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ABINIT: Overview and focus on selected capabilities
ABINIT: Overview and focus on selected capabilities
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ABINIT: Overview and focus on selected capabilities

Romero, Aldo H. ; Allan, Douglas C. ; Amadon, Bernard ; Antonius, Gabriel ; Applencourt, Thomas ; Baguet, Lucas ; Bieder, Jordan ; Bottin, François ; Bouchet, Johann ; Bousquet, Eric ; Bruneval, Fabien ; Brunin, Guillaume ; Caliste, Damien ; Côté, Michel ; Denier, Jules ; Dreyer, Cyrus ; Ghosez, Philippe ; Giantomassi, Matteo ; Gillet, Yannick ; Gingras, Olivier ; Hamann, Donald R. ; Hautier, Geoffroy ; Jollet, François ; Jomard, Gérald ; Martin, Alexandre ; Miranda, Henrique P. C. ; Naccarato, Francesco ; Petretto, Guido ; Pike, Nicholas A. ; Planes, Valentin ; Prokhorenko, Sergei ; Rangel, Tonatiuh ; Ricci, Fabio ; Rignanese, Gian-Marco ; Royo, Miquel ; Stengel, Massimiliano ; Torrent, Marc ; van Setten, Michiel J. ; Van Troeye, Benoit ; Verstraete, Matthieu J. ; Wiktor, Julia ; Zwanziger, Josef W. ; Gonze, Xavier

The Journal of chemical physics, 2020-03, Vol.152 (12), p.124102-124102 [Peer Reviewed Journal]

United States: American Institute of Physics

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2
Temperature dependence of the electronic structure of semiconductors and insulators
Temperature dependence of the electronic structure of semiconductors and insulators
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Temperature dependence of the electronic structure of semiconductors and insulators

Poncé, S ; Gillet, Y ; Laflamme Janssen, J ; Marini, A ; Verstraete, M ; Gonze, X

The Journal of chemical physics, 2015-09, Vol.143 (10), p.102813-102813 [Peer Reviewed Journal]

United States: American Institute of Physics

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3
A walk through the approximations of ab initio multiple spawning
A walk through the approximations of ab initio multiple spawning
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A walk through the approximations of ab initio multiple spawning

Mignolet, Benoit ; Curchod, Basile F. E.

The Journal of chemical physics, 2018-04, Vol.148 (13), p.134110-134110 [Peer Reviewed Journal]

United States: American Institute of Physics

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4
Alchemical and structural distribution based representation for universal quantum machine learning
Alchemical and structural distribution based representation for universal quantum machine learning
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Alchemical and structural distribution based representation for universal quantum machine learning

Faber, Felix A. ; Christensen, Anders S. ; Huang, Bing ; von Lilienfeld, O. Anatole

The Journal of chemical physics, 2018-06, Vol.148 (24), p.241717-241717 [Peer Reviewed Journal]

United States

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5
The Molpro quantum chemistry package
The Molpro quantum chemistry package
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The Molpro quantum chemistry package

Werner, Hans-Joachim ; Knowles, Peter J. ; Manby, Frederick R. ; Black, Joshua A. ; Doll, Klaus ; Heßelmann, Andreas ; Kats, Daniel ; Köhn, Andreas ; Korona, Tatiana ; Kreplin, David A. ; Ma, Qianli ; Miller, Thomas F. ; Mitrushchenkov, Alexander ; Peterson, Kirk A. ; Polyak, Iakov ; Rauhut, Guntram ; Sibaev, Marat

The Journal of chemical physics, 2020-04, Vol.152 (14), p.144107-144107 [Peer Reviewed Journal]

United States: American Institute of Physics

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6
Scalable molecular dynamics on CPU and GPU architectures with NAMD
Scalable molecular dynamics on CPU and GPU architectures with NAMD
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Scalable molecular dynamics on CPU and GPU architectures with NAMD

Phillips, James C. ; Hardy, David J. ; Maia, Julio D. C. ; Stone, John E. ; Ribeiro, João V. ; Bernardi, Rafael C. ; Buch, Ronak ; Fiorin, Giacomo ; Hénin, Jérôme ; Jiang, Wei ; McGreevy, Ryan ; Melo, Marcelo C. R. ; Radak, Brian K. ; Skeel, Robert D. ; Singharoy, Abhishek ; Wang, Yi ; Roux, Benoît ; Aksimentiev, Aleksei ; Luthey-Schulten, Zaida ; Kalé, Laxmikant V. ; Schulten, Klaus ; Chipot, Christophe ; Tajkhorshid, Emad

The Journal of chemical physics, 2020-07, Vol.153 (4), p.044130-044130 [Peer Reviewed Journal]

United States: American Institute of Physics

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7
SchNet – A deep learning architecture for molecules and materials
SchNet – A deep learning architecture for molecules and materials
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SchNet – A deep learning architecture for molecules and materials

Schütt, K. T. ; Sauceda, H. E. ; Kindermans, P.-J. ; Tkatchenko, A. ; Müller, K.-R.

The Journal of chemical physics, 2018-06, Vol.148 (24), p.241722-241722 [Peer Reviewed Journal]

United States

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8
Perspective on integrating machine learning into computational chemistry and materials science
Perspective on integrating machine learning into computational chemistry and materials science
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Perspective on integrating machine learning into computational chemistry and materials science

Westermayr, Julia ; Gastegger, Michael ; Schütt, Kristof T. ; Maurer, Reinhard J.

The Journal of chemical physics, 2021-06, Vol.154 (23), p.230903-230903 [Peer Reviewed Journal]

United States

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9
Sparse learning of stochastic dynamical equations
Sparse learning of stochastic dynamical equations
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Sparse learning of stochastic dynamical equations

Boninsegna, Lorenzo ; Nüske, Feliks ; Clementi, Cecilia

The Journal of chemical physics, 2018-06, Vol.148 (24), p.241723-241723 [Peer Reviewed Journal]

United States

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10
Natural triple excitations in local coupled cluster calculations with pair natural orbitals
Natural triple excitations in local coupled cluster calculations with pair natural orbitals
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Natural triple excitations in local coupled cluster calculations with pair natural orbitals

Riplinger, Christoph ; Sandhoefer, Barbara ; Hansen, Andreas ; Neese, Frank

The Journal of chemical physics, 2013-10, Vol.139 (13), p.134101-134101 [Peer Reviewed Journal]

United States

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