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Materials System Modeling

Home / Solutions / Materials System Modeling

For more than two decades, QuesTek has been developing and improving its proprietary computational technologies, vetted thermodynamic and kinetic databases of elemental properties and calibrated physics-based predictive property models to identify microstructures needed to meet key properties and ensure enhanced performance. As a team we are experienced in a wide range of material systems including iron, aluminum, copper, cobalt, magnesium, molybdenum, nickel, titanium, niobium, tungsten and property / performance prediction (e.g. yielding, fracture, fatigue, creep, corrosion, and oxidation). These models are used to (i) design, discover, and/or optimize materials and thermal processes and (ii) assess a material’s performance capabilities. See below for more details on QuesTek’s material system modeling capabilities.

QuesTek is also a provider of other ICME-based materials solutions, including materials design and materials process optimization.

Atomistic Simulations

Computational Thermodynamics

Mechanistic Models

Machine Learning

Atomistic Simulations

Density Functional Theory (DFT) calculations are used to provide key inputs for CALPHAD assessment and ICME model development. DFT calculations also provide critical data for ICME models

Expansion of Fe-X-Y enthalpy of mixing database for high entropy alloy design

Grain boundary energetics for first-princples brittle fracture susceptibility predictions

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Computational Thermodynamics

The evolution of material’s microstructure is modeled as a function of processing parameters and composition using computational thermodynamics tools such as those based off of CALPHAD (Computer Coupling of Phase Diagrams and Thermochemistry)

Predictions of phase constitution as a function of
composition ranges and heat treatment

Modeling of size evolution of precipitate phases as a
function of aging time and temperature

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Mechanistic Models

QuesTek has developed and utilized mechanistic models that 1) enhance understanding of underlying mechanism that governs materials properties and behavior, and enables QuesTek to design new materials. Our models simulate complex mechanism spanning multiple length and time scales, creating reliable and useful tools to perform materials design.

Modeling of multi-scale creep deformation mechanisms in steels

Dislocation-dynamics based yield strength prediction model

Martensite start temperature modelling for steels as a function of alloy composition computed using QuesTek’s Integrated Computational Materials Design (iCMD®) platform

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Machine Learning

QuesTek uses machine learning models for various kinds of studies and ICME-aided materials design workflows, including but not limited to Bayesian inference for uncertainty quantification (UQ), Bayesian optimization (BO), regression models based on neural network (NN), random forest (RF), and SISSO.

Bayesian optimization for high entropy alloy (HEA) design

Neural network regression modeling of predicted vs. DFT calculated bulk modulus values

Nb alloy DBTT prediction using SISSO

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Interested in learning how QuesTek can support you with materials system modeling services?

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