jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sheng Zhang | Puhan Zhang | Gia-Wei Chern
Source: arXiv (Cornell University) (preprint, 2021-05-27)
Field: Materials Science — Materials Chemistry

Anomalous phase separation dynamics in a correlated electron system: machine-learning enabled large-scale kinetic Monte Carlo simulations Phase separation plays a central role in the emergence of novel functionalities of correlated electron materials. The structure of the mixed-phase states depends strongly on the nonequilibrium phase-separation dynamics, which has so far yet to be systematically investigated, especially on the theoretical side. With the aid of modern machine learning methods, we demonstrate the first-ever large-scale kinetic Monte Carlo simulations of the phase separation process for the Falicov-Kimball model, which is one of the canonical strongly correlated electron systems. We uncover an unusual phase-separation scenario where domain coarsening occurs simultaneously at two different scales: the growth of checkerboard clusters at smaller length scales and the expansion of super-clusters, which are aggregates of the checkerboard clusters of the same sign, at a larger scale. We show that the emergence of super-clusters is due to a hidden dynamical breaking of the sublattice symmetry. Arrested growth of the checkerboard patterns and of the super-clusters is shown to result from a correlation-induced self-trapping mechanism. Glassy behaviors similar to the one reported in this work could be generic for other correlated electron systems. Monte Carlo method ; Statistical physics ; Kinetic Monte Carlo ; Physics ; Phase (matter) ; Electron ; Kinetic energy ; Instability ; Scale (ratio) ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Theoretical and Computational Physics
Authors: Cheng‐Wei Ju | Ethan French | Nadav Geva | Alexander W. Kohn | Zhou Lin
Source:  (preprint, 2021-08-03)
Field: Materials Science — Materials Chemistry

Stacked Ensemble Machine Learning for Range-Separation Parameters High-throughput virtual materials and drug discovery based on density functional theory has achieved tremendous success in recent decades, but its power on organic semiconducting molecules suffers catastrophically from self-interaction error until the optimally tuned range-separated hybrid (OT-RSH) exchange--correlation functionals were developed. The accurate but expensive fi�rst-principles OT-RSH transitions from a short-range (semi-)local functional to a long-range Hartree--Fock exchange at a distance characterized by the inverse of a molecule-specifi�c, non-empirically-determined range-separation parameter (ω). In the present study, we proposed a promising stacked ensemble machine learning model that provides an accelerated alternative of OT-RSH based on system-dependent structural and electronic con�figurations. We trained ML-ωPBE, the �first functional in our series, using a database of 1,970 organic semiconducting molecules with sufficient structural diversity, and assessed its accuracy and efficiency using another 1,956 molecules. Compared with the �first-principles OT-ωPBE, our ML-ωPBE reached a mean absolute error of 0:00504a0^{-1} for the optimal value of ω, reduced the computational cost for the test set by 2.66 orders of magnitude, and achieved a comparable predictive power in various optical properties. Range (aeronautics) ; Density functional theory ; Molecule ; Inverse ; Statistical physics ; Hybrid functional ; Series (stratigraphy) ; Ensemble learning ; Test set ; Materials science ; Computer science ; Physics ; Computational physics ; Biological system ; Algorithm ; Computational chemistry ; Chemistry ; Artificial intelligence ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Electrochemical Analysis and Applications ; Analytical Chemistry and Sensors
Authors: Alexander Goscinski | Guillaume Fraux | Giulio Imbalzano | Michele Ceriotti
Source: Machine Learning Science and Technology (preprint, 2021-01-14)
Field: Materials Science — Materials Chemistry

The role of feature space in atomistic learning Abstract Efficient, physically-inspired descriptors of the structure and composition of molecules and materials play a key role in the application of machine-learning techniques to atomistic simulations. The proliferation of approaches, as well as the fact that each choice of features can lead to very different behavior depending on how they are used, e.g. by introducing non-linear kernels and non-Euclidean metrics to manipulate them, makes it difficult to objectively compare different methods, and to address fundamental questions on how one feature space is related to another. In this work we introduce a framework to compare different sets of descriptors, and different ways of transforming them by means of metrics and kernels, in terms of the structure of the feature space that they induce. We define diagnostic tools to determine whether alternative feature spaces contain equivalent amounts of information, and whether the common information is substantially distorted when going from one feature space to another. We compare, in particular, representations that are built in terms of n -body correlations of the atom density, quantitatively assessing the information loss associated with the use of low-order features. We also investigate the impact of different choices of basis functions and hyperparameters of the widely used SOAP and Behler–Parrinello features, and investigate how the use of non-linear kernels, and of a Wasserstein-type metric, change the structure of the feature space in comparison to a simpler linear feature space. Feature (linguistics) ; Metric (unit) ; Space (punctuation) ; Feature vector ; Euclidean space ; Computer science ; Basis (linear algebra) ; Artificial intelligence ; Pattern recognition (psychology) ; Theoretical computer science ; Data mining ; Mathematics ; Engineering ; Pure mathematics Machine Learning in Materials Science ; Theoretical and Computational Physics ; Protein Structure and Dynamics
Authors: Jaber Rezaei Mianroodi | Nima H. Siboni | Dierk Raabe
Source: arXiv (Cornell University) (preprint, 2021-03-16)
Field: Materials Science — Materials Chemistry

Teaching Solid Mechanics to Artificial Intelligence: a fast solver for heterogeneous solids We propose a deep neural network (DNN) as a fast surrogate model for local stress (and in principle strain) calculation in inhomogeneous non-linear material systems. We show that the DNN predicts the local stresses with about 3.8% mean absolute percentage error (MAPE) for the case of heterogeneous elastic media and a mechanical phase contrast of up to factor of 1.5 among neighboring domains, while performing 103 times faster than spectral solvers. The speed-up arises from the fact that after training, the DNN predicts the stress without any iterations, as opposed to the iterative nature of standard non-linear solvers. The new DNN surrogate model also proves suited for general purposes: it is capable to reproduce the stress distribution in geometries topologically far different from those used for training, implying effective learning of scenarios described by the underlying partial differential equations. Even in the case of elasto-plastic materials with up to 2 times mechanical contrast in elastic stiffness and 4 times in yield stress among adjacent regions, where conventional solvers typically require a substantial number of iterations to arrive at stress predictions, the trained model simulates the micromechanics with a MAPE of 6.4% in one single forward evaluation step of the network, i.e. without any iterative calculations even for the case of such a non-linear problem. The results reveal a completely new and highly efficient approach to solve non-linear mechanical boundary value problems and/or augment existing solution methods in corresponding hybrid variants, with an acceleration up to factor of 8300 for heterogeneous elastic-plastic materials in comparison to the currently fastest available solvers. Solver ; Computer science ; Artificial neural network ; Linear elasticity ; Boundary value problem ; Micromechanics ; Algorithm ; Stress (linguistics) ; Stiffness ; Acceleration ; Applied mathematics ; Mathematical optimization ; Artificial intelligence ; Mathematics ; Finite element method ; Mathematical analysis ; Structural engineering ; Physics ; Engineering Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Advanced Electron Microscopy Techniques and Applications
Authors: Cheng‐Wei Ju | Ethan French | Nadav Geva | Alexander W. Kohn | Zhou Lin
Source:  (preprint, 2021-09-13)
Field: Materials Science — Materials Chemistry

Stacked Ensemble Machine Learning for Range-Separation Parameters High-throughput virtual materials and drug discovery based on density functional theory has achieved tremendous success in recent decades, but its power on organic semiconducting molecules suffered catastrophically from the self-interaction error until the optimally tuned range-separated hybrid (OT-RSH) exchange-correlation functionals were developed. The accurate but expensive �first-principles OT-RSH transitions from a short-range (semi-)local functional to a long-range Hartree-Fock exchange at a distance characterized by the inverse of a molecule-specific, non-empirically-determined range-separation parameter (ω). In the present study, we proposed a promising stacked ensemble machine learning (SEML) model that provides an accelerated alternative of OT-RSH based on system-dependent structural and electronic configurations. We trained ML-ωPBE, the first functional in our series, using a database of 1,970 organic semiconducting molecules with sufficient structural diversity, and assessed its accuracy and efficiency using another 1,956 molecules. Compared with the �first-principles OT-ωPBE, our ML-ωPBE reached a mean absolute error of 0:00504a_0^{-1} for the optimal value of ω, reduced the computational cost for the test set by 2.66 orders of magnitude, and achieved comparable predictive powers in various optical properties. Range (aeronautics) ; Density functional theory ; Molecule ; Ensemble learning ; Computer science ; Inverse ; Statistical physics ; Hybrid functional ; Series (stratigraphy) ; Materials science ; Algorithm ; Computational physics ; Biological system ; Physics ; Chemistry ; Computational chemistry ; Artificial intelligence ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Electrochemical Analysis and Applications ; Analytical Chemistry and Sensors
Authors: Hui Lian | Zhiyu You | Rong Zhang | Tao Zhou | Yan Xia
Source:  (article, 2021-06-18)
Field: Chemistry — Analytical Chemistry

Engine oil quality analysis based on probabilistic neural network The engine oil life detection methods generally include chemical detection and optical detection. The former is inconvenient to operate, while the latter has poor accuracy. In order to obtain the life of engine oil by measuring the physical properties such as viscosity, density and dielectric constant of engine oil, the fluid property sensor(FPS) oil sensor can be used to collect the parameters such as viscosity, density and dielectric constant of engine oil, and map them to the actual life of engine oil. The probabilistic neural network(PNN) is used to learn the test samples, forming a nonlinear evaluation mechanism, and establishing an oil expert judgment system to achieve the purpose of independently judging the life of engine oil. After the training of 15 groups of sample data, 7 groups of samples were evaluated and tested in this paper. The test results show that this method can be used for the identification and online monitoring of engine oil quality, which provides a reference for the life judgment and quality replacement of engine oil, conforms to the actual application requirements, and has certain application value. Artificial neural network ; Probabilistic logic ; Probabilistic neural network ; Computer science ; Oil analysis ; Motor oil ; Automotive engineering ; Artificial intelligence ; Petroleum engineering ; Engineering ; Time delay neural network Spectroscopy and Chemometric Analyses ; Fault Detection and Control Systems ; Water Quality Monitoring and Analysis
Authors: Chunming Tang | Shan Fu | Fengyang Liu
Source:  (article, 2021-05-28)
Field: Chemistry — Inorganic Chemistry

Design and Implementation of System for Generating MOFs for Hydrogen Storage in Hydrogen-Fueled vehicles New energy vehicles replace nonrenewable energy such as gasoline with renewable resources. On the one hand, it effectively reduces the use of nonrenewable energy and protects the natural environment. On the other hand, it improves the atmospheric environment. Because hydrogen energy has the characteristics of high efficiency and energy saving, the use of hydrogen energy has become one of the directions for the development of clean energy. Metal-organic framework (MOF) has been widely studied in the field of gas adsorption due to its porous structure and large specific surface area. In this paper, we propose a system for generating MOF based on Monte Carlo Tree Search (MCTS). By improving the activation function in the gated recurrent unit (GRU) structure, the convergence speed and accuracy of the neural network can be improved. The improved GRU is used as a policy network to guide MCTS to generate MOFs with superior hydrogen adsorption performance. The improved GRU has an accuracy of 90.31% on the SMILES string dataset, which is 1.19% higher than the accuracy of the traditional GRU; For specific metal nodes and topologies, the system can generate MOFs with larger hydrogen adsorption capacity than the experimentally synthesized MOF materials. Computer science ; Hydrogen storage ; Hydrogen ; Network topology ; Renewable energy ; Hydrogen fuel ; Adsorption ; Materials science ; Process engineering ; Engineering ; Chemistry ; Electrical engineering ; Computer network Metal-Organic Frameworks: Synthesis and Applications ; Carbon dioxide utilization in catalysis ; Hydrogen Storage and Materials
Authors: Mehmet F. Demirel | Shengchao Liu | Siddhant Garg | Yingyu Liang
Source: arXiv (Cornell University) (preprint, 2021-09-29)
Field: Materials Science — Materials Chemistry

An Analysis of Attentive Walk-Aggregating Graph Neural Networks Graph neural networks (GNNs) have been shown to possess strong representation power, which can be exploited for downstream prediction tasks on graph-structured data, such as molecules and social networks. They typically learn representations by aggregating information from the K-hop neighborhood of individual vertices or from the enumerated walks in the graph. Prior studies have demonstrated the effectiveness of incorporating weighting schemes into GNNs; however, this has been primarily limited to K-hop neighborhood GNNs so far. In this paper, we aim to extensively analyze the effect of incorporating weighting schemes into walk-aggregating GNNs. Towards this objective, we propose a novel GNN model, called AWARE, that aggregates information about the walks in the graph using attention schemes in a principled way to obtain an end-to-end supervised learning method for graph-level prediction tasks. We perform theoretical, empirical, and interpretability analyses of AWARE. Our theoretical analysis provides the first provable guarantees for weighted GNNs, demonstrating how the graph information is encoded in the representation, and how the weighting schemes in AWARE affect the representation and learning performance. We empirically demonstrate the superiority of AWARE over prior baselines in the domains of molecular property prediction (61 tasks) and social networks (4 tasks). Our interpretation study illustrates that AWARE can successfully learn to capture the important substructures of the input graph. Interpretability ; Computer science ; Weighting ; Graph ; Theoretical computer science ; Artificial intelligence ; Expressive power ; Machine learning ; Random walk ; Data mining ; Mathematics Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Takao Onishi | Yuji Matsukawa | Yuto Yamazaki | Daisuke Miyashiro
Source: C – Journal of Carbon Research (article, 2021-11-25)
Field: Materials Science — Materials Chemistry

Prediction of the Near-Infrared Absorption Spectrum of Single-Walled Carbon Nanotubes Using a Bayesian Regularized Back Propagation Neural Network Model DNA-wrapped single-walled carbon nanotubes (DNA-SWCNTs) in stable dispersion are expected to be used as biosensors in the future, because they have the property of absorption of light in the near infrared (NIR) region, which is safe for the human body. However, this practical application requires the understanding of the DNA-SWCNTs’ detailed response characteristics. The purpose of this study is to predict, in detail, the response characteristics of the absorption spectra that result when the antioxidant catechin is added to oxidized DNA-SWCNTs, from a small amount of experimental data. Therefore, in the present study, we predicted the characteristics of the absorption spectra of DNA-SWCNTs using the Bayesian regularization backpropagation neural network (BRBPNN) model. The BRBPNN model was trained with the catechin concentration and initial absorption peaks as inputs and the absorption spectra after catechin addition as outputs. The accuracy of the predicted absorption peaks and wavelengths after the addition of catechin, as predicted by the BRBPNN model, was within 1% of the error of the experimental data. By inputting the catechin concentrations under hundreds of conditions into this BRBPNN model, we were able to obtain detailed prediction curves for the absorption peaks. This method has the potential to help to reduce the experimental costs and improve the efficiency of investigating the properties of high-cost materials such as SWCNTs. Carbon nanotube ; Materials science ; Catechin ; Absorption (acoustics) ; Absorption spectroscopy ; Biological system ; Dispersion (optics) ; Spectral line ; Backpropagation ; Artificial neural network ; Analytical Chemistry (journal) ; Computer science ; Nanotechnology ; Optics ; Chemistry ; Antioxidant ; Composite material ; Artificial intelligence ; Physics ; Chromatography ; Organic chemistry Carbon Nanotubes in Composites ; Neuroscience and Neural Engineering ; Conducting polymers and applications
Authors: Rishi E. Kumar | Armi Tiihonen | Shijing Sun | David P. Fenning | Zhe Liu | Tonio Buonassisi
Source: arXiv (Cornell University) (preprint, 2021-10-08)
Field: Materials Science — Materials Chemistry

Opportunities for Machine Learning to Accelerate Halide Perovskite Commercialization and Scale-Up While halide perovskites attract significant academic attention, examples of at-scale industrial production are still sparse. In this perspective, we review practical challenges hindering the commercialization of halide perovskites, and discuss how machine-learning (ML) tools could help: (1) active-learning algorithms that blend institutional knowledge and human expertise could help stabilize and rapidly update baseline manufacturing processes; (2) ML-powered metrology, including computer imaging, could help narrow the performance gap between large- and small-area devices; and (3) inference methods could help accelerate root-cause analysis by reconciling multiple data streams and simulations, focusing research effort on areas with highest probability for improvement. We conclude that to satisfy many of these challenges, incremental -- not radical -- adaptations of existing ML and statistical methods are needed. We identify resources to help develop in-house data-science talent, and propose how industry-academic partnerships could help adapt "ready-now" ML tools to specific industry needs, further improve process control by revealing underlying mechanisms, and develop "gamechanger" discovery-oriented algorithms to better navigate vast materials combination spaces and the literature. Commercialization ; Data science ; Inference ; Machine learning ; Scale (ratio) ; Computer science ; Process (computing) ; Artificial intelligence ; Perovskite (structure) ; Automation ; Halide ; Knowledge management ; Industrial engineering ; Process management ; Nanotechnology ; Engineering ; Business ; Materials science ; Marketing Machine Learning in Materials Science ; Perovskite Materials and Applications ; Electronic and Structural Properties of Oxides
Authors: Vasily A. Eliferov | Evgeny Zhvansky | Anatoly Sorokin | Vsevolod Shurkhay | Denis S. Bormotov | Stanislav I. Pekov | Pavel V. Nikitin | Marina Ryzhova | Eugene E. Kulikov | Potapov Aa | Е. Н. Николаев | Igor Popov
Source: Biochemistry (Moscow) Supplement Series B Biomedical Chemistry (article, 2021-04-01)
Field: Chemistry — Spectroscopy

The Role of Lipids in the Classification of Astrocytoma and Glioblastoma Using Mass Spectrometry Tumor Profiling Express MS identification of biological tissues has become a much more accessible research method due to the application of direct specimen ionization at atmospheric pressure. In contrast to traditional methods of analysis employing GC-MS methods for determining the molecular composition of the analyzed objects it eliminates the influence of mutual ion suppression. Despite significant progress in the field of direct MS of biological tissues, the question of mass spectrometric profile attribution to a certain type of tissue still remains open. The use of modern machine learning methods and protocols (e.g., “random forests”) enables us to trace possible relationships between the components of the sample MS profile and the result of brain tumor tissue classification (astrocytoma or glioblastoma). In this study it has been shown that the most pronounced differences in the mass spectrometric profiles of these tumors are due to their lipid composition. Detection of statistically significant differences in lipid profiles of astrocytoma and glioblastoma may be used to perform an express analysis during surgery and inform the neurosurgeon what type of malignant tissue he is working with. The ability of accurate determination of the tumor boundaries significantly improves the quality of surgical intervention and postoperative rehabilitation, as well as the duration and quality of life of patients. Astrocytoma ; Glioblastoma ; Mass spectrometry ; Brain tumor ; Chemistry ; Pathology ; Medicine ; Chromatography ; Cancer research Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Advanced Proteomics Techniques and Applications