Generative Ensemble Regression: Learning Particle Dynamics from Observations of Ensembles with Physics-informed Deep Generative Models

<p>The chemical space for novel electronic donor-acceptor oligomers with targeted properties was explored using deep generative models and transfer learning. A General Recurrent Neural Network model was trained from the ChEMBL database to generate chemically valid SMILES strings. The parameters of the General Recurrent Neural Network were fine-tuned via transfer learning using the electronic donor-acceptor database from the Computational Material Repository to generate novel donor-acceptor oligomers. Six different transfer learning models were developed with different subsets of the donor-acceptor database as training sets. We concluded that electronic properties such as HOMO-LUMO gaps and dipole moments of the training sets can be learned using the SMILES representation with deep generative models, and that the chemical space of the training sets can be efficiently explored. This approach identified approximately 1700 new molecules that have promising electronic properties (HOMO-LUMO gap <2 eV and dipole moment <2 Debye), 6-times more than in the original database. Amongst the molecular transformations, the deep generative model has learned how to produce novel molecules by trading off between selected atomic substitutions (such as halogenation or methylation) and molecular features such as the spatial extension of the oligomer. The method can be extended as a plausible source of new chemical combinations to effectively explore the chemical space for targeted properties.</p>

Download Full-text

The Self-Assembly of an Amphiphilic Block Copolymer: A Dissipative Particle Dynamics Study

Tenside Surfactants Detergents ◽

10.3139/113.100254 ◽

2005 ◽

Vol 42 (3) ◽

pp. 180-183 ◽

Cited By ~ 2

Author(s):

S. G. Schulz ◽

U. Frieske ◽

H. Kuhn ◽

G. Schmid ◽

F. Müller ◽

...

Keyword(s):

Block Copolymer ◽

Self Assembly ◽

Dissipative Particle Dynamics ◽

Particle Dynamics ◽

The Self ◽

Amphiphilic Block Copolymer

Download Full-text

Particle Dynamics in the Bottom Boundary Layer at the Coastal Mixing and Optics Experiment Site

10.21236/ada630446 ◽

1999 ◽

Author(s):

Yogesh C. Agrawal

Keyword(s):

Boundary Layer ◽

Particle Dynamics ◽

Bottom Boundary Layer ◽

Bottom Boundary

Download Full-text

Networks

10.1093/oso/9780198805090.001.0001 ◽

2018 ◽

Cited By ~ 347

Author(s):

Mark Newman

Keyword(s):

Biological Networks ◽

Generative Models ◽

The Internet ◽

Network Data ◽

Computer Algorithms ◽

Graph Models ◽

Random Graph Models ◽

The Social ◽

Spectral Algorithms ◽

Wide Availability

The study of networks, including computer networks, social networks, and biological networks, has attracted enormous interest in recent years. The rise of the Internet and the wide availability of inexpensive computers have made it possible to gather and analyse network data on an unprecendented scale, and the development of new theoretical tools has allowed us to extract knowledge from networks of many different kinds. The study of networks is broadly interdisciplinary and developments have occurred in many fields, including mathematics, physics, computer and information sciences, biology, and the social science. This book brings together the most important breakthroughts in each of these fields and presents them in a unified fashion, highlighting the strong interconnections between work in different areas. Topics covered include the measurement of networks; methods for analysing network data, including methods developed in physics, statistics, and sociology; fundamentals of graph theory; computer algorithms, including spectral algorithms and community detection; mathematical models of networks such as random graph models and generative models; and models of processes taking place on networks.

Download Full-text