jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: James P. Horwath | Xiaomin Lin | Hongrui He | Qingteng Zhang | Eric M. Đufresne | Miaoqi Chu | Subramanian K. R. S. Sankaranarayanan | Wei Chen | Suresh Narayanan | Mathew J. Cherukara
Source: arXiv (Cornell University) (preprint, 2022-12-07)
Field: Materials Science — Materials Chemistry

Elucidation of Relaxation Dynamics Beyond Equilibrium Through AI-informed X-ray Photon Correlation Spectroscopy Understanding and interpreting dynamics of functional materials \textit{in situ} is a grand challenge in physics and materials science due to the difficulty of experimentally probing materials at varied length and time scales. X-ray photon correlation spectroscopy (XPCS) is uniquely well-suited for characterizing materials dynamics over wide-ranging time scales, however spatial and temporal heterogeneity in material behavior can make interpretation of experimental XPCS data difficult. In this work we have developed an unsupervised deep learning (DL) framework for automated classification and interpretation of relaxation dynamics from experimental data without requiring any prior physical knowledge of the system behavior. We demonstrate how this method can be used to rapidly explore large datasets to identify samples of interest, and we apply this approach to directly correlate bulk properties of a model system to microscopic dynamics. Importantly, this DL framework is material and process agnostic, marking a concrete step towards autonomous materials discovery. Relaxation (psychology) ; Interpretation (philosophy) ; Dynamics (music) ; Dynamic light scattering ; Spectroscopy ; Statistical physics ; Characterization (materials science) ; Photon ; Computer science ; Biological system ; Physics ; Chemical physics ; Materials science ; Artificial intelligence ; Nanotechnology ; Optics ; Biology Machine Learning in Materials Science ; Functional Brain Connectivity Studies ; Phase Equilibria and Thermodynamics
Authors: Clemens Schulte | Alice Solda | Sebastian Spänig | Nathan B. P. Adams | Ivana Bekić | Werner Streicher | Dominik Heider | Ralf Strasser | Hans Michael Maric
Source: Research Square (preprint, 2022-04-28)
Field: Materials Science — Polymers and Plastics

Multivalent Binding Kinetics Resolved by Fluorescence Proximity Sensing Kinetics ; Fluorescence ; Receptor–ligand kinetics ; Chemistry ; Biophysics ; Physics ; Optics ; Biology Dendrimers and Hyperbranched Polymers ; RNA Interference and Gene Delivery ; Chemical Synthesis and Analysis
Authors: Jose Paulo G. De Oliveira | Carmelo J. A. Bastos-Filho | Sérgio Campello Oliveira
Source:  (article, 2022-10-13)
Field: Chemistry — Analytical Chemistry

Chemical sample classification using autoencoder-based spectroscopy We present a Proof of Concept of a chemical sample classifier. Instead of a spectrometer, we use autoencoders, machine learning models that try to reconstruct their output images presented at their input. The autoencoder is trained with spectrograms generated from optical signals detected after propagating through the sample. The signal shape is specifically designed, and the emitted wavelength varies as a function of time. Over propagation through the sample, the signal is partially absorbed. Since absorption depends on the sample's transmittance, the detected signal represents a signature. Hence, based on reconstruction error, the autoencoder performs classification with 100% accuracy. Autoencoder ; Spectrogram ; Sample (material) ; Artificial intelligence ; SIGNAL (programming language) ; Classifier (UML) ; Pattern recognition (psychology) ; Computer science ; Transmittance ; Spectrometer ; Optics ; Deep learning ; Physics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Manolis Chatzigeorgiou | Michalis Vrigkas | Nikos Boukos | Margarita Beazi‐Katsioti | Marios S. Katsiotis | Vassilios Constantoudis
Source:  (article, 2022-09-07)
Field: Materials Science — Surfaces, Coatings and Films

Machine Learning evaluation of microscopy image segmentation methods: The case of Gaussian Mixture Models Multiphase materials are encountered in several areas of science and technology. Their properties are determined by the fraction of the phases (material compounds) constituting the composite material. Therefore, the quantitative characterization of phase fractions is highly demanded and has been the subject of extensive studies. To this end, a widely used technique is the segmentation of top-down back-scattered electron SEM (BSE-SEM) images given that different phases are depicted with pixel collections of different luminosity. Gaussian mixture models (GMM) are one the most popular and easily implemented methods to segment the BSE-SEM images through the deconvolution of their histograms. However, the accuracy and the limitations of their application have not been fully investigated. The aim of this paper is to design a neural-network approach to fill this gap and provide a fast tool for the automatic evaluation of the accuracy of GMM predictions for all material phases based on the inspection of the measured SEM image histogram alone. The proposed tool facilitates the decision-making process of an SEM user concerning the optimum choice of a segmentation method. Histogram ; Artificial intelligence ; Computer science ; Deconvolution ; Mixture model ; Artificial neural network ; Segmentation ; Pixel ; Pattern recognition (psychology) ; Image segmentation ; Gaussian ; Process (computing) ; Computer vision ; Image (mathematics) ; Algorithm ; Physics Electron and X-Ray Spectroscopy Techniques ; Thermography and Photoacoustic Techniques ; Machine Learning in Materials Science
Authors: Jiho Kim | Geon Kim | Gyunyoung Heo | Kunok Chang
Source: Nuclear Engineering and Technology (article, 2022-02-23)
Field: Materials Science — Materials Chemistry

Defect structure classification of neutron-irradiated graphite using supervised machine learning Molecular dynamics simulations were performed to predict the behavior of graphite atoms under neutron irradiation using large-scale atomic/molecular massively parallel simulator (LAMMPS) package with adaptive intermolecular reactive empirical bond order (AIREBOM) potential. Defect structures of graphite were compared with results from previous studies by means of density functional theory (DFT) calculations. The quantitative relation between primary knock-on atom (PKA) energy and irradiation damage on graphite was calculated. and the effect of PKA direction on the amount of defects is estimated by counting displaced atoms. Defects are classified into four groups: structural defects, energy defects, vacancies, and near-defect structures, where a structural defect is further subdivided into six types by decision tree method which is one of the supervised machine learning techniques. Graphite ; Materials science ; Irradiation ; Neutron ; Molecular dynamics ; Atom (system on chip) ; Massively parallel ; Chemical physics ; Computational chemistry ; Computer science ; Chemistry ; Nuclear physics ; Physics ; Composite material ; Parallel computing Graphite, nuclear technology, radiation studies ; Machine Learning in Materials Science ; Nuclear Materials and Properties
Authors: Mohamed Azouz Mrad | Kristóf Csorba | Dorián László Galata | Zsombor Kristóf Nagy | Brigitta Nagy
Source: Periodica Polytechnica Electrical Engineering and Computer Science (article, 2022-03-16)
Field: Chemistry — Analytical Chemistry

Spectroscopy-Based Partial Prediction of In Vitro Dissolution Profile Using Artificial Neural Networks In pharmaceutical industry, dissolution testing is part of the target product quality that essentials are in the approval of new products. The prediction of the dissolution profile based on spectroscopic data is an alternative to the current destructive and time-consuming method. RAMAN and Near Infrared (NIR) spectroscopy are two complementary methods, that provide information on the physical and chemical properties of the tablets and can help in predicting their dissolution profiles. This work aims to use the information collected by these methods to support the decision of how much of the dissolution profile should be measured and which methods to use, so that by estimating the remaining part, the accuracy requirement of the industry is met. Artificial neural network models were created, in which parts of the measured dissolution profiles, along with the spectroscopy data and the measured compression curves were used as an input to estimate the remaining part of the dissolution profiles. It was found that by measuring the dissolution profiles for 30 minutes, the remaining part was estimated within the acceptance limits of the f2 similarity factor. Adding further spectroscopy methods along with the measured parts of the dissolution profile significantly increased the prediction accuracy. Dissolution ; Artificial neural network ; Raman spectroscopy ; Spectroscopy ; Biological system ; Computer science ; Dissolution testing ; Process engineering ; Materials science ; Artificial intelligence ; Data mining ; Chemistry ; Optics ; Engineering ; Physics Spectroscopy and Chemometric Analyses ; Crystallization and Solubility Studies ; Drug Solubulity and Delivery Systems
Authors: Chang Liu | Lehan Yao | Qian Chen
Source: Microscopy and Microanalysis (article, 2022-07-22)
Field: Materials Science — Materials Chemistry

Machine Learning Based Tracking of Single Nanoparticle Vibrations from a Projected 3D Moiré Lattice Journal Article Machine Learning Based Tracking of Single Nanoparticle Vibrations from a Projected 3D Moiré Lattice Get access Chang Liu, Chang Liu Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States Search for other works by this author on: Oxford Academic Google Scholar Lehan Yao, Lehan Yao Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States Search for other works by this author on: Oxford Academic Google Scholar Qian Chen Qian Chen Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States Search for other works by this author on: Oxford Academic Google Scholar Microscopy and Microanalysis, Volume 28, Issue S1, 1 August 2022, Pages 94–95, https://doi.org/10.1017/S1431927622001283 Published: 01 August 2022 Science and engineering ; Library science ; Tracking (education) ; Engineering physics ; Engineering ; Artificial intelligence ; Art history ; Computer science ; History ; Sociology Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Ion-surface interactions and analysis
Authors: N. Jakse | Johannes Sandberg | Granz, Leon F. | Anthony Saliou | Philippe Jarry | Émilie Devijver | Thomas Voigtmann | Jürgen Horbach | Andreas Meyer
Source: arXiv (Cornell University) (preprint, 2022-01-04)
Field: Materials Science — Materials Chemistry

Interatomic machine learning potentials for aluminium: application to solidification phenomena In studying solidification process by simulations on the atomic scale, the modeling of crystal nucleation or amorphisation requires the construction of interatomic interactions that are able to reproduce the properties of both the solid and the liquid states. Taking into account rare nucleation events or structural relaxation under deep undercooling conditions requires much larger length scales and longer time scales than those achievable by \textit{ab initio} molecular dynamics (AIMD). This problem is addressed by means of classical MD simulations using a well established high dimensional neural network potential trained on a relevant set of configurations generated by AIMD. Our dataset contains various crystalline structures and liquid states at different pressures, including their time fluctuations in a wide range of temperatures considering only their energy labels. Applied to elemental aluminium, the resulting potential is shown to be efficient to reproduce the basic structural, dynamics and thermodynamic quantities in the liquid and undercooled states without the need to include neither explicitly the forces nor all kind of configurations in the training procedure. The early stage of crystallization is further investigated on a much larger scale with one million atoms, allowing us to unravel features of the homogeneous nucleation mechanisms in the fcc phase at ambient pressure as well as in the bcc phase at high pressure with unprecedented accuracy close to the \textit{ab initio} one. In both case, a single step nucleation process is observed. Nucleation ; Supercooling ; Interatomic potential ; Crystallization ; Molecular dynamics ; Phase (matter) ; Ab initio ; Materials science ; Relaxation (psychology) ; Thermodynamics ; Crystal (programming language) ; Chemical physics ; Atomic units ; Aluminium ; Statistical physics ; Chemistry ; Physics ; Computational chemistry ; Computer science ; Metallurgy Machine Learning in Materials Science ; Microstructure and mechanical properties ; Aluminum Alloy Microstructure Properties
Authors: Tianshou Liang | Pengpeng Shi | Sanqing Su | Zhi Zeng
Source: Chinese Physics B (article, 2022-10-10)
Field: Materials Science — Materials Chemistry

Learning physical states of bulk crystalline materials from atomic trajectories in molecular dynamics simulation Melting of crystalline material is a common physical phenomenon, yet it remains elusive owing to the diversity in physical pictures. In this work, we proposed a deep learning architecture to learn the physical states (solid- or liquid-phase) from the atomic trajectories of the bulk crystalline materials with four typical lattice types. The method has ultra-high accuracy (higher than 95%) for the classification of solid–liquid atoms during the phase transition process and is almost insensitive to temperature. The atomic physical states are identified from atomic behaviors without considering any characteristic threshold parameter, which yet is necessary for the classical methods. The phase transition of bulk crystalline materials can be correctly predicted by learning from the atomic behaviors of different materials, which confirms the close correlation between atomic behaviors and atomic physical states. These evidences forecast that there should be a more general undiscovered physical quantity implicated in the atomic behaviors and elucidate the nature of bulk crystalline melting. Molecular dynamics ; Materials science ; Phase transition ; Atomic radius ; Chemical physics ; Lattice (music) ; Melting temperature ; Statistical physics ; Condensed matter physics ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Material Dynamics and Properties ; nanoparticles nucleation surface interactions
Authors: Jacek Harazin | Andrzej Wróbel
Source: IOP Conference Series Materials Science and Engineering (article, 2022-03-01)
Field: Materials Science — Materials Chemistry

A research of new potential vibration cancellation techniques with the use of piezo stacks Abstract Piezoelectric materials offer an ability to exchange energy between electrical and mechanical systems with fair ease, by using the direct and inverse piezoelectric effect. Piezoelectric transceivers, sensors, microphones, actuators or active acoustic noise cancellation devices are some of many applications for piezoelectric materials. Due to the natural ability of the material to convert electricity and mechanical strain, solutions based on piezoelectric materials are often compact and allow applications on micro scales. This article aims at expanding the applicability of piezoelectric materials to dampen vibrations of broader spectrum and bigger amplitudes, in hopes for finding new possible solutions for damping vibrations generated by heavy duty equipment or machines associated with manufacturing processes. The study involves parallel attempts at creating algorithms and mathematical models using classical mathematical algebra and deep learning techniques to actively dampen incoming vibrations with the use of piezoelectric plates. The novelty of this study would involve using piezoelectric plates of different sizes and properties in a single stack, to dampen the frequencies and amplitudes of incoming vibrations. Piezoelectricity ; Vibration ; Acoustics ; Stack (abstract data type) ; Piezoelectric motor ; Energy harvesting ; Computer science ; Mechanical engineering ; Electricity ; Electronic engineering ; Engineering ; Energy (signal processing) ; Electrical engineering ; Physics Ferroelectric and Piezoelectric Materials ; Advanced MEMS and NEMS Technologies ; Piezoelectric Actuators and Control
Authors: Qifang Xu | Roland L. Dunbrack
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2022-08-15)
Field: Materials Science — Materials Chemistry

The Protein Common Assembly Database (ProtCAD) – A comprehensive structural resource of protein complexes ABSTRACT Proteins often act through oligomeric interactions with other proteins. X-ray crystallography and cryo-electron microscopy provide detailed information on the structures of biological assemblies, defined as the most likely biologically relevant structures derived from experimental data. In crystal structures, the most relevant assembly may be ambiguously determined, since multiple assemblies observed in the crystal lattice may be plausible. It is estimated that 10-15% of PDB entries may have incorrect or ambiguous assembly annotations. Accurate assemblies are required for understanding functional data and training of deep learning methods for predicting assembly structures. As with any other kind of biological data, replication via multiple independent experiments provides important validation for the determination of biological assembly structures. Here we present the Protein Common Assembly Database (ProtCAD), which presents clusters of protein assembly structures observed in independent structure determinations of homologous proteins in the Protein Data Bank (PDB). ProtCAD is searchable by PDB entry, UniProt identifiers, or Pfam domain designations and provides downloads of coordinate files, PyMol scripts, and publicly available assembly annotations for each cluster of assemblies. About 60% of PDB entries contain assemblies in clusters of at least 2 independent experiments. All clusters and coordinates are available on ProtCAD web site ( http://dunbrack2.fccc.edu/protcad ). Protein Data Bank (RCSB PDB) ; UniProt ; Protein Data Bank ; Database ; Computer science ; Protein structure ; Identifier ; Computational biology ; Crystallography ; Information retrieval ; Data mining ; Chemistry ; Biology ; Biochemistry ; Programming language Enzyme Structure and Function ; Protein Structure and Dynamics ; RNA and protein synthesis mechanisms
Authors: Keerthi Chadalavada | Krithika Anbazhagan | Adama Ndour | Sunita Choudhary | William M. Palmer | Jamie R. Flynn | Srikanth Mallayee | P. N. Sharada | K.V.S.V. Prasad | Vishakh Padmakumar | Chris S. Jones | Jana Kholová
Source: Preprints.org (preprint, 2022-02-25)
Field: Chemistry — Analytical Chemistry

Machine Learning-Powered Models for Near-Infrared Spectrometers: Prediction of Protein in Multiple Grain Cereals Achieving global goals on sustainable nutrition, health, and wellbeing will depend on delivering enhanced diets to humankind. This will require, among others, instantaneous access to information on food quality at key points within agri-food systems. Although stationary methods are usually used to quantify grain quality (wet-lab chemistry, benchtop NIR spectrometer); these do not suit many required user-cases, such as stakeholders in decentralized agri-food-chains that are typical for emerging economies. Therefore, we explored new technologies and models that might aid these particular user-cases. For this purpose, we generated the NIR spectra of 328 grain samples from multiple cereals (finger millet, foxtail millet, maize, pearl millet, sorghum) with a standard benchtop NIR Spectrometer (DS2500, FOSS) and a novel mobile NIR-based sensor (HL-EVT5, Hone). We explored a range of classical deterministic and novel machine learning (ML)-driven models to build calibrations out of the NIR spectra. We were able to build relevant calibrations out of both types of spectra. At the same time, ML-based methods enhanced the prediction capacity of calibration models compared to classical deterministic methods. We also documented that the prediction of grain protein content based on NIR spectra generated by a mobile sensor (HL-EVT5, Hone) was highly relevant for quantitative protein predictions (R2 = 0.91, RMSE = 0.97, RPD = 3.48). Thus, the findings of this study lay the foundations on which to expand the utilization of NIR spectroscopy applications for agricultural research and development. Spectrometer ; Near-infrared spectroscopy ; Calibration ; Sorghum ; Computer science ; Grain quality ; Remote sensing ; Environmental science ; Mathematics ; Agronomy ; Geography ; Physics ; Statistics ; Biology ; Optics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Smart Agriculture and AI
Authors: Stephan Thaler | Gregor Doehner | Julija Zavadlav
Source: arXiv (Cornell University) (preprint, 2022-12-15)
Field: Materials Science — Materials Chemistry

Scalable Bayesian Uncertainty Quantification for Neural Network Potentials: Promise and Pitfalls Neural network (NN) potentials promise highly accurate molecular dynamics (MD) simulations within the computational complexity of classical MD force fields. However, when applied outside their training domain, NN potential predictions can be inaccurate, increasing the need for Uncertainty Quantification (UQ). Bayesian modeling provides the mathematical framework for UQ, but classical Bayesian methods based on Markov chain Monte Carlo (MCMC) are computationally intractable for NN potentials. By training graph NN potentials for coarse-grained systems of liquid water and alanine dipeptide, we demonstrate here that scalable Bayesian UQ via stochastic gradient MCMC (SG-MCMC) yields reliable uncertainty estimates for MD observables. We show that cold posteriors can reduce the required training data size and that for reliable UQ, multiple Markov chains are needed. Additionally, we find that SG-MCMC and the Deep Ensemble method achieve comparable results, despite shorter training and less hyperparameter tuning of the latter. We show that both methods can capture aleatoric and epistemic uncertainty reliably, but not systematic uncertainty, which needs to be minimized by adequate modeling to obtain accurate credible intervals for MD observables. Our results represent a step towards accurate UQ that is of vital importance for trustworthy NN potential-based MD simulations required for decision-making in practice. Markov chain Monte Carlo ; Computer science ; Uncertainty quantification ; Scalability ; Bayesian probability ; Bayesian inference ; Artificial intelligence ; Machine learning ; Monte Carlo method ; Hyperparameter ; Mathematics ; Statistics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Gaussian Processes and Bayesian Inference