CASTING

A Continuous Action Space Tree search for INverse desiGn (CASTING) Framework and Materials Discovery

Table of Contents

• Introduction
• Prerequisites
• Installation
• Running the code
• Optimization of Gold nanocluster
• Carbon metastable polymorphs
• Citation
• License

Introduction

A pseudocode implementation of CASTING framework (Paper) for optimization of atomic nanoclusters only. This code uses MCTS (Monte Carlo Tree Search) as base optimizer.

    Fast and accurate prediction of optimal crystal structure, topology, and microstructures is important for accelerating the design and discovery of new materials. Material properties are strongly correlated to the underlying structure and topology – inverse design is emerging as a powerful tool to discover new and increasingly complex materials that meet targeted functionalities. CASTING provides a unified framework for fast, scalable and accurate prediction & design of materials.

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Prerequisites

This package requires: - scipy - LAMMPS - pymatgen - pandas - numpy - networkx - ase

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Installation

Manual Installation

Install the anaconda package. Then, conda env create --name CASTING conda activate CASTING git clone https://github.com/sbanik2/CASTING.git pip install -r requirements.txt python setup.py install

Installation with pypi

``` pip install CASTING

``` ***The package requires python lammps binding to run. First, lammps package needs to be downloaded from LAMMPS download and compiled. The instructions on python integration can be found here LAMMPS-Python.

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Running the code

First, all the parameters crystal (constrains), LAMMPS parameters (pair style, pair coefficient etc.) and the perturbation parameter need to be set. The composition is given for e.g., a Au<2>Al<3> as "composition":{"Au":2,"Al:3"}. In a file (for e.g., RunOpt.py) we define,

``` python from CASTING.utilis import rdatafame,getlattice from CASTING.MCTS import MCTS from CASTING.clusterfun import createRandomData from CASTING.lammpsEvaluate import LammpsEvaluator from CASTING.perturb import perturbate

In[6]:

import random import numpy as np

seed = 12

random.seed(seed) np.random.seed(seed)

In[9]:

rmin = {"Au-Au":2} # minimum allowed interatomic distance rmax = {"Au-Au":4} # maximum allowed interatomic distance box_dim = 50 # box dimension

--------crystal constrains-----------

constrains = { "composition":{"Au":1}, "atoms":13, "rmin":rdatafame(rmin), "rmax":rdatafame(rmax), "lattice":getlattice(boxdim),
}

-------------perturbation------------

pt = { 'max_mutation': 0.05, # mutation in fraction of box length }

-----------------lammps parameters-------------------

lammpspar = { 'constrains':constrains, 'pairstyle': "pairstyle eam", 'paircoeff': "pair_coeff * * Au.eam" # Provide full path of potential file here }

```

Once the parameters are set, the evaluator (LAMMPS calculator) & the perturbator is to be initialized and a structure for root node is created.

``` python

rootdata = createRandomData(constrains,multiplier= 10)

perturb = perturbate(pt). perturb evaluator = LammpsEvaluator(lammps_par).evaluate

```

Finally, Call MCTS with all the hyperparameters added. Details of individual hyperparameters for the optimizer can be found here Paper.

``` python

MCTS( rootdata, perturb , evaluator, niterations=2000, headexpand=10, nexpand=3, nsimulate=3, nplayouts=10, exploreconstant=1, maxdepth=12, a=0, selected_node=0, )

```

Run the code

python RunOpt.py

The optimization produces a "dumpfile.dat" output containing all the crystal parameters and the energy values as the output. To extract the structures in either 'poscar' or 'cif' format, one can use the 'StructureWriter' module.

``` python

from CASTING.writer import StructureWriter

numtowrite = 10 # number of stuctures to extract writer = StructureWriter( "dumpfile.dat", outpath="structures", objfile="energy.dat", fileformat="poscar" # "poscar" or "cif" ) writer.write( numto_write, sort=True) # sort to arrange in increasing order of energy

``` This will extract 'numtowrite' number of structures in ascending order of objective.

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Optimization of Gold nanocluster

An example optimization of Gold (Au) nanocluster is given in "example" directory. We have used CASTING to optimize the already known global minima (Sutton-Chen) of 13 atom Au nanocluster (Icosahedral structure). Details on additional examples can be found in Paper.

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Launch in Colab

An example optimization of a Gold (Au) - Sutton-Chen nanocluster in the form of a Jupyter notebook that does not require LAMMPS Python binding has been provided in the notebooks directory. This can be launched in Google Colab using the link.

Carbon metastable polymorphs

We have also used CASTING to sample metastable polymorphs of Carbon(C). All the structures are then further relaxed with DFT. The unique polymorphs and their corresponding DFT energies have been provided in “C_polymorphs” directory.

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Citation

``` @article{banik2023continuous, title={A continuous action space tree search for INverse desiGn (CASTING) framework for materials discovery}, author={Banik, Suvo and Loefller, Troy and Manna, Sukriti and Chan, Henry and Srinivasan, Srilok and Darancet, Pierre and Hexemer, Alexander and Sankaranarayanan, Subramanian KRS}, journal={npj Computational Materials}, volume={9}, number={1}, pages={1--16}, year={2023}, publisher={Nature Publishing Group} }

```

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License

CASTING is distributed under MIT License. See LICENSE for details.

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