jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zeyu Liu | Meng Jiang | Tengfei Luo
Source: arXiv (Cornell University) (preprint, 2022-08-28)
Field: Materials Science — Materials Chemistry

Leveraging Low-Fidelity Data to Improve Machine Learning of Sparse High-Fidelity Thermal Conductivity Data via Transfer Learning Lattice thermal conductivity (TC) of semiconductors is crucial for various applications, ranging from microelectronics to thermoelectrics. Data-driven approach can potentially establish the critical composition-property relationship needed for fast screening of candidates with desirable TC, but the small number of available data remains the main challenge. TC can be efficiently calculated using empirical models, but they have inferior accuracy compared to the more resource-demanding first-principles calculations. Here, we demonstrate the use of transfer learning (TL) to improve the machine learning models trained on small but high-fidelity TC data from experiments and first-principles calculations, by leveraging a large but low-fidelity data generated from empirical TC models, where the trainings on high- and low-fidelity TC data are treated as different but related tasks. TL improves the model accuracy by as much as 23% in R2 and reduces the average factor difference by as much as 30%. Using the TL model, a large semiconductor database is screened, and several candidates with room temperature TC &gt; 350 W/mK are identified and further verified using first-principles simulations. This study demonstrates that TL can leverage big low-fidelity data as a proxy task to improve models for the target task with high-fidelity but small data. Such a capability of TL may have important implications to materials informatics in general. Computer science ; Leverage (statistics) ; Fidelity ; High fidelity ; Transfer of learning ; Machine learning ; Materials informatics ; Experimental data ; Ranging ; Artificial intelligence ; Data mining ; Health informatics ; Physics ; Mathematics Machine Learning in Materials Science ; Thermal properties of materials ; Advanced Thermoelectric Materials and Devices
Authors: Masanori Kohyama | Shingo Tanaka | Yoshinori Shiihara
Source: Journal of the Japan Institute of Metals and Materials (article, 2022-11-28)
Field: Materials Science — Materials Chemistry

&lt;i&gt;Ab Initio&lt;/i&gt; Local-Energy and Local-Stress Calculations for Materials Science and Engineering Revealing atomic-scale distributions of energy and stress in defective or complex systems, based on the behavior of electrons, should contribute much to materials science and engineering, while only few practical ab initio methods were developed for this purpose. Thus, we developed computational techniques of local-energy and local-stress calculations within the plane-wave PAW (projector augmented wave)-GGA (generalized gradient approximation) framework. This is natural extension of ab initio energy-density and stress-density schemes, while the inherent gauge dependency is removed by integrating these densities in each local region where the contained gauge-dependent terms are integrated to be zero. In this overview, we explain our scheme with some details and discuss the concepts or physical meanings of local energy and local stress via the comparison with related schemes using similar densities, LCAO (linear combination of atomic orbitals) methods, Green’s function formulation implemented by multiple scattering or TB (tight-binding) methods, or EAM (embedded-atom method) potentials. We present recent applications to metallic surfaces, grain boundaries (GBs) with and without solute segregation, tensile tests of metallic GBs, local elastic constants of microstructures in alloys, and machine-learning based GB-energy prediction, where the local-energy and local-stress analyses provide novel aspects of phenomena, deep insights into the mechanism, and effective data for novel machine-learning based modelling. We discuss unsettled issues and future applications, especially for large-scale metallic systems. Ab initio ; Local-density approximation ; Atom (system on chip) ; Atomic orbital ; Atomic units ; Embedded atom model ; Ab initio quantum chemistry methods ; Materials science ; Statistical physics ; Physics ; Density functional theory ; Electron ; Computer science ; Quantum mechanics ; Molecular dynamics Microstructure and mechanical properties ; Boron and Carbon Nanomaterials Research ; Metal and Thin Film Mechanics
Authors: Shanglin Yang | Lin Yang | Yong Li | Defu Xu | Suyi Zhang | Lihui Peng
Source: IEEE Open Journal of Instrumentation and Measurement (article, 2022-01-01)
Field: Chemistry — Analytical Chemistry

Deep Neural Network-Based Sorghum Adulteration Detection in Baijiu Brewing In Baijiu brewing process, suppliers may adulterate glutinous sorghum with commercially inferior japonica sorghum, which can affect the yield and quality of the final Baijiu production. Currently, sorghum adulteration detection in Baijiu brewing process in China is carried out manually by sampling and observation, which strongly depends on the experiences of workers. In this paper, we proposed a method that uses sorghum images as input and combines image processing and deep neural networks to identify grain varieties and calculate the adulteration ratio. Two derivative networks of CNN, i.e., ResNet and SqueezeNet are used to implement the deep neural networks for sorghum grain identification and adulteration ratio calculation. The classification accuracy of the ResNet and SqueezeNet based models reached 93.34% and 87.98% on test set, respectively. The root mean squared error (RSME) for adulteration ratio estimation is 4.95% and 7.73%, respectively. The mean absolute error (MAE) is 4.20% and 6.29% accordingly. The proposed pipeline is capable of realizing rapid and non-destructive adulteration detection of raw materials in industrial production, thus conducing to the industrial digital transformation and efficiency improvement. Sorghum ; Brewing ; Artificial intelligence ; Artificial neural network ; Mathematics ; Raw material ; Agricultural engineering ; Computer science ; Pulp and paper industry ; Environmental science ; Pattern recognition (psychology) ; Food science ; Engineering ; Agronomy ; Chemistry ; Biology Spectroscopy and Chemometric Analyses ; Industrial Vision Systems and Defect Detection ; Smart Agriculture and AI
Authors: Nikos S. Hatzakis | Min Zhang | Henrik Dahl Pinholt | Xin Zhou | Søren S.-R. Bohr | Luca Banetta | Alessio Zaccone | Vito Foderà
Source: Research Square (preprint, 2022-02-15)
Field: Chemistry — Spectroscopy

Direct Observation of Heterogeneous Formation of Amyloid Spherulites in Real-time by Super-resolution Microscopy Resolution (logic) ; Microscopy ; Amyloid (mycology) ; Materials science ; Superresolution ; Super-resolution microscopy ; Nanotechnology ; Chemistry ; Optics ; Computer science ; Physics ; Image (mathematics) ; Computer vision ; Artificial intelligence ; Scanning confocal electron microscopy Advanced NMR Techniques and Applications ; Advanced Fluorescence Microscopy Techniques ; Protein Structure and Dynamics
Authors: Jacob B. J. Chapman | Pui-Wai Ma
Source: arXiv (Cornell University) (preprint, 2022-05-10)
Field: Materials Science — Materials Chemistry

A Machine-Learned Spin-Lattice Potential for Dynamic Simulations of Defective Magnetic Iron A machine-learned spin-lattice interatomic potential (MSLP) for magnetic iron is developed and applied to mesoscopic scale defects. It is achieved by augmenting a spin-lattice Hamiltonian with a neural network term trained to descriptors representing a mix of local atomic configuration and magnetic environments. It reproduces the cohesive energy of BCC and FCC phases with various magnetic states. It predicts the formation energy and complex magnetic structure of point defects in quantitative agreement with density functional theory (DFT) including the reversal and quenching of magnetic moments near the core of defects. The Curie temperature is calculated through spin-lattice dynamics showing good computational stability at high temperature. The potential is applied to study magnetic fluctuations near sizable dislocation loops. The MSLP transcends current treatments using DFT and molecular dynamics, and surpasses other spin-lattice potentials that only treat near-perfect crystal cases. Condensed matter physics ; Mesoscopic physics ; Curie temperature ; Magnetic moment ; Lattice (music) ; Embedded atom model ; Physics ; Molecular dynamics ; Materials science ; Ferromagnetism ; Quantum mechanics Machine Learning in Materials Science ; Magnetic Properties and Applications ; Microstructure and mechanical properties
Authors: Daniele Rapetti | Massimo Delle Piane | Matteo Cioni | Daniela Polino | Riccardo Ferrando | Giovanni M. Pavan
Source: ChemRxiv (preprint, 2022-11-02)
Field: Materials Science — Materials Chemistry

Machine Learning of Atomic Dynamics and Statistical Surface Identities in Gold Nanoparticles It is known that metal nanoparticles (NPs) may be dynamic and atoms may move within them even at fairly low temperatures. Characterizing such complex dynamics is key for understanding NPs’ properties in realistic regimes, but detailed information on, e.g., the stability, survival, and interconversion rates of the atomic environments (AEs) populating them are non-trivial to attain. Here we show a machine learning approach that allows us to reconstruct the complex atomic dynamics of metal NPs from high-dimensional data extracted from molecular dynamics simulations. Using different-shape gold NPs as a representative example, an AEs’ dictionary allows us to label step-by-step the individual atoms in the NPs, identifying the native and non-native AEs and populating them along the MD simulations at various temperatures. Tracking the emergence, annihilation, lifetime, and dynamic interconversion of the AEs, our approach permits estimating a “statistical equivalent identity” for metal NPs based on the intrinsic atomic dynamics present within them. Molecular dynamics ; Nanoparticle ; Dynamics (music) ; Metal ; Colloidal gold ; Identity (music) ; Stability (learning theory) ; Nanotechnology ; Chemical physics ; Chemistry ; Materials science ; Computer science ; Physics ; Computational chemistry ; Machine learning Machine Learning in Materials Science ; Gold and Silver Nanoparticles Synthesis and Applications ; nanoparticles nucleation surface interactions
Authors: Daniele Rapetti | Massimo Delle Piane | Matteo Cioni | Daniela Polino | Riccardo Ferrando | Giovanni M. Pavan
Source: ChemRxiv (preprint, 2022-11-04)
Field: Materials Science — Materials Chemistry

Machine Learning of Atomic Dynamics and Statistical Surface Identities in Gold Nanoparticles It is known that metal nanoparticles (NPs) may be dynamic and atoms may move within them even at fairly low temperatures. Characterizing such complex dynamics is key for understanding NPs’ properties in realistic regimes, but detailed information on, e.g., the stability, survival, and interconversion rates of the atomic environments (AEs) populating them are non-trivial to attain. Here we show a machine learning approach that allows us to reconstruct the complex atomic dynamics of metal NPs from high-dimensional data extracted from molecular dynamics simulations. Using different-shape gold NPs as a representative example, an AEs’ dictionary allows us to label step-by-step the individual atoms in the NPs, identifying the native and non-native AEs and populating them along the MD simulations at various temperatures. Tracking the emergence, annihilation, lifetime, and dynamic interconversion of the AEs, our approach permits estimating a “statistical equivalent identity” for metal NPs based on the intrinsic atomic dynamics present within them. Molecular dynamics ; Dynamics (music) ; Nanoparticle ; Metal ; Colloidal gold ; Identity (music) ; Stability (learning theory) ; Chemical physics ; Nanotechnology ; Chemistry ; Materials science ; Computer science ; Physics ; Computational chemistry ; Machine learning Machine Learning in Materials Science ; Gold and Silver Nanoparticles Synthesis and Applications ; nanoparticles nucleation surface interactions
Authors: Gaoqiang Niu | Yi Zhuang | Yushen Hu | Liu Zong | Bo Wu | Mingjie Li | Fei Wang
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Chemistry — Spectroscopy

Selective Discrimination of Vocs Gases at Ppb-Level Using Mos Gas Sensor in Pulse-Heating Mode with Modified Hill's Model Mode (computer interface) ; Materials science ; Pulse (music) ; Analytical Chemistry (journal) ; Environmental science ; Chemistry ; Environmental chemistry ; Engineering ; Electrical engineering ; Computer science Spectroscopy and Laser Applications ; Gas Sensing Nanomaterials and Sensors ; Atomic and Subatomic Physics Research
Authors: Qian Li | Lingyu Liu | Guang Han | Xiaoyun Sun | Zheng Haiqing | Jun Zhang | Baoan Liu | Kang Guo
Source: 2022 37th Youth Academic Annual Conference of Chinese Association of Automation (YAC) (article, 2022-11-19)
Field: Materials Science — Materials Chemistry

Research on Classification Method of Urban Underground Cable Partial Discharge Defects After the urban underground power transmission cable is put into use, the cable section is prone to metal residue, semiconductor residue, insulation moisture and other defects when the cable joint is connected, resulting in partial discharge phenomenon of the cable. This paper analyzes the partial discharge of cable, proposes a convolutional neural network to identify the local discharge signal, and improves the network to obtain better results. This topic has a wide range of application scenarios and is suitable for the identification and analysis of cable discharge signals generated during cable operation. Partial discharge ; Power cable ; Direct-buried cable ; Convolutional neural network ; Computer science ; Transmission (telecommunications) ; Environmental science ; Electrical engineering ; Engineering ; Telecommunications ; Materials science ; Voltage ; Cable theory ; Layer (electronics) ; Artificial intelligence High voltage insulation and dielectric phenomena ; Power Systems Fault Detection ; Power Transformer Diagnostics and Insulation
Authors: Luis Itza Vazquez-Salazar | Eric D. Boittier | M. Meuwly
Source: arXiv (Cornell University) (preprint, 2022-07-14)
Field: Materials Science — Materials Chemistry

Uncertainty quantification for predictions of atomistic neural networks The value of uncertainty quantification on predictions for trained neural networks (NNs) on quantum chemical reference data is quantitatively explored. For this, the architecture of the PhysNet NN was suitably modified and the resulting model was evaluated with different metrics to quantify calibration, quality of predictions, and whether prediction error and the predicted uncertainty can be correlated. The results from training on the QM9 database and evaluating data from the test set within and outside the distribution indicate that error and uncertainty are not linearly related. The results clarify that noise and redundancy complicate property prediction for molecules even in cases for which changes - e.g. double bond migration in two otherwise identical molecules - are small. The model was then applied to a real database of tautomerization reactions. Analysis of the distance between members in feature space combined with other parameters shows that redundant information in the training dataset can lead to large variances and small errors whereas the presence of similar but unspecific information returns large errors but small variances. This was, e.g., observed for nitro-containing aliphatic chains for which predictions were difficult although the training set contained several examples for nitro groups bound to aromatic molecules. This underlines the importance of the composition of the training data and provides chemical insight into how this affects the prediction capabilities of a ML model. Finally, the approach put forward can be used for information-based improvement of chemical databases for target applications through active learning optimization. Computer science ; Artificial neural network ; Redundancy (engineering) ; Training set ; Data mining ; Property (philosophy) ; Set (abstract data type) ; Calibration ; Artificial intelligence ; Chemical space ; Machine learning ; Algorithm ; Mathematics ; Statistics ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Electrochemical Analysis and Applications
Authors: T. Arumuga Maria Devi | P. Darwin
Source: International Journal of Uncertainty Fuzziness and Knowledge-Based Systems (article, 2022-06-01)
Field: Chemistry — Analytical Chemistry

Performance Metrics on Hyperspectral Images in Fuzzy Contextual Convolutional Neural Network for Food Quality Analyzer The quality of food and the safety of consumer is one of the major essential things in our day-to-day life. To ensure the quality of foods through their various attributes, different types of methods have been introduced. In this proposed method, three underlying blocks namely Hyperspectral Food Image Context Extractor (HFICE), Hyperspectral Context Fuzzy Classifier (HCFC) and Convolutional Neural Network (CNN) for Food Quality Analyzer (CFQA). Hyperspectral Food Image Context Extractor module is used as the preprocess to get food attributes such as texture, color, size, shape and molecular particulars. Hyperspectral Context Fuzzy Classifier module identifies a particular part of the food (zone entity) is whether carbohydrate, fat, protein, water or unusable core. CNN for Food Quality Analyzer module uses a Tuned Convolutional Layer, Heuristic Activation Operation, Parallel Element Merge Layer and a regular fully connected layer. Indian Pines, Salinas and Pavia are the benchmark dataset to evaluate hyperspectral image-based machine learning procedures. These datasets are used along with a dedicated chicken meat Hyper Spectral Imaging dataset is used in the training and testing process. Results are obtained that about 7.86% of average values in various essential evaluation metrics such as performance metrics such as accuracy, precision, sensitivity and specificity have improved when compared to existing state of the art results. Hyperspectral imaging ; Computer science ; Convolutional neural network ; Artificial intelligence ; Classifier (UML) ; Pattern recognition (psychology) ; Fuzzy logic ; Extractor ; Data mining ; Engineering Spectroscopy and Chemometric Analyses ; Remote-Sensing Image Classification ; Advanced Chemical Sensor Technologies
Authors: Weijie Yang | Jiajia Li | Xuelu Chen | Yajun Feng | Chongchong Wu | Ian D. Gates | Zhengyang Gao | Xunlei Ding | Jianxi Yao | Hao Li
Source: ChemPhysChem (article, 2022-04-05)
Field: Materials Science — Materials Chemistry

Cover Feature: Exploring the Effects of Ionic Defects on the Stability of CsPbI<sub>3</sub> with a Deep Learning Potential (ChemPhysChem 7/2022) The Cover Feature illustrates the concept of using machine learning and data science to predict the energies and properties of CsPbI3 materials. More information can be found in the Research Article by Z. Gao, X. Ding, J. Yao, H. Li and co-workers. Cover (algebra) ; Feature (linguistics) ; Ionic bonding ; Stability (learning theory) ; Chemistry ; Nanotechnology ; Computer science ; Artificial intelligence ; Computational chemistry ; Materials science ; Machine learning ; Engineering ; Philosophy ; Ion ; Organic chemistry ; Mechanical engineering Machine Learning in Materials Science
Authors: Samrajya Bikram Thapa
Source: International Journal of Research Publication and Reviews (article, 2022-11-01)
Field: Chemistry — Analytical Chemistry

Using ROC-curve to Illustrate the Use of Binomial Logistic Regression Model in Wine Quality Analysis The major objective of this paper is to predict wine quality based on chemical attributes using binary logistic regression model. The evaluation of wine quality in this study is analyzed based on statistical methods, correlation analysis, and regression analysis. This study highlights several features are not relevant (only six essential variables were enough) to predict wine quality. The result showed that this method is robust enough to predict wine quality with an overall accuracy of more than 75%. Similar techniques or machine learning methods can help in predicting more accurately. Wine ; Logistic regression ; Binomial regression ; Statistics ; Regression analysis ; Quality (philosophy) ; Computer science ; Regression ; Econometrics ; Mathematics ; Machine learning ; Artificial intelligence ; Food science Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Fermentation and Sensory Analysis
Authors: Jigyasa Nigam | Sergey Pozdnyakov | Guillaume Fraux | Michele Ceriotti
Source: The Journal of Chemical Physics (article, 2022-04-27)
Field: Materials Science — Materials Chemistry

Unified theory of atom-centered representations and message-passing machine-learning schemes Data-driven schemes that associate molecular and crystal structures with their microscopic properties share the need for a concise, effective description of the arrangement of their atomic constituents. Many types of models rely on descriptions of atom-centered environments, which are associated with an atomic property or with an atomic contribution to an extensive macroscopic quantity. Frameworks in this class can be understood in terms of atom-centered density correlations (ACDC), which are used as a basis for a body-ordered, symmetry-adapted expansion of the targets. Several other schemes that gather information on the relationship between neighboring atoms using "message-passing" ideas cannot be directly mapped to correlations centered around a single atom. We generalize the ACDC framework to include multi-centered information, generating representations that provide a complete linear basis to regress symmetric functions of atomic coordinates, and provide a coherent foundation to systematize our understanding of both atom-centered and message-passing and invariant and equivariant machine-learning schemes. Atom (system on chip) ; Invariant (physics) ; Equivariant map ; Computer science ; Basis (linear algebra) ; Property (philosophy) ; Topology (electrical circuits) ; Physics ; Pure mathematics ; Mathematics ; Quantum mechanics ; Combinatorics ; Geometry Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Advanced Chemical Physics Studies
Authors: Vida Jamali | A. Paul Alivisatos
Source: Microscopy and Microanalysis (article, 2022-07-22)
Field: Chemistry — Physical and Theoretical Chemistry

Studying Diffusion of Colloidal Nanoparticles in Solution Using Liquid Phase TEM and Machine Learning Journal Article Studying Diffusion of Colloidal Nanoparticles in Solution Using Liquid Phase TEM and Machine Learning Get access Vida Jamali, Vida Jamali Department of Chemistry, University of California – Berkeley, Berkeley, CA, United States Corresponding author: vidaj@berkeley.edu, paul.alivisatos@berkeley.edu Search for other works by this author on: Oxford Academic Google Scholar A Paul Alivisatos A Paul Alivisatos Department of Chemistry, University of California – Berkeley, Berkeley, CA, United States Corresponding author: vidaj@berkeley.edu, paul.alivisatos@berkeley.edu Search for other works by this author on: Oxford Academic Google Scholar Microscopy and Microanalysis, Volume 28, Issue S1, 1 August 2022, Pages 142–143, https://doi.org/10.1017/S1431927622001465 Published: 01 August 2022 Nanoparticle ; Liquid phase ; Phase (matter) ; Colloid ; Diffusion ; Nanotechnology ; Engineering physics ; Chemistry ; Art history ; Polymer science ; Analytical Chemistry (journal) ; Library science ; Materials science ; Computer science ; Chemical engineering ; Engineering ; Art ; Physics ; Thermodynamics ; Chromatography ; Organic chemistry Electrostatics and Colloid Interactions ; Nanopore and Nanochannel Transport Studies ; nanoparticles nucleation surface interactions