M. Kornbluth

发表

Anharmonic Phonons in Graphene from First Principles

M. Kornbluth, 2017 .

Resistance to fracture in the glassy solid electrolyte Lipon

A. Westover, N. Dudney, S. Kalnaus, 2021, Journal of Materials Research.

Plasma Synthesis of Spherical Crystalline and Amorphous Electrolyte Nanopowders for Solid-State Batteries.

A. Westover, N. Dudney, D. Cullen, 2020, ACS Applied Materials and Interfaces.

Origin of Spinel Nanocheckerboards via First Principles.

C. Marianetti, M. Kornbluth, 2015, Physical review letters.

Analysis of shot-to-shot variability in post-disruption runaway electron currents for diverted DIII-D discharges

D. Humphreys, M. Kornbluth, V. Izzo, 2012 .

E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials

Jonathan P. Mailoa, M. Geiger, T. Smidt, 2021, Nature communications.

Orbital Mixer: Using Atomic Orbital Features for Basis Dependent Prediction of Molecular Wavefunctions

Jonathan P. Mailoa, J. Z. Kolter, M. Kornbluth, 2022, 2205.06133.

Electronic structure manipulation via composition tuning for the development of highly conductive and acid-stable oxides

Jonathan P. Mailoa, M. Kornbluth, Haegyeom Kim, 2022, Journal of Materials Chemistry A.

Orbital Mixer: Using Atomic Orbital Features for Basis-Dependent Prediction of Molecular Wavefunctions.

Jonathan P. Mailoa, J. Z. Kolter, M. Kornbluth, 2022, Journal of chemical theory and computation.

Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture

Jonathan P. Mailoa, Cheol Woo Park, C. Wolverton, 2021, npj Computational Materials.

E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials

Jonathan P. Mailoa, Simon L. Batzner, M. Geiger, 2021, Nature Communications.

A fast neural network approach for direct covariant forces prediction in complex multi-element extended systems

Mordechai Kornbluth, Boris Kozinsky, Chris Ablitt, 2019, Nature Machine Intelligence.

Learning local equivariant representations for large-scale atomistic dynamics

Simon L. Batzner, B. Kozinsky, Lixin Sun, 2022, Nature Communications.

Accurate and scalable multi-element graph neural network force field and molecular dynamics with direct force architecture

Mordechai Kornbluth, Jonathan Vandermause, Boris Kozinsky, 2020, 2007.14444.

Group theoretical approach to computing phonons and their interactions

Mordechai Kornbluth, C. Marianetti, M. Kornbluth, 2019, Physical Review B.

A fast neural network approach for direct covariant forces prediction in complex multi-element extended systems

Jonathan P. Mailoa, Simon L. Batzner, G. Samsonidze, 2019, Nature Machine Intelligence.

Medium-Range Ordering in the Ionic Glass Electrolytes LiPON and LiSiPON

Steve W. Martin, A. Westover, N. Dudney, 2023, Chemistry of Materials.

Light-induced electronic non-equilibrium in plasmonic particles.

A. Nitzan, T. Seideman, M. Kornbluth, 2013, The Journal of chemical physics.

A community-powered search of machine learning strategy space to find NMR property prediction models pdf

Youngsoo Lee, David R. Glowacki, J. Zico Kolter, 2020, PloS one.

Spectral Denoising for Accelerated Analysis of Correlated Ionic Transport.

B. Kozinsky, M. Kornbluth, Aris Marcolongo, 2020, Physical review letters.

SE(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials pdf

Mordechai Kornbluth, Simon Batzner, Boris Kozinsky, 2021, ArXiv.