jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Trias Mahmudiono | Raed Obaid Saleh | Gunawan Widjaja | Tzu-Chia Chen | Ghulam Yasin | Lakshmi Thangavelu | Usama S. Altimari | Supat Chupradit | Mustafa M. Kadhim | Haydar Abdulameer Marhoon
Source: Food Science and Technology (review, 2022-01-01)
Field: Chemistry — Analytical Chemistry

A review on material analysis of food safety based on fluorescence spectrum combined with artificial neural network technology Aiming at the problem that it is difficult to achieve rapid and accurate detection of pesticide residues, the artificial neural network method is used to separate the mixed fluorescence spectra in the measurement of acetamiprid pesticide residues, and a fluorescence spectrum that can quickly detect the pesticide residues of acetamiprid on solid surfaces is designed. According to the back-propagation algorithm, the three-layer artificial neural network principle is used to detect the acetamiprid residue in the mixed system of acetamiprid and filter paper with severely overlapping fluorescence spectra. In the range of 340nm~400nm, using the fluorescence intensity values ​​at 20 characteristic wavelengths as the characteristic network parameters, after network training and testing, the recovery rates of acetamiprid concentrations of 40mg/kg and 90mg/kg are 102% and 97%, respectively. The relative standard deviations of the determination results were 1.4% and 1.9%, respectively. The experimental results show that the BP neural network-assisted fluorescence spectroscopy method for the determination of acetamiprid pesticide residues on filter paper has the characteristics of fast network training, short detection period, and high measurement accuracy. Acetamiprid ; Artificial neural network ; Fluorescence ; Biological system ; Fluorescence spectroscopy ; Pesticide residue ; Materials science ; Analytical Chemistry (journal) ; Computer science ; Chemistry ; Pesticide ; Artificial intelligence ; Chromatography ; Optics ; Biology ; Physics ; Ecology Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Water Quality Monitoring and Analysis
Authors: Thankappan Sasilatha | S. Rajendran | Senthil Kumaran Selvaraj | Časlav Lacnjevac | Rajendran Rathish
Source: Zastita materijala (article, 2022-09-03)
Field: Materials Science — Materials Chemistry

Application of machine learning in corrosion inhibition study Artificial intelligence is a branch of science concerned with teaching machines to think and act like humans. Machine learning is concerned with enabling computers to perform tasks without the need for explicit programming. Machine Learning enables computers to learn without the need for explicit programming. Machine Learning is a broad field that encompasses a wide range of machine learning operations such as clustering, classification, and the development of predictive models. Machine Learning (ML) and Deep Learning (DL) research is now finding a home in both industry and academia. Machine Learning technologies are increasingly being used in medical imaging. To detect tumours and other malignant growths in the human body. Deep Learning is making significant contributions to the advancement of industrial robotics. Machine learning algorithms are used in the self-driving car industry to guide the vehicle to its destination. Deep Learning and Machine Learning are also used in corrosion science and engineering. They are used to choose the inhibitor molecules from a large pool of available molecules. Artificial intelligence ; Machine learning ; Computer science ; Deep learning ; Field (mathematics) ; Robot learning ; Cluster analysis ; Computational learning theory ; Unsupervised learning ; Robotics ; Active learning (machine learning) ; Instance-based learning ; Robot ; Mobile robot Corrosion Behavior and Inhibition ; Oil and Gas Production Techniques ; Non-Destructive Testing Techniques
Authors: Oliver Lerstøl Hebnes | Marianne Etzelmüller Bathen | Øyvind Sigmundson Schøyen | Sebastian Gregorius Winther-Larsen | Lasse Vines | M. Hjorth‐Jensen
Source: npj Computational Materials (article, 2022-09-28)
Field: Materials Science — Materials Chemistry

Predicting solid state material platforms for quantum technologies Abstract Semiconductor materials provide a compelling platform for quantum technologies (QT). However, identifying promising material hosts among the plethora of candidates is a major challenge. Therefore, we have developed a framework for the automated discovery of semiconductor platforms for QT using material informatics and machine learning methods. Different approaches were implemented to label data for training the supervised machine learning (ML) algorithms logistic regression, decision trees, random forests and gradient boosting. We find that an empirical approach relying exclusively on findings from the literature yields a clear separation between predicted suitable and unsuitable candidates. In contrast to expectations from the literature focusing on band gap and ionic character as important properties for QT compatibility, the ML methods highlight features related to symmetry and crystal structure, including bond length, orientation and radial distribution, as influential when predicting a material as suitable for QT. Computer science ; Machine learning ; Artificial intelligence ; Materials informatics ; Data mining ; Health informatics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; X-ray Diffraction in Crystallography
Authors: Kamal Choudhary | Brian DeCost
Source: npj Computational Materials (article, 2022-10-28)
Field: Materials Science — Materials Chemistry

Author Correction: Atomistic Line Graph Neural Network for improved materials property predictions Property (philosophy) ; Line (geometry) ; Computer science ; Graph ; Artificial neural network ; Artificial intelligence ; Theoretical computer science ; Mathematics ; Philosophy ; Epistemology ; Geometry Machine Learning in Materials Science
Authors: Margaret R. Quinn | Tyrel M. McQueen
Source: Frontiers in Electronic Materials (article, 2022-05-10)
Field: Materials Science — Materials Chemistry

Identifying New Classes of High Temperature Superconductors With Convolutional Neural Networks Applying machine learning to aid the search for high temperature superconductors has recently been a topic of significant interest due to the broad applications of these materials but is challenging due to the lack of a quantitative microscopic model. Here we analyze over 33,000 entries from the Superconducting Materials Database, maintained by the National Institute for Materials Science of Japan, assigning crystal structures to each entry by correlation with Materials project and other structural databases. These augmented inputs are combined with material-specific properties, including critical temperature, to train convolutional neural networks (CNNs) to identify superconductors. Classification models achieve accuracy &amp;gt;95% and regression models trained to predict critical temperature achieve R 2 &amp;gt;0.92 and mean absolute error ≈ 5.6 K. A crystal-graph representation whereby an undirected graph encodes atom sites (graph vertices) and their bonding relationships (graph edges), is used to represent materials’ periodic crystal structure to the CNNs. Trained networks are used to search though 130,000 crystal structures in the Materials Project for high temperature superconductor candidates and predict their critical temperature; several materials with model-predicted T C &amp;gt;30 K are proposed, including rediscovery of the recently explored infinite layer nickelates. Convolutional neural network ; Superconductivity ; Graph ; Crystal (programming language) ; Computer science ; High-temperature superconductivity ; Crystal structure ; Representation (politics) ; Artificial neural network ; Deep learning ; Artificial intelligence ; Algorithm ; Theoretical computer science ; Machine learning ; Materials science ; Physics ; Condensed matter physics ; Crystallography ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Iron-based superconductors research
Authors: Mathias Schreiner | Arghya Bhowmik | Tejs Vegge | Jonas Busk | Ole Winther
Source: arXiv (Cornell University) (preprint, 2022-07-25)
Field: Materials Science — Materials Chemistry

Transition1x -- a Dataset for Building Generalizable Reactive Machine Learning Potentials Machine Learning (ML) models have, in contrast to their usefulness in molecular dynamics studies, had limited success as surrogate potentials for reaction barrier search. It is due to the scarcity of training data in relevant transition state regions of chemical space. Currently, available datasets for training ML models on small molecular systems almost exclusively contain configurations at or near equilibrium. In this work, we present the dataset Transition1x containing 9.6 million Density Functional Theory (DFT) calculations of forces and energies of molecular configurations on and around reaction pathways at the wB97x/6-31G(d) level of theory. The data was generated by running Nudged Elastic Band (NEB) calculations with DFT on 10k reactions while saving intermediate calculations. We train state-of-the-art equivariant graph message-passing neural network models on Transition1x and cross-validate on the popular ANI1x and QM9 datasets. We show that ML models cannot learn features in transition-state regions solely by training on hitherto popular benchmark datasets. Transition1x is a new challenging benchmark that will provide an important step towards developing next-generation ML force fields that also work far away from equilibrium configurations and reactive systems. Benchmark (surveying) ; Computer science ; Machine learning ; Artificial intelligence ; Work (physics) ; Density functional theory ; Computational chemistry ; Chemistry ; Physics ; Geography Machine Learning in Materials Science ; Topic Modeling
Authors: Xun Huang | Xue‐Gang Li | Wen‐De Xiao | Zidong Wei
Source: AIChE Journal (article, 2022-10-25)
Field: Chemistry — Inorganic Chemistry

Machine learning‐assisted multiscale modeling of an autothermal fixed‐bed reactor for methanol to propylene process Abstract The current commercial multistage reactor for methanol to propylene (MTP) process suffers from poor propylene selectivity and catalyst efficiency, mainly because of the low inlet methanol concentration and long residence time. In this work, we proposed an autothermal co‐current flow reactor for MTP process, where the reaction heat is continuously removed through heat exchange with cold reactants, thus single‐stage reactor can be used with higher methanol inlet concentration. The reactor feasibility was investigated by a three‐dimensional multiscale model, in which the diffusion–reaction interaction inside catalyst particle was described by a neural network model trained by machine learning. With the feeding methanol fraction increasing to 30%, propylene selectivity reaches 82.27% while the space velocity approaches 2.68 g MeOH g cat −1 h −1 at 99.97% methanol conversion, about 1.4 and 3.8 times those of a commercial multibed reactor, respectively. With proper catalyst bed dilution, the reaction temperature is well controlled between 700 and 754 K. Methanol ; Catalysis ; Dilution ; Space velocity ; Chemistry ; Chemical engineering ; Residence time (fluid dynamics) ; Selectivity ; Chemical reaction engineering ; Materials science ; Process engineering ; Thermodynamics ; Organic chemistry ; Engineering Zeolite Catalysis and Synthesis ; Catalysis and Oxidation Reactions ; Catalysis and Hydrodesulfurization Studies
Authors: Mehdi Shams | Pratyush Tiwary
Source: arXiv (Cornell University) (preprint, 2022-06-27)
Field: Materials Science — Materials Chemistry

Thermodynamics-inspired Explanations of Artificial Intelligence In recent years, predictive machine learning methods have gained prominence in various scientific domains. However, due to their black-box nature, it is essential to establish trust in these models before accepting them as accurate. One promising strategy for assigning trust involves employing explanation techniques that elucidate the rationale behind a black-box model's predictions in a manner that humans can understand. However, assessing the degree of human interpretability of the rationale generated by such methods is a nontrivial challenge. In this work, we introduce interpretation entropy as a universal solution for assessing the degree of human interpretability associated with any linear model. Using this concept and drawing inspiration from classical thermodynamics, we present Thermodynamics-inspired Explainable Representations of AI and other black-box Paradigms (TERP), a method for generating accurate, and human-interpretable explanations for black-box predictions in a model-agnostic manner. To demonstrate the wide-ranging applicability of TERP, we successfully employ it to explain various black-box model architectures, including deep learning Autoencoders, Recurrent Neural Networks, and Convolutional Neural Networks, across diverse domains such as molecular simulations, text, and image classification. Interpretability ; Black box ; Interpretation (philosophy) ; Computer science ; Convolutional neural network ; Artificial intelligence ; Deep learning ; Machine learning ; Entropy (arrow of time) ; Deep neural networks ; Physics Machine Learning in Materials Science ; Explainable Artificial Intelligence (XAI) ; Computational Drug Discovery Methods
Authors: Daniel Schwartz | Tien Hong Nguyen | Zhengtao Chen | Kenneth K. S. Lau | Michael C. Grady | Ali Shokoufandeh | Masoud Soroush
Source: AIChE Journal (article, 2022-03-01)
Field: Materials Science — Surfaces, Coatings and Films

Data‐driven prediction and optimization of liquid wettability of an initiated chemical vapor deposition‐produced fluoropolymer Abstract Initiated chemical vapor deposition (iCVD) is a reactive process that creates polymeric materials on a surface from vapor‐phase monomers and thermal initiators. Our iCVD synthesis of poly(perfluorodecyl acrylate) (PPFDA) resulted in the growth of micro‐ and nano‐worms normal to the surface. The micro‐ and nanostructures of the worms directly depend on iCVD process conditions. They in turn influence bulk properties such as their liquid wettability. The current absence of a physiochemical model that can explain the relationships between iCVD process conditions and bulk properties of the polymers motivates the use of data‐driven modeling to capture and describe the relationships. In this work, we report iCVD data (contact angles of heptane, octane, and water on PPFDA and process conditions) from 49 batches and use artificial neural networks to model the relationships. The models are then used to determine the optimal iCVD process conditions that maximize the contact angles on PPFDA. Fluoropolymer ; Contact angle ; Wetting ; Chemical vapor deposition ; Polymer ; Materials science ; Chemical engineering ; Deposition (geology) ; Monomer ; Acrylate ; Process (computing) ; Work (physics) ; Octane ; Nanotechnology ; Chemistry ; Organic chemistry ; Composite material ; Thermodynamics ; Computer science Surface Modification and Superhydrophobicity ; Advanced Sensor and Energy Harvesting Materials ; Adhesion, Friction, and Surface Interactions
Authors: Ivan S. Novikov | O. Kovalyova | Alexander V. Shapeev | Max Hodapp
Source: Journal of materials research/Pratt's guide to venture capital sources (article, 2022-10-13)
Field: Materials Science — Materials Chemistry

AI-accelerated materials informatics method for the discovery of ductile alloys Alloy ; Materials science ; Density functional theory ; Statistical physics ; Materials informatics ; Interatomic potential ; Ductility (Earth science) ; Computer science ; Molecular dynamics ; Computational chemistry ; Physics ; Metallurgy ; Chemistry ; Health informatics Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Titanium Alloys Microstructure and Properties
Authors: Ting Zhou | Xing Gao | Zhiwei Ma | Hailong Chang | Tielong Shen | Minghuan Cui | Zhiguang Wang
Source: Chinese Physics B (article, 2022-07-22)
Field: Materials Science — Materials Chemistry

Atomistic insights into early stage corrosion of bcc Fe surfaces in oxygen dissolved liquid lead-bismuth eutectic (LBE-O) Classical molecular dynamics simulations with global neural network machine learning potential are used to study early stage oxidation and dissolution behaviors of bcc Fe surfaces contacting with stagnant oxygen dissolved liquid lead-bismuth eutectic (LBE-O). Both static and dynamic simulation results indicate that the early stage oxidation and dissolution behaviors of bcc Fe show strong orientation dependence under the liquid LBE environments, which may explain the experimental observations of uneven interface between iron-based materials and liquid LBE. Our investigations show that it is the delicate balance between the oxide growth and metal dissolution that leads to the observed corrosion anisotropy for bcc Fe contacting with liquid LBE-O. Dissolution ; Eutectic system ; Materials science ; Liquid metal ; Oxide ; Corrosion ; Chemical engineering ; Molecular dynamics ; Chemical physics ; Oxygen ; Anisotropy ; Metal ; Bismuth ; Metallurgy ; Alloy ; Chemistry ; Organic chemistry ; Optics ; Computational chemistry ; Physics Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Metallurgical Processes and Thermodynamics
Authors: Junzhong Xie | Xu-Yuan Zhou | Dong Luan | Hong Jiang
Source: Journal of Chemical Theory and Computation (article, 2022-06-03)
Field: Materials Science — Materials Chemistry

Machine Learning Force Field Aided Cluster Expansion Approach to Configurationally Disordered Materials: Critical Assessment of Training Set Selection and Size Convergence Cluster expansion (CE) is a powerful theoretical tool to study the configuration-dependent properties of substitutionally disordered systems. Typically, a CE model is built by fitting a few tens or hundreds of target quantities calculated by first-principles approaches. To validate the reliability of the model, a convergence test of the cross-validation (CV) score to the training set size is commonly conducted to verify the sufficiency of the training data. However, such a test only confirms the convergence of the predictive capability of the CE model within the training set, and it is unknown whether the convergence of the CV score would lead to robust thermodynamic simulation results such as order-disorder phase transition temperature <i>T</i><sub>c</sub>. In this work, using carbon defective MoC<sub>1-<i>x</i></sub> as a model system and aided by the machine-learning force field technique, a training data pool with about 13000 configurations has been efficiently obtained and used to generate different training sets of the same size randomly. By conducting parallel Monte Carlo simulations with the CE models trained with different randomly selected training sets, the uncertainty in calculated <i>T</i><sub>c</sub> can be evaluated at different training set sizes. It is found that the training set size that is sufficient for the CV score to converge still leads to a significant uncertainty in the predicted <i>T</i><sub>c</sub> and that the latter can be considerably reduced by enlarging the training set to that of a few thousand configurations. This work highlights the importance of using a large training set to build the optimal CE model that can achieve robust statistical modeling results and the facility provided by the machine-learning force field approach to efficiently produce adequate training data. Convergence (economics) ; Computer science ; Test set ; Set (abstract data type) ; Monte Carlo method ; Training set ; Machine learning ; Cluster (spacecraft) ; Field (mathematics) ; Artificial intelligence ; Training (meteorology) ; Data set ; Work (physics) ; Algorithm ; Mathematics ; Statistics ; Physics ; Engineering ; Mechanical engineering Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Advanced Chemical Physics Studies
Authors: Diyi Liu | Mitchell Luskin | Stephen Carr
Source: Physical Review Research (article, 2022-12-30)
Field: Materials Science — Materials Chemistry

Seeing moiré: Convolutional network learning applied to twistronics Moir\'e patterns made of two-dimensional (2D) materials represent highly tunable electronic Hamiltonians, allowing a wide range of quantum phases to emerge in a single material. Current modeling techniques for moir\'e electrons require significant technical work specific to each material, impeding large-scale searches for useful moir\'e materials. In order to address this difficulty, we have developed a material-agnostic machine learning approach and test it here on prototypical one-dimensional (1D) moir\'e tight-binding models. We utilize the stacking dependence of the local density of states (SD-LDOS) to convert information about electronic band structure into physically relevant images. We then train a neural network that successfully predicts moir\'e electronic structure from the easily computed SD-LDOS of aligned bilayers. This network can satisfactorily predict moir\'e electronic structures, even for materials that are not included in its training data. Moiré pattern ; Stacking ; Convolutional neural network ; Computer science ; Electronic structure ; Deep learning ; Materials science ; Artificial neural network ; Artificial intelligence ; Physics ; Computer vision ; Condensed matter physics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Ferroelectric and Negative Capacitance Devices
Authors: Xiaona Huang | Yidi Shen | Qi An
Source: Energy and AI (article, 2022-01-10)
Field: Materials Science — Materials Chemistry

Nanotwinning induced decreased lattice thermal conductivity of high temperature thermoelectric boron subphosphide (B12P2) from deep learning potential simulations Boron subphosphide (B12P2) is a promising high temperature thermoelectric material due to its good thermal stability, and chemical inertness. However, the thermal properties of B12P2 have not been well revealed so far. Here, we first develop a deep learning potential for B12P2 based on quantum mechanical calculations. Then the isotropic lattice thermal conductivity (LTC) of crystalline B12P2 is predicted to be 39.70 ± 4.38 W/m⋅K from molecular dynamics simulations using this deep learning potential. The LTC exhibits the relationship of κL∼1/T in the temperature range of 300 ∼ 1500 K. More important, a twin boundary strategy is proposed to reduce the LTC of B12P2. In nanotwinned B12P2, the phonon transport in all directions is significantly suppressed by twin boundaries (TBs) with the isotropic LTC of 15.85 ± 2.70 W/m⋅K, especially in the direction normal to the TB plane. The decrease of vibrational density of states and phonon participation ratio due to TBs' phonon scattering is the main reason of the low LTC in nanotwinned B12P2. In addition, the elastic moduli (B and G) of B12P2 crystal decrease by less than 7% after inducing TBs, which suggests that the mechanical properties are not significantly affected by TBs. Overall, this work enriches our understanding of the thermal properties of B12P2 and offers a promising approach, i.e., introducing TBs, to design high-performance thermoelectric materials. Phonon ; Materials science ; Thermal conductivity ; Phonon scattering ; Thermoelectric effect ; Isotropy ; Grain boundary ; Condensed matter physics ; Scattering ; Atmospheric temperature range ; Thermodynamics ; Composite material ; Optics ; Microstructure ; Physics Thermal properties of materials ; Machine Learning in Materials Science ; Boron and Carbon Nanomaterials Research