Human-in-the-Loop Layout and Geometry Optimization of Structures and Components

2017 ◽  
pp. 1364-1373
Author(s):  
Linwei He ◽  
Matthew Gilbert ◽  
Thomas Johnson ◽  
Chris Smith
Keyword(s):  
Geometry Optimization ◽  
Human In The Loop ◽  
Loop Layout

scholarly journals NEXTorch: A Design and Bayesian Optimization Toolkit for Chemical Sciences and Engineering

2021 ◽  
Author(s):  
Yifan Wang ◽  
Tai-Ying Chen ◽  
Dionisios Vlachos
Keyword(s):  
Active Learning ◽  
Geometry Optimization ◽  
Optimization Theory ◽  
Computational Design ◽  
Bayesian Optimization ◽  
Optimization Approach ◽  
Loop Optimization ◽  
Human In The Loop ◽  
Condition Optimization ◽  
Reactor Geometry

<div> <div> <div> <p>Automation and optimization of chemical systems require well-inform decisions on what experiments to run to reduce time, materials, and/or computations. Data-driven active learning algorithms have emerged as valuable tools to solve such tasks. Bayesian optimization, a sequential global optimization approach, is a popular active-learning framework. Past studies have demonstrated its efficiency in solving chemistry and engineering problems. We introduce NEXTorch, a library in Python/PyTorch, to facilitate laboratory or computational design using Bayesian optimization. NEXTorch offers fast predictive modeling, flexible optimization loops, visualization capabilities, easy interfacing with legacy software, and multiple types of parameters and data type conversions. It provides GPU acceleration, parallelization, and state-of-the-art Bayesian Optimization algorithms and supports both automated and human-in-the-loop optimization. The comprehensive online documentation introduces Bayesian optimization theory and several examples from catalyst synthesis, reaction condition optimization, parameter estimation, and reactor geometry optimization. NEXTorch is open-source and available on GitHub. </p> </div> </div> </div>

scholarly journals NEXTorch: A Design and Bayesian Optimization Toolkit for Chemical Sciences and Engineering

2021 ◽  
Author(s):  
Yifan Wang ◽  
Tai-Ying Chen ◽  
Dionisios Vlachos
Keyword(s):  
Active Learning ◽  
Geometry Optimization ◽  
Optimization Theory ◽  
Computational Design ◽  
Bayesian Optimization ◽  
Optimization Approach ◽  
Loop Optimization ◽  
Human In The Loop ◽  
Condition Optimization ◽  
Reactor Geometry

<div> <div> <div> <p>Automation and optimization of chemical systems require well-inform decisions on what experiments to run to reduce time, materials, and/or computations. Data-driven active learning algorithms have emerged as valuable tools to solve such tasks. Bayesian optimization, a sequential global optimization approach, is a popular active-learning framework. Past studies have demonstrated its efficiency in solving chemistry and engineering problems. We introduce NEXTorch, a library in Python/PyTorch, to facilitate laboratory or computational design using Bayesian optimization. NEXTorch offers fast predictive modeling, flexible optimization loops, visualization capabilities, easy interfacing with legacy software, and multiple types of parameters and data type conversions. It provides GPU acceleration, parallelization, and state-of-the-art Bayesian Optimization algorithms and supports both automated and human-in-the-loop optimization. The comprehensive online documentation introduces Bayesian optimization theory and several examples from catalyst synthesis, reaction condition optimization, parameter estimation, and reactor geometry optimization. NEXTorch is open-source and available on GitHub. </p> </div> </div> </div>

GEOMETRY OPTIMIZATION OF AIR-ASSISTED SWIRL NOZZLE BASED ON SURROGATE MODELS AND COMPUTATIONAL FLUID DYNAMICS

2019 ◽  
Vol 29 (7) ◽  
pp. 605-628
Author(s):  
Zongli Yi ◽  
Li Hou ◽  
Qi Zhang ◽  
Yousheng Wang ◽  
Yunxia You
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Geometry Optimization ◽  
Surrogate Models

Influences of Uncertainties and Traffic Scenario Difficulties in a Human-in-the-Loop Simulation

2014 ◽  
Vol 22 (2) ◽  
pp. 179-193
Author(s):  
Nancy Bienert ◽  
Joey Mercer ◽  
Jeffrey R. Homola ◽  
Susan E. Morey ◽  
Thomas Prevot
Keyword(s):  
Human In The Loop ◽  
Traffic Scenario

CLB18: A New Structural Database with Unusual Carbon–Carbon Long Bonds

2020 ◽  
Author(s):  
Pierpaolo Morgante ◽  
Roberto Peverati
Keyword(s):  
Transition Metal Complexes ◽  
Low Cost ◽  
Geometry Optimization ◽  
Structure Calculation ◽  
Unique Property ◽  
Spin States ◽  
Calculation Methods ◽  
Large Molecules ◽  
Barrier Heights ◽  
Structural Database

<div><div><div><p>In this Letter, we introduce a new database called carbon long bond 18 (CLB18), composed of 18 structures with one long C–C bond. We use this new database to evaluate the performance of several low-cost methods commonly used for geometry optimization of medium and large molecules. We found that the long bonds in CLB18 are electronically different from those found in barrier heights databases. We also report the unexpected correlation between the results of CLB18 and those of the energetics of spin states in transition-metal complexes. Given this unique property, CLB18 can be a useful tool for assessing existing electronic structure calculation methods and developing new ones.</p></div></div></div>

BAND NN: A Deep Learning Framework For Energy Prediction and Geometry Optimization of Organic Small Molecules

2019 ◽  
Author(s):  
Siddhartha Laghuvarapu ◽  
Yashaswi Pathak ◽  
U. Deva Priyakumar
Keyword(s):  
Machine Learning ◽  
Density Functional ◽  
Computational Cost ◽  
Geometry Optimization ◽  
Dft Methods ◽  
Energy Prediction ◽  
Machine Learning Model ◽  
Equilibrium Structures ◽  
High Level ◽  
Non Equilibrium

Recent advances in artificial intelligence along with development of large datasets of energies calculated using quantum mechanical (QM)/density functional theory (DFT) methods have enabled prediction of accurate molecular energies at reasonably low computational cost. However, machine learning models that have been reported so far requires the atomic positions obtained from geometry optimizations using high level QM/DFT methods as input in order to predict the energies, and do not allow for geometry optimization. In this paper, a transferable and molecule-size independent machine learning model (BAND NN) based on a chemically intuitive representation inspired by molecular mechanics force fields is presented. The model predicts the atomization energies of equilibrium and non-equilibrium structures as sum of energy contributions from bonds (B), angles (A), nonbonds (N) and dihedrals (D) at remarkable accuracy. The robustness of the proposed model is further validated by calculations that span over the conformational, configurational and reaction space. The transferability of this model on systems larger than the ones in the dataset is demonstrated by performing calculations on select large molecules. Importantly, employing the BAND NN model, it is possible to perform geometry optimizations starting from non-equilibrium structures along with predicting their energies.

Investigating the Impact of Human in-the-Loop Digital Twin in an Industrial Maintenance Context

2020 ◽  
Author(s):  
Ali Al-Yacoubb ◽  
Will Eaton ◽  
Melanie Zimmer ◽  
Achim Buerkle ◽  
Dedy Ariansyaha ◽  
...  
Keyword(s):  
Digital Twin ◽  
Human In The Loop ◽  
Industrial Maintenance ◽  
The Impact
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