jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jianping Xu | Tao Zu | Yi‐Cheng Hsu | Xiaoli Wang | Dan Wu | Yi Zhang
Source: arXiv (Cornell University) (preprint, 2022-05-20)
Field: Materials Science — Materials Chemistry

Accelerating Chemical Exchange Saturation Transfer Imaging Using a Model-based Deep Neural Network With Synthetic Training Data Purpose: To develop a model-based deep neural network for high-quality image reconstruction of undersampled multi-coil chemical exchange saturation transfer (CEST) data. Theory and Methods: Inspired by the variational network, the CEST image reconstruction equation is unrolled into a deep neural network (CEST-VN) with a k-space data-sharing block that takes advantage of the inherent redundancy in adjacent CEST frames and 3D spatial-frequential convolution kernels that exploit correlations in the x-ω domain. Additionally, a new pipeline based on multiple-pool Bloch-McConnell simulations is devised to synthesize multi-coil CEST data from publicly available anatomical MRI data. The proposed neural network is trained on simulated data with a CEST-specific loss function that jointly measures the structural and CEST contrast. The performance of CEST-VN was evaluated on three healthy volunteers and five brain tumor patients using retrospectively undersampled data with various acceleration factors, and compared with other state-of-the-art reconstruction methods. Results: The proposed CEST-VN method generated high-quality CEST source images and APT-weighted (APTw) maps in healthy and brain tumor subjects, consistently outperforming GRAPPA, blind compressed sensing, and the original variational network. With the acceleration factors increasing from 3 to 6, CEST-VN with the same hyperparameters yielded similar and accurate reconstruction without apparent loss of details or increase of artifacts. The ablation studies confirmed the effectiveness of the joint CEST-specific loss function and data-sharing block used. Conclusions: The proposed CEST-VN method can offer high-quality CEST source images and APTw maps from highly undersampled multi-coil data by integrating the deep-learning prior and multi-coil sensitivity encoding model. Computer science ; Artificial intelligence ; Image quality ; Iterative reconstruction ; Block (permutation group theory) ; Artificial neural network ; Pattern recognition (psychology) ; Algorithm ; Image (mathematics) ; Mathematics Lanthanide and Transition Metal Complexes ; Advanced MRI Techniques and Applications ; MRI in cancer diagnosis
Authors: Kan Hatakeyama‐Sato | Hiroki Adachi | Momoka Umeki | Takahiro Kashikawa | Koichi Kimura | Kenichi Oyaizu
Source: ChemRxiv (preprint, 2022-04-14)
Field: Materials Science — Materials Chemistry

Automated design of Li+-conducting polymer by quantum-inspired annealing Automated molecule design by computers has been an essential topic in materials informatics. Still, generating practical structures is not easy because of the difficulty in treating material stability, synthetic difficulty, mechanical properties, and other miscellaneous parameters, often leading to the generation of junk molecules. We tackle the problem by introducing supervised/unsupervised machine learning and quantum-inspired annealing. Our autonomous molecular design system can help experimental researchers discover practical materials more efficiently. Like the human design process, new molecules are explored based on knowledge of existing compounds. A new solid-state polymer electrolyte for lithium-ion batteries is designed and synthesized, giving a promising room temperature conductivity of 10^-5 S/cm with reasonable thermal, chemical, and mechanical properties. Computer science ; Simulated annealing ; Polymer ; Annealing (glass) ; Molecule ; Electrolyte ; Quantum chemical ; Materials science ; Nanotechnology ; Chemistry ; Machine learning ; Organic chemistry ; Composite material Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Surface Chemistry and Catalysis
Authors: Youzhi Zhou | Pengcheng Lin | Xin Ke | Qiang Hu | Qi Shi | Jingguo Zhang | Zhong Wang | Limin Wang
Source: Materials and Corrosion (article, 2022-01-02)
Field: Materials Science — Materials Chemistry

Machine learning‐based analysis and prediction of the interfacial corrosion processes of copper cathode plates during the electrolytic production of copper powders Abstract Present efforts to support the essential industrial‐scale electrolytic production of copper‐based metal powders urgently require approaches to the real‐time predicting of corrosion of copper cathodes employed in electrolytic production processes. However, current approaches are extremely limited owing to the difficulty of accurately modeling the complex cathode corrosion process. In this study, the corrosion process under different parameters was analyzed by a self‐designed continuous electrolytic corrosion experimental device, clarify the influence mechanism of current density on the corrosion of the solid–liquid–gas interface area, and addresses this issue by applying a random forest machine learning approach based on three process parameters, including the electrolyte temperature, liquid‐level fluctuation cycle period, and current density. The dataset employed in the model is obtained using a novel experimental corrosion test method based on electrode arrays. The experimental results include the corrosion rates of copper cathode plates at different positions relative to the liquid electrolyte level during the electrolysis process. The resulting stochastic model is demonstrated to obtain a high prediction accuracy of 97% for the various regions of copper cathode plates defined according to liquid electrolyte level. Corrosion ; Electrolyte ; Cathode ; Copper ; Materials science ; Electrolysis ; Electrolytic process ; Metallurgy ; Electrode ; Chemistry ; Engineering ; Electrical engineering Corrosion Behavior and Inhibition ; Electrodeposition and Electroless Coatings ; Anodic Oxide Films and Nanostructures
Authors: Teja Pathour | Nasrin Akter | James D. Dormer | Sugandha Chaudhary | Baowei Fei | Shashank R. Sirsi
Source:  (article, 2022-03-30)
Field: Materials Science — Materials Chemistry

Identifying unique acoustic signatures from chemically-crosslinked microbubble clusters using deep learning Ultrasound contrast agents (UCA) are gas encapsulated microspheres that oscillate volumetrically when exposed to an ultrasound field producing a backscattered signal which can be used for improved ultrasound imaging and drug delivery. UCA's are being used widely for contrast-enhanced ultrasound imaging, but there is a need for improved UCAs to develop faster and more accurate contrast agent detection algorithms. Recently, we introduced a new class of lipid based UCAs called Chemically Cross-linked Microbubble Clusters (CCMCs). CCMCs are formed by the physical tethering of individual lipid microbubbles into a larger aggregate cluster. The advantages of these novel CCMCs are their ability to fuse together when exposed to low intensity pulsed ultrasound (US), potentially generating unique acoustic signatures that can enable better contrast agent detection. In this study, our main objective is to demonstrate that the acoustic response of CCMCs is unique and distinct when compared to individual UCAs using deep learning algorithms. Acoustic characterization of CCMCs and individual bubbles was performed using a broadband hydrophone or a clinical transducer attached to a Verasonics Vantage 256. A simple artificial neural network (ANN) was trained and used to classify raw 1D RF ultrasound data as either from CCMC or non-tethered individual bubble populations of UCAs. The ANN was able to classify CCMCs at an accuracy of 93.8% for data collected from broadband hydrophone and 90% for data collected using Verasonics with a clinical transducer. The results obtained suggest the acoustic response of CCMCs is unique and has the potential to be used in developing a novel contrast agent detection technique. Computer science ; Materials science ; Deep learning ; Nanotechnology ; Artificial intelligence Ultrasound and Cavitation Phenomena ; Photoacoustic and Ultrasonic Imaging ; Ultrasound and Hyperthermia Applications
Authors: Yakubu Wudil | M.A. Gondal
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Predicting the Thermoelectric Energy Figure of Merit of Bi2te3-Based Semiconducting Materials: A Machine Learning Approach Figure of merit ; Thermoelectric effect ; Mean squared error ; Seebeck coefficient ; Materials science ; Linear regression ; Support vector machine ; Machine learning ; Thermoelectric materials ; Gradient boosting ; Thermal conductivity ; Artificial intelligence ; Mathematics ; Algorithm ; Computer science ; Statistics ; Thermodynamics ; Physics ; Optoelectronics ; Composite material Advanced Thermoelectric Materials and Devices ; Neural Networks and Applications ; Machine Learning in Materials Science
Authors: Dong Hyeon Mok | Jong Seung Kim | Seoin Back
Source: ChemRxiv (preprint, 2022-09-30)
Field: Materials Science — Materials Chemistry

Direction-based Graph Representation to Accelerate Stable Catalysts Discovery To realize renewable and sustainable energy cycle, there has been a lot of effort put into discovering catalysts with desired properties from a large chemical space. To achieve this goal, several screening strategies have been proposed, most of which require validation of thermodynamic stability and synthesizability of candidate materials via computationally intensive quantum chemistry or solid-state physics calculations. This problem can be overcome by reducing the number of calculations through machine learning methods, which predict target properties using unrelaxed crystal structures as inputs. However, numerical input representations of most of the previous models are based on either too specific (e.g., atomic coordinates) or too ambiguous (e.g., stoichiometry) information, practically inapplicable to energy prediction of unrelaxed initial structures. In this work, we develop direction-based crystal graph convolutional neural network (D-CGCNN) with the highest accuracy toward formation energy predictions of the relaxed structures using the initial structures as inputs. By comparing with other approaches, we revealed correlations between crystal graph similarities and model performances, elucidating the origin of the improved accuracy of our model. We applied this model to the on-going high-throughput virtual screening project, where the model discovered 1,725 stable materials from 15,318 unrelaxed structures by performing 3,966 structure optimizations (~25 %). Crystal structure prediction ; Computer science ; Graph ; Representation (politics) ; Chemical space ; Stability (learning theory) ; Theoretical computer science ; Statistical physics ; Crystal structure ; Machine learning ; Chemistry ; Physics Machine Learning in Materials Science ; Topic Modeling ; Advanced Graph Neural Networks
Authors: Quang Binh Ho | Tomohiro Kawashima | Yoshinobu Murakami | Naohiro Hozumi
Source: 2022 9th International Conference on Condition Monitoring and Diagnosis (CMD) (article, 2022-11-13)
Field: Materials Science — Materials Chemistry

Fundamental Study on Condition Assessment of Insulating Material using Deep Learning based on Waveform Characteristics of Partial Discharge The waveform characteristics of the partial discharge (PD) reflect the process of electron avalanche in the discharge space. The authors developed a measurement system for the PD waveforms with the broadest possible frequency band and showed that the waveform characteristics depend on the condition of the insulating material. For example, when a semi-conductive sheet was attached to an insulator to simulate the decrease in surface resistance due to deterioration, it was clarified that shoulders appeared at the rising part of the PD waveform as the surface resistance decreased. Furthermore, the surface resistance of the insulator could be assessed by simulating the change of the rising part of the PD waveform using the equivalent circuit model. In this paper, the authors classified the PD waveforms obtained using samples with different surface resistivity by deep learning based on the waveform characteristics. By appropriately processing PD waveforms with the broadest possible frequency band, it is possible to identify the deterioration condition by deep learning using only the information in the PD waveform. Waveform ; Partial discharge ; Sheet resistance ; Insulator (electricity) ; Electrical conductor ; Acoustics ; Materials science ; Electronic engineering ; Voltage ; Computer science ; Electrical engineering ; Optoelectronics ; Composite material ; Engineering ; Physics High voltage insulation and dielectric phenomena ; Electrostatic Discharge in Electronics ; Power Transformer Diagnostics and Insulation
Authors: Vaidish Sumaria | Luan Nguyen | Franklin Tao | Philippe Sautet
Source: ChemRxiv (preprint, 2022-11-18)
Field: Materials Science — Materials Chemistry

Atomic scale mechanism of platinum catalystrestructuring under a pressure of reactant gas Heterogeneous catalysis is key for chemical transformations. Understanding how catalyst active sites dynamically evolve at the atomic scale under reaction conditions is a prerequisite for accurate determination of catalytic mechanisms and predictably developing catalysts. We combine in-situ time-dependent observation and machine learning-accelerated first-principle atomistic simulations to uncover the mechanism of restructuring of Pt catalysts under a pressure of carbon monoxide (CO). We show that a high CO coverage at a Pt step edge triggers the formation of atomic protrusions of low-coordination Pt atoms, which then detach from the step edge to create subnano-islands on the terraces, where undercoordinated sites are stabilized by the CO adsorbates. The fast and accurate machine learning potential is key to enable the exploration of ten of thousands of configurations for the CO covered restructuring catalyst. These studies open an avenue to achieve atom-scale understanding of structural dynamics of more complex metal nanoparticle catalysts under reaction. Catalysis ; Restructuring ; Atomic units ; Carbon monoxide ; Atom (system on chip) ; Chemical physics ; Chemical engineering ; Nanoscopic scale ; Nanotechnology ; Platinum ; Materials science ; Chemistry ; Computer science ; Physics ; Engineering Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; nanoparticles nucleation surface interactions
Authors: P. Gosz | Marek Krośnicki | J. Koperski | Tomasz Urbańczyk
Source: Acta Physica Polonica A (article, 2022-01-01)
Field: Chemistry — Analytical Chemistry

Application of Deep Neural Network in Finding of Repulsive Part of Molecular Potential Based on Dispersed Emission Spectra A new method of obtaining parameters of the repulsive part of the analytical potential energy curve of a diatomic molecule, based on the deep neural network approach, is presented. The method was successfully tested on artificially created spectra as well as an experimental boundfree spectrum of the G0 + u (6 1 P1)( = 39) X0 + g transition in Hg2. The method was tested for a Morse and fourparameter expanded Morse oscillator potentials but can be easily adaptable to other forms of analytical potential energy curves. topics Spectral line ; Artificial neural network ; Deep neural networks ; Materials science ; Statistical physics ; Physics ; Computer science ; Artificial intelligence ; Quantum mechanics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: Nobuya Tsuji | Pavel Sidorov | Chendan Zhu | Yuuya Nagata | Timur Gimadiev | Alexandre Varnek | Benjamin List
Source: ChemRxiv (preprint, 2022-07-29)
Field: Materials Science — Materials Chemistry

Predicting Highly Enantioselective Catalysts Using Tunable Fragment Descriptors Catalyst optimization process is typically relying on an inductive and qualitative assumption of chemists based on screening data. While machine learning models using molecular properties or calculated 3D structures enable quantitative data evaluation, costly quantum chemical calculations are often required. In contrast, readily available binary fingerprint descriptors are time- and cost-efficient, but their predictive performance remains insufficient. Here, we describe a machine learning model based on fragment descriptors, which are fine-tuned for asymmetric catalysis and represent cyclic or polyaromatic hydrocarbons, enabling robust and efficient virtual screening. Using training data with only moderate selectivities, we designed theoretically and validated experimentally new catalysts showing higher selectivities in a previously unaddressed transformation. Quantum chemical ; Enantioselective synthesis ; Fingerprint (computing) ; Computer science ; Catalysis ; Binary number ; Process (computing) ; Fragment (logic) ; Virtual screening ; Transformation (genetics) ; Training set ; Molecular descriptor ; Artificial intelligence ; Machine learning ; Biological system ; Chemistry ; Computational chemistry ; Algorithm ; Quantitative structure–activity relationship ; Molecular dynamics ; Molecule ; Organic chemistry ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Surface Chemistry and Catalysis
Authors: Felix Döppel | Martin Votsmeier
Source: ChemRxiv (preprint, 2022-03-29)
Field: Materials Science — Materials Chemistry

Efficient machine learning based surrogate models for surface kinetics by approximating the rates of the rate-determining steps Machine learning based surrogate models that interpolate precomputed solutions of the rate equations can greatly accelerate simulations of catalytic systems. Established schemes for the construction of surrogate models depend on a logarithmic scaling of the source terms, which limits their application to cases where species are exclusively consumed or produced under all conditions of interest. We propose a new approach based on interpolating the forward- and reverse rates of the rate-determining reactions and thus overcoming this limitation. The new scheme is demonstrated using a surface reaction mechanism describing the oxidation of CO and H2 as well as the water gas shift reaction including 5 gas species, 9 surface species and 18 reversible reactions. Multivariate spline interpolation is used for a first evaluation of our approach. With the splines, the new method reproduces the source terms of CO with an error of 0.5 % which is 50 to 100 times more accurate than the established approaches of either mapping the source terms directly (49.9 % error) or mapping adsorption/desorption rates (23.7 % error). The true potential of the new approach develops in combination with machine learning techniques like neural networks. Even very small neural networks with a single hidden layer of 20 nodes yield an error of 0.35 %. This is about 200 times more accurate than the same neural networks used with the established approaches. Increasing the network size to still moderate 30 nodes in two hidden layers (2342 parameters in total) reduces the error to 0.0058 %. Besides the increased accuracy, the neural networks also outperform spline interpolation by at least an order of magnitude with respect to interpolation time, storage space and required amount of training data. Due to the extremely low errors achieved with moderate size neural networks and small training data sets, the proposed method based on mapping the rate-determining steps shows promise to scale to much larger and more complex reaction systems in catalysis and other fields like combustion, atmospheric chemistry or systems biology. Artificial neural network ; Interpolation (computer graphics) ; Logarithm ; Surrogate model ; Computer science ; Scaling ; Algorithm ; Reduction (mathematics) ; Spline interpolation ; Spline (mechanical) ; Surface (topology) ; Machine learning ; Artificial intelligence ; Mathematical optimization ; Mathematics ; Thermodynamics ; Geometry ; Physics Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Advanced Multi-Objective Optimization Algorithms
Authors: Vasilis Krokos | Stéphane Bordas | Pierre Kerfriden
Source: arXiv (Cornell University) (preprint, 2022-05-13)
Field: Materials Science — Materials Chemistry

A graph-based probabilistic geometric deep learning framework with online enforcement of physical constraints to predict the criticality of defects in porous materials Stress prediction in porous materials and structures is challenging due to the high computational cost associated with direct numerical simulations. Convolutional Neural Network (CNN) based architectures have recently been proposed as surrogates to approximate and extrapolate the solution of such multiscale simulations. These methodologies are usually limited to 2D problems due to the high computational cost of 3D voxel based CNNs. We propose a novel geometric learning approach based on a Graph Neural Network (GNN) that efficiently deals with three-dimensional problems by performing convolutions over 2D surfaces only. Following our previous developments using pixel-based CNN, we train the GNN to automatically add local fine-scale stress corrections to an inexpensively computed coarse stress prediction in the porous structure of interest. Our method is Bayesian and generates densities of stress fields, from which credible intervals may be extracted. As a second scientific contribution, we propose to improve the extrapolation ability of our network by deploying a strategy of online physics-based corrections. Specifically, we condition the posterior predictions of our probabilistic predictions to satisfy partial equilibrium at the microscale, at the inference stage. This is done using an Ensemble Kalman algorithm, to ensure tractability of the Bayesian conditioning operation. We show that this innovative methodology allows us to alleviate the effect of undesirable biases observed in the outputs of the uncorrected GNN, and improves the accuracy of the predictions in general. Computer science ; Probabilistic logic ; Artificial intelligence ; Convolutional neural network ; Microscale chemistry ; Inference ; Extrapolation ; Discretization ; Scalability ; Graph ; Machine learning ; Algorithm ; Theoretical computer science ; Mathematics Machine Learning in Materials Science ; Medical Image Segmentation Techniques
Authors: Jianwei Miao | Yao Yang | Jihan Zhou | Zipeng Zhao | Geng Sun | Saman Moniri | Colin Ophus | Yongsoo Yang | Ziyang Wei | Yakun Yuan | Cheng Zhu | Yang Liu | Qiang Sun | Qingying Jia | Hendrik Heinz | Jim Ciston | Peter Ercius | Philippe Sautet | Yu Huang
Source: Research Square (preprint, 2022-05-02)
Field: Materials Science — Materials Chemistry

Atomic-scale identification of the active sites of nanocatalysts Nanomaterial-based catalyst ; Identification (biology) ; Scale (ratio) ; Atomic units ; Computer science ; Nanotechnology ; Materials science ; Physics ; Biology Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalytic Processes in Materials Science