TY - JOUR

T1 - Applications of Data Driven Methods in Computational Materials Design

AU - Dösinger, Christoph Alexander

AU - Spitaler, Tobias

AU - Reichmann, Alexander

AU - Scheiber, Daniel

AU - Romaner, Lorenz

PY - 2022

Y1 - 2022

N2 - In today’s digitized world, large amounts of data are becoming available at rates never seen before. This holds true also for materials science where high-throughput simulations and experiments continuously produce new data. Data driven methods are required which can make best use of the information stored in large data repositories. In the present article, two of such data driven methods are presented. First, we apply machine learning to generalize and extend the results obtained from computationally intense density functional theory (DFT) simulations. We show how grain boundary segregation energies can be trained with gradient boosting regression and extended to many more positions in the grain boundary for a complete description. The second method relies on Bayesian inference, which can be used to calibrate models to give data and quantification of the model uncertainty. The method is applied to calibrate parameters in thermodynamic models of the Gibbs energy of Ti-W alloys. The uncertainty of the model parameters is quantified and propagated to the phase boundaries of the Ti-W system.

AB - In today’s digitized world, large amounts of data are becoming available at rates never seen before. This holds true also for materials science where high-throughput simulations and experiments continuously produce new data. Data driven methods are required which can make best use of the information stored in large data repositories. In the present article, two of such data driven methods are presented. First, we apply machine learning to generalize and extend the results obtained from computationally intense density functional theory (DFT) simulations. We show how grain boundary segregation energies can be trained with gradient boosting regression and extended to many more positions in the grain boundary for a complete description. The second method relies on Bayesian inference, which can be used to calibrate models to give data and quantification of the model uncertainty. The method is applied to calibrate parameters in thermodynamic models of the Gibbs energy of Ti-W alloys. The uncertainty of the model parameters is quantified and propagated to the phase boundaries of the Ti-W system.

U2 - 10.1007/s00501-021-01182-3

DO - 10.1007/s00501-021-01182-3

M3 - Article

VL - 167-2022

SP - 29

EP - 35

JO - Berg- und hüttenmännische Monatshefte : BHM

JF - Berg- und hüttenmännische Monatshefte : BHM

SN - 0005-8912

IS - 1

ER -