jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Qi Yuan | Hong Ren | Hong Li | Dinglin Zhang | Hongqiang Cui | Junben Weng | Guohui Li | Guiyan Wang | Yan Li
Source: Chinese Journal of Chemical Physics (article, 2020-11-25)
Field: Materials Science — Materials Chemistry

Interaction energy prediction of organic molecules using deep tensor neural network The interaction energy of two molecules system plays a critical role in analyzing the interacting effect in molecular dynamic simulation. Since the limitation of quantum mechanics calculating resources, the interaction energy based on quantum mechanics can not be merged into molecular dynamic simulation for a long time scale. A deep learning framework, deep tensor neural network, is applied to predict the interaction energy of three organic related systems within the quantum mechanics level of accuracy. The geometric structure and atomic types of molecular conformation, as the data descriptors, are applied as the network inputs to predict the interaction energy in the system. The neural network is trained with the hierarchically generated conformations data set. The complex tensor hidden layers are simplified and trained in the optimization process. The predicted results of different molecular systems indicate that deep tensor neural network is capable to predict the interaction energy with 1 kcal/mol of the mean absolute error in a relatively short time. The prediction highly improves the efficiency of interaction energy calculation. The whole proposed framework provides new insights to introducing deep learning technology into the interaction energy calculation. Artificial neural network ; Computer science ; Tensor (intrinsic definition) ; Interaction energy ; Energy (signal processing) ; Artificial intelligence ; Quantum ; Deep learning ; Process (computing) ; Statistical physics ; Physics ; Molecule ; Quantum mechanics ; Mathematics ; Geometry Machine Learning in Materials Science ; Protein Structure and Dynamics ; Advanced NMR Techniques and Applications
Authors: Sen Liu | Branden B. Kappes | Behnam Amin-Ahmadi | Othmane Benafan | Xiaoli Zhang | Aaron P. Stebner
Source: arXiv (Cornell University) (preprint, 2020-03-03)
Field: Materials Science — Materials Chemistry

Physics-informed machine learning for composition-process-property alloy\n design: shape memory alloy demonstration Machine learning (ML) is shown to predict new alloys and their performances\nin a high dimensional, multiple-target-property design space that considers\nchemistry, multi-step processing routes, and characterization methodology\nvariations. A physics-informed featured engineering approach is shown to enable\notherwise poorly performing ML models to perform well with the same data.\nSpecifically, previously engineered elemental features based on alloy\nchemistries are combined with newly engineered heat treatment process features.\nThe new features result from first transforming the heat treatment parameter\ndata as it was previously recorded using nonlinear mathematical relationships\nknown to describe the thermodynamics and kinetics of phase transformations in\nalloys. The ability of the ML model to be used for predictive design is\nvalidated using blind predictions. Composition - process - property\nrelationships for thermal hysteresis of shape memory alloys (SMAs) with complex\nmicrostructures created via multiple\nmelting-homogenization-solutionization-precipitation processing stage\nvariations are captured, in addition to the mean transformation temperatures of\nthe SMAs. The quantitative models of hysteresis exhibited by such highly\nprocessed alloys demonstrate the ability for ML models to design for physical\ncomplexities that have challenged physics-based modeling approaches for\ndecades.\n Shape-memory alloy ; Homogenization (climate) ; Hysteresis ; Alloy ; Property (philosophy) ; Materials science ; Artificial intelligence ; Process (computing) ; Computer science ; Mechanical engineering ; Characterization (materials science) ; Precipitation ; Nonlinear system ; Biological system ; Machine learning ; Thermodynamics ; Process engineering ; Metallurgy ; Nanotechnology ; Physics ; Engineering Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; High Entropy Alloys Studies
Authors: Ekaterina V. Panfilova | A. R. Ibragimov | K V Mozer
Source: Journal of Physics Conference Series (article, 2020-07-01)
Field: Materials Science — Biomaterials

Neural network module for tuning an atomic force microscope in the study of photonic crystal films Abstract The article describes the use of neural network algorithms for setting the modes of investigation of photonic crystal films by tapping mode of atomic force microscopy. The photonic crystal thin films and monolayers obtained from colloidal suspensions by methods of sedimentation, centrifugation, vertical deposition, electrophoresis, and Langmuir-Blodgett are studied in this work. Study of films growth kinetics revealed that many factors were critical for reproducible deposition of the structure. To obtain high-quality images of samples, it is very important to get rid of artifacts and correctly configure the scanning parameters. To solve these problems it was proposed to use neural network algorithms. They allow to choose individual settings for each sample. We got acceptable results using a multilayer perceptron with one hidden layer. Artificial neural network ; Photonic crystal ; Materials science ; Monolayer ; Perceptron ; Multilayer perceptron ; Layer (electronics) ; Deposition (geology) ; Thin film ; Colloidal crystal ; Colloid ; Crystal (programming language) ; Nanotechnology ; Sample (material) ; Atomic force microscopy ; Computer science ; Optoelectronics ; Artificial intelligence ; Chemistry ; Chemical engineering ; Chromatography ; Engineering Diatoms and Algae Research
Authors: JCS Kadupitiya | Geoffrey Fox | Vikram Jadhao
Source: The International Journal of High Performance Computing Applications (preprint, 2020-01-14)
Field: Materials Science — Materials Chemistry

Machine learning for parameter auto-tuning in molecular dynamics simulations: Efficient dynamics of ions near polarizable nanoparticles Simulating the dynamics of ions near polarizable nanoparticles (NPs) using coarse-grained models is extremely challenging due to the need to solve the Poisson equation at every simulation timestep. Recently, a molecular dynamics (MD) method based on a dynamical optimization framework bypassed this obstacle by representing the polarization charge density as virtual dynamic variables and evolving them in parallel with the physical dynamics of ions. We highlight the computational gains accessible with the integration of machine learning (ML) methods for parameter prediction in MD simulations by demonstrating how they were realized in MD simulations of ions near polarizable NPs. An artificial neural network–based regression model was integrated with MD simulation and predicted the optimal simulation timestep and optimization parameters characterizing the virtual system with 94.3% success. The ML-enabled auto-tuning of parameters generated accurate dynamics of ions for ≈ 10 million steps while improving the stability of the simulation by over an order of magnitude. The integration of ML-enhanced framework with hybrid Open Multi-Processing / Message Passing Interface (OpenMP/MPI) parallelization techniques reduced the computational time of simulating systems with thousands of ions and induced charges from thousands of hours to tens of hours, yielding a maximum speedup of ≈ 3 from ML-only acceleration and a maximum speedup of ≈ 600 from the combination of ML and parallel computing methods. Extraction of ionic structure in concentrated electrolytes near oil–water emulsions demonstrates the success of the method. The approach can be generalized to select optimal parameters in other MD applications and energy minimization problems. Molecular dynamics ; Speedup ; Computer science ; Ion ; Polarizability ; Approx ; Kriging ; Multiphysics ; Computational science ; Statistical physics ; Physics ; Computational chemistry ; Parallel computing ; Chemistry ; Machine learning ; Quantum mechanics ; Finite element method Machine Learning in Materials Science ; Electrostatics and Colloid Interactions ; Spectroscopy and Quantum Chemical Studies
Authors: Xiaolin Chen | Wenbo Zhang | Xinran Li | Ruien Zhang | Xiaotao Fu | Yuan Li
Source:  (article, 2020-09-06)
Field: Chemistry — Analytical Chemistry

Degree of Polymerization Evaluation of Field Bushing with Near-Infrared Spectroscopy using Radial Basis Function Neural Network The degree of polymerization (DP) is the most direct parameter to characterize the aging condition of the oilpaper insulation. Recently, near infrared spectroscopy (NIRS) is used to evaluate the DP of oil-paper insulation for it is a rapid and non-destructive detection compared with the traditional viscometric method. However, the real applications of NIRS are constrained by the poor generalization ability of current prediction algorithms. This paper studies the intelligent algorithms for precisely predicting the degree of polymerization (DP) of the oil-paper insulation. The radial basis function (RBF) neural network is adopted due to its fast learning speed and strong nonlinear mapping capability. The basic principles of RBF neural network are introduced and the model for the diagnosis is trained. Especially, considering the noise at the both ends parts of the spectrum produced by the portable near-infrared, these spectral data are removed by the algorithm. A numerical example is presented to demonstrate the capability of the proposed method and the results indicate that the aging state of oil-paper insulation can be diagnosed with a trained RBF neural network of high precision. Our proposed model is applied on the Oil-impregnated paper bushing and the application results show that this method could accurately assess the aging condition of the oil-paper insulation. Radial basis function ; Artificial neural network ; Bushing ; Computer science ; Generalization ; Radial basis function network ; Noise (video) ; Field (mathematics) ; Algorithm ; Artificial intelligence ; Materials science ; Mathematics ; Composite material Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Cultural Heritage Materials Analysis
Authors: Toto Haryanto | Anuraga Jayanegara | Dias Febrisahrozi | Aziz Kustiyo
Source:  (article, 2020-09-16)
Field: Chemistry — Analytical Chemistry

Artificial Neural Network For Estimation Nutrient Utilization Based on Chemical Composition on Ruminant Animal Feed The production of ruminant livestock is used to meet the needs of human food. The productivity of ruminants is determined by the feed given. Therefore, a proper feeding strategy is needed. This research aims to estimate the nutrient utilization of ruminant forage plants based on chemical content using Artificial Neural Network (ANN). Chemical content used as the estimator is the main analysis category; meanwhile, ruminant nutritive values are predicted from our model. A total of 136 species of forage plants were obtained from http://www.feedipedia.org. Data were divided into training and testing data using K-fold cross-validation with k=3. The process of estimation was performed using the ANN structure with several variation numbers of neurons in the hidden layer from 4, 6, and 8. As a result, the ANN structure built could estimate the nutrient properly, indicated by small values of RMSE (3.33) revealed from ANN structure with four neurons. The determination coefficient (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) from this ANN model more than 0.50 for all output, except in the production of Metabolizable energy, which was 0.49. Ruminant ; Nutrient ; Artificial neural network ; Composition (language) ; Estimation ; Computer science ; Artificial intelligence ; Biology ; Agronomy ; Ecology ; Engineering ; Pasture Spectroscopy and Chemometric Analyses
Authors: Elizabeth Lee | Thomas Ludwig | Boyuan Yu | Aayush R. Singh | François Gygi | Jens K. Nørskov | Juan Pablo
Source: ChemRxiv (preprint, 2020-12-18)
Field: Materials Science — Materials Chemistry

Neural Network Sampling of the Free Energy Landscape for Nitrogen Dissociation on Ruthenium &lt;p&gt;In heterogeneous catalysis, free energy profiles of reactions govern the mechanisms, rates, and equilibria. Energetics are conventionally computed using the harmonic approximation (HA), which requires determination of critical states &lt;i&gt;a priori&lt;/i&gt;. Here, we use neural networks to efficiently sample and directly calculate the free energy surface (FES) of a prototypical heterogeneous catalysis reaction—the dissociation of molecular nitrogen on ruthenium—at density functional theory-level accuracy. We find vibrational entropy of surface atoms, often neglected in HA for transition metal catalysts, contributes significantly to the reaction barrier. The minimum free energy path for dissociation reveals an “on-top” adsorbed molecular state prior to the transition state. While a previously reported flat-lying molecular metastable state can be identified in the potential energy surface, it is absent in the FES at relevant reaction temperatures. These findings demonstrate the importance of identifying critical points self-consistently on the FES for reactions that involve considerable entropic effects.&lt;/p&gt; Metastability ; Ruthenium ; Catalysis ; Dissociation (chemistry) ; Chemistry ; Potential energy surface ; Energy landscape ; Transition state ; Chemical physics ; Density functional theory ; Potential energy ; Computational chemistry ; Molecule ; Physical chemistry ; Atomic physics ; Physics ; Organic chemistry Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Catalysis and Oxidation Reactions
Authors: Michael Fortunato | Connor W. Coley | Brian C. Barnes | Klavs F. Jensen
Source: ChemRxiv (preprint, 2020-03-31)
Field: Materials Science — Materials Chemistry

Machine Learned Prediction of Reaction Template Applicability for Data-Driven Retrosynthetic Predictions of Energetic Materials State of the art computer-aided synthesis planning models are naturally biased toward commonly reported chemical reactions, thus reducing the usefulness of those models for the unusual chemistry relevant to shock physics. To address this problem, a neural network was trained to recognize reaction template applicability for small organic molecules to supplement the rare reaction examples of relevance to energetic materials. The training data for the neural network was generated by brute force determination of template subgraph matching for product molecules from a database of reactions in U.S. patent literature. This data generation strategy successfully augmented the information about template applicability for rare reaction mechanisms in the reaction database. The increased ability to recognize rare reaction templates was demonstrated for reaction templates of interest for energetic material synthesis such as heterocycle ring formation.The following article has been submitted to by the 21st Biennial APS Conference on Shock Compression of Condensed Matter. After it is published, it will be found at https://publishing.aip.org/resources/librarians/products/journals/. Template ; Computer science ; Artificial neural network ; Retrosynthetic analysis ; Relevance (law) ; Combinatorial chemistry ; Chemistry ; Artificial intelligence ; Programming language ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Thermal and Kinetic Analysis
Authors: Yifei Mo
Source: ECS Meeting Abstracts (article, 2020-11-23)
Field: Materials Science — Materials Chemistry

Data-Driven Discovery of New Materials for Solid-State Batteries All-solid-state Li-ion battery based on solid electrolytes is a promising next-generation battery technology with high energy density, intrinsic safety, long cycle life, and wide operational temperatures. However, the lack of solid electrolyte materials that satisfy multiple requirements, such as high ionic conductivity, good stability, and interfacial compatibility with the electrode, are impeding the development of this new battery technology. To resolve these materials challenges, we develop and leverage an array of data-driven computation techniques including machine learning to discover and design novel solid-state Li-ion conductors as solid electrolytes for all-solid-state batteries. The data-driven approach enables rapid searching over a large materials space of tens of thousands of materials with highly diverse structures and chemistries. Dozens of novel solid-state conductors are discovered through our data-driven materials search. Our data-driven analyses provide unique insights into the fundamental understanding about solid-state Li-ion conductors beyond traditional physical mechanistic studies. Our study demonstrates a new paradigm of using machine learning techniques for materials discovery that overcome the data-scarcity challenges. Zhang et al., “Unsupervised Discovery of Solid-State Lithium Ion Conductors”, Nature Communications, 10, 5260 (2019) DOI: 10.1038/s41467-019-13214-1 He et al., “Crystal Structural Framework of Lithium Super-Ionic Conductors”, Advanced Energy Materials 9, 1902078 (2019) DOI: 10.1002/aenm.201902078 Nolan et al., “Computation-Accelerated Design of Materials and Interfaces for All-Solid-State Lithium-Ion Batteries”, Joule, 2, 2016-2046 (2018) DOI: 10.1016/j.joule.2018.08.017 Fast ion conductor ; Materials science ; Electrical conductor ; Nanotechnology ; Leverage (statistics) ; Solid-state ; Battery (electricity) ; Engineering physics ; Computer science ; Electrolyte ; Electrode ; Chemistry ; Engineering ; Power (physics) ; Artificial intelligence ; Physics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Advanced Battery Materials and Technologies
Authors: Bryan M. Williams | J. Kreuzer | Thomas Giddens | Alp Kucukelbir
Source: AISTech2020 Proceedings of the Iron and Steel Technology Conference (article, 2020-01-01)
Field: Chemistry — Spectroscopy

Accelerating Product Chemistry Refinement Using Machine Learning Computer science ; Product (mathematics) ; Artificial intelligence ; Machine learning ; Chemistry ; Mathematics Analytical Chemistry and Chromatography ; Process Optimization and Integration ; Crystallization and Solubility Studies
Authors: Elizabeth Lee | Thomas Ludwig | Boyuan Yu | Aayush R. Singh | François Gygi | Jens K. Nørskov | Juan Pablo
Source: ChemRxiv (preprint, 2020-12-23)
Field: Materials Science — Materials Chemistry

Neural Network Sampling of the Free Energy Landscape for Nitrogen Dissociation on Ruthenium Reaction rates in heterogeneous catalysis are predicted using the free energy profiles of elementary reactions. Conventionally, the energetics are computed from critical points of the potential energy surface, with harmonic free energy corrections. Here we use ab initio molecular dynamics and neural network-assisted enhanced sampling simulations to directly calculate the free energy landscape of a prototypical heterogeneous catalysis reaction, the dissociation of molecular nitrogen on ruthenium. We show that accelerating force- and frequency-based enhanced sampling using neural networks can characterize reactive phenomena at density functional theory-level accuracy. A previously reported molecularly adsorbed metastable state is found in the potential energy surface but is absent in the free energy surface. The potential of mean force for the dissociation reaction shows significant temperature-dependent effects beyond the standard harmonic approximation. We demonstrate that these thermodynamic effects can be important for elementary reactions on transition metal surfaces. Metastability ; Energy landscape ; Dissociation (chemistry) ; Potential energy surface ; Chemical physics ; Umbrella sampling ; Catalysis ; Potential energy ; Chemistry ; Ruthenium ; Molecular dynamics ; Ab initio ; Reaction dynamics ; Materials science ; Computational chemistry ; Molecule ; Thermodynamics ; Physical chemistry ; Atomic physics ; Physics ; Organic chemistry Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Topic Modeling
Authors: Elizabeth Lee | Thomas Ludwig | Boyuan Yu | Aayush R. Singh | François Gygi | Jens K. Nørskov | Juan Pablo
Source: ChemRxiv (preprint, 2020-12-18)
Field: Materials Science — Materials Chemistry

Neural Network Sampling of the Free Energy Landscape for Nitrogen Dissociation on Ruthenium Reaction rates in heterogeneous catalysis are predicted using the free energy profiles of elementary reactions. Conventionally, the energetics are computed from critical points of the potential energy surface, with harmonic free energy corrections. Here we use ab initio molecular dynamics and neural network-assisted enhanced sampling simulations to directly calculate the free energy landscape of a prototypical heterogeneous catalysis reaction, the dissociation of molecular nitrogen on ruthenium. We show that accelerating force- and frequency-based enhanced sampling using neural networks can characterize reactive phenomena at density functional theory-level accuracy. A previously reported molecularly adsorbed metastable state is found in the potential energy surface but is absent in the free energy surface. The potential of mean force for the dissociation reaction shows significant temperature-dependent effects beyond the standard harmonic approximation. We demonstrate that these thermodynamic effects can be important for elementary reactions on transition metal surfaces. Metastability ; Energy landscape ; Dissociation (chemistry) ; Potential energy surface ; Umbrella sampling ; Ruthenium ; Chemical physics ; Chemistry ; Catalysis ; Potential energy ; Molecular dynamics ; Ab initio ; Reaction dynamics ; Computational chemistry ; Thermodynamics ; Molecule ; Physical chemistry ; Atomic physics ; Physics ; Organic chemistry Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Topic Modeling
Authors: Michael Fortunato | Connor W. Coley | Brian C. Barnes | Klavs F. Jensen
Source: ChemRxiv (preprint, 2020-02-06)
Field: Materials Science — Materials Chemistry

Data Augmentation and Pretraining for Template-Based Retrosynthetic Prediction in Computer-Aided Synthesis Planning This work presents efforts to augment the performance of data-driven machine learning algorithms for reaction template recommendation used in computer-aided synthesis planning software. Often, machine learning models designed to perform the task of prioritizing reaction templates or molecular transformations are focused on reporting high accuracy metrics for the one-to-one mapping of product molecules in reaction databases to the template extracted from the recorded reaction. The available templates that get selected for inclusion in these machine learning models have been previously limited to those that appear frequently in the reaction databases and exclude potentially useful transformations. By augmenting open-access datasets of organic reactions with artificially calculated template applicability and pretraining a template relevance neural network on this augmented applicability dataset, we report an increase in the template applicability recall and an increase in the diversity of predicted precursors. The augmentation and pretraining effectively teaches the neural network an increased set of templates that could theoretically lead to successful reactions for a given target. Even on a small dataset of well curated reactions, the data augmentation and pretraining methods resulted in an increase in top-1 accuracy, especially for rare templates, indicating these strategies can be very useful for small datasets. Template ; Computer science ; Relevance (law) ; Artificial neural network ; Retrosynthetic analysis ; Machine learning ; Artificial intelligence ; Set (abstract data type) ; Training set ; Software ; Task (project management) ; Data mining ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis