scholarly journals Thermodynamics and the potential energy landscape: case study of small water clusters

2019 ◽  
Vol 21 (14) ◽  
pp. 7305-7312 ◽  
Author(s):  
Jordan Dorrell ◽  
Livia B. Pártay
Keyword(s):  
Thermodynamic Properties ◽  
Potential Energy ◽  
Water Clusters ◽  
Energy Landscape ◽  
Coarse Grained ◽  
Computational Technique ◽  
Small Water ◽  
Potential Energy Landscape ◽  
Water Models

We investigated the structure and the thermodynamic properties of small water clusters with the nested sampling computational technique, using two different water models, the coarse-grained mW (up to 25 molecules) and the flexible version of TIP3P (up to 16 molecules).

Active Learning the Potential Energy Landscape for Water Clusters from Sparse Training Data

2020 ◽  
Vol 124 (8) ◽  
pp. 4907-4916 ◽  
Author(s):  
Troy D. Loeffler ◽  
Tarak K. Patra ◽  
Henry Chan ◽  
Mathew Cherukara ◽  
Subramanian K. R. S. Sankaranarayanan
Keyword(s):  
Active Learning ◽  
Potential Energy ◽  
Water Clusters ◽  
Energy Landscape ◽  
Training Data ◽  
Potential Energy Landscape

scholarly journals An energy landscape approach to locomotor transitions in complex 3D terrain

2020 ◽  
Vol 117 (26) ◽  
pp. 14987-14995 ◽  
Author(s):  
Ratan Othayoth ◽  
George Thoms ◽  
Chen Li
Keyword(s):  
Potential Energy ◽  
Complex Terrain ◽  
Statistical Physics ◽  
Energy Landscape ◽  
Three Dimensional ◽  
Single Mode ◽  
Physical Interaction ◽  
Landscape Approach ◽  
3D Terrain ◽  
Potential Energy Landscape

Effective locomotion in nature happens by transitioning across multiple modes (e.g., walk, run, climb). Despite this, far more mechanistic understanding of terrestrial locomotion has been on how to generate and stabilize around near–steady-state movement in a single mode. We still know little about how locomotor transitions emerge from physical interaction with complex terrain. Consequently, robots largely rely on geometric maps to avoid obstacles, not traverse them. Recent studies revealed that locomotor transitions in complex three-dimensional (3D) terrain occur probabilistically via multiple pathways. Here, we show that an energy landscape approach elucidates the underlying physical principles. We discovered that locomotor transitions of animals and robots self-propelled through complex 3D terrain correspond to barrier-crossing transitions on a potential energy landscape. Locomotor modes are attracted to landscape basins separated by potential energy barriers. Kinetic energy fluctuation from oscillatory self-propulsion helps the system stochastically escape from one basin and reach another to make transitions. Escape is more likely toward lower barrier direction. These principles are surprisingly similar to those of near-equilibrium, microscopic systems. Analogous to free-energy landscapes for multipathway protein folding transitions, our energy landscape approach from first principles is the beginning of a statistical physics theory of multipathway locomotor transitions in complex terrain. This will not only help understand how the organization of animal behavior emerges from multiscale interactions between their neural and mechanical systems and the physical environment, but also guide robot design, control, and planning over the large, intractable locomotor-terrain parameter space to generate robust locomotor transitions through the real world.

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