Adaptive Quantum Design for Nanoscience

Jason Thalken, Stephan Haas, Anthony Levi

University of Southern CaliforniaDepartment of Physics and Astronomy

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Nano-Scale Design

• Quantum effects can not be ignored

– Complex interactions require computationally expensive quantum models

– Classical devices will not maintain functionality when scaled into this regime

– New functionalities may exist which have no counterpart at larger length scales

• Broken-symmetry configurations must be examined

– Breaking symmetry often has effects for which we have no a priori intuition

– The desired functionality may result from only a small fraction of the nearly infinite set of all possible configurations

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Adaptive Quantum Design

• A useful device functionality is specified by humans.

• Computers evaluate the functionality of potential designs using an efficient and accurate quantum model.

• Advanced search algorithms find optimal design solutions to best fit the specified functionality.

It is also possible to remove human input entirely, allowing machines to search for solutions which exhibit any useful or “interesting” functionality.

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First Example: Density of States of 4 Atoms in 1D

)(__

EN

E

Target DOS:

4 equidistant peaks

0

0

0

0

434241

343231

242321

141312

ttt

ttt

ttt

ttt

H )()(4

1

i

iEEEN

0|)()(| 2__

ENENE

Feedback Loop

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Adaptive Quantum Design: 9 Atoms in 2D

02040608010012014016018020022024026028030032005101520253035404550556065Message size (Kbytes)Application throughput (Mbits/s), CPU usage (%) Full-duplex UDP throughputFull-duplex TCP throughputCPU usage for full-duplex UDPCPU usage for full-duplex TCP

• Start with 2D periodic array of atoms.• Use tight-binding description of electrons around atoms.• Break symmetry of 2D atom array to emulate flat density of states.• Local update: guided random walk.

0-5 50

40

80

120(3 × 3) 2D periodic array density of states

Target density of states is quasi-2D

N(E

)

Energy, E/t

Atom position, x Energy, E/t

N(E

)

Ato

m p

osit

ion,

y

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Second Example: Excitonic Absorption in AlGaAs Quantum Well Structures

Eg = 1.43 eV

Effective Masses: Electron: 0.067 me, Heavy hole: 0.340 me

F = 0 kV/cm F = 70 kV/cm

Apply anElectric Field

Position, z (nm) Position, z (nm)

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• When an electric field is applied to a symmetric square well, both the absorption peak strength and absorbed photon energy diminishes (quantum confined Stark effect)

Effects of Applied Electric Field on Absorption

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Target: An Absorption Frequency Switch

Specifications:

Match absorption strength at 0 and 70 kV/cm

Separate the two peaks by more than two line widths

Both peaks should have large absorption strength

Ab

sorp

tion

Photon Energy

F = 0 kV/cm

F = 70 kV/cm

•A target function represents the desired quantum physical model output. In this case, the target function is represented by two points of equal absorption strength separated in energy by at least 0.012 eV

•A fitness function represents the weighted distance between the physical model’s output for a particular solution and the target function. The most desirable solution will have the lowest possible fitness value.

||02.0451

101||

2 700700

4

70

700 EEF

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• This solution was discovered using a machine-based genetic algorithm search

• Exponential loss in peak strength intensity as hole ionizes suggests an intensity modulator can be developed from a similar structure

Solution: Field Induced Ionization

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A New Approach to Design:Automated Device Synthesis

• Motivation:– Removing human input from the design process will lift

many time and target related limitations– It is unreasonable to expect humans to perform an

exhaustive search of n-dimensional configuration space

Solutions

ComputerSorting

InterestingSolutions

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Computer-Sorted “Interesting” Absorption Paths

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Conclusions

• Adaptive Quantum Design: search for optimum system configurations which closely match target functions, which leads to the discovery of new molecular building blocks.

• New paradigm for nanoscience: target dictates system shape.

• Removing the target: machines that search for optimal configurations can perform exploratory searches for “interesting” solutions as well