | Sebastian Boecker | Michael Witting
Source: ChemRxiv (preprint, 2023-07-05)
Field: Chemistry — Spectroscopy

RepoRT: A comprehensive repository for small molecule retention times Liquid chromatography is frequently employed for the separation of metabolites and other small molecules. Prediction of retention times via machine learning methods can assist compound annotation. Yet, transferable predictions are intrinsically complicated for novel compounds and novel chromatographic conditions because retention times depend both on compound structure and the employed chromatographic system. We present RepoRT, the first repository for retention time data. RepoRT presently contains 373 datasets, 8809 unique compounds, and 88,325 retention time entries measured on 49 different chromatographic columns using varying eluents, flow rates, and temperatures. We put particular effort on making RepoRT “machine learning-ready”: We performed an extensive manual curation, cleaning and completion of the available data; we developed automated methods for data validation during upload; we collected more than 45,000 different columns of different vendors with their lengths, particle sizes, inner diameters, and pore sizes to create a database with normalized column names; and, we ensured that features required for transferable predictions, such as parameters numerically describing the selectivity of chromatographic columns, are readily available. For version control and reproducible research, RepoRT is hosted on GitHub. Column (typography) ; Upload ; Retention time ; Chromatography ; Computer science ; Chromatographic separation ; Chemistry ; High-performance liquid chromatography Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Jeremy Cleeman | Rajiv Malhotra
Source:  (article, 2023-06-12)
Field: Materials Science — Materials Chemistry

Highly Parsimonious Multi-Fidelity Learning of Process Parameter-Performance Relationships: A Case Study With Fused Filament Fabrication Abstract Modeling the effects of the process parameters on the process or product performance is critical for predictive process design and control. While physics-based modeling has long been the path to such predictions, machine learning models are of increasing interest recently. But the data-hungry nature of machine learning entails prohibitive experimental cost, is hindered by large oversimplifications of computationally efficiency analytical models, or is limited by the time and cost of creating and executing more accurate numerical models. The situation becomes worse for newer processes or material for which the underlying coupled physics is often not fully known, thus decelerating process or material deployment. Transfer learning has been proposed as a solution for this problem, but the above issues remain since a source model of sufficient fidelity is still needed. This paper takes a first stab at this issue. We propose a physics-informed transfer learning that combines a large amount of data from an extremely low-fidelity but equally computationally efficient process model based on first principles with a small experimental dataset that represents ground truth. We demonstrate this approach by creating a support vector regression model of printed line width in Fused Filament Fabrication. Orders of magnitude reduction in computational cost and 60% reduction in experimental data needed is achieved, while retaining similar or lower error as compared to naïve learning on a much larger experimental dataset. We demonstrate the importance of the number and location of high-fidelity experimental data points and discuss future work in this context. Computer science ; Process (computing) ; Context (archaeology) ; Machine learning ; Reduction (mathematics) ; Fidelity ; Ground truth ; High fidelity ; Artificial intelligence ; Transfer of learning ; Engineering ; Mathematics Machine Learning in Materials Science ; Fault Detection and Control Systems ; Machine Learning and Algorithms
Authors: Shashikant Kumar | Xin Jing | John E. Pask | Andrew J. Medford | Phanish Suryanarayana
Source: arXiv (Cornell University) (preprint, 2023-10-10)
Field: Materials Science — Materials Chemistry

Kohn-Sham accuracy from orbital-free density functional theory via $Δ$-machine learning We present a $Δ$-machine learning model for obtaining Kohn-Sham accuracy from orbital-free density functional theory (DFT) calculations. In particular, we employ a machine learned force field (MLFF) scheme based on the kernel method to capture the difference between Kohn-Sham and orbital-free DFT energies/forces. We implement this model in the context of on-the-fly molecular dynamics simulations, and study its accuracy, performance, and sensitivity to parameters for representative systems. We find that the formalism not only improves the accuracy of Thomas-Fermi-von Weizs{ä}cker (TFW) orbital-free energies and forces by more than two orders of magnitude, but is also more accurate than MLFFs based solely on Kohn-Sham DFT, while being more efficient and less sensitive to model parameters. We apply the framework to study the structure of molten Al$_{0.88}$Si$_{0.12}$, the results suggesting no aggregation of Si atoms, in agreement with a previous Kohn-Sham study performed at an order of magnitude smaller length and time scales. Density functional theory ; Formalism (music) ; Kernel (algebra) ; Physics ; Context (archaeology) ; Kohn–Sham equations ; Orbital-free density functional theory ; Time-dependent density functional theory ; Statistical physics ; Computer science ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Electron and X-Ray Spectroscopy Techniques
Authors: Yang Xu | Xiuhong Hu | Chenchen Wang | Yongrui Liu | Quan Chen | Haiyan Liu
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Biomaterials

De novo Design of Cavity-Containing Proteins with a Backbone-Centered Neural Network Energy Function Function (biology) ; Artificial neural network ; Energy (signal processing) ; Computer science ; Neuroscience ; Cell biology ; Biology ; Physics ; Artificial intelligence Supramolecular Self-Assembly in Materials
Authors: S. Merouane
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Surfaces, Coatings and Films

A New Automatic Analysis Tool for the Determination of Primary Particle Size from Electron Microscopy Images: Application of the Cellpose Software Electron microscope ; Software ; Single particle analysis ; Particle size ; Particle (ecology) ; Microscopy ; Computer science ; Artificial intelligence ; Chemistry ; Physics ; Chemical engineering ; Optics ; Biology ; Engineering Electron and X-Ray Spectroscopy Techniques
Authors: Michael Oberaigner | Manuel Ederer | Sandeep Kumar Chaluvadi | P. Orgiani | Regina Ciancio | Stefan Löffler | Gerald Kothleitner | Daniel Knez
Source: ArXiv.org (preprint, 2023-10-05)
Field: Materials Science — Materials Chemistry

Visualising emergent phenomena at oxide interfaces Knowledge of atomic-level details of structure, chemistry, and electronic states is paramount for a comprehensive understanding of emergent properties at oxide interfaces. We utilise a novel methodology based on atomic-scale electron energy loss spectroscopy (EELS) to spatially map the electronic states tied to the formation of a two-dimensional electron gas (2DEG) at the prototypical non-polar/polar $TiO_2$/$LaAlO_3$ interface. Combined with differential phase contrast analysis we directly visualise the microscopic locations of ions and charge and find that 2DEG states and $Ti^{3+}$ defect states exhibit different spatial distributions. Supported by density functional theory (DFT) and inelastic scattering simulations we examine the role of oxygen vacancies in 2DEG formation. Our work presents a general pathway to directly image emergent phenomena at interfaces using this unique combination of arising microscopy techniques with machine learning assisted data analysis procedures. Oxide ; Human–computer interaction ; Computer science ; Materials science ; Metallurgy Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Electronic and Structural Properties of Oxides
Authors: Sreyas Chintapalli | Tina Gao | Luna Singh | Serene Kamal | Arlene Chiu | Yijun Zhang | Susanna M. Thon
Source:  (article, 2023-06-11)
Field: Materials Science — Materials Chemistry

Inverse Design of Spectrally-Selective Films for PbS-CQD Tandem Solar Cells We present a method for designing spectrally-selective optoelectronic films with a finite absorption bandwidth. We demonstrate the process by designing a film composed of lead sulfide colloidal quantum dots (PbS-CQDs). Designs incorporate the patterning of absorbing PbS-CQD films into photonic crystal-like slabs which couple incident light into leaky modes within the plane of the absorbing films, modulating the absorption spectrum. Computational times required to calculate optical spectra are drastically decreased by implementing the Fourier Modal Method. Furthermore, a supervised machine-learning-based inverse design methodology is presented which allows tailoring of the PbS-CQD film optical properties for use in a variety of photovoltaic applications, such as tandem cells in which spectral tailoring can enable current-matching flexilibility. Materials science ; Lead sulfide ; Optoelectronics ; Photonic crystal ; Quantum dot ; Absorption (acoustics) ; Broadband ; Tandem ; Optics ; Nanotechnology ; Physics Quantum Dots Synthesis And Properties ; Chalcogenide Semiconductor Thin Films ; Photonic Crystals and Applications
Authors: Ryo Yamada | Keisuke Haraguchi | Munekazu Ohno
Source: Modelling and Simulation in Materials Science and Engineering (article, 2023-01-04)
Field: Materials Science — Materials Chemistry

Controlling thermal noise in phase-field simulation for nucleation during isothermal solidification of a pure metal Abstract It is currently challenging to quantitatively describe the nucleation behavior using the phase-field model because it depends on the computational grid size. In this study, the grid size dependence of nucleation behavior during isothermal solidification in pure Fe was investigated by changing the magnitude of thermal noise inducing the nucleation. To remove such dependence, a neural network was utilized to compute the magnitude of thermal noise needed to describe the desired nucleation rate for a given grid size and degree of undercooling. It was found that the nucleation rate can be controlled using the present approach, and the nucleation rates in pure Fe obtained by molecular dynamics simulations and experiments could be accurately reproduced. Nucleation ; Supercooling ; Materials science ; Isothermal process ; Phase (matter) ; Thermal ; Noise (video) ; Thermodynamics ; Molecular dynamics ; Chemical physics ; Physics ; Computer science ; Computational chemistry ; Chemistry Solidification and crystal growth phenomena ; Aluminum Alloy Microstructure Properties ; Microstructure and Mechanical Properties of Steels
Authors: Daniil Nikitin | Kateryna Biliak | Jonathan Lemke | Mariia Protsak | Pavel Pleskunov | Marco Tosca | Suren Ali‐Ogly | Veronika Červenková | Blessing Adejube | Lucia Bajtošová | Zulfiya Černochová | Ján Prokeš | Ivo Křivka | Hynek Biederman | Franz Faupel | Alexander Vahl | Andrei Choukourov
Source: NANOCOM ... (article, 2023-01-01)
Field: Materials Science — Biomaterials

SURFACTANT-FREE SILVER NANOFLUIDS As LIQUID SYSTEMS WITH NEUROMORPHIC POTENTIAL Neuromorphic engineering is a rapidly developing branch of science that aims to implement the unique attributes of biological neural networks in artificial devices. Most neuromorphic devices are based on the resistive switching effect, which involves changing the device’s conductivity in response to an external electric field. For instance, percolating nanoparticle (NP) networks produced by gas aggregation cluster sources (GAS) show collective spiking behavior in conductivity reminiscent of brain-like dynamics. Nevertheless, the problem of dynamic spatial reconfiguration in solid-state neuromorphic systems remains unsolved. Herein, novel nanofluids with resistive switching properties are proposed as neuromorphic media. They are produced by depositing silver NPs from GAS into vacuum-compatible liquids (paraffin, silicon oil, and PEG) without the use of surfactants or other chemicals. When the electric field is applied between two electrodes, the migration of NPs toward biased electrode is detected in all liquids. The electrophoretic nature of the NP movement was proved by means of ζ-potential measurements. Such movement led to the self-assembly of NPs in conductive paths connecting the electrodes and, as a result, to resistive switching. The electrical response was strongly dependent on the dielectric constant of the base liquid. The Ag-PEG nanofluid demonstrated the best switching performance reproducible during several tens of current-voltage cycles. The growth of flexible and reconfigurable conductive filaments in nanofluids makes them suitable media for potential realization of 3D neural networks. Neuromorphic engineering ; Nanofluid ; Pulmonary surfactant ; Computer science ; Materials science ; Nanotechnology ; Physics ; Artificial intelligence ; Artificial neural network ; Thermodynamics ; Nanoparticle Supramolecular Self-Assembly in Materials ; Ionic liquids properties and applications ; Conducting polymers and applications
Authors: Monday Omotioma | Okechukwu Domonic Onukwuli | Obiora-Okafor Ifeoma Amaoge | FRIDAY Nwankwo ARCHIBONG | Peace Ugochinyerem Nlemedim
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

Modeling Inhibition Efficiency of Mango Leaf Extract for Corrosion Control of Mild Steel in Hcl Solution: Response Surface Methodology and Artificial Neural Network Artificial neural network ; Corrosion ; Response surface methodology ; Biological system ; Process engineering ; Materials science ; Metallurgy ; Biochemical engineering ; Chemical engineering ; Computer science ; Artificial intelligence ; Chemistry ; Engineering ; Chromatography ; Biology Corrosion Behavior and Inhibition