Machine learning for metallurgy I. A neural-network potential for Al-Cu

High-strength metal alloys achieve their performance via careful control of precipitates and solutes. The nucleation, growth, and kinetics of precipitation, and the resulting mechanical properties, are inherently atomic scale phenomena, particularly during early-stage nucleation and growth. Atomistic modeling using interatomic potentials is a desirable tool for understanding the detailed phenomena involved in precipitation and strengthening, which requires length and timescales far larger than those accessible by first-principles methods. Current interatomic potentials for alloys are not, however, sufficiently accurate for such studies. Here a family of neural-network potentials (NNPs) for the Al-Cu system are presented as a first example of a machine learning potential that can achieve near-first-principles accuracy for many different metallurgically important aspects of this alloy. High-fidelity predictions of intermetallic compounds, elastic constants, dilute solid-solution energetics, precipitate-matrix interfaces, generalized stacking fault energies and surfaces for slip in matrix and precipitates, antisite defect energies, and other quantities, are shown. The NNPs also captures the subtle entropically induced transition between θ and θ at temperatures around 600 K. Many comparisons are made with the state-of-the-art angular-dependent potential for Al-Cu, demonstrating the significant quantitative benefit of a machine learning approach. A preliminary kinetic Monte Carlo study shows the NNP to predict the emergence of GP zones in Al-4at%Cu at T = 300 K in agreement with experiments. These studies show that the NNP has significant transferability to defects and properties outside the structures used to train the NNP but also shows some errors highlighting that the use of any interatomic potential requires careful validation in application to specific metallurgical problems of interest

Phonon Lifetimes Throughout the Brillouin Zone at Elevated Temperatures from Experiment and ab initio

We obtain phonon lifetimes in aluminium by inelastic neutron scattering experiments, by ab initio molecular dynamics, and by perturbation theory. At elevated temperatures significant discrepancies are found between experiment and perturbation theory, which disappear when using molecular dynamics due to the inclusion of full anharmonicity and the correct treatment of the multiphonon background. We show that multiple-site interactions are small and that local pairwise anharmonicity dominates phonon-phonon interactions, which permits an efficient computation of phonon lifetimes

Anomalous Phonon Lifetime Shortening in Paramagnetic CrN Caused by Spin-Lattice Coupling: A Combined Spin and Ab Initio Molecular Dynamics Study

We study the mutual coupling of spin fluctuations and lattice vibrations in paramagnetic CrN by combining atomistic spin dynamics and ab initio molecular dynamics. The two degrees of freedom are dynamically coupled, leading to nonadiabatic effects. Those effects suppress the phonon lifetimes at low temperature compared to an adiabatic approach. The dynamic coupling identified here provides an explanation for the experimentally observed unexpected temperature dependence of the thermal conductivity of magnetic semiconductors above the magnetic ordering temperature.Funding Agencies|FP7 Marie Sklodowska-Curie COFUND Programme [600382]; priority programme SPP1599 "Ferroic cooling" [HI1300/6-2]; Netherlands Organisation for Scientific Research (NWO) under the VIDI research programme [15707]; European Research Council (ERC) under the EUs Horizon 2020 Research and Innovation Programme [639211]; Swedish Research Council (VR) [330-2014-6336]; Marie Sklodowska Curie Actions [INCA 600398]; Swedish Government Strategic Research Area in Materials Science on Functional Materials at Linkoping University (Faculty Grant SFOMatLiU) [2009 00971]; Swedish Foundation for Strategic Research</p