Defect tolerance in CsPbI3: Reconstruction of potential energy landscape and band degeneracy in spin-orbit coupling

2022 ◽  
Author(s):  
Chen Ming ◽  
Han Wang ◽  
Damien West ◽  
Shengbai Zhang ◽  
Yi-Yang Sun
Keyword(s):  
Potential Energy ◽  
High Performance ◽  
Energy Landscape ◽  
Spin Orbit Coupling ◽  
Spin Orbit ◽  
Defect Tolerance ◽  
Optoelectronic Materials ◽  
Potential Energy Landscape ◽  
Halide Perovskites ◽  
Lead Halide

Lead halide perovskites have been intensively developed to be high-performance photovoltaic and optoelectronic materials, where the unique defect tolerance property is believed to contribute to their excellence. However, the defect...

Enhancing Defect Tolerance with Ligands at the Surface of Lead Halide Perovskites

2021 ◽  
pp. 6299-6304
Author(s):  
Tyler J. Smart ◽  
Hiroyuki Takenaka ◽  
Tuan Anh Pham ◽  
Liang Z. Tan ◽  
Jin Z. Zhang ◽  
...  
Keyword(s):  
Defect Tolerance ◽  
Halide Perovskites ◽  
Lead Halide

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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