scholarly journals Unbiased Global Optimization of Lennard-Jones Clusters forN≤201Using the Conformational Space Annealing Method

2003 ◽  
Vol 91 (8) ◽  
Author(s):  
Julian Lee ◽  
In-Ho Lee ◽  
Jooyoung Lee
Keyword(s):  
Global Optimization ◽  
Conformational Space ◽  
Lennard Jones ◽  
Annealing Method

scholarly journals Thermodynamics and the global optimization of Lennard-Jones clusters

1998 ◽  
Vol 109 (19) ◽  
pp. 8143-8153 ◽  
Author(s):  
Jonathan P. K. Doye ◽  
David J. Wales ◽  
Mark A. Miller
Keyword(s):  
Global Optimization ◽  
Lennard Jones

Close-packing transitions in clusters of Lennard-Jones spheres

2005 ◽  
Vol 1 (4) ◽  
pp. 183-191 ◽  
Author(s):  
F. Calvo ◽  
M. Benali ◽  
V. Gerbaud ◽  
M. Hemati
Keyword(s):  
Global Optimization ◽  
Cluster Structure ◽  
Particle Diameter ◽  
Optimization Methods ◽  
Close Packing ◽  
Small Radius ◽  
Large Radius ◽  
Lennard Jones ◽  
Global Optimization Methods ◽  
Insight Into

The structures of clusters of spherical and homogeneous particles are investigated using a combination of global optimization methods. The pairwise potential between particles is integrated exactly from elementary Lennard-Jones interactions, and the use of reduced units allows us to get insight into the effects of the particle diameter. As the diameter increases, the potential becomes very sharp, and the cluster structure generally changes from icosahedral (small radius) to close-packed cubic (large radius), possibly through intermediate decahedral shapes. The results are interpreted in terms of the effective range of the potential.

scholarly journals MolFinder: An Efficient Global Molecular Property Optimization and Search Algorithm Using SMILES

2020 ◽  
Author(s):  
Yongbeom Kwon ◽  
Juyong Lee
Keyword(s):  
Global Optimization ◽  
Reinforcement Learning ◽  
Search Algorithm ◽  
Optimization Methods ◽  
Optimization Method ◽  
Conformational Space ◽  
Efficient Global Optimization ◽  
Global Optimization Algorithm ◽  
Annealing Algorithm ◽  
Shed Light

<div> <div> <div> <p>Here, we introduce a new molecule optimization method, MolFinder, based on an efficient global optimization algorithm, the conformational space annealing algorithm, and the SMILES representation. MolFinder finds diverse molecules with desired properties efficiently without any training and a large molecular database. Compared with recently proposed reinforcement-learning-based molecule optimization algorithms, MolFinder consistently outperforms in terms of both the optimization of a given target property and the generation of a set of diverse and novel molecules. The efficiency of MolFinder demonstrates that combinatorial optimization using the SMILES representation is a promising approach for molecule optimization, which has not been well investigated despite its simplicity. We believe that our results shed light on new possibilities for advances in molecule optimization methods. </p> </div> </div> </div>

scholarly journals MolFinder: An Evolutionary Algorithm for the Global Optimization of Molecular Properties and the Extensive Exploration of Chemical Space Using SMILES

2020 ◽  
Author(s):  
Yongbeom Kwon ◽  
Juyong Lee
Keyword(s):  
Global Optimization ◽  
Reinforcement Learning ◽  
Chemical Space ◽  
Optimization Methods ◽  
Optimization Method ◽  
Conformational Space ◽  
Efficient Global Optimization ◽  
Global Optimization Algorithm ◽  
Annealing Algorithm ◽  
Shed Light

Abstract Here, we introduce a new molecule optimization method, MolFinder, based on an efficient global optimization algorithm, the conformational space annealing algorithm, and the SMILES representation. MolFinder finds diverse molecules with desired properties efficiently without any training and a large molecular database. Compared with recently proposed reinforcement-learning-based molecule optimization algorithms, MolFinder consistently outperforms in terms of both the optimization of a given target property and the generation of a set of diverse and novel molecules. The efficiency of MolFinder demonstrates that combinatorial optimization using the SMILES representation is a promising approach for molecule optimization, which has not been well investigated despite its simplicity. We believe that our results shed light on new possibilities for advances in molecule optimization methods.

scholarly journals Generating Reservoir Conformations for Replica Exchange through the Use of the Conformational Space Annealing Method

2013 ◽  
Vol 9 (2) ◽  
pp. 1115-1124 ◽  
Author(s):  
Asim Okur ◽  
Benjamin T. Miller ◽  
Keehyoung Joo ◽  
Jooyoung Lee ◽  
Bernard R. Brooks
Keyword(s):  
Conformational Space ◽  
Replica Exchange ◽  
Annealing Method
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