The field of materials science is the study of materials with a focus on their composition, structure, and properties. The interdisciplinary field combines physics, chemistry and engineering and encompasses all-natural and artificial materials. This includes metals, ceramics, glasses, polymers, semiconductors, and composites. 

With a basic understanding of the origins of properties, materials can be selected or designed for an enormous variety of applications. Material Science is integrated in design, manufacturing, research, innovation, development.

Understanding and predicting material properties requires understanding the behavior of individual atoms and molecules and the behavior of large collections of these components. Materials scientists can use this understanding to develop new materials that follow a set list of desired properties.

Computational materials science and engineering uses modeling, simulation, theory, and to understand materials. The main goals include discovering new materials, determining material behavior and mechanisms, explaining experiments, and exploring materials theories.

What Materials Modelling Can Do for Industry

  • More efficient experimentation – for instance, using modelling to determine which direction to move in composition space to create a material with better properties.
  • Broader exploration and deeper understanding – in the case where modelling is cheaper than experiment and accurate enough using modelling to perform a more extensive parameter search than could have been afforded using experiment alone.
  • Saving a product development project and/or accelerated product development – for instance, modelling providing a solution to a problem that had not been solved using the tools previously available for development.

A more credible value for the RoI for materials modelling in the range of 3 to 9 – with the higher value associated with a higher use of modelling suggesting that the more resource a company invests in materials modelling, the higher their rate of return on this investment.

Goldbeck (2012). Goldbeck G (2012) The economic impact of molecular modelling of chemicals and materials.

Simcenter CULGI

  • Acquired by Siemens in 2021, is a part of Xcelerator portfolio
  • Founded in 1999, spinoff from Leiden University
  • Specialization in computational multiscale modelling simulation methods
  • This unique engineering workflow can deliver cost savings and accelerate innovation in the materials design and process development
  • Materials domain includes specialty chemicals, batteries, pharmaceuticals, and cosmetics

Property Estimation

  • All properties that are well-posed in classical mechanics and statistical mechanics can in principle be computed.
  • The issues remaining are accuracy (the error comes from the interatomic potential) and computational efficiency.
  • The properties can be roughly grouped into four categories:
      • Structural characterization. Examples include radial distribution function, dynamic structure factor, etc.
      • Equation of state. Examples include free-energy functions, phase diagrams, static response functions like thermal expansion coefficient, etc.
      • Transport. Examples include viscosity, thermal conductivity (electronic contribution excluded), correlation functions, diffusivity, etc.
      • Non-equilibrium response. Examples include plastic deformation, pattern formation, etc.

Examples of Property determination in Industrial Sectors


  • Cohesive strength and mechanical failure of the polymer-filler interface
  • Screen sizing agents for production of carbon fibers of desired shape and size
  • Simulate crosslinking of resin around the fibers and characterize the structure and binding strength


  • Predict the behavior of pure polymers and their properties such as glass transition temperature (Tg), Young’s modulus, yield stress and critical strain
  • Crosslink simulation to understand polymer network formation and impact of chemical structure, additives and processing on mechanical properties and Tg
  • Calculate reaction energies and kinetics for polymerization and degradation reactions
  • Explore catalyst features such as stereochemical selectivity
  • Predict thermodynamic properties 
  • Use Quantitative Structure-Property Relationship (QSPR) models to correlate polymer repeat unit structure with bulk properties such as Tg, Poisson’s ratio, thermal conductivity, refractive index, fracture stress and permeability


  • Calculate the diffusion of lithium ions throughout pure electrolytes or mixtures
  • Investigate how electrolyte molecules are broken up and incorporated into the solid electrolyte interface during operation
  • Determine the properties of electrolyte additives to assess how they influence the performance
  • Predict the viscosity of electrolyte formulations
  • Materials Studio may also be combined with COSMOtherm to predict the safety of electrolytes based on properties like vapor pressure and flash point


  • Predict the properties of polymers and blends to optimize them for food packaging and transport
  • Determine degradation products of packaging due to the environment
  • Calculate strength of adhesives and understand the mechanism of binding

Household products

  • Predict micelle formation to assess detergent activity and behavior
  • Determine the impacts of substituting ingredients with “green” alternatives
  • Calculate thermodynamic properties such as solvent vapor pressure to determine their use in aerosols and sprays

Food and beverage

  • Explore the impacts of environmental conditions on food freshness and appearance, such as chocolate bloom
  • Assess the impacts of substituting plant-based or “green” fats and oils with quantitative structure-property relationship (QSPR) models
  • Predict the properties of product formulations and optimize them for end markets with native machine learning workflows
  • Generate pharmacophore models to predict new odorants and flavorants 

Personal care

  • Predict skin permeation of active ingredients and additives 
  • Calculate solubility
  • Determine the emission and absorption spectra of pigment molecules
  • Identify molecular features that impart desirable consumer properties such as foam formation and stability with QSPR models

Battery : Anode

  • Predict the transport of lithium ions in the anode and how the lithium ions arrange within the anode during charging and discharging
  • Determine the processes which lead to graphene exfoliation and overall anode degradation
  • Explore the factors which control the growth and stabilization of the solid electrolyte interface
  • Determine the atomic-scale mechanisms for degradation in battery performance such as the formation of metal dendrites

Molecular Dynamics

  • Molecular dynamics(MD) is a computer simulation method for analyzing the physical movements of atoms and molecules.
  • The atoms and molecules are allowed to interact for a fixed period of time, giving a view of the dynamic”evolution” of the system.
  • In the most common version, the trajectoriesof atoms and molecules are determined by numerically solvingNewton’s equations of motion for a system of interacting particles, where forces between the particles and their potential energies are often calculated using interatomic potentials or molecular mechanical force fields.
  • The method is applied mostly in chemical physics, materials science, and biophysics.

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There are two main families of MD methods, which can be distinguished according to the model (and the resulting mathematical formalism) chosen to represent a physical system.

  • In the ‘classical’ mechanics approach to MD simulations molecules are treated as classical objects, resembling very much the ‘ball and stick’ model. Atoms correspond to soft balls and elastic sticks correspond to bonds. The laws of classical mechanics define the dynamics of the system.
  • The ‘quantum’ or ‘first-principles’ MD simulations, which started in the 1980s with the seminal work of Car and Parinello, take explicitly into account the quantum nature of the chemical bond. The electron density function for the valence electrons that determine bonding in the system is computed using quantum equations, whereas the dynamics of ions (nuclei with their inner electrons) is followed classically.

Quantum MD simulations represent an important improvement over the classical approach and they are used in providing information on a number of biological problems. However, they require more computational resources. At present only the classical MD is practical for simulations of biomolecular systems comprising many thousands of atoms over time scales of nanoseconds.

Particle based modeling refers to modeling approaches where a molecule is represented by beads and bonds, with varying degrees of coarse-graining. In Culgi, two such modeling approaches are available: (1) atomistic/molecular modeling; and (2) soft-core bead mesoscopic modeling.


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