Abstract

We develop a novel deep learning tool, Alchemite, for materials discovery. The approach merges together both experimental data and computer simulations, in particular learning property-property correlations, to exploit all possible sources of information. The tool has been used to design several materials that have been experimentally verified and patented. We present just one case study where we discover and characterize the new nickel-base alloy for direct laser deposition most likely to simultaneously satisfy targets of processibility, cost, density, phase stability, creep resistance, oxidation, and resistance to thermal stresses. Experimental testing confirms that the physical properties of the proposed alloy exceed those of other commercially available Ni-base alloys for combustor liner applications. The Alchemite machine learning algorithm is now being commercialized by startup Intellegens.

Probabilistic neural network identification of an alloy for direct laser deposition B.D. Conduit, T. Illston, S. Baker, D. Vadegadde Duggappa, S. Harding, H.J. Stone & G.J. Conduit Materials & Design 168, 107644 (2019)

Probabilistic design of a molybdenum-base alloy using a neural network B.D. Conduit, N.G. Jones, H.J. Stone & G.J. Conduit Scripta Materialia 146, 82 (2018)

Materials data validation and imputation with an artificial neural network P.C. Verpoort, P. MacDonald & G.J. Conduit Computational Materials Science 147, 176 (2018)

Design of a nickel-base superalloy using a neural network B.D. Conduit, N.G. Jones, H.J. Stone & G.J. Conduit Materials & Design 131, 358 (2017)