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Rolf Krause | Nicolas Castin | Alessio Quaglino | Christophe Domain | Mihai-Cosmin Marinica | Luca Messina | Alexandra Goryaeva | Giovanni Bonny | A. Quaglino | R. Krause | L. Messina | A. Goryaeva | M. Marinica | C. Domain | N. Castin | G. Bonny
[1] D. Varshalovich,et al. Quantum Theory of Angular Momentum , 1988 .
[2] G. Kresse,et al. Ab initio molecular dynamics for liquid metals. , 1993 .
[3] Georg Kresse,et al. Norm-conserving and ultrasoft pseudopotentials for first-row and transition elements , 1994 .
[4] Hafner,et al. Ab initio molecular-dynamics simulation of the liquid-metal-amorphous-semiconductor transition in germanium. , 1994, Physical review. B, Condensed matter.
[5] Blöchl,et al. Projector augmented-wave method. , 1994, Physical review. B, Condensed matter.
[6] Burke,et al. Generalized Gradient Approximation Made Simple. , 1996, Physical review letters.
[7] G. Kresse,et al. From ultrasoft pseudopotentials to the projector augmented-wave method , 1999 .
[8] G. Henkelman,et al. Improved tangent estimate in the nudged elastic band method for finding minimum energy paths and saddle points , 2000 .
[9] G. Henkelman,et al. A climbing image nudged elastic band method for finding saddle points and minimum energy paths , 2000 .
[10] L. Malerba,et al. Interatomic potentials consistent with thermodynamics: The Fe–Cu system , 2007 .
[11] Risi Kondor,et al. A novel set of rotationally and translationally invariant features for images based on the non-commutative bispectrum , 2007, cs/0701127.
[12] Frédéric Soisson,et al. Cu-precipitation kinetics in α − Fe from atomistic simulations: Vacancy-trapping effects and Cu-cluster mobility , 2007 .
[13] Carl E. Rasmussen,et al. Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.
[14] Phaedon-Stelios Koutsourelakis,et al. Accurate Uncertainty Quantification Using Inaccurate Computational Models , 2009, SIAM J. Sci. Comput..
[15] C. Domain,et al. Introducing chemistry in atomistic kinetic Monte Carlo simulations of Fe alloys under irradiation , 2010 .
[16] Christophe Domain,et al. Ab initio study of solute transition-metal interactions with point defects in bcc Fe , 2010 .
[17] Duc Nguyen-Manh,et al. Cluster expansion models for Fe–Cr alloys, the prototype materials for a fusion power plant , 2010 .
[18] A. Bartók. Gaussian Approximation Potential: an interatomic potential derived from first principles Quantum Mechanics , 2010, 1003.2817.
[19] Manuel Roussel,et al. Kinetic study of phase transformation in a highly concentrated Fe–Cr alloy: Monte Carlo simulation versus experiments , 2011 .
[20] Charlotte Becquart,et al. Modeling Microstructure and Irradiation Effects , 2011 .
[21] M. I. Pascuet,et al. Modeling the first stages of Cu precipitation in α-Fe using a hybrid atomistic kinetic Monte Carlo approach. , 2011, The Journal of chemical physics.
[22] Ramakrishna Kakarala. The Bispectrum as a Source of Phase-Sensitive Invariants for Fourier Descriptors: A Group-Theoretic Approach , 2012, Journal of Mathematical Imaging and Vision.
[23] Lorenzo Malerba,et al. Mobility and stability of large vacancy and vacancy–copper clusters in iron: An atomistic kinetic Monte Carlo study , 2012 .
[24] Marco Buongiorno Nardelli,et al. The high-throughput highway to computational materials design. , 2013, Nature materials.
[25] N. Castin,et al. On the mobility of vacancy clusters in reduced activation steels: an atomistic study in the Fe–Cr–W model alloy , 2013, Journal of physics. Condensed matter : an Institute of Physics journal.
[26] Charlotte Becquart,et al. First principle-based AKMC modelling of the formation and medium-term evolution of point defect and solute-rich clusters in a neutron irradiated complex Fe-CuMnNiSiP alloy representative of reactor pressure vessel steels , 2013 .
[27] R. Kondor,et al. On representing chemical environments , 2012, 1209.3140.
[28] Nongnuch Artrith,et al. Neural network potentials for metals and oxides – First applications to copper clusters at zinc oxide , 2013 .
[29] J Behler,et al. Representing potential energy surfaces by high-dimensional neural network potentials , 2014, Journal of physics. Condensed matter : an Institute of Physics journal.
[30] Christian Trott,et al. Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials , 2014, J. Comput. Phys..
[31] Benjamin Peherstorfer,et al. Optimal Model Management for Multifidelity Monte Carlo Estimation , 2016, SIAM J. Sci. Comput..
[32] J. Knaster,et al. Materials research for fusion , 2016, Nature Physics.
[33] Christophe Domain,et al. Improved atomistic Monte Carlo models based on ab-initio -trained neural networks : Application to FeCu and FeCr alloys , 2017 .
[34] P. Erhart,et al. Mechanism of Re precipitation in irradiated W-Re alloys from kinetic Monte Carlo simulations , 2017, 1702.03019.
[35] Nicolas Castin,et al. Introducing ab initio-based neural networks for transition-rate prediction in kinetic Monte Carlo simulations , 2017 .
[36] A. Thompson,et al. Quantum-Accurate Molecular Dynamics Potential for Tungsten , 2017, 1702.07042.
[37] K J Kurzydłowski,et al. A first-principles model for anomalous segregation in dilute ternary tungsten-rhenium-vacancy alloys , 2017, Journal of physics. Condensed matter : an Institute of Physics journal.
[38] Aidan P Thompson,et al. Extending the accuracy of the SNAP interatomic potential form. , 2017, The Journal of chemical physics.
[39] Benjamin Peherstorfer,et al. Survey of multifidelity methods in uncertainty propagation, inference, and optimization , 2018, SIAM Rev..
[40] A. Quaglino,et al. Fast uncertainty quantification of activation sequences in patient‐specific cardiac electrophysiology meeting clinical time constraints , 2018, International journal for numerical methods in biomedical engineering.
[41] W. A. Wall,et al. The impact of personalized probabilistic wall thickness models on peak wall stress in abdominal aortic aneurysms , 2018, International journal for numerical methods in biomedical engineering.