Machine learning and molecular dynamics simulations assisted evolutionary design and discovery pipeline to screen the efficient small molecule acceptors for PTB7-Th based organic solar cells with over 15% efficiency

Small Molecule ◽

Organic Solar Cells ◽

Evolutionary Design ◽

New Materials ◽

Dynamics Simulations ◽

Discovery Pipeline

Organic solar cells are the most promising candidates for future commercialization. This goal can be quickly achieved by designing new materials and predicting their performance without experimentation to reduce the...

Developing efficient small molecule acceptors with sp2‐hybridized nitrogen at different positions by density functional theory calculations, molecular dynamics simulations and machine learning

Chemistry - A European Journal ◽

10.1002/chem.202103712 ◽

2021 ◽

Author(s):

Asif Mahmood ◽

Ahmad Irfan ◽

Jin-Liang Wang

Keyword(s):

Machine Learning ◽

Molecular Dynamics ◽

Density Functional Theory ◽

Small Molecule ◽

Density Functional ◽

Density Functional Theory Calculations ◽

Functional Theory ◽

Resolving Atomic‐Scale Interactions in Non‐Fullerene Acceptor Organic Solar Cells with Solid‐State NMR Spectroscopy, Crystallographic Modelling, and Molecular Dynamics Simulations

Advanced Materials ◽

10.1002/adma.202105943 ◽

2021 ◽

pp. 2105943

Author(s):

Benjamin R. Luginbuhl ◽

Parth Raval ◽

Tomasz Pawlak ◽

Zhifang Du ◽

Tonghui Wang ◽

...

Keyword(s):

Molecular Dynamics ◽

Solar Cells ◽

Solid State ◽

Nmr Spectroscopy ◽

Organic Solar Cells ◽

Solid State Nmr ◽

Atomic Scale ◽

Solid State Nmr Spectroscopy ◽

Molecular dynamics simulations of lithium superionic conductor Li10GeP2S12 using a machine learning potential

Solid State Ionics ◽

10.1016/j.ssi.2021.115567 ◽

2021 ◽

Vol 361 ◽

pp. 115567

Author(s):

Kazutoshi Miwa ◽

Ryoji Asahi

Keyword(s):

Machine Learning ◽

Molecular Dynamics ◽

Superionic Conductor ◽

Learning Potential ◽

Molecular dynamics simulations with machine learning potential for Nb-doped lithium garnet-type oxide Li7−xLa3(Zr2−xNbx)O12

Physical Review Materials ◽

10.1103/physrevmaterials.2.105404 ◽

2018 ◽

Vol 2 (10) ◽

Cited By ~ 8

Author(s):

Kazutoshi Miwa ◽

Ryoji Asahi

Keyword(s):

Machine Learning ◽

Molecular Dynamics ◽

Learning Potential ◽

Lithium Garnet ◽

DNA triplex structure, thermodynamics, and destabilisation: insight from molecular simulations

Physical Chemistry Chemical Physics ◽

10.1039/c8cp02385a ◽

2018 ◽

Vol 20 (20) ◽

pp. 14013-14023 ◽

Cited By ~ 6

Author(s):

Belinda J. Boehm ◽

Charles Whidborne ◽

Alexander L. Button ◽

Tara L. Pukala ◽

David M. Huang

Keyword(s):

Molecular Dynamics ◽

Neurodegenerative Disease ◽

Small Molecule ◽

Molecular Simulations ◽

Friedreich’S Ataxia ◽

Dna Triplexes ◽

Dna Triplex ◽

Triplex Structure ◽

Molecular dynamics simulations are used to elucidate the structure and thermodynamics of DNA triplexes associated with the neurodegenerative disease Friedreich's ataxia (FRDA), as well as complexes of these triplexes with the small molecule netropsin, which is known to destabilise triplexes.