Machine-learning potentials for nanoscale simulations of deformation and fracture: example of TiB${}_2$ ceramic

Abstract

Machine-learning interatomic potentials (MLIPs) offer a powerful avenue for simulations beyond length and timescales of ab initio methods. Their development for investigation of mechanical properties and fracture, however, is far from trivial since extended defects -- governing plasticity and crack nucleation in most materials -- are too large to be included in the training set. Using TiB${}_2$ as a model ceramic material, we propose a strategy for fitting MLIPs suitable to simulate mechanical response of monocrystals until fracture. Our MLIP accurately reproduces ab initio stresses and failure mechanisms during room-temperature uniaxial tensile deformation of TiB${}_2$ at the atomic scale ($\approx {10}^3$ atoms). More realistic tensile tests (low strain rate, Poisson's contraction) at the nanoscale ($\approx {10}^4$--10${}^6$ atoms) require MLIP up-fitting, i.e. learning from additional ab initio configurations. Consequently, we elucidate trends in theoretical strength, toughness, and crack initiation patterns under different loading directions. To identify useful environments for further up-fitting, i.e., making the MLIP applicable to a wider spectrum of simulations, we asses transferability to other deformation conditions and phases not explicitly trained on.

References in corpus (23)

• Restoring the density-gradient expansion for exchange in solids and surfaces
• Generalized gradient approximation for solids and their surfaces
• Gaussian Approximation Potentials: the accuracy of quantum mechanics, without the electrons
• ANI-1: An extensible neural network potential with DFT accuracy at force field computational cost
• Moment Tensor Potentials: a class of systematically improvable interatomic potentials
• A Spectral Analysis Method for Automated Generation of Quantum-Accurate Interatomic Potentials
• A Performance and Cost Assessment of Machine Learning Interatomic Potentials
• Active learning of linearly parametrized interatomic potentials
• A Machine Learning Potential for Graphene
• Machine Learning Inter-Atomic Potentials Generation Driven by Active Learning: A Case Study for Amorphous and Liquid Hafnium dioxide
• Review of Transition-Metal Diboride Thin Films
• Quantum-Accurate Spectral Neighbor Analysis Potential Models for Ni-Mo Binary Alloys and FCC Metals
• Enhancing plasticity in high-entropy refractory ceramics via tailoring valence electron concentration
• Predicting electronic structures at any length scale with machine learning
• Temperature-dependent elastic properties of binary and multicomponent high-entropy refractory carbides
• Application of machine learning potentials to predict grain boundary properties in fcc elemental metals
• Group-theoretical high-order rotational invariants for structural representations: Application to linearized machine learning interatomic potential
• Elucidating dislocation core structures in titanium nitride through high-resolution imaging and atomistic simulations
• Strength, transformation toughening and fracture dynamics of rocksalt-structure Ti1-xAlxN (0 <= x <= 0.75) alloys
• Elinvar effect in $\beta -$Ti simulated by on-the-fly trained moment tensor potential
• In operando active learning of interatomic interaction during large-scale simulations
• Descriptor for slip-induced crack-blunting in refractory ceramics
• On energetics of allotrope transformations in transition-metal diborides via plane-by-plane shearing

Cited by in corpus (4)

• Random sampling versus active learning algorithms for machine learning potentials of quantum liquid water
• Accurate prediction of structural and mechanical properties on amorphous materials enabled through machine-learning potentials: a case study of silicon nitride
• Machine-learning potentials for structurally and chemically complex MAB phases: strain hardening and ripplocation-mediated plasticity
• Machine-Learning Potentials Predict Orientation- and Mode-Dependent Fracture in Refractory Diborides