jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shujing Zhao | N. Birbilis
Source: arXiv (Cornell University) (preprint, 2022-08-11)
Field: Materials Science — Metals and Alloys

Searching for chromate replacements using natural language processing and machine learning algorithms The past few years has seen the application of machine learning utilised in the exploration of new materials. As in many fields of research - the vast majority of knowledge is published as text, which poses challenges in either a consolidated or statistical analysis across studies and reports. Such challenges include the inability to extract quantitative information, and in accessing the breadth of non-numerical information. To address this issue, the application of natural language processing (NLP) has been explored in several studies to date. In NLP, assignment of high-dimensional vectors, known as embeddings, to passages of text preserves the syntactic and semantic relationship between words. Embeddings rely on machine learning algorithms and in the present work, we have employed the Word2Vec model, previously explored by others, and the BERT model - applying them towards a unique challenge in materials engineering. That challenge is the search for chromate replacements in the field of corrosion protection. From a database of over 80 million records, a down-selection of 5990 papers focused on the topic of corrosion protection were examined using NLP. This study demonstrates it is possible to extract knowledge from the automated interpretation of the scientific literature and achieve expert human level insights. Computer science ; Artificial intelligence ; Field (mathematics) ; Machine learning ; Word2vec ; Natural language processing ; Interpretation (philosophy) ; Domain (mathematical analysis) ; Programming language Hydrogen embrittlement and corrosion behaviors in metals
Authors: Yi Wang | Fan Zhang | Mengru Wang | Xiaoling Mou | Shu-Hui Liu | Zheng Jiang | Wei Liu | Ronghe Lin | Yunjie Ding
Source: Angewandte Chemie (article, 2022-10-18)
Field: Materials Science — Materials Chemistry

Discerning the Contributions of Gold Species in Butadiene Hydrogenation: From Single Atoms to Nanoparticles Abstract Identification of the roles of different active sites is vital for the rational design of catalysts. We present a cutting‐edge strategy to discern the contributions of different single‐atom gold species and nanoparticles in 1,3‐butadiene hydrogenation, through coupling of advanced spectroscopic techniques, electron microscopy‐based automated image analyses, and steady‐state and kinetic studies. While all the carbon‐hosted single gold atoms display negligible initial activity, the in situ‐evolved gold nanoparticles are highly active. Full metal‐species quantification is realized by combining electron‐microscopy‐based atom recognition statistics and deep‐learning‐driven nanoparticle segmentation algorithm, allowing the structure–activity correlations for the hybrid catalysts containing different Au architectures to be established. Surface exposure density of Au nanoparticles, as revealed by electron‐microscopy‐based statistics, is revealed as a new and reliable activity descriptor. Nanoparticle ; Colloidal gold ; Catalysis ; Atom (system on chip) ; Nanotechnology ; Materials science ; Microscopy ; Carbon fibers ; Electron microscope ; Chemistry ; Chemical physics ; Computer science ; Physics ; Organic chemistry ; Optics Catalytic Processes in Materials Science ; Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Yuxing Zhou | Wei Zhang | E. Ma | Volker L. Deringer
Source: arXiv (Cornell University) (preprint, 2022-07-28)
Field: Materials Science — Materials Chemistry

Unlocking device-scale atomistic modelling of phase-change memory materials Quantum-accurate computer simulations play a central role in understanding phase-change materials (PCMs) for advanced memory technologies. However, direct quantum-mechanical simulations are necessarily limited to simplified models, containing no more than a few hundred or a thousand atoms. Machine learning (ML) based potential models that are "trained" on quantum-mechanical data are an emerging alternative approach, currently evolving from highly specialised to more widely applied simulation tools. Here we show that a universal, compositionally flexible ML model can describe a wide range of flagship Ge-Sb-Te PCMs under real device conditions, including non-isothermal heating and chemical disorder which are relevant for memory applications. The speed of the ML model enables atomistic simulations of multiple thermal cycles and delicate operations for neuro-inspired computing, namely, cumulative SET and iterative RESET. A device-scale capability demonstration (40 x 20 x 20 nm3) shows that the new ML potential can directly describe technologically relevant processes in PCM-based memory products. In a wider context, our work demonstrates how ML-driven materials simulations are now entering a stage where they can guide architecture design for high-performance electronic devices. Computer science ; Context (archaeology) ; Phase-change memory ; Quantum ; Scale (ratio) ; Range (aeronautics) ; Isothermal process ; Computational science ; Nanotechnology ; Materials science ; Physics Phase-change materials and chalcogenides ; Machine Learning in Materials Science ; Advanced Memory and Neural Computing
Authors: Jens Oppliger | M. Michael Denner | J. Küspert | Ruggero Frison | Qisi Wang | A. Morawietz | Oleh Ivashko | Ann‐Christin Dippel | M. v. Zimmermann | I. Biało | Leonardo Martinelli | Benoît Fauqué | Jaewon Choi | Mirian García‐Fernández | Ke‐Jin Zhou | Niels Christensen | T. Kurosawa | N. Momono | Migaku Oda | Fabian Donat Natterer | Mark H. Fischer | Titus Neupert | J. Chang
Source: arXiv (Cornell University) (preprint, 2022-09-19)
Field: Materials Science — Materials Chemistry

Weak-signal extraction enabled by deep-neural-network denoising of diffraction data Removal or cancellation of noise has wide-spread applications for imaging and acoustics. In every-day-life applications, denoising may even include generative aspects, which are unfaithful to the ground truth. For scientific use, however, denoising must reproduce the ground truth accurately. Here, we show how data can be denoised via a deep convolutional neural network such that weak signals appear with quantitative accuracy. In particular, we study X-ray diffraction on crystalline materials. We demonstrate that weak signals stemming from charge ordering, insignificant in the noisy data, become visible and accurate in the denoised data. This success is enabled by supervised training of a deep neural network with pairs of measured low- and high-noise data. We demonstrate that using artificial noise does not yield such quantitatively accurate results. Our approach thus illustrates a practical strategy for noise filtering that can be applied to challenging acquisition problems. Noise reduction ; Ground truth ; Computer science ; Noise (video) ; Convolutional neural network ; Artificial intelligence ; Artificial neural network ; Pattern recognition (psychology) ; SIGNAL (programming language) ; Deep learning ; Image (mathematics) Machine Learning in Materials Science ; Seismic Imaging and Inversion Techniques ; Seismology and Earthquake Studies
Authors: Arkaprava Banerjee | Agnieszka Gajewicz | Kunal Roy
Source: ChemRxiv (preprint, 2022-11-16)
Field: Materials Science — Materials Chemistry

A machine learning q-RASPR approach for efficient predictions of the specific surface area of perovskites In this study, the specific surface area of various perovskites was modeled using a novel quantitative read-across structure-property relationship (q-RASPR) approach, which clubs both Read-Across (RA) and quantitative structure-property relationship (QSPR) together. After optimization of the hyper-parameters, certain similarity-based error measures for each query compound were obtained. Clubbing some of these error-based measures with the previously selected features along with the Read-Across prediction function, a number of machine learning models were developed using Partial Least Squares (PLS), ridge regression (RR), linear support vector regression (LSVR), and random forest (RF) regression. Based on the external prediction quality and interpretability, the PLS model was selected as the best predictor which underscored the previously reported results. The finally selected model should efficiently predict specific surface areas of other perovskites for their use in photocatalysis. The new q-RASPR method also appears promising for the prediction of several other property endpoints of interest in materials science. Interpretability ; Random forest ; Quantitative structure–activity relationship ; Partial least squares regression ; Similarity (geometry) ; Support vector machine ; Regression ; Property (philosophy) ; Ridge ; Artificial intelligence ; Computer science ; Surface (topology) ; Linear regression ; Machine learning ; Mean squared error ; Regression analysis ; Mathematics ; Statistics ; Geology Machine Learning in Materials Science ; Advanced Photocatalysis Techniques ; Pigment Synthesis and Properties
Authors: Maksim Zhdanov | Nico Hoffmann | Gabriele Cesa
Source: arXiv (Cornell University) (preprint, 2022-12-12)
Field: Materials Science — Materials Chemistry

Implicit Convolutional Kernels for Steerable CNNs Steerable convolutional neural networks (CNNs) provide a general framework for building neural networks equivariant to translations and transformations of an origin-preserving group $G$, such as reflections and rotations. They rely on standard convolutions with $G$-steerable kernels obtained by analytically solving the group-specific equivariance constraint imposed onto the kernel space. As the solution is tailored to a particular group $G$, implementing a kernel basis does not generalize to other symmetry transformations, complicating the development of general group equivariant models. We propose using implicit neural representation via multi-layer perceptrons (MLPs) to parameterize $G$-steerable kernels. The resulting framework offers a simple and flexible way to implement Steerable CNNs and generalizes to any group $G$ for which a $G$-equivariant MLP can be built. We prove the effectiveness of our method on multiple tasks, including N-body simulations, point cloud classification and molecular property prediction. Equivariant map ; Kernel (algebra) ; Simple (philosophy) ; Convolutional neural network ; Perceptron ; Group (periodic table) ; Point cloud ; Computer science ; Symmetry group ; Artificial intelligence ; Representation (politics) ; Constraint (computer-aided design) ; Pattern recognition (psychology) ; Pure mathematics ; Mathematics ; Artificial neural network ; Geometry ; Physics Machine Learning in Materials Science ; Neural Networks and Applications
Authors: Bing Huang | O. Anatole von Lilienfeld | Jaron T. Krogel | Anouar Benali
Source: arXiv (Cornell University) (preprint, 2022-10-12)
Field: Materials Science — Materials Chemistry

Towards DMC accuracy across chemical space with scalable $Δ$-QML In the past decade, quantum diffusion Monte Carlo (DMC) has been demonstrated to successfully predict the energetics and properties of a wide range of molecules and solids by numerically solving the electronic many-body Schrödinger equation. We show that when coupled with quantum machine learning (QML) based surrogate methods the computational burden can be alleviated such that QMC shows clear potential to undergird the formation of high quality descriptions across chemical space. We discuss three crucial approximations necessary to accomplish this: The fixed node approximation, universal and accurate references for chemical bond dissociation energies, and scalable minimal amons set based QML (AQML) models. Numerical evidence presented includes converged DMC results for over one thousand small organic molecules with up to 5 heavy atoms used as amons, and 50 medium sized organic molecules with 9 heavy atoms to validate the AQML predictions. Numerical evidence collected for $Δ$-AQML models suggests that already modestly sized QMC training data sets of amons suffice to predict total energies with near chemical accuracy throughout chemical space. Chemical space ; Scalability ; Quantum Monte Carlo ; Quantum chemical ; Dissociation (chemistry) ; Monte Carlo method ; Space (punctuation) ; Statistical physics ; Molecule ; Quantum chemistry ; Computer science ; Basis set ; Physics ; Chemistry ; Quantum mechanics ; Mathematics ; Physical chemistry ; Statistics Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Protein Structure and Dynamics
Authors: Shaorui Qin | Taiyun Zhu | Shenglong Zhu | Jianlin Li | Yanguo Ke | Han Li | Yuan Li
Source: 2022 4th Asia Energy and Electrical Engineering Symposium (AEEES) (article, 2022-03-25)
Field: Chemistry — Analytical Chemistry

Quantitative Assessment of Degree of Polymerization of Insulating Paper by Optimized Bayesian-SVR Algorithm Insulation paper is the main insulating material for oil-immersed power (OIP) equipment and the diagnosis of its aging condition is important to maintain the health of OIP equipment. In recent years, researchers have proposed the use of near-infrared spectroscopy (NIRS) as an alternative to the viscosity method for detecting the degree of polymerization (DP) of insulating paper, but the complex mapping relationship between the spectra and DP has led to poor performance of the traditional algorithm. In this paper, an optimized support vector regression (SVR) algorithm is proposed for the first time to model the quantitative analysis of NIRS and DP of insulating paper. We firstly obtained the insulating paper spectral dataset in the laboratory and carried out noise reduction and abnormal sample rejection, then analyzed the effect of the SVR algorithm hyperparameters <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\varepsilon,\ \sigma$</tex> ; C on the model performance, and used the Bayesian optimization algorithm to optimize the SVR hyperparameters, and finally used the SVR algorithm to build an evaluation model and compared it with the partial least squares (PLS) and BP neural network (BPNN) models. The results show that the Bayesian-SVR model performs better on both the training and test sets. The algorithm proposed in this paper significantly improved the current algorithms. Hyperparameter ; Algorithm ; Support vector machine ; Computer science ; Bayesian network ; Partial least squares regression ; Artificial neural network ; Bayesian probability ; Degree (music) ; Artificial intelligence ; Pattern recognition (psychology) ; Machine learning Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Fault Detection and Control Systems
Authors: Stephan Rosenkranz | Matthew Krogstad | R. Osborn
Source: Acta Crystallographica Section A Foundations and Advances (article, 2022-07-29)
Field: Materials Science — Materials Chemistry

New developments in the use of single-crystal diffuse scattering to probe nanoscale correlations in crystalline materials Many emerging phenomena of high technological and scientific interest derive from the presence of local disorder and nanoscale correlations embedded within a long-range ordered average crystalline structure. Materials that harbor such disorder often show strongly enhanced responses to external stimuli, with improved properties desirable for future applications. Examples include relaxor behavior, thermoelectricity, ionic conduction, colossal magnetoresistance, unconventional superconductivity, negative thermal expansion, and more. In order to control and further enhance such functional responses requires a detailed microscopic understanding of these short-range ordered correlations. Single crystal diffuse scattering provides detailed insight regarding the existence and morphology of local distortions as well as defect-defect correlations, e.g., the tendency of local disorder to cluster into nanoscale ordered structures [1]. We will discuss recent developments in both instrumentation and novel analysis methods that now provide unprecedented insight into the unusual patterns that persist within short-range ordered states. In particular, the ability to efficiently measure diffuse scattering intensities from single crystals over large volumes of reciprocal space with high dynamic range allows modeling of complex disorder with high fidelity and enables new ways of analyzing the data in real space through three-dimensional pair-distribution functions A novel method to obtain the 3D-dPDF from select regions in momentum space was recently shown to provide novel insight into the origin of structural phase transitions Finally, recent advances in applying Machine Learning methods to diffuse scattering data can provide new, rapid insight into the information contained in these large data sets Nanoscopic scale ; Materials science ; Scattering ; Crystal (programming language) ; Single crystal ; Nanotechnology ; Crystallography ; Optics ; Chemistry ; Physics ; Computer science X-ray Diffraction in Crystallography
Authors: Sukhmander Singh | A. S. Luyt | R. S. Bhoopal | Sonia Yogi | Bhavna Vidhani
Source: Journal of Vibration Engineering & Technologies (article, 2022-04-16)
Field: Materials Science — Polymers and Plastics

Estimation of Mechanical Properties of Copper Powder Filled Linear Low-Density Polyethylene Composites Materials science ; Composite material ; Copper ; Volume fraction ; Artificial neural network ; Linear low-density polyethylene ; Modulus ; Phase (matter) ; Elongation ; Stress (linguistics) ; Polyethylene ; Ultimate tensile strength ; Computer science ; Machine learning ; Metallurgy Natural Fiber Reinforced Composites ; Polymer Nanocomposites and Properties ; Tribology and Wear Analysis
Authors: Haotian Feng | Sabarinathan P Subramaniyan | Tewani, Hridyesh | Prabhakar, Pavana
Source: arXiv (Cornell University) (preprint, 2022-09-19)
Field: Materials Science — Polymers and Plastics

Physics-Constrained Neural Network for Design and Feature-Based Optimization of Weave Architectures Woven fabrics play an essential role in everyday textiles for clothing/sportswear, water filtration, and retaining walls, to reinforcements in stiff composites for lightweight structures like aerospace, sporting, automotive, and marine industries. Several possible combinations of weave patterns and material choices, which comprise weave architecture, present a challenging question about how they could influence the physical and mechanical properties of woven fabrics and reinforced structures. In this paper, we present a novel Physics-Constrained Neural Network (PCNN) to predict the mechanical properties like the modulus of weave architectures and the inverse problem of predicting pattern/material sequence for a design/target modulus value. The inverse problem is particularly challenging as it usually requires many iterations to find the appropriate architecture using traditional optimization approaches. We show that the proposed PCNN can effectively predict weave architecture for the desired modulus with higher accuracy than several baseline models considered. We present a feature-based optimization strategy to improve the predictions using features in the Grey Level Co-occurrence Matrix (GLCM) space. We combine PCNN with this feature-based optimization to discover near-optimal weave architectures to facilitate the initial design of weave architecture. The proposed frameworks will primarily enable the woven composite analysis and optimization process, and be a starting point to introduce Knowledge-guided Neural Networks into the complex structural analysis. Artificial neural network ; Inverse ; Aerospace ; Woven fabric ; Architecture ; Process (computing) ; Computer science ; Feature (linguistics) ; Automotive industry ; Plain weave ; Artificial intelligence ; Engineering ; Mechanical engineering ; Yarn ; Mathematics Textile materials and evaluations ; Mechanical Behavior of Composites ; Color perception and design
Authors: Cameron Diao | Kaixiong Zhou | Liu, Zirui | Xiao Huang | Xia Hu
Source: arXiv (Cornell University) (preprint, 2022-12-20)
Field: Materials Science — Materials Chemistry

MolCPT: Molecule Continuous Prompt Tuning to Generalize Molecular Representation Learning Molecular representation learning is crucial for the problem of molecular property prediction, where graph neural networks (GNNs) serve as an effective solution due to their structure modeling capabilities. Since labeled data is often scarce and expensive to obtain, it is a great challenge for GNNs to generalize in the extensive molecular space. Recently, the training paradigm of "pre-train, fine-tune" has been leveraged to improve the generalization capabilities of GNNs. It uses self-supervised information to pre-train the GNN, and then performs fine-tuning to optimize the downstream task with just a few labels. However, pre-training does not always yield statistically significant improvement, especially for self-supervised learning with random structural masking. In fact, the molecular structure is characterized by motif subgraphs, which are frequently occurring and influence molecular properties. To leverage the task-related motifs, we propose a novel paradigm of "pre-train, prompt, fine-tune" for molecular representation learning, named molecule continuous prompt tuning (MolCPT). MolCPT defines a motif prompting function that uses the pre-trained model to project the standalone input into an expressive prompt. The prompt effectively augments the molecular graph with meaningful motifs in the continuous representation space; this provides more structural patterns to aid the downstream classifier in identifying molecular properties. Extensive experiments on several benchmark datasets show that MolCPT efficiently generalizes pre-trained GNNs for molecular property prediction, with or without a few fine-tuning steps. Molecular graph ; Computer science ; Artificial intelligence ; Classifier (UML) ; Leverage (statistics) ; Machine learning ; Graph ; Chemical space ; Representation (politics) ; Theoretical computer science ; Chemistry ; Drug discovery Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Joe D. Morrow | John L. A. Gardner | Volker L. Deringer
Source: arXiv (Cornell University) (preprint, 2022-11-22)
Field: Materials Science — Materials Chemistry

How to validate machine-learned interatomic potentials Machine learning (ML) approaches enable large-scale atomistic simulations with near-quantum-mechanical accuracy. With the growing availability of these methods there arises a need for careful validation, particularly for physically agnostic models - that is, for potentials which extract the nature of atomic interactions from reference data. Here, we review the basic principles behind ML potentials and their validation for atomic-scale materials modeling. We discuss best practice in defining error metrics based on numerical performance as well as physically guided validation. We give specific recommendations that we hope will be useful for the wider community, including those researchers who intend to use ML potentials for materials "off the shelf". Computer science ; Scale (ratio) ; Atomic units ; Quantum ; Machine learning ; Data science ; Physics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Electronic and Structural Properties of Oxides