scholarly journals A universal density matrix functional from molecular orbital-based machine learning: Transferability across organic molecules

2019 ◽  
Vol 150 (13) ◽  
pp. 131103 ◽  
Author(s):  
Lixue Cheng ◽  
Matthew Welborn ◽  
Anders S. Christensen ◽  
Thomas F. Miller
Keyword(s):  
Machine Learning ◽  
Density Matrix ◽  
Molecular Orbital ◽  
Organic Molecules

scholarly journals Deep Learning Total Energies and Orbital Energies of Large Organic Molecules Using Hybridization of Molecular Fingerprints

2020 ◽  
Author(s):  
Obaidur Rahaman ◽  
Alessio Gagliardi
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Molecular Orbital ◽  
Large Scale ◽  
Organic Molecules ◽  
Chemical Properties ◽  
Many Body ◽  
Molecular Fingerprints ◽  
Total Energies ◽  
The Individual

<p>The ability to predict material properties without the need of resource consuming experimental efforts can immensely accelerate material and drug discovery. Although ab initio methods can be reliable and accurate in making such predictions, they are computationally too expensive at a large scale. The recent advancements in artificial intelligence and machine learning as well as availability of large quantum mechanics derived datasets enable us to train models on these datasets as benchmark and to make fast predictions on much larger datasets. The success of these machine learning models highly depends on the machine-readable fingerprints of the molecules that capture their chemical properties as well as topological information. In this work we propose a common deep learning based framework to combine different types of molecular fingerprints to enhance prediction accuracy. Graph Neural Network (GNN), Many Body Tensor Representation (MBTR) and a set of simple Molecular Descriptors (MD) were used to predict the total energies, Highest Occupied Molecular Orbital (HOMO) energies and Lowest Unoccupied Molecular Orbital (LUMO) energies of a dataset containing ~62k large organic molecules with complex aromatic rings and remarkably diverse functional groups. The results demonstrate that a combination of best performing molecular fingerprints can produce better results than the individual ones. The simple and flexible deep learning framework developed in this work can be easily adapted to incorporate other types of molecular fingerprints.<br></p>

scholarly journals Deep Learning Total Energies and Orbital Energies of Large Organic Molecules Using Hybridization of Molecular Fingerprints

2020 ◽  
Author(s):  
Obaidur Rahaman ◽  
Alessio Gagliardi
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Molecular Orbital ◽  
Large Scale ◽  
Organic Molecules ◽  
Chemical Properties ◽  
Many Body ◽  
Molecular Fingerprints ◽  
Total Energies ◽  
The Individual

<p>The ability to predict material properties without the need of resource consuming experimental efforts can immensely accelerate material and drug discovery. Although ab initio methods can be reliable and accurate in making such predictions, they are computationally too expensive at a large scale. The recent advancements in artificial intelligence and machine learning as well as availability of large quantum mechanics derived datasets enable us to train models on these datasets as benchmark and to make fast predictions on much larger datasets. The success of these machine learning models highly depends on the machine-readable fingerprints of the molecules that capture their chemical properties as well as topological information. In this work we propose a common deep learning based framework to combine different types of molecular fingerprints to enhance prediction accuracy. Graph Neural Network (GNN), Many Body Tensor Representation (MBTR) and a set of simple Molecular Descriptors (MD) were used to predict the total energies, Highest Occupied Molecular Orbital (HOMO) energies and Lowest Unoccupied Molecular Orbital (LUMO) energies of a dataset containing ~62k large organic molecules with complex aromatic rings and remarkably diverse functional groups. The results demonstrate that a combination of best performing molecular fingerprints can produce better results than the individual ones. The simple and flexible deep learning framework developed in this work can be easily adapted to incorporate other types of molecular fingerprints.<br></p>

scholarly journals Analytical gradients for molecular-orbital-based machine learning

2021 ◽  
Vol 154 (12) ◽  
pp. 124120
Author(s):  
Sebastian J. R. Lee ◽  
Tamara Husch ◽  
Feizhi Ding ◽  
Thomas F. Miller
Keyword(s):  
Machine Learning ◽  
Molecular Orbital ◽  
Analytical Gradients

scholarly journals A deep neural network model for packing density predictions and its application in the study of 1.5 million organic molecules

2019 ◽  
Vol 10 (36) ◽  
pp. 8374-8383 ◽  
Author(s):  
Mohammad Atif Faiz Afzal ◽  
Aditya Sonpal ◽  
Mojtaba Haghighatlari ◽  
Andrew J. Schultz ◽  
Johannes Hachmann
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Refractive Index ◽  
High Throughput ◽  
Neural Network Model ◽  
High Throughput Screening ◽  
Deep Neural Network ◽  
Organic Molecules ◽  
High Refractive Index ◽  
Computational Pipeline

Computational pipeline for the accelerated discovery of organic materials with high refractive index via high-throughput screening and machine learning.

scholarly journals A Physics-Infused Deep Learning Model for the Prediction of Refractive Indices and Its Use for the Large-Scale Screening of Organic Compound Space

2019 ◽  
Author(s):  
Mojtaba Haghighatlari ◽  
Gaurav Vishwakarma ◽  
Mohammad Atif Faiz Afzal ◽  
Johannes Hachmann
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Large Scale ◽  
Organic Molecules ◽  
Learning Model ◽  
Training Data ◽  
Refractive Indices ◽  
Learning Models ◽  
Deep Learning Model ◽  
Machine Learning Models

<div><div><div><p>We present a multitask, physics-infused deep learning model to accurately and efficiently predict refractive indices (RIs) of organic molecules, and we apply it to a library of 1.5 million compounds. We show that it outperforms earlier machine learning models by a significant margin, and that incorporating known physics into data-derived models provides valuable guardrails. Using a transfer learning approach, we augment the model to reproduce results consistent with higher-level computational chemistry training data, but with a considerably reduced number of corresponding calculations. Prediction errors of machine learning models are typically smallest for commonly observed target property values, consistent with the distribution of the training data. However, since our goal is to identify candidates with unusually large RI values, we propose a strategy to boost the performance of our model in the remoter areas of the RI distribution: We bias the model with respect to the under-represented classes of molecules that have values in the high-RI regime. By adopting a metric popular in web search engines, we evaluate our effectiveness in ranking top candidates. We confirm that the models developed in this study can reliably predict the RIs of the top 1,000 compounds, and are thus able to capture their ranking. We believe that this is the first study to develop a data-derived model that ensures the reliability of RI predictions by model augmentation in the extrapolation region on such a large scale. These results underscore the tremendous potential of machine learning in facilitating molecular (hyper)screening approaches on a massive scale and in accelerating the discovery of new compounds and materials, such as organic molecules with high-RI for applications in opto-electronics.</p></div></div></div>

LOCALIZED MOLECULAR ORBITAL MODEL ——INVESTIGATION OF SOME ORGANIC MOLECULES

1986 ◽  
Vol 2 (03) ◽  
pp. 263-270
Author(s):  
Yang Zhongzhi ◽  
◽  
Yu Hengtai ◽  
Shen Erzhong ◽  
Wang Zhizhong ◽  
...  
Keyword(s):  
Molecular Orbital ◽  
Organic Molecules ◽  
Model Investigation ◽  
Molecular Orbital Model
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