BIOVIA MATERIALS STUDIOAMORPHOUS CELLDATASHEET

What Does BIOVIA Materials Studio Amorphous Cell Do?
BIOVIA Materials Studio Amorphous Cell is a comprehensivemodel building tool for creating a wide range of amorphousmaterials. Its flexible1 approach to forcefield choice, combinedwith several different construction techniques, enables thebuilding of a bulk structures of a wide range of systems
including polymers and glasses, as well as nanostructures.
BIOVIA Materials Studio Amorphous Cell can also be usedto generate layered structures to enable calculations oninterfaces.

KEY USES OF BIOVIA MATERIALS STUDIOAMORPHOUS CELL
The behavior of amorphous materials is critical to products
such as plastics, glasses, foods, and chemicals. Researchers
studying amorphous polymers, for example, seek to optimize
their mechanical behavior, the transport of molecules throughthe system, and their surface and interface interactions. Theseproperties impact the polymer’s performance in applications
such as separation processes, packaging, and drug delivery.

Polymers
Building accurate models of polymers is key to predicting
the properties of these materials. BIOVIA Materials StudioAmorphous Cell has been designed to build realistic modelsof chain molecules enabling fast prediction of properties suchas density, mechanical properties, and diffusivity. Combine
models of small molecules such as drugs, with the polymer
models to predict compatibility.

Figure 1: BIOVIA Materials Studio Amorphous Cell
allows packing of molecules into nonorthogonal cells,
enabling simulations of solvents on cleaved surfaces.

Inorganic glasses
Use specialised forcefields to construct initial starting structures
for inorganic glassy materials. These can be further used withother classical simulations modules such as BIOVIA Materials
Studio Forcite Plus and BIOVIA Materials Studio GULP topredict key properties.

Solvation of systems
Use the “Packing” task to soak molecules or surfaces witha solvent. Use the classical simulations modules to predict
interaction energies between surfaces and solvents.

HOW DOES BIOVIA MATERIALS STUDIOAMORPHOUS CELL WORK?
In BIOVIA Materials Studio Amorphous Cell, molecules areadded into a three-dimensional box by growing the candidatemolecules inside the box segment by segment, taking into
account interaction with all atoms already positioned, whilst
continuously monitoring the single chain conformations. Aftereach head segment is placed, the chain molecules are grownby selecting the next segment from a number of candidatestructures. Each of the candidate structures is subjected to anumber of checks such as ring spearing and close contacts. For
the candidates that pass the initial tests, a probability, basedon the energy of adding the segement, is calculated. These arecompared to a random number and the next candidate is chosen.
The result of this sampling method is that low energy sites arepreferred over high energy sites. Using this method, BIOVIA
Materials Studio Amorphous Cell generates bulk disorderedsystems containing chains molecules in realistic equilibrium
conformations1.

Figure 2: Polymers with rigid backbones suchas polyethylene terepthalate can be packed
into cells at experimental densities.

BIOVIA Materials Studio Amorphous Cell is operated fromwithin the BIOVIA Materials Studio environment. A flexible
client- server architecture means that BIOVIA Materials Studio
Amorphous Cell calculations can be run on servers locatedelsewhere on your network.

• Flexible torsion definition:-- Backbone atoms in polymer chains-- All flexible torsions-- Torsions defined on the model• Ring spearing and catenation checks.
• Set temperature to control ability to build higher energyconformations.
• Modify number of bonds that are used to generate candidate• structures.
• Control the number of attempts to add molecules into the cell.
• Optimization of the new amorphous cell after construction.

Energy options• Support for Universal, BIOVIA Materials Studio Compass,Dreiding, PCFF, CVFF or customized forcefields.
• Charging using forcefield defined charges, Qeq or Gasteiger
methodologies.

Construction task

Figure 3: The packing task enables fast solvation ofexisting structures such as this nanoparticle of silica.

• Construct an amorphous cell at a set density using one ormore input molecules.
• Specify loading as number of molecules.
• Build at a specific density or ramp to a target density.
• Build cubic, tetragonal or orthorhombic cells.

Packing task

To use the program, you begin with the molecular structureof the polymer or material you wish to study, which can beconstructed using BIOVIA Materials Studio Visualizer’s polymer
building and sketching tools or loaded from a database. You
then set simulation parameters, including system compositionand density, and BIOVIA Materials Studio Amorphous Cell
constructs a periodic model of the system for you.

• Pack one or more molecules into a volume at a specified
density.
• Specify loading by the molar ratio of the molecules.
• Pack into:-- non-orthogonal cells-- cells already containing atoms-- isosurfaces

The resulting amorphous cell can be used with classical andquantum simulations for property prediction. You can also use
the coarse-graining tools to generate bead representations of
the amorphous materials for use in mesoscale modeling.

Confined layer task

FEATURES AND CAPABILITIESGeneral Model Building• Flexible building algorithm-- Model building using a modified Markov process2 withbond conformational probabilities chosen to accountfor both intramolecular and intermolecular non-bondedinteractions-- Fast model building using a geometrical close contact check-- Combined build with both energy and close contact check• Construct an amorphous cell at a set density with themolecules confined to a layer.
• Specify loading as number of one or more molecules.
• Build cubic, tetragonal, or orthorhombic cells.
• Choose to confine along A, B or C lattice vectors.
To learn more about BIOVIA Materials Studio, go toaccelrys.com/materials-studio

REFERENCES:
1. Theodorou, D.N. and Suter, U.W., Macromolecules, 1985, 18, 1485; 1986, 19, 139.
2. Allen, M.P. and Tildesley, D.J., Computer Simulation of Liquids, Oxford University Press, 1987.

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