README.md

This repository contains python3 and C code for the design of mechanical metamaterials as explained in

S Bonfanti, R Guerra, F Font-Clos, D Rayneau-Kirkhope, and S Zapperi. 2020.
Automatic Design of Mechanical Metamaterial Actuators
preprint version arXiv:2002.03032
journal version [ add link to journal ]

Requirements

To build the metamech library you need the following external packages:

• keras
• matplotlib
• numpy
• ordered_set
• pandas
• pytest
• scipy
• tqdm

See requirements.txt for exact pinned versions (but usually earlier versions work as well).

Installation

We recommend that you install all requirements in an isolated environment via conda, pyenv, pipenv or a similar environment manager. If you are a conda user

conda create --name metamech pip

will create and activate an isolated python environment called metamech. Then you can activate the environment and install all dependencies automatically without interfering with your standard python installation

conda activate metamech
pip install -r requirements.txt

Compile the C part of the code, which runs the FIRE algorithm, into a shared library. Depending on your operating system, do as follows:

On a macOS with Clang:

clang -std=c11 -Wall -Wextra -pedantic -c -fPIC metamech/minimize.c -o metamech/minimize.o
clang -shared metamech/minimize.o -o metamech/minimize.dylib

On a Linux system with GCC you can use:

gcc -Wall -Wextra -pedantic -c -fPIC metamech/minimize.c -o metamech/minimize.o
gcc -shared metamech/minimize.o -o metamech/minimize.sso

WARNING do not change .sso to the more standard .so  extension.

Install the python part of the code in development mode.

pip install -e .

Run unit tests, to make sure everything went well

pytest -v

Usage

A set of handy pre-configured loader functions for the structures used in the manuscript are available at the accompanying repository metamech_datasets. Here we show how to design a small mechanical metamaterial from scratch, to illustrate how the library is structured.

import numpy as np

from metamech.lattice import Lattice
from metamech.actuator import Actuator

# triangular regular lattice of side length 8
nodes_positions = np.array([
    [0.     , 0.86603], [0.5    , 0.     ], [1.     , 0.86603], [1.5    , 0.     ],
    [2.     , 0.86603], [2.5    , 0.     ], [3.     , 0.86603], [3.5    , 0.     ],
    [4.     , 0.86603], [4.5    , 0.     ], [5.     , 0.86603], [5.5    , 0.     ],
    [6.     , 0.86603], [6.5    , 0.     ], [7.     , 0.86603], [7.5    , 0.     ],
    [8.     , 0.86603], [0.     , 2.59808], [0.5    , 1.73205], [1.     , 2.59808],
    [1.5    , 1.73205], [2.     , 2.59808], [2.5    , 1.73205], [3.     , 2.59808],
    [3.5    , 1.73205], [4.     , 2.59808], [4.5    , 1.73205], [5.     , 2.59808],
    [5.5    , 1.73205], [6.     , 2.59808], [6.5    , 1.73205], [7.     , 2.59808],
    [7.5    , 1.73205], [8.     , 2.59808], [0.     , 4.33013], [0.5    , 3.4641 ],
    [1.     , 4.33013], [1.5    , 3.4641 ], [2.     , 4.33013], [2.5    , 3.4641 ],
    [3.     , 4.33013], [3.5    , 3.4641 ], [4.     , 4.33013], [4.5    , 3.4641 ],
    [5.     , 4.33013], [5.5    , 3.4641 ], [6.     , 4.33013], [6.5    , 3.4641 ],
    [7.     , 4.33013], [7.5    , 3.4641 ], [8.     , 4.33013], [0.     , 6.06218],
    [0.5    , 5.19615], [1.     , 6.06218], [1.5    , 5.19615], [2.     , 6.06218],
    [2.5    , 5.19615], [3.     , 6.06218], [3.5    , 5.19615], [4.     , 6.06218],
    [4.5    , 5.19615], [5.     , 6.06218], [5.5    , 5.19615], [6.     , 6.06218],
    [6.5    , 5.19615], [7.     , 6.06218], [7.5    , 5.19615], [8.     , 6.06218],
    [0.     , 7.79423], [0.5    , 6.9282 ], [1.     , 7.79423], [1.5    , 6.9282 ],
    [2.     , 7.79423], [2.5    , 6.9282 ], [3.     , 7.79423], [3.5    , 6.9282 ],
    [4.     , 7.79423], [4.5    , 6.9282 ], [5.     , 7.79423], [5.5    , 6.9282 ],
    [6.     , 7.79423], [6.5    , 6.9282 ], [7.     , 7.79423], [7.5    , 6.9282 ],
    [8.     , 7.79423]])

edges_indices = np.array([
       [ 0,  1],[ 0,  2],[ 0, 18],[ 1,  2],[ 1,  3],[ 2, 18],
       [ 2,  3],[ 2,  4],[ 2, 20],[ 3,  5],[ 3,  4],[ 4,  5],
       [ 4, 22],[ 4, 20],[ 4,  6],[ 5,  7],[ 5,  6],[ 6, 22],
       [ 6,  7],[ 6, 24],[ 6,  8],[ 7,  9],[ 7,  8],[ 8, 10],
       [ 8, 24],[ 8,  9],[ 8, 26],[ 9, 10],[ 9, 11],[10, 26],
       [10, 11],[10, 12],[10, 28],[11, 12],[11, 13],[12, 28],
       [12, 13],[12, 14],[12, 30],[13, 15],[13, 14],[14, 15],
       [14, 32],[14, 30],[14, 16],[15, 16],[16, 32],[17, 19],
       [17, 18],[17, 35],[18, 19],[18, 20],[19, 35],[19, 21],
       [19, 20],[19, 37],[20, 22],[20, 21],[21, 22],[21, 39],
       [21, 37],[21, 23],[22, 24],[22, 23],[23, 24],[23, 39],
       [23, 41],[23, 25],[24, 26],[24, 25],[25, 27],[25, 26],
       [25, 41],[25, 43],[26, 27],[26, 28],[27, 43],[27, 29],
       [27, 28],[27, 45],[28, 29],[28, 30],[29, 45],[29, 31],
       [29, 30],[29, 47],[30, 32],[30, 31],[31, 32],[31, 49],
       [31, 47],[31, 33],[32, 33],[33, 49],[34, 35],[34, 36],
       [34, 52],[35, 36],[35, 37],[36, 52],[36, 37],[36, 38],
       [36, 54],[37, 39],[37, 38],[38, 39],[38, 56],[38, 54],
       [38, 40],[39, 41],[39, 40],[40, 56],[40, 41],[40, 58],
       [40, 42],[41, 43],[41, 42],[42, 44],[42, 58],[42, 43],
       [42, 60],[43, 44],[43, 45],[44, 60],[44, 45],[44, 46],
       [44, 62],[45, 46],[45, 47],[46, 62],[46, 47],[46, 48],
       [46, 64],[47, 49],[47, 48],[48, 49],[48, 66],[48, 64],
       [48, 50],[49, 50],[50, 66],[51, 53],[51, 52],[51, 69],
       [52, 53],[52, 54],[53, 69],[53, 55],[53, 54],[53, 71],
       [54, 56],[54, 55],[55, 56],[55, 73],[55, 71],[55, 57],
       [56, 58],[56, 57],[57, 58],[57, 73],[57, 75],[57, 59],
       [58, 60],[58, 59],[59, 61],[59, 60],[59, 75],[59, 77],
       [60, 61],[60, 62],[61, 77],[61, 63],[61, 62],[61, 79],
       [62, 63],[62, 64],[63, 79],[63, 65],[63, 64],[63, 81],
       [64, 66],[64, 65],[65, 66],[65, 83],[65, 81],[65, 67],
       [66, 67],[67, 83],[68, 69],[68, 70],[69, 70],[69, 71],
       [70, 71],[70, 72],[71, 73],[71, 72],[72, 73],[72, 74],
       [73, 75],[73, 74],[74, 75],[74, 76],[75, 77],[75, 76],
       [76, 78],[76, 77],[77, 78],[77, 79],[78, 79],[78, 80],
       [79, 80],[79, 81],[80, 81],[80, 82],[81, 83],[81, 82],
       [82, 83],[82, 84],[83, 84],
       ])

# construct the lattice
lattice = Lattice(
    nodes_positions=nodes_positions,
    edges_indices=edges_indices,
    linear_stiffness=10,
    angular_stiffness=0.2
)

# we want to start off with a configuration
# will all possible edges present
for edge in lattice._possible_edges:
    lattice.flip_edge(edge)

# input displacement is 0.1 units in -y direction
# applied to top-right nodes
input_nodes = [81, 82, 83, 84]
input_vectors = np.array([
    [ 0.       , -0.1],
    [ 0.       , -0.1],
    [ 0.       , -0.1],
    [ 0.       , -0.1]
])

# measure output at top-left nodes along -x direction
output_nodes = [68, 69, 70, 71]
output_vectors = np.array([
    [-1, 0],
    [-1, 0],
    [-1, 0],
    [-1, 0],
])

# freeze the bottomn layer
frozen_nodes = [1, 3, 5, 7, 9, 11, 13, 15]

# construct the actuator
actuator = Actuator(
    lattice=lattice,
    input_nodes=input_nodes,
    input_vectors=input_vectors,
    output_nodes=output_nodes,
    output_vectors=output_vectors,
    frozen_nodes=frozen_nodes
)

If you're on a jupyter notebook or ipython, you can now visualize the actuator.

# creates a matplotlib figure
# of displaced configuration
from metamech.viz import show_actuator
show_actuator(actuator)

Initially, the the structure is a full lattice which does not perform the desired mechanical action, so the efficiency is close to 0:

print("initial efficiency", actuator.efficiency)
# initial efficiency -0.08673312423435053

To find a more efficienct configuration, we need to run the Monte Carlo part of the algorithm which adds/removes bonds as explained in the manuscript. We do this with a Metropolis object.

from metamech.metropolis import Metropolis
metropolis = Metropolis(
    actuator=actuator
)
metropolis.run(
    initial_temperature=0.1,
    final_temperature=0,
    num_steps=1000
)

While the Monte Carlo simulation runs, you can see a progress bar which shows some key variables such as temperature, efficiency and acceptance rate. When the simulation finishes, we check that the final efficiency is indeed much higher:

print("final efficiency", actuator.efficiency)
# final efficiency 2.8265941145286715

The metroplis.history attirbute stores the evolution of key parameters in a dictionary of lists, which we can easily turn into a pandas dataframe, to then export to our preferred format

import pandas as pd
history_df = pd.DataFrame(metropolis.history)
history_df.to_xlsx("/path/to/MC_history.xlsx")
history_df.to_csv("/path/to/MC_history.csv")

We can store the final configuration in LAMMPS format, to visualize it with other software such as Ovito.

actuator.to_lammps("/path/to/output.lammps")
actuator._get_displaced_actuator().to_lammps("/path/to/output_displaced.lammps")