Noam Bernstein

发表

Construction of permittivity functions for high-explosives using density functional theory

Daniel Finkenstadt, Sam Lambrakos, Noam Bernstein, 2012, Int. J. Intell. Def. Support Syst..

Research data supporting "De novo exploration and self-guided learning of potential-energy surfaces"

Noam Bernstein, Volker L. Deringer, Gábor Csányi, 2019 .

Performance of inverse atomistic scale fracture modeling on GPGPU architectures

Sam Lambrakos, Noam Bernstein, Athanasios Iliopoulos, 2011, J. Comput. Sci..

Spanning the length scales in dynamic simulation

Noam Bernstein, Efthimios Kaxiras, Farid F. Abraham, 1998 .

Expressive Programming for Computational Physics in Fortran 950

Noam Bernstein, Gábor Csányi, Alessandro De Vita, 2007 .

Inverse Molecular Dynamics Modeling Performance on GPU Architectures for a Problem of Fracture

Noam Bernstein, John G. Michopoulos, Athanasios Iliopoulos, 2010 .

Multi-scale modeling of contacts between self-affine surfaces

Noam Bernstein, Jean-François Molinari, Mark O. Robbins, 2004 .

The adaptive buffered force QM/MM method in the CP2K and AMBER software packages

Andrew Jones, Noam Bernstein, Andreas W. Götz, 2014, J. Comput. Chem..

Free energy calculation from umbrella sampling using Bayesian inference

Noam Bernstein, Thomas Stecher, G 'abor Cs 'anyi, 2013 .

Machine learning unifies the modeling of materials and molecules

Noam Bernstein, James R Kermode, Michele Ceriotti, 2017, Science Advances.

A universal preconditioner for simulating condensed phase materials.

Noam Bernstein, Christoph Ortner, Nicholas Gould, 2016, The Journal of chemical physics.

Initial Construction of a General Framework for Numerical Simulation of IED Detection and Remote Activation Scenarios

Daniel Finkenstadt, Noam Bernstein, Verne L. Jacobs, 2010 .

Simultaneous structure exploration and machine-learned potential fitting

Noam Bernstein, Volker L. Deringer, Gábor Csányi, 2019 .

Phase diagrams of complex systems with nested sampling

Noam Bernstein, Gábor Csányi, K. Michael Salerno, 2018 .

Machine learning interatomic potentials with and without (much) human labor

Noam Bernstein, 2019 .

Free Energy Surface Reconstruction from Umbrella Samples Using Gaussian Process Regression.

Noam Bernstein, Gábor Csányi, Thomas Stecher, 2013, Journal of chemical theory and computation.

Thermal effects in 10 keV Si PKA cascades in 3C-SiC

Noam Bernstein, Wing Kam Liu, N. Bernstein, 2009 .

QM/MM simulation of liquid water with an adaptive quantum region.

Noam Bernstein, István Simon, Gábor Csányi, 2012, Physical chemistry chemical physics : PCCP.

Tests of an adaptive QM/MM calculation on free energy profiles of chemical reactions in solution.

Noam Bernstein, Gábor Csányi, Letif Mones, 2013, The journal of physical chemistry. B.

Spanning the continuum to quantum length scales in a dynamic simulation of brittle fracture

Noam Bernstein, Efthimios Kaxiras, Farid F. Abraham, 1998 .

Exploration, Sampling, And Reconstruction of Free Energy Surfaces with Gaussian Process Regression.

Noam Bernstein, Gábor Csányi, Letif Mones, 2016, Journal of chemical theory and computation.

Incommensurate transverse anisotropy induced by disorder and spin-orbit-vibron coupling in Mn12 acetate

Tunna Baruah, Noam Bernstein, Jens Kortus, 2005 .

Quantifying Chemical Structure and Machine‐Learned Atomic Energies in Amorphous and Liquid Silicon

Noam Bernstein, Volker L. Deringer, Gábor Csányi, 2018, Angewandte Chemie.

Simulations of nanocylinders self-assembled from cyclic β-tripeptides.

Noam Bernstein, N. Bernstein, J. Kulp, 2010, The journal of physical chemistry. A.

Realistic Atomistic Structure of Amorphous Silicon from Machine-Learning-Driven Molecular Dynamics.

Noam Bernstein, Volker L. Deringer, Gábor Csányi, 2018, The journal of physical chemistry letters.

Nonorthogonal tight-binding Hamiltonians for defects and interfaces in silicon

Noam Bernstein, Efthimios Kaxiras, E. Kaxiras, 1997 .

Quantifying Chemical Structure and Atomic Energies in Amorphous Silicon Networks.

Noam Bernstein, Volker L. Deringer, Stephen R. Elliott, 2018, 1811.11069.

Machine-learned interatomic potentials for alloys and alloy phase diagrams

Conrad W. Rosenbrock, Gus L. W. Hart, Noam Bernstein, 2019, npj Computational Materials.

Mixed finite element and atomistic formulation for complex crystals

Noam Bernstein, Efthimios Kaxiras, Ellad B. Tadmor, 1999 .

Modeling the Phase-Change Memory Material, Ge2Sb2Te5, with a Machine-Learned Interatomic Potential.

Noam Bernstein, Volker L. Deringer, Gábor Csányi, 2018, The journal of physical chemistry. B.

Density functional studies of single molecule magnets

Tunna Baruah, Noam Bernstein, Jens Kortus, 2003 .

Machine Learning a General-Purpose Interatomic Potential for Silicon

Noam Bernstein, Gabor Csanyi, James Kermode, 2018, Physical Review X.

A first-principles measure for the twinnability of FCC metals

Noam Bernstein, Ellad B. Tadmor, N. Bernstein, 2004 .

De novo exploration and self-guided learning of potential-energy surfaces

Noam Bernstein, Volker L. Deringer, Gábor Csányi, 2019, npj Computational Materials.

Dynamic Fracture of Silicon: Concurrent Simulation of Quantum Electrons, Classical Atoms, and the Continuum Solid

Noam Bernstein, Farid F. Abraham, Jeremy Q. Broughton, 2000 .

Hybrid atomistic simulation methods for materials systems

Noam Bernstein, Gábor Csányi, James R. Kermode, 2009 .

Multiscale simulations of silicon nanoindentation

Noam Bernstein, Efthimios Kaxiras, Ellad B. Tadmor, 2001 .