Overview
Introduction
Sierra provides a uniform Python API for Entos simulations, such as workflows, energy calculations, and dynamics. The API provides access to low-cost, high-accuracy methods (OrbNet) as well as more traditional quantum chemistry methods (HF and DFT).
Sierra includes a number of prebuilt workflows for tasks more complicated than simple energy calculations, such as conformer generation, grid optimizations, bond dissociation energies, and many more.
Models
The Entos ecosystem is built up of the idea of Model
objects which contain attributes. A Model
in Sierra can be created by specifying attributes with the general form model = Model(attribute=data,...)
. The data in the Model
can then be accessed by model.attribute
. For example, the Molecule
model contains attributes like symbols
and geometry
that can be called to return the atomic symbols and molecular geometry, respectively.
from sierra.inputs import *
mol = Molecule(pubchem="water")
print(mol.symbols)
#> ['O' 'H' 'H']
print(mol.geometry)
"""
[[ 0. 0. 0. ]
[ 0.52421003 1.68733646 0.48074633]
[ 1.14668581 -0.45032174 -1.35474466]]
"""
Computation
There is a special type of model called Input
which provides the input structure for a computation. Each Input
object is paired with a corresponding Result
object, which contains all information in the Input
together with any additional attributes evaluated during the computation. All Input
objects can be computed to Result
objects with the sierra.run
command.
import sierra
from sierra.inputs import *
water = Molecule(pubchem="water")
print(water.measure([0, 1])) # O-H bond distance
#> 1.8311246545702178
inp = OptimizationInput(initial_molecule=water, method=XTBMethod(model="gfn1"))
result = sierra.run(inp)
print(result.final_molecule.measure([0, 1])) # Optimized O-H bond distance
#> 1.8104715425523041
Documentation Tree
For further documentation and examples see:
- Molecule - Details on creating and/or importing a molecular structure.
- Units - A description of Sierra's unit system.
- Building Blocks - Energy, gradient, AIMD, etc, which form the basis of Entos workflows.
- Workflow - Automated technologies which perform commonly used discovery tasks.
- Energy Methods - Details of DFT, xTB, OrbNet, and other energy methods.