scholarly journals Machine learning meets volcano plots: computational discovery of cross-coupling catalysts

2018 ◽  
Vol 9 (35) ◽  
pp. 7069-7077 ◽  
Author(s):  
Benjamin Meyer ◽  
Boodsarin Sawatlon ◽  
Stefan Heinen ◽  
O. Anatole von Lilienfeld ◽  
Clémence Corminboeuf
Keyword(s):  
Machine Learning ◽  
Atomistic Simulation ◽  
Cross Coupling ◽  
Volcano Plots ◽  
Computational Discovery ◽  
Modern Machine

The application of modern machine learning to challenges in atomistic simulation is gaining attraction.

scholarly journals Fingerprint-Based Detection of Non-Local Effects in the Electronic Structure of a Simple Single Component Covalent System

2021 ◽  
Vol 6 (1) ◽  
pp. 9
Author(s):  
Behnam Parsaeifard ◽  
Deb Sankar De ◽  
Jonas A. Finkler ◽  
Stefan Goedecker
Keyword(s):  
Machine Learning ◽  
Charge Transfer ◽  
Long Range ◽  
Atomistic Simulation ◽  
Energy Surface ◽  
Driving Mechanism ◽  
Non Local ◽  
Band Structure Energy ◽  
Learning Schemes ◽  
Modern Machine

Using fingerprints used mainly in machine learning schemes of the potential energy surface, we detect in a fully algorithmic way long range effects on local physical properties in a simple covalent system of carbon atoms. The fact that these long range effects exist for many configurations implies that atomistic simulation methods, such as force fields or modern machine learning schemes, that are based on locality assumptions, are limited in accuracy. We show that the basic driving mechanism for the long range effects is charge transfer. If the charge transfer is known, locality can be recovered for certain quantities such as the band structure energy.

scholarly journals Response to Comment on “Predicting reaction performance in C–N cross-coupling using machine learning”

Science ◽  
2018 ◽  
Vol 362 (6416) ◽  
pp. eaat8763 ◽  
Author(s):  
Jesús G. Estrada ◽  
Derek T. Ahneman ◽  
Robert P. Sheridan ◽  
Spencer D. Dreher ◽  
Abigail G. Doyle
Keyword(s):  
Machine Learning ◽  
Cross Coupling ◽  
Feature Model ◽  
Learning Models ◽  
Chemical Feature ◽  
Out Of Sample ◽  
Reaction Performance ◽  
Machine Learning Models

We demonstrate that the chemical-feature model described in our original paper is distinguishable from the nongeneralizable models introduced by Chuang and Keiser. Furthermore, the chemical-feature model significantly outperforms these models in out-of-sample predictions, justifying the use of chemical featurization from which machine learning models can extract meaningful patterns in the dataset, as originally described.

scholarly journals Modern Machine Learning as a Benchmark for Fitting Neural Responses

2018 ◽  
Vol 12 ◽  
Author(s):  
Ari S. Benjamin ◽  
Hugo L. Fernandes ◽  
Tucker Tomlinson ◽  
Pavan Ramkumar ◽  
Chris VerSteeg ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Responses ◽  
Modern Machine

scholarly journals Comment on “Predicting reaction performance in C–N cross-coupling using machine learning”

Science ◽  
2018 ◽  
Vol 362 (6416) ◽  
pp. eaat8603 ◽  
Author(s):  
Kangway V. Chuang ◽  
Michael J. Keiser
Keyword(s):  
Machine Learning ◽  
Experimental Design ◽  
Coupling Reaction ◽  
Cross Coupling ◽  
Learning Models ◽  
Applied Machine Learning ◽  
Cross Coupling Reaction ◽  
Reaction Performance ◽  
Test Scenarios ◽  
Machine Learning Models

Ahneman et al. (Reports, 13 April 2018) applied machine learning models to predict C–N cross-coupling reaction yields. The models use atomic, electronic, and vibrational descriptors as input features. However, the experimental design is insufficient to distinguish models trained on chemical features from those trained solely on random-valued features in retrospective and prospective test scenarios, thus failing classical controls in machine learning.

scholarly journals Non-intrusive nonlinear model reduction via machine learning approximations to low-dimensional operators

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Zhe Bai ◽  
Liqian Peng
Keyword(s):  
Machine Learning ◽  
Nonlinear Dynamical Systems ◽  
Reduced Order Models ◽  
Simulation Code ◽  
Nonlinear Dynamical ◽  
Reduced Order ◽  
Nonlinear Model Reduction ◽  
Regression Techniques ◽  
Low Dimensional ◽  
Modern Machine

AbstractAlthough projection-based reduced-order models (ROMs) for parameterized nonlinear dynamical systems have demonstrated exciting results across a range of applications, their broad adoption has been limited by their intrusivity: implementing such a reduced-order model typically requires significant modifications to the underlying simulation code. To address this, we propose a method that enables traditionally intrusive reduced-order models to be accurately approximated in a non-intrusive manner. Specifically, the approach approximates the low-dimensional operators associated with projection-based reduced-order models (ROMs) using modern machine-learning regression techniques. The only requirement of the simulation code is the ability to export the velocity given the state and parameters; this functionality is used to train the approximated low-dimensional operators. In addition to enabling nonintrusivity, we demonstrate that the approach also leads to very low computational complexity, achieving up to $$10^3{\times }$$ 10 3 × in run time. We demonstrate the effectiveness of the proposed technique on two types of PDEs. The domain of applications include both parabolic and hyperbolic PDEs, regardless of the dimension of full-order models (FOMs).

scholarly journals Identification of Neural Network Model of Robot to Solve the Optimal Control Problem

2021 ◽  
Vol 20 (6) ◽  
pp. 1254-1278
Author(s):  
Elizaveta Shmalko ◽  
Yuri Rumyantsev ◽  
Ruslan Baynazarov ◽  
Konstantin Yamshanov
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Mathematical Model ◽  
Optimal Control ◽  
Neural Network Model ◽  
Software Package ◽  
Control Object ◽  
Robotic Systems ◽  
Object Model ◽  
Modern Machine

To calculate the optimal control, a satisfactory mathematical model of the control object is required. Further, when implementing the calculated controls on a real object, the same model can be used in robot navigation to predict its position and correct sensor data, therefore, it is important that the model adequately reflects the dynamics of the object. Model derivation is often time-consuming and sometimes even impossible using traditional methods. In view of the increasing diversity and extremely complex nature of control objects, including the variety of modern robotic systems, the identification problem is becoming increasingly important, which allows you to build a mathematical model of the control object, having input and output data about the system. The identification of a nonlinear system is of particular interest, since most real systems have nonlinear dynamics. And if earlier the identification of the system model consisted in the selection of the optimal parameters for the selected structure, then the emergence of modern machine learning methods opens up broader prospects and allows you to automate the identification process itself. In this paper, a wheeled robot with a differential drive in the Gazebo simulation environment, which is currently the most popular software package for the development and simulation of robotic systems, is considered as a control object. The mathematical model of the robot is unknown in advance. The main problem is that the existing mathematical models do not correspond to the real dynamics of the robot in the simulator. The paper considers the solution to the problem of identifying a mathematical model of a control object using machine learning technique of the neural networks. A new mixed approach is proposed. It is based on the use of well-known simple models of the object and identification of unaccounted dynamic properties of the object using a neural network based on a training sample. To generate training data, a software package was written that automates the collection process using two ROS nodes. To train the neural network, the PyTorch framework was used and an open source software package was created. Further, the identified object model is used to calculate the optimal control. The results of the computational experiment demonstrate the adequacy and performance of the resulting model. The presented approach based on a combination of a well-known mathematical model and an additional identified neural network model allows using the advantages of the accumulated physical apparatus and increasing its efficiency and accuracy through the use of modern machine learning tools.

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