jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sherif Abdulkader Tawfik | Salvy P. Russo
Source: arXiv (Cornell University) (preprint, 2022-03-02)
Field: Materials Science — Materials Chemistry

Naturally-meaningful and efficient descriptors: machine learning of material properties based on robust one-shot ab initio descriptors Establishing a data-driven pipeline for the discovery of novel materials requires the engineering of material features that can be feasibly calculated and can be applied to predict a material's target properties. Here we propose a new class of descriptors for describing crystal structures, which we term Robust One-Shot Ab initio (ROSA) descriptors. ROSA is computationally cheap and is shown to accurately predict a range of material properties. These simple and intuitive class of descriptors are generated from the energetics of a material at a low level of theory using an incomplete ab initio calculation. We demonstrate how the incorporation of ROSA descriptors in ML-based property prediction leads to accurate predictions over a wide range of crystals, amorphized crystals, metal-organic frameworks and molecules. We believe that the low computational cost and ease of use of these descriptors will significantly improve ML-based predictions. Ab initio ; Class (philosophy) ; Shot (pellet) ; Computer science ; Pipeline (software) ; Simple (philosophy) ; Range (aeronautics) ; Artificial intelligence ; Property (philosophy) ; Ab initio quantum chemistry methods ; Statistical physics ; Machine learning ; Biological system ; Materials science ; Molecule ; Physics ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: V. Sharma | Dibakar Datta
Source: Journal of Electrochemical Energy Conversion and Storage (article, 2022-06-13)
Field: Materials Science — Materials Chemistry

Developing Potential Energy Surfaces for Graphene-Based 2D–3D Interfaces From Modified High-Dimensional Neural Networks for Applications in Energy Storage Abstract Designing a new heterostructure electrode has many challenges associated with interface engineering. Demanding simulation resources and lack of heterostructure databases continue to be a barrier to understanding the chemistry and mechanics of complex interfaces using simulations. Mixed-dimensional heterostructures composed of two-dimensional (2D) and three-dimensional (3D) materials are undisputed next-generation materials for engineered devices due to their changeable properties. The present work computationally investigates the interface between 2D graphene and 3D tin (Sn) systems with density functional theory (DFT) method. This computationally demanding simulation data is further used to develop machine learning (ML)-based potential energy surfaces (PES). The approach to developing PES for complex interface systems in the light of limited data and the transferability of such models has been discussed. To develop PES for graphene-tin interface systems, high-dimensional neural networks (HDNN) are used that rely on atom-centered symmetry function to represent structural information. HDNN are modified to train on the total energies of the interface system rather than atomic energies. The performance of modified HDNN trained on 5789 interface structures of graphene|Sn is tested on new interfaces of the same material pair with varying levels of structural deviations from the training dataset. Root-mean-squared error (RMSE) for test interfaces fall in the range of 0.01–0.45 eV/atom, depending on the structural deviations from the reference training dataset. By avoiding incorrect decomposition of total energy into atomic energies, modified HDNN model is shown to obtain higher accuracy and transferability despite a limited dataset. Improved accuracy in the ML-based modeling approach promises cost-effective means of designing interfaces in heterostructure energy storage systems with higher cycle life and stability. Heterojunction ; Graphene ; Interface (matter) ; Tin ; Materials science ; Density functional theory ; Atom (system on chip) ; Computer science ; Computational science ; Nanotechnology ; Optoelectronics ; Computational chemistry ; Chemistry ; Embedded system Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; 2D Materials and Applications
Authors: K. Arunteja | Amudha | Vikraman Rakshagan
Source: 2022 14th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS) (article, 2022-11-12)
Field: Chemistry — Analytical Chemistry

Retraction Notice: Identification of Diseased Apple Fruit using 3D and 2D Convolutional Neural Network for Improving Accuracy To identify the diseased apple fruits using 2D and 3D convolution neural network. Materials and Methods: Kaggle dataset is used to characterize the images, dataset contains 100 images. In training 50 images, (Healthy 25 and Diseased 25) and testing 50 images (Healthy 25 and Diseased 25) are used and the algorithm is iterated 25 times (N) for all samples. The images are tested using two machine learning algorithms 2D Con-volutional Neural Network and novel 3D Convolutional Neural Network. The samples are calculated using Gpower with pretest power of 80confidence interval of 95Novel 3D Convolutional Neural Network achieved classification accuracy 84.64 % and 2D Convolutional neural network achieved accuracy of 70.92 % with 2 tailed significance value of 0.001 (P!0.05). Conclusion: Novel 3D Convolution Neural Network appears to be significantly better than the 2D convolutional neural network in terms of accuracy for identifying diseased fruits. Convolutional neural network ; Identification (biology) ; Computer science ; Expression (computer science) ; Artificial intelligence ; Artificial neural network ; Pattern recognition (psychology) ; Computational biology ; Machine learning ; Biology ; Botany Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Maciej J. Karcz | Luca Messina | Eiji Kawasaki | Serenah Rajaonson | Didier Bathellier | Émeric Bourasseau
Source: arXiv (Cornell University) (preprint, 2022-11-22)
Field: Materials Science — Materials Chemistry

Semi-supervised generative approach to point-defect formation in chemically disordered compounds: application to uranium-plutonium mixed oxides Machine-learning methods are nowadays of common use in the field of material science. For example, they can aid in optimizing the physicochemical properties of new materials, or help in the characterization of highly complex chemical compounds. An especially challenging problem arises in the modeling of chemically disordered solid solutions, for which some properties depend on the distribution of chemical species in the crystal lattice. This is the case of defect properties of uranium-plutonium mixed oxides nuclear fuels. The number of possible configurations is so high that the problem becomes intractable if treated with direct sampling. We thus propose a machine learning approach, based on generative modeling, to optimize the exploration of this large configuration space. A probabilistic, semi-supervised approach using Mixture Density Network is applied to estimate the concentration of thermal defects in (U, Pu)O2. We show that this method, based on the prediction of the density of states of formation energy of a defect, is computationally much more cost-efficient compared to other approaches available in the literature. Plutonium ; Uranium ; Computer science ; Point (geometry) ; Probabilistic logic ; Lattice (music) ; Generative grammar ; Characterization (materials science) ; Materials science ; Artificial intelligence ; Chemistry ; Nanotechnology ; Mathematics ; Physics ; Metallurgy ; Radiochemistry Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Radioactive element chemistry and processing
Authors: Phong Thanh Nguyen | Joseph Choi | Yen Nguyen | H.S. Udaykumar | Stephen Baek
Source:  (preprint, 2022-07-07)
Field: Materials Science — Materials Chemistry

Establishing the structure-property-performance linkage of pressed energetic materials using physics-aware recurrent convolutional neural networks (PARC) Property (philosophy) ; J-PARC ; Linkage (software) ; Convolutional neural network ; Recurrent neural network ; Computer science ; Physics ; Materials science ; Artificial neural network ; Artificial intelligence ; Optics ; Chemistry Machine Learning in Materials Science
Authors: Stephen Lam | Yu Shi | Thomas Beck
Source: ECS Meeting Abstracts (article, 2022-07-07)
Field: Materials Science — Materials Chemistry

Modeling Solvation Thermodynamics in Molten Salts with Quasichemical Theory and Ab Initio-Accurate Deep Learning-Accelerated Simulations Molten salts are a promising class of ionic liquids used in advanced energy applications including next-generation nuclear reactors, batteries, and solar thermal energy storage. In these applications, understanding corrosion processes and predicting phase behavior remains a critical challenge. This requires accurate prediction of the solvation thermodynamics of ionic species in a variety of chemical and configurational states. In this work, we fundamentally address these challenges by combining quasichemical theory (QCT), ab initio simulation with density functional theory (DFT), and neural network interatomic potentials (NNIP) to accurately predict the solvation free energy of solute ions in molten salt. Ab initio data is used to train neural networks that learn the environment-dependent atomic forces and energies. This enables acceleration of atomistic simulation by more than three orders of magnitude. Using chemically accurate and highly efficient neural network-based molecular simulations, we perform free energy calculations within the QCT framework. Namely, QCT provides an exact partitioning of the free energy that includes contributions from 1) formation of a cavity in solution, 2) insertion of a solute ion into the cavity, and 3) relaxation of the cavity surrounding the solute ion. This requires simulations in timescales totaling tens of nanoseconds. As such, using AIMD alone is impractical for exploring a wide range of solutes, compositions, and thermodynamic conditions. In this work, we show that the NNIPs can accurately predict molten salt thermodynamics and local coordination structures. We provide a demonstration of the combined methods (DFT-NNIP-QCT) on molten NaCl, in which we obtain the total excess potentials of Na + and Cl - ions, and perform corrections to errors in electrostatic energy caused by finite size of the simulation cell. The calculated excess chemical potential for Na+/Cl− was predicted to be -161.7±10.6 kcal/mol, which is consistent with previous calculations and an experimental value of -163.5 kcal/mol from thermochemical tables. These results provide initial validation of the methods for predicting excess chemical potentials, which can be directly exploited for the determination of solute chemistry, and the solubility of dissolved gases and metallic ions in molten salts. This provides motivation for the use of these methods to understanding solute chemistry in a wide range of molten salt systems in advanced energy applications. Solvation ; Ab initio ; Ionic bonding ; Molten salt ; Chemistry ; Thermodynamics ; Work (physics) ; Ion ; Molecular dynamics ; Density functional theory ; Chemical physics ; Computational chemistry ; Physics ; Organic chemistry Machine Learning in Materials Science ; Ionic liquids properties and applications ; Zeolite Catalysis and Synthesis
Authors: Jonathan Schmidt | Haichen Wang | Georg Schmidt | Miguel A. L. Marques
Source: arXiv (Cornell University) (preprint, 2022-08-29)
Field: Materials Science — Materials Chemistry

Machine Learning guided high-throughput search of non-oxide garnets Garnets, known since the early stages of human civilization, have found important applications in modern technologies including magnetorestriction, spintronics, lithium batteries, etc. The overwhelming majority of experimentally known garnets are oxides, while explorations (experimental or theoretical) for the rest of the chemical space have been limited in scope. A key issue is that the garnet structure has a large primitive unit cell, requiring an enormous amount of computational resources. To perform a comprehensive search of the complete chemical space for new garnets,we combine recent progress in graph neural networks with high-throughput calculations. We apply the machine learning model to identify the potential (meta-)stable garnet systems before systematic density-functional calculations to validate the predictions. In this way, we discover more than 600 ternary garnets with distances to the convex hull below 100~meV/atom with a variety of physical and chemical properties. This includes sulfide, nitride and halide garnets. For these, we analyze the electronic structure and discuss the connection between the value of the electronic band gap and charge balance. Computer science ; Ternary operation ; Materials science ; Spintronics ; Nanotechnology ; Physics ; Condensed matter physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Rishikesh Magar | Yuyang Wang | Amir Barati Farimani
Source: arXiv (Cornell University) (preprint, 2022-05-04)
Field: Materials Science — Materials Chemistry

Crystal Twins: Self-supervised Learning for Crystalline Material Property Prediction Machine learning (ML) models have been widely successful in the prediction of material properties. However, large labeled datasets required for training accurate ML models are elusive and computationally expensive to generate. Recent advances in Self-Supervised Learning (SSL) frameworks capable of training ML models on unlabeled data have mitigated this problem and demonstrated superior performance in computer vision and natural language processing tasks. Drawing inspiration from the developments in SSL, we introduce Crystal Twins (CT): an SSL method for crystalline materials property prediction. Using a large unlabeled dataset, we pre-train a Graph Neural Network (GNN) by applying the redundancy reduction principle to the graph latent embeddings of augmented instances obtained from the same crystalline system. By sharing the pre-trained weights when fine-tuning the GNN for regression tasks, we significantly improve the performance for 7 challenging material property prediction benchmarks Computer science ; Property (philosophy) ; Redundancy (engineering) ; Artificial intelligence ; Machine learning ; Graph ; Artificial neural network ; Reduction (mathematics) ; Pattern recognition (psychology) ; Data mining ; Theoretical computer science ; Mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Yinan Bu | Liangliang Shi | Bin Ma
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Chemistry — Organic Chemistry

Method for Establishing Predictive Models for Total Organic Halogen Based on Piecewise Interpolation and Machine Learning Artificial neural network ; Backpropagation ; Piecewise ; Interpolation (computer graphics) ; Support vector machine ; Process (computing) ; Sensitivity (control systems) ; Sigmoid function Chemical Thermodynamics and Molecular Structure ; Water Quality Monitoring and Analysis
Authors: 伸宏 中村 | Nobuhiro Nakamura | 朋史 多田 | Tomofumi Tada (1674688) | 秀雄 細野 | HIDEO HOSONO
Source: Institutional Repositories DataBase (IRDB) (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Suppression of Rayleigh Scattering in Silica Glass by Codoping Boron and Fluorine: Molecular Dynamics Simulations with ForceMatching and Neural Network Potentials Silica glass ; Molecular dynamics ; Rayleigh scattering ; Boron ; Dynamics (music) ; Scattering Material Dynamics and Properties ; Glass properties and applications ; High-pressure geophysics and materials
Authors: Timo Sommer | Roland Willa | Jörg Schmalian | Pascal Friederich
Source: arXiv (Cornell University) (preprint, 2022-12-12)
Field: Materials Science — Materials Chemistry

3DSC - A New Dataset of Superconductors Including Crystal Structures Data-driven methods, in particular machine learning, can help to speed up the discovery of new materials by finding hidden patterns in existing data and using them to identify promising candidate materials. In the case of superconductors, which are a highly interesting but also a complex class of materials with many relevant applications, the use of data science tools is to date slowed down by a lack of accessible data. In this work, we present a new and publicly available superconductivity dataset ('3DSC'), featuring the critical temperature $T_\mathrm{c}$ of superconducting materials additionally to tested non-superconductors. In contrast to existing databases such as the SuperCon database which contains information on the chemical composition, the 3DSC is augmented by the approximate three-dimensional crystal structure of each material. We perform a statistical analysis and machine learning experiments to show that access to this structural information improves the prediction of the critical temperature $T_\mathrm{c}$ of materials. Furthermore, we see the 3DSC not as a finished dataset, but we provide ideas and directions for further research to improve the 3DSC in multiple ways. We are confident that this database will be useful in applying state-of-the-art machine learning methods to eventually find new superconductors. Superconductivity ; Computer science ; Crystal (programming language) ; Class (philosophy) ; Machine learning ; Artificial intelligence ; State (computer science) ; Data mining ; Physics ; Condensed matter physics ; Algorithm Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Topic Modeling
Authors: Cancan Huang | Brenda M. Rubenstein
Source: arXiv (Cornell University) (preprint, 2022-11-14)
Field: Materials Science — Materials Chemistry

Machine Learning Diffusion Monte Carlo Forces Diffusion Monte Carlo (DMC) is one of the most accurate techniques available for calculating the electronic properties of molecules and materials, yet it often remains a challenge to economically compute forces using this technique. As a result, ab initio molecular dynamics simulations and geometry optimizations that employ Diffusion Monte Carlo forces are often out of reach. One potential approach for accelerating the computation of "DMC forces" is to machine learn these forces from DMC energy calculations. In this work, we employ Behler-Parrinello Neural Networks to learn DMC forces from DMC energy calculations for geometry optimization and molecular dynamics simulations of small molecules. We illustrate the unique challenges that stem from learning forces without explicit force data and from noisy energy data by making rigorous comparisons of potential energy surface, dynamics, and optimization predictions among ab initio Density Functional Theory (DFT) simulations and machine learning models trained on DFT energies with forces, DFT energies without forces, and DMC energies without forces. We show for three small molecules - C2, H2O, and CH3Cl - that machine learned DMC dynamics can reproduce average bond lengths and angles within a few percent of known experimental results at a 100th of the typical cost. Our work describes a much-needed means of performing dynamics simulations on high-accuracy, DMC PESs and for generating DMC-quality molecular geometries given current algorithmic constraints. Monte Carlo method ; Molecular dynamics ; Diffusion Monte Carlo ; Statistical physics ; Density functional theory ; Work (physics) ; Computation ; Ab initio ; Potential energy ; Computer science ; Quantum Monte Carlo ; Force field (fiction) ; Computational chemistry ; Physics ; Monte Carlo molecular modeling ; Chemistry ; Algorithm ; Artificial intelligence ; Classical mechanics ; Mathematics ; Quantum mechanics ; Markov chain Monte Carlo Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Phase Equilibria and Thermodynamics
Authors: Xingzou Liu | Siyuan Shang | Xinyu Zhao | Liguo Fang
Source:  (article, 2022-06-24)
Field: Chemistry — Analytical Chemistry

The Study on the Optimization Model for Producing Olefin by Coupling Reaction of Ethanol Based on Clustering Analysis and Multiple Regression As a basic chemical raw material, olefins play a very important role in optimizing product structure and promoting economic development. In recent years, the olefin industry has faced unfavorable external environmental challenges such as low oil prices and the spread of the epidemic, and the uncertainty of industry development has greatly increased. Due to the huge demand, technological innovation in olefin production has become a top priority. Ethanol, which is the feedstock for C4 olefins, has an impact on the selectivity and yield of C4 olefins under different catalyst combinations and temperatures. Therefore, the study of ethanol-coupled preparation of C4 olefins under different conditios is conducive to improving the synthesis efficiency of C4 olefins and providing decision-making suggestions for the optimization and nconfiguration of chemicals. In this paper, 120 sets of experimental data are analyzed and processed, which provides a universal calculation step and solution for the parameter selection of ethanol-coupled to prepare C4 olefins under specific conditions. First, the by-product relationship was determined by cluster analysis in order to analyze the factors affecting ethanol utilization and C4 yield. Then, using multiple linear regression and partial least squares regression, the quantitative relationship between the variables was found, and the yield of C4 olefins at a temperature of 400 degrees, catalyst quality of 200 mg Co/SiO2-200 mg HAP, Co support of 1.1 Wt%, Co/SiO2 concentration of 1.06 wt%, and C4 olefin yield of 1.15 ml/min of ethanol concentration reached a maximum value of 77.21%. Finally, after error analysis and validity test, it is concluded that it is suitable for chemical reactions in most industrial production and can be used for efficient extraction of the target product in actual production. In the future, it will be optimized in a larger amount of experimental data, further optimized in combination with deep learning, to improve accuracy and accuracy. Olefin fiber ; Yield (engineering) ; Raw material ; Regression analysis ; Linear regression ; Ethanol fuel ; Production (economics) ; Ethanol ; Partial least squares regression ; Cluster analysis ; Catalysis ; Product (mathematics) ; Chemistry ; Organic chemistry ; Mathematics ; Materials science ; Economics ; Statistics ; Microeconomics Petroleum Processing and Analysis ; Catalysis and Hydrodesulfurization Studies ; Catalysis and Oxidation Reactions
Authors: Dylan Walters | Jacob Herrin | Thomas D. Sewell | Stephen Baek | H.S. Udaykumar
Source:  (preprint, 2022-07-06)
Field: Materials Science — Materials Chemistry

Learning continuum strength models for meso-scale simulations of HMX from molecular dynamics using deep neural networks Molecular dynamics ; Scale (ratio) ; Artificial neural network ; Dynamics (music) ; Statistical physics ; Deep neural networks ; Computer science ; Artificial intelligence ; Physics ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Fuel Cells and Related Materials
Authors: Brigitte Walla | Daniel Bischoff | S. Franz | Robert Janowski | Dirk Weuster‐Botz
Source: Chemie Ingenieur Technik (article, 2022-08-25)
Field: Materials Science — Materials Chemistry

Crystallization of Rationally Engineered <i>Lactobacillus kefir</i> Alcoholdehydrogenases Monitored by Machine‐Learning Based Protein Crystal Detection Technical university ; Engineering ; Library science ; Computer science Enzyme Structure and Function ; Microbial Metabolic Engineering and Bioproduction ; Protein Structure and Dynamics