Publications using the ænet code

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ænet Core

2021

A.M. Miksch*, T. Morawietz, J. Kästner, A. Urban, N. Artrith*,
“Strategies for the Construction of Machine-Learning Potentials for Accurate and Efficient Atomic-Scale Simulations”, https://arxiv.org/abs/2101.10468 (2021). Open Access.

T. Morawietz* and N. Artrith*,
“Machine Learning-Accelerated Quantum Mechanics-Based Atomistic Simulations for Industrial Applications”, J. Comput. Aided Mol. Des. (2021) in press DOI: https://doi.org/10.1007/s10822-020-00346-6.

2020

A.M. Cooper, J. Kästner, A. Urban, N. Artrith*,
“Efficient training of ANN potentials by including atomic forces via Taylor expansion and application to water and a transition-metal oxide”, npj Comput Mater 6 (2020) 54. Open Access.
The database can be obtained from the Materials Cloud repository.

2019

N. Artrith*,
“Machine Learning for the Modeling of Interfaces in Energy Storage and Conversion Materials”, J. Phys. Energy 1, (2019) 032002. Open Access.

N. Artrith*, A. Urban, Y. Wang, G. Ceder*,
“Atomic-Scale Factors that Control the Rate Capability of Nanostructured Amorphous Si for High-Energy-Density Batteries”, https://arxiv.org/abs/1901.09272 (2019).

2018

V. Lacivita*, N. Artrith, and G. Ceder*,
“The Structural and Compositional Factors that Control the Li-Ion Conductivity in LiPON Electrolytes”, Chem. Mater. 30 (2018) 7077-7090. Open Access.

N. Artrith*, A. Urban, and G. Ceder*,
“Constructing First-Principles Phase Diagrams of Amorphous Li_xSi using Machine-Learning-Assisted Sampling with an Evolutionary Algorithm”, J. Chem. Phys. 148 (2018) 241711. (Editor’s Pick) (preprint)

2017

N. Artrith*, A. Urban, and G. Ceder*,
“Efficient and Accurate Machine-Learning Interpolation of Atomic Energies in Compositions with Many Species”, Phys. Rev. B 96 (2017) 014112.(preprint)

2016

J.S. Elias, N. Artrith, M. Bugnet, L. Giordano, G.A. Botton, A.M. Kolpak, and Y. Shao-Horn*,
“Elucidating the Nature of the Active Phase in Copper/Ceria Catalysts for CO Oxidation”, ACS Catal. 6 (2016) 1675-1679.

N. Artrith* and A. Urban,
“An Implementation of Artificial Neural-Network Potentials for Atomistic Materials Simulations: Performance for TiO₂”, Comput. Mater. Sci. 114 (2016) 135-150. (Editor’s Choice)

2015

N. Artrith* and A.M. Kolpak,
“Grand Canonical Molecular Dynamics Simulations of Cu-Au Nanoalloys in Thermal Equilibrium using Reactive ANN Potentials”, Comput. Mater. Sci. 110 (2015) 20-28.

2014

N. Artrith* and A.M. Kolpak,
“Understanding the Composition and Activity of Electrocatalytic Nanoalloys in Aqueous Solvents: A Combination of DFT and Accurate Neural Network Potentials”, Nano Lett. 14 (2014) 2670–2676.

ænet Users

Please let us know if you would like to have your publications listed on the ænet website. Thank you for your support and for your help improving ænet.

2020

K. Shimamura, Y. Takeshita, S. Fukushima, A. Koura, F. Shimojo,
“Computational and Training Requirements for Interatomic Potential Based on Artificial Neural Network for Estimating Low Thermal Conductivity of Silver Chalcogenides”, J. Chem. Phys. 153 (2020) 234301.

Y. Nagai, M. Okumura, K. Kobayashi, M. Shiga,
“Self-learning Hybrid Monte Carlo: A First-principles Approach”, Phys. Rev. B 102 (2020) 041124.

H. Mori and T. Ozaki,
“Neural Network Atomic Potential to Investigate the Dislocation Dynamics in BCC Iron”, Phys. Rev. Materials 4 (2020) 040601.

T.D. Loeffler, S. Manna, T.K. Patra, H. Chan, B. Narayanan, S.K.R.S. Sankaranarayanan, “Active Learning A Neural Network Model For Gold Clusters & Bulk From Sparse First Principles Training Data”, arXiv (2020) 2006.03674.

T.D. Loeffler, T.K. Patra, H. Chan, S.K.R.S. Sankaranarayanan, “Active Learning a Coarse-grained Neural Network Model for Bulk Water from Sparse Training Data”, Mol. Syst. Des. Eng. 5 (2020) 902−910.

T.D. Loeffler, T.K. Patra, H. Chan, M. Cherukara, S.K.R.S. Sankaranarayanan, “Active Learning the Potential Energy Landscape for Water Clusters from Sparse Training Data”, J. Phys. Chem. C 124 (2020) 4907−4916.

2019

G. Sun, P. Sautet,
“Toward Fast and Reliable Potential Energy Surfaces for Metallic Pt Clusters by Hierarchical Delta Neural Networks”, J. Chem. Theory Comput. 15 (2019) 5614−5627.

T.K. Patra, T.D. Loeffler, H. Chan, M.J. Cherukara, B. Narayanan, and S.K.R.S. Sankaranarayanan, “A Coarse-grained Deep Neural Network Model for Liquid Water”, Appl. Phys. Lett. 115, (2019) 193101.

K. Shimamura, S. Fukushima, A. Koura, F. Shimojo, M. Misawa, R.K. Kalia, A. Nakano, P. Vashishta, T. Matsubara, and S. Tanaka, “Guidelines for Creating Artificial Neural Network Empirical Interatomic Potential from First-Principles Molecular Dynamics Data Under Specific Conditions and Its Application to α-Ag2Se”, J. Chem. Phys. 151 (2019) 124303.

Y. Umeno and A. Kubo,
“Prediction of Electronic Structure in Atomistic Model Using Artificial Neural Network”, Comput. Mater. Sci. 168 (2019) 164-171.

2018

I. Sukuba, L. Chen, M. Probst, and A. Kaiser,
“A Neural Network Interface for DL_POLY and its Application to Liquid Water”, Mol. Simulat. (2018) DOI: 10.1080/08927022.2018.1560440.

G. Sun, P. Sautet,
“Metastable Structures in Cluster Catalysis from First-Principles: Structural Ensemble in Reaction Conditions and Metastability Triggered Reactivity”, J. Am. Chem. Soc. 140 (2018) 2812–2820.

Acknowledgments

This work has been using the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant number ACI-1053575. Since 2019 ænet development has used resources of the Center for Functional Nanomaterials, which is a U.S. DOE Office of Science Facility, at Brookhaven National Laboratory under Contract No. DE-SC0012704. Development has also been supported by the Department of Chemical Engineering at Columbia University.