jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Muhamad Haziq Hasnul Hadi | Pin Jern Ker | Hui Jing Lee | Yang Sing Leong | M. A. Hannan | M. Z. Jamaludin | Mohd Adzir Mahdi
Source: Sensors (article, 2021-11-02)
Field: Chemistry — Analytical Chemistry

Color Index of Transformer Oil: A Low-Cost Measurement Approach Using Ultraviolet-Blue Laser The color of transformer oil can be one of the first indicators determining the quality of the transformer oil and the condition of the power transformer. The current method of determining the color index (CI) of transformer oil utilizes a color comparator based on the American Society for Testing and Materials (ASTM) D1500 standard, which requires a human observer, leading to human error and a limited number of samples tested per day. This paper reports on the utilization of ultra violet-blue laser at 405- and 450-nm wavelengths to measure the CI of transformer oil. In total, 20 transformer oil samples with CI ranging from 0.5 to 7.5 were measured at optical pathlengths of 10 and 1 mm. A linear regression model was developed to determine the color index of the transformer oil. The equation was validated and verified by measuring the output power of a new batch of transformer oil samples. Data obtained from the measurements were able to quantify the CI accurately with root-mean-square errors (RMSEs) of 0.2229 for 405 nm and 0.4129 for 450 nm. This approach shows the commercialization potential of a low-cost portable device that can be used on-site for the monitoring of power transformers. Transformer ; Transformer oil ; Materials science ; Electrical engineering ; Environmental science ; Engineering ; Voltage Spectroscopy and Chemometric Analyses ; Cultural Heritage Materials Analysis ; Water Quality Monitoring and Analysis
Authors: Yuya Murakami | Atsushi Shono
Source: Chemical Engineering Journal Advances (article, 2021-11-27)
Field: Materials Science — Materials Chemistry

Reaction engineering with recurrent neural network: Kinetic study of Dushman reaction A recurrent neural network (RNN) was used to predict the time evolution of the Dushman reaction for the first time. The structure of the RNN was optimized to reduce the training time and amount of data required by introducing four significant features: concentration change as an output; log-scale calculation in the neural network; mass balance calculation; and pre-training of an RNN cell using two different rate laws, followed by fine-tuning. The pre-trained RNN models showed similar accuracy to that of a method using integration of the original rate laws (mean squared error of 3.30 and 1.28 μM2 for each rate law), as expected. The fine-tuned models exhibited significant improvements (mean squared error of approximately 0.10 μM2 for both rate laws) regardless of the accuracy of the original rate laws. No overtraining was observed during the fine-tuning because of the implementation of log-scale and mass balance calculations, which helped the RNN models to effectively learn from training data scattered by several orders of magnitude and avoid theoretically invalid curvature. The proposed RNN model is suitable for practical applications because it can achieve high accuracy with limited training time and a limited amount of experimental data (only 88 conditions were used to prepare training dataset in this work) and does not require detailed investigation of the reaction mechanism. The results show that if a theoretical background is properly introduced by the features mentioned above, the RNN models are highly versatile in terms of reaction kinetics. Recurrent neural network ; Computer science ; Mean squared error ; Artificial neural network ; Work (physics) ; Algorithm ; Artificial intelligence ; Mathematics ; Statistics ; Physics ; Thermodynamics Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Fuel Cells and Related Materials
Authors: John P. Stoppelman | Jesse G. McDaniel
Source: The Journal of Chemical Physics (article, 2021-09-13)
Field: Materials Science — Materials Chemistry

Physics-based, neural network force fields for reactive molecular dynamics: Investigation of carbene formation from [EMIM<b>+</b>][OAc<b>−</b>] Reactive molecular dynamics simulations enable a detailed understanding of solvent effects on chemical reaction mechanisms and reaction rates. While classical molecular dynamics using reactive force fields allows significantly longer simulation time scales and larger system sizes compared with ab initio molecular dynamics, constructing reactive force fields is a difficult and complex task. In this work, we describe a general approach following the empirical valence bond framework for constructing ab initio reactive force fields for condensed phase simulations by combining physics-based methods with neural networks (PB/NNs). The physics-based terms ensure the correct asymptotic behavior of electrostatic, polarization, and dispersion interactions and are compatible with existing solvent force fields. NNs are utilized for a versatile description of short-range orbital interactions within the transition state region and accurate rendering of vibrational motion of the reacting complex. We demonstrate our methodology for a simple deprotonation reaction of the 1-ethyl-3-methylimidazolium cation with acetate to form 1-ethyl-3-methylimidazol-2-ylidene and acetic acid. Our PB/NN force field exhibits ∼1 kJ mol<sup>-1</sup> mean absolute error accuracy within the transition state region for the gas-phase complex. To characterize the solvent modulation of the reaction profile, we compute potentials of mean force for the gas-phase reaction as well as the reaction within a four-ion cluster and benchmark against ab initio molecular dynamics simulations. We find that the surrounding ionic environment significantly destabilizes the formation of the carbene product, and we show that this effect is accurately captured by the reactive force field. By construction, the PB/NN potential may be directly employed for simulations of other solvents/chemical environments without additional parameterization. Molecular dynamics ; Force field (fiction) ; Reaction dynamics ; Ab initio ; Carbene ; Coupled cluster ; Physics ; Solvent effects ; Computational chemistry ; Chemical physics ; Chemistry ; Molecule ; Solvent ; Quantum mechanics ; Organic chemistry Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Ionic liquids properties and applications
Authors: Luis Cesar de Azevedo | Gabriel A. Pinheiro | Marcos G. Quiles | Juarez L. F. Da Silva | Ronaldo C. Prati
Source: Journal of Chemical Information and Modeling (article, 2021-08-13)
Field: Materials Science — Materials Chemistry

Systematic Investigation of Error Distribution in Machine Learning Algorithms Applied to the Quantum-Chemistry QM9 Data Set Using the Bias and Variance Decomposition Most machine learning applications in quantum-chemistry (QC) data sets rely on a single statistical error parameter such as the mean square error (MSE) to evaluate their performance. However, this approach has limitations or can even yield incorrect interpretations. Here, we report a systematic investigation of the two components of the MSE, i.e., the bias and variance, using the QM9 data set. To this end, we experiment with three descriptors, namely (i) symmetry functions (SF, with two-body and three-body functions), (ii) many-body tensor representation (MBTR, with two- and three-body terms), and (iii) smooth overlap of atomic positions (SOAP), to evaluate the prediction process's performance using different numbers of molecules in training samples and the effect of bias and variance on the final MSE. Overall, low sample sizes are related to higher MSE. Moreover, the bias component strongly influences the larger MSEs. Furthermore, there is little agreement among molecules with higher errors (outliers) across different descriptors. However, there is a high prevalence among the outliers intersection set and the convex hull volume of geometric coordinates (VCH). According to the obtained results with the distribution of MSE (and its components bias and variance) and the appearance of outliers, it is suggested to use ensembles of models with a low bias to minimize the MSE, more specifically when using a small number of molecules in the training set. Outlier ; Mean squared error ; Variance (accounting) ; Algorithm ; Data set ; Mathematics ; Statistics ; Set (abstract data type) ; Pattern recognition (psychology) ; Representation (politics) ; Convex hull ; Artificial intelligence ; Computer science ; Regular polygon Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Yoshifumi Amamoto | Hiroteru Kikutake | Ken Kojio | Atsushi Takahara | Kei Terayama
Source: Polymer Journal (article, 2021-07-28)
Field: Materials Science — Materials Chemistry

Visualization of judgment regions in convolutional neural networks for X-ray diffraction and scattering images of aliphatic polyesters Convolutional neural network ; Diffraction ; Scattering ; Visualization ; Small-angle X-ray scattering ; Artificial intelligence ; Deep learning ; Materials science ; Computer science ; Polyester ; X-ray crystallography ; Optics ; Pattern recognition (psychology) ; Physics ; Composite material Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Samuel Kim | Peter Y. Lu | Charlotte Loh | Jamie Smith | Jasper Snoek | Marin Soljačić
Source: arXiv (Cornell University) (preprint, 2021-04-23)
Field: Materials Science — Materials Chemistry

Deep Learning for Bayesian Optimization of Scientific Problems with High-Dimensional Structure Bayesian optimization (BO) is a popular paradigm for global optimization of expensive black-box functions, but there are many domains where the function is not completely a black-box. The data may have some known structure (e.g. symmetries) and/or the data generation process may be a composite process that yields useful intermediate or auxiliary information in addition to the value of the optimization objective. However, surrogate models traditionally employed in BO, such as Gaussian Processes (GPs), scale poorly with dataset size and do not easily accommodate known structure. Instead, we use Bayesian neural networks, a class of scalable and flexible surrogate models with inductive biases, to extend BO to complex, structured problems with high dimensionality. We demonstrate BO on a number of realistic problems in physics and chemistry, including topology optimization of photonic crystal materials using convolutional neural networks, and chemical property optimization of molecules using graph neural networks. On these complex tasks, we show that neural networks often outperform GPs as surrogate models for BO in terms of both sampling efficiency and computational cost. Bayesian optimization ; Computer science ; Curse of dimensionality ; Artificial intelligence ; Gaussian process ; Surrogate model ; Deep learning ; Artificial neural network ; Optimization problem ; Machine learning ; Algorithm ; Gaussian Machine Learning in Materials Science ; Advanced Multi-Objective Optimization Algorithms ; Computational Drug Discovery Methods
Authors: Zheyong Fan | Zezhu Zeng | Cunzhi Zhang | Yanzhou Wang | Keke Song | Haikuan Dong | Yue Chen | Tapio Ala-Nissilä
Source: Physical review. B./Physical review. B (preprint, 2021-09-20)
Field: Materials Science — Materials Chemistry

Neuroevolution machine learning potentials: Combining high accuracy and low cost in atomistic simulations and application to heat transport We develop a neuroevolution-potential (NEP) framework for generating neural network-based machine-learning potentials. They are trained using an evolutionary strategy for performing large-scale molecular dynamics (MD) simulations. A descriptor of the atomic environment is constructed based on Chebyshev and Legendre polynomials. The method is implemented in graphic processing units within the open-source gpumd package, which can attain a computational speed over ${10}^{7}$ atom-step per second using one Nvidia Tesla V100. Furthermore, per-atom heat current is available in NEP, which paves the way for efficient and accurate MD simulations of heat transport in materials with strong phonon anharmonicity or spatial disorder, which usually cannot be accurately treated either with traditional empirical potentials or with perturbative methods. Computer science ; Neuroevolution ; Anharmonicity ; Artificial intelligence ; Artificial neural network ; Molecular dynamics ; Atom (system on chip) ; Chebyshev filter ; Legendre polynomials ; Machine learning ; Algorithm ; Physics ; Parallel computing ; Computer vision Machine Learning in Materials Science ; Thermal properties of materials ; Fuel Cells and Related Materials
Authors: Hangrui Bi | Hengyi Wang | Chence Shi | Connor W. Coley | Jian Tang | Hongyu Guo
Source: arXiv (Cornell University) (preprint, 2021-06-08)
Field: Materials Science — Materials Chemistry

Non-Autoregressive Electron Redistribution Modeling for Reaction Prediction Reliably predicting the products of chemical reactions presents a fundamental challenge in synthetic chemistry. Existing machine learning approaches typically produce a reaction product by sequentially forming its subparts or intermediate molecules. Such autoregressive methods, however, not only require a pre-defined order for the incremental construction but preclude the use of parallel decoding for efficient computation. To address these issues, we devise a non-autoregressive learning paradigm that predicts reaction in one shot. Leveraging the fact that chemical reactions can be described as a redistribution of electrons in molecules, we formulate a reaction as an arbitrary electron flow and predict it with a novel multi-pointer decoding network. Experiments on the USPTO-MIT dataset show that our approach has established a new state-of-the-art top-1 accuracy and achieves at least 27 times inference speedup over the state-of-the-art methods. Also, our predictions are easier for chemists to interpret owing to predicting the electron flows. Autoregressive model ; Computer science ; Speedup ; Inference ; Decoding methods ; Redistribution (election) ; Chemical reaction ; Algorithm ; Computation ; Pointer (user interface) ; Theoretical computer science ; Artificial intelligence ; Chemistry ; Mathematics ; Parallel computing Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Mass Spectrometry Techniques and Applications
Authors: John Rogers | Tsung-Han Lee | Sahar Pakdel | Wenhu Xu | Vladimir Dobrosavljević | Yong-Xin Yao | Ove Christiansen | Nicola Lanatà
Source: Physical Review Research (article, 2021-01-29)
Field: Materials Science — Materials Chemistry

Bypassing the computational bottleneck of quantum-embedding theories for strong electron correlations with machine learning A cardinal obstacle to performing quantum-mechanical simulations of strongly correlated matter is that, with the theoretical tools presently available, sufficiently accurate computations are often too expensive to be ever feasible. Here we design a computational framework combining quantum-embedding (QE) methods with machine learning. This allows us to bypass altogether the most computationally expensive components of QE algorithms, making their overall cost comparable to bare density functional theory. We perform benchmark calculations of a series of actinide systems, where our method accurately describes the correlation effects, reducing by orders of magnitude the computational cost. We argue that, by producing a larger-scale set of training data, it will be possible to apply our method to systems with arbitrary stoichiometries and crystal structures, paving the way to virtually infinite applications in condensed matter physics, chemistry, and materials science. Bottleneck ; Computation ; Series (stratigraphy) ; Set (abstract data type) ; Benchmark (surveying) ; Computational learning theory ; Density functional theory Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Electron Microscopy Techniques and Applications
Authors: Masanobu Nakayama
Source: Journal of the Ceramic Society of Japan (article, 2021-05-31)
Field: Materials Science — Materials Chemistry

Materials informatics for discovery of ion conductive ceramics for batteries In this review, we introduce our work in the field of materials informatics for the prediction of ionic conduction properties in inorganic crystalline solids. Rational material development based on information-derived prediction of the ionic conductivity for the materials listed in the crystal structure database is attractive to reduce processing time and labor costs. For this purpose, the development of general descriptors and a sufficient volume of ionic conductivity datasets are required. As an example, herein we describe machine learning regression and Bayes optimization schemes and their results by using histogram descriptors and a bond valence-based force field approach. Materials informatics ; Materials science ; Ionic conductivity ; Ionic bonding ; Conductivity ; Ceramic ; Valence (chemistry) ; Bayes' theorem ; Work (physics) ; Ion ; Computer science ; Machine learning ; Data mining ; Artificial intelligence ; Health informatics ; Thermodynamics ; Composite material ; Physics ; Bayesian probability ; Electrolyte ; Physical chemistry ; Chemistry Machine Learning in Materials Science ; Fuel Cells and Related Materials ; X-ray Diffraction in Crystallography
Authors: O. E. Osafile | Nelson Nenuwe
Source: Journal of the Nigerian Society of Physical Sciences (article, 2021-05-29)
Field: Materials Science — Materials Chemistry

Lattice Dynamics and thermodynamic Responses of XNbSn Half-Heusler Semiconductors: A First-Principles Approach In this work, we have evaluated how CoNbSn, IrNbSn, and RhNbSn half Heusler alloys respond to temperature change and the accompanying lattice vibrations as a cubic crystal. There are reports in the literature for CoNbSn with which we compared our result; there are, however, no reports for the other two alloys except for their Debye temperature obtained via machine learning, and our results compare well. Considering that results in the literature for IrNbSn and RhNbSn are scanty, we first computed the alloys' structural and electronic properties to establish their structural stability using the density functional theory and generalised gradient approximation as implemented in the quantum espresso computational suite. We confirmed the equilibrium lattice structure by exploring the three possibilities for a half Heusler alloy and fitting the results to the state's Murnaghan equation. The negative formation energies obtained supports experimental simulation of the alloys. Results from the lattice dynamics and thermodynamic evaluation show that the alloys favour ionic bonding and are ductile. The Debye temperature positions IrNbSn to be the most promising material for thermoelectric application because it has the least Debye temperature; hence it is supposed to have the lowest thermal conductivity. The Dulong-Petit law is obeyed at high temperature as expected. The phonon dispersion and density of states show that the d orbitals of Co and Nb are the significant contributors to the dispersions at both the acoustic and optical modes of the alloys. Debye model ; Phonon ; Condensed matter physics ; Materials science ; Thermoelectric effect ; Density functional theory ; Debye ; Thermodynamics ; Physics ; Quantum mechanics Advanced Thermoelectric Materials and Devices ; Heusler alloys: electronic and magnetic properties ; Machine Learning in Materials Science
Authors: Fenglong Ding | Ying Liu | Zilong Zhuang | Zhengguang Wang
Source: Sensors (article, 2021-05-26)
Field: Chemistry — Organic Chemistry

A Sawn Timber Tree Species Recognition Method Based on AM-SPPResNet Sawn timber is an important component material in furniture manufacturing, decoration, construction and other industries. The mechanical properties, surface colors, textures, use and other properties of sawn timber possesed by different tree species are different. In order to meet the needs of reasonable timber use and product quality of sawn timber products, sawn timber must be identified according to tree species to ensure the best use of materials. In this study, an optimized convolution neural network was proposed to process sawn timber image data to identify the tree species of the sawn timber. The spatial pyramid pooling and attention mechanism were used to improve the convolution layer of ResNet101 to extract the feature vector of sawn timber images. The optimized ResNet (simply called "AM-SPPResNet") was used to identify the sawn timber image, and the basic recognition model was obtained. Then, the weight parameters of the feature extraction layer of the basic model were frozen, the full connection layer was removed, and using support vector machine (SVM) and XGBoost classifier which were commonly used in machine learning to train and learn the 21 × 1024 dimension feature vectors extracted by feature extraction layer. Through a number of comparative experiments, it is found that the prediction model using linear function as the kernel function of support vector machine learning the feature vectors extracted from the improved convolution layer performed best, and the F1 score and overall accuracy of all kinds of samples were above 99%. Compared with the traditional methods, the accuracy was improved by up to 12%. Support vector machine ; Artificial intelligence ; Pattern recognition (psychology) ; Feature extraction ; Classifier (UML) ; Feature vector ; Computer science ; Tree (set theory) ; Artificial neural network ; Kernel (algebra) ; Convolution (computer science) ; Convolutional neural network ; Data mining ; Mathematics Wood and Agarwood Research ; Spectroscopy and Chemometric Analyses ; Industrial Vision Systems and Defect Detection
Authors: Yuki Inada | Yukari Katsura | Masaya Kumagai | Kaoru Kimura
Source: Science and Technology of Advanced Materials Methods (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Atomic descriptors generated from coordination polyhedra in crystal structures We developed atomic descriptors from local crystal structures, which will facilitate researchers’ use of machine learning to predict the properties of inorganic materials via materials informatics. We applied singular value decomposition to the occurrence matrix of local coordination polyhedra in crystal structures. We generated two atomic descriptors, each based on the coordination atoms and topology of the coordination polyhedra. As a result of atomic clustering using these descriptors, the composition descriptor proposed in previous research depends on the similarity between same-group atoms in the periodic table. In contrast, when using our original descriptors based on the coordination atoms and topology of the coordination polyhedra, the similarity between adjacent atoms in the periodic table as well as the similarity between same-group atoms was pertinent. When we used machine learning to predict the formation energy and band gap using these descriptors as inputs, the prediction accuracy and generalization ability increased compared with using a physical property descriptor. Polyhedron ; Similarity (geometry) ; Crystal structure ; Crystal (programming language) ; Topology (electrical circuits) ; Generalization ; Cluster analysis ; Coordination number ; Periodic table ; Group (periodic table) ; Distance matrix ; Computer science ; Crystallography ; Artificial intelligence ; Mathematics ; Chemistry ; Algorithm ; Combinatorics ; Image (mathematics) Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods