Quantum Chemistry methods for materials science - Andreas Grüneis TU Wien, Austria
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Quantum Chemistry methods for
materials science
June 8 - 10, 2022
CECAM-HQ-EPFL, Lausanne, Switzerland
Andreas Grüneis
TU Wien, Austria
Matthias Scheffler
Fritz-Haber-Institut der Max-Planck-Gesellschaft, Germany1. Description
Density--functional theory (DFT) [1,2] has been the method of choice for electronic--structure
calculations in materials science over many decades. However, certain well--documented
failures such as unsatisfactory prediction of atomization energies and underestimation of weak
interactions and reaction barriers, limit the predictive power of current density- functional
approximations, including (semi-)-local and hybrid functionals in materials science [3,4].
The desire for general--purpose electronic--structure methods with high accuracy is pressing,
especially in materials science. Together with the rapid growth of computational capacity, this
has drawn attention to the sophisticated quantum--chemistry methodologies rooted in
wave-function theory (WFT). WFT offers a systematic hierarchy to approach the exact solution
of the many--electron Schrödinger equation. The Møller-Plesset perturbation theory and the
coupled--cluster approach are two popular choices in quantum chemistry. In contrast to DFT,
these WFT--based quantum--chemistry methods go beyond the single--electron mean--field
model and take the correlation effects into account in an explicit many--body picture. The
improvable accuracy together with potentially richer electronic--structure information and
the ability to study electronically excited states via the equation of motion (EOM) formalism
make them very promising in materials science. The implementation of popular quantum-
-chemistry methods to condensed matter systems, including the second--order Møller-Plesset
perturbation method (MP2) and (EOM) coupled--cluster approaches, has been done in several
mainstream computational platforms [5,6,7,8,9,10]. Their applications to study properties in
solids and surfaces have been presented by the world's leading researchers and their groups
[11,12,13,14,15,16]. More recently, an increasing number of applications using EOM type
methods have focused on the prediction of electronic band structures and optical excitation
energies in solids [10,16,17,18].
However, there is still a long way to go before making quantum--chemistry methods practical
for solids. Compared to popular density functionals, the quantum--chemistry methods are
often much more expensive, and encounter more difficulties when converging results with
respect to all computational parameters involved such as the size of the simulation cell
and basis set [9,13,14,17]. In this context, the central goal of the workshop is to discuss the
state of the art and challenges of using quantum chemistry methods in materials science, to
share the recent progresses in quantum chemistry, and to deepen the coalescence of two
communities: molecular quantum-chemistry and solid-state physics.
In addition to the theoretical topics described above, this workshop will focus on the computer
implementation of massive parallel algorithms to perform quantum chemical calculations
on modern supercomputers. The evolution of computer architecture towards larger multicore
machines partly equipped with GPUs makes it necessary to adapt existing simulation software
and employ libraries tailored to run ab initio calculations efficiently on modern hardware
[20,21,22,23,24,25]. This workshop will bring together some of the world's leading experts in
the development of massively parallel algorithms for quantum chemistry calculations to foster
cooperation and catalyze scientific software innovation.
Key References
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[13] T. Gruber, K. Liao, T. Tsatsoulis, F. Hummel, A. Grüneis, Phys. Rev. X, 8, 021043 (2018)
[14] B. Lau, G. Knizia, T. Berkelbach, J. Phys. Chem. Lett., 12, 1104 (2021)
[15] T. Schäfer, F. Libisch, G. Kresse, A. Grüneis, J. Chem. Phys., 154, 011101 (2021)
[16] A. Dittmer, R. Izsák, F. Neese, D. Maganas, Inorg. Chem., 58, 9303 (2019)
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[19] D. Usvyat, L. Maschio, M. Schütz, WIREs. Comput. Mol. Sci., 8 (2018)
[20] C. Peng, J. Calvin, F. Pavošević, J. Zhang, E. Valeev, J. Phys. Chem. A, 120, 10231 (2016)
[21] L. Gyevi-Nagy, M. Kállay, P. Nagy, J. Chem. Theory Comput., 17, 860 (2021)
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[24] C. Peng, J. Calvin, E. Valeev, Int. J. Quantum. Chem., 119 (2019)
[25] A. DePrince, J. Hammond, J. Chem. Theory Comput., 7, 1287 (2011)
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Computing, 74, 3176 (2014)
Page 3 of 102. Program
Day 1 - Wednesday June 8th 2022
• 09:00 to 13:45 - Registration
• 13:45 to 14:30 - Welcome & Introduction
• 14:30 to 15:20 - Juerg Hutter
Double-hybrid density functionals: energy, forces, stress tensor in condensed phase
simulations
• 15:20 to 16:10 - Peter Nagy
Gold standard Quantum Chemistry up to 1000 atoms: method optimization and large-
scale applications
• 16:10 to 16:30 - Coffee break
• 16:10 to 17:00 - James Spencer
Learning ab initio wavefunctions with deep neural networks
• 17:00 to 17:50 - Timothy Berkelbach
• 17:50 to 18:10 - Discussion
• 18:10 to 20:00 - Poster session & aperitif
Day 2 - Thursday June 9th 2022
• 09:00 to 09:10 - Introduction
• 09:10 to 10:00 - Seiichiro L. Ten-no
• 10:00 to 10:50 - Carmen Herrmann
Molecular electronics and spintronics as a challenge for first-principles methods
• 10:50 to 11:10 - Coffee break
• 11:10 to 12:00 - Dimitrios Manganas
Core level spectroscopy of antiferromagnetically coupled systems
• 12:00 to 12:30 - Discussion
• 12:30 to 14:30 - Lunch
• 14:30 to 14:40 - Introduction
• 14:40 to 15:30 - Jörg Kussmann
Efficient and low-scaling ab initio methods for (non-)adiabatic molecular dynamics
simulations on CPUs and GPUs
• 15:30 to 16:20 - Denis Usvyat
Embedded-fragment quantum chemical treatment of periodic systems
• 16:20 to 16:40 - Coffee break
• 16:40 to 17:30 - Edgar Solomonik
Tensor algorithms and software for Quantum Chemistry
• 19:00 to 21:00 - Social dinner
Page 4 of 10Day 3 - Friday June 10th 2022
• 09:00 to 09:10 - Introduction
• 09:10 to 10:00 - Jeff Hammond
Programming methods for implementing coupled-cluster methods on GPUs
• 10:00 to 10:50 - Edward F. Valeev
• 10:50 to 11:10 - Coffee break
• 11:10 to 12:00 - TBA
• 12:00 to 12:30 - Discussion
• 12:30 to 12:40 - Closing Word
Page 5 of 103. Abstracts
Core level spectroscopy of antiferromagnetically coupled systems
Dimitrios Manganas, Frank Neese, Tiago Da Costa Gouveia Leyser, Anneke Dittmer
Max-Planck-Institut für Kohlenforschung, Germany
TBA
Double-hybrid density functionals: energy, forces, stress tensor in condensed
phase simulations.
Juerg Hutter
University of Zurich, Switzerland
Double-hybrid (DH) density functionals have been shown to improve thermodynamic properties of
molecular systems. They are therefore also interesting candidates for higher accuracy simulations in
molecular condensed phase systems. The improved accuracy would be beneficial for many such
systems with properties driven by subtle energy differences (polymorphs).
We report on the implementation of a series of double-hybrid functionals, their energy gradients and
stress tensors into the CP2K code using the GPW framework. We explore different correlation methods
and model approximations in order to overcome memory and scaling bottlenecks, as well as slow
convergence with basis set size. A long-range SOS-MP2 based DH functional using RI and Laplace
transform algorithm showed a scaling performance similar to GGA calculations for systems sizes of up
to 1000 atoms. However, the increase in prefactor is close to two orders of magnitude.
Efficient and low-scaling ab initio methods for (non-)adiabatic molecular
dynamics simulations on CPUs and GPUs
Jörg Kussmann, Henryk Laqua, Laurens D.M. Peters, Christian Ochsenfeld
University of Munich (LMU), Germany
We present our recent method developments that aim at enabling highly efficient ab initio (non-
)adiabatic molecular dynamics (MD) simulations [1-4]. These methods allow for the investigation of
biochemical, catalytic, or photo-induced chemical processes and can be a crucial tool in the design of
new drugs or photoactive materials.
The major bottleneck in these calculations is the evaluation of the electron-electron interaction terms
that prevents the application to larger systems. Our recent developments of linear-scaling semi-
numerical exact exchange methods [5-7] and a highly efficient RI-Coulomb method [6] for both central
(CPU) and graphics processing units (GPU) strongly reduce the overall computational time required for
these steps, thus enabling not only the simulation of large molecular systems, but also significantly
speed up simulations of smaller to medium sized systems due to the strong-scaling parallelization of
our methods.
Furthermore, we investigated several pathways to further improve MD simulations. While ensuring a
small number of self-consistent field (SCF) steps by using the extended-Lagrangian method (XL-
BOMD) [8], we further reduced the overall computational effort by developing a method for a combined
evaluation of energies and nuclear gradients [4].
For non-adiabatic molecular dynamics (NAMD) we developed the Fermi-smearing TDA method (FS-
TDA) [2] to overcome a major shortcoming of time-dependent DFT in the context of non-adiabatic MD
simulations. While conventional TDDFT often fails at conical intersections, our FS-TDA method enables
to capture the multi-reference character of the electronic structure by employing a thermal electronic
density that ensures a physically correct description of the dynamics in the vicinity of conical
intersections.
Page 6 of 10With the improved description of conical intersections by the FS-TDA method and our high-performance
integral methods, new possibilities for gaining insights into energy conversion processes open up.
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[2] L. Peters, J. Kussmann, C. Ochsenfeld, J. Chem. Phys., 153, 094104 (2020)
[3] L. Peters, J. Kussmann, C. Ochsenfeld, J. Phys. Chem. Lett., 11, 3955 (2020)
[4] H. Laqua, J. Dietschreit, J. Kussmann, C. Ochsenfeld, submitted (2022).
[5] H. Laqua, J. Kussmann, C. Ochsenfeld, J. Chem. Phys., 154, 214116 (2021)
[6] H. Laqua, T. Thompson, J. Kussmann, C. Ochsenfeld, J. Chem. Theory Comput., 16, 1456 (2020)
[7] J. Kussmann, H. Laqua, C. Ochsenfeld, J. Chem. Theory Comput., 17, 1512 (2021)
[8] A. Niklasson, M. Cawkwell, The Journal of Chemical Physics, 141, 164123 (2014)
Embedded-fragment quantum chemical treatment of periodic systems
Denis Usvyat
Humboldt University, Germany
High-level quantum chemical treatment of periodic systems has become an essential tool in solid state
applications. It generally allows for a systematic control of error and a higher quantitative accuracy than
density functional theory. However, due to a substantial computational cost, quantum chemical models
usually require special techniques and approximations to be feasible in practical applications to periodic
systems. One of such approaches is the embedded-fragment method [1,2,3], where a group of localized
occupied and virtual orbitals, corresponding to a periodic HF calculation, are subjected to a high-level
quantum chemical treatment. This method is especially effective for point defects in solids and on
surfaces. We provide an overview on the current developments and applications of the embedded-
fragment approach.
[1] O. Masur, M. Schütz, L. Maschio, D. Usvyat, J. Chem. Theory Comput., 12, 5145-5156 (2016)
[2] T. Mullan, L. Maschio, P. Saalfrank, D. Usvyat, J. Chem. Phys., 156, 074109 (2022)
[3] E. Christlmaier, D. Kats, A. Alavi, D. Usvyat, J. Chem. Phys., 156, 154107 (2022)
Gold standard Quantum Chemistry up to 1000 atoms: method optimization and
large-scale applications
Peter Nagy, László Gyevi-Nagy, Bernát Szabó, Balázs Lőrincz, Mihály Kállay
Budapest University of Technology and Economics, Hungary
Recent optimization efforts [1-4] and large-scale applications [1,5-7] are presented illustrating the
accuracy and efficiency of the local natural orbital (LNO) coupled-cluster (CC) family of methods.
Besides the highly-optimized and parallel closed-shell and restricted open-shell MP2 and CC with
single-, double, - and perturbative triple excitations [LNO-CCSD(T)] [1-4], general-order LNO-CC [1],
and an LNO-ADC (2) approach for excited states are implemented in the MRCC program package [8].
An LNO-CCSD(T)/CBS(T,Q) level or similar composite energy expressions are often an affordable
choice if applied in computational protocols involving geometry optimization or frequency evaluation
with DFT methods above rung three. The exceptionally low minimal memory requirement and the
negligible disk use of the LNO-CCSD(T) method make it routinely applicable for closed- and open-shell
molecules with single-reference character up to a few hundred atoms even with easily accessible,
single-CPU workstations.
The efficiency of LNO-CCSD(T) allowed us to approach the complete basis set limit of conventional
CCSD(T) with robust basis set incompleteness and local error estimates for large molecular complexes
in the 100-1000 atom range [1,5,7]. These rigorous error estimates were useful to exclude the local and
basis set errors as the primary source of large, up to 8 kcal/mol deviations between CCSD(T) and fixed-
node DMC obtained for challenging intermolecular interactions [5]. However, this puzzling
disagreement between the two highly-regarded benchmark methods remain unexplained.
The capabilities of the current LNO-CCSD(T) implementation are demonstrated on large, three-
dimensional applications, such as ionic crystal surfaces; supramolecular complexes [5]; organo-,
transition metal-, and enzyme catalysis [1,6,7]; and protein-ligand binding [1].
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[3] L. Gyevi-Nagy, M. Kállay, P. Nagy, J. Chem. Theory Comput., 16, 366 (2019)
[4] P. Szabó, J. Csóka, M. Kállay, P. Nagy, J. Chem. Theory Comput., 17, 2886 (2021)
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(2021)
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[7] B. Hégely, P. Nagy, M. Kállay, J. Chem. Theory Comput., 14, 4600-4615 (2018)
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Ladjánszki, L. Szegedy, B. Ladóczki, K. Petrov, M. Farkas, P. Mezei, Á. Ganyecz, J. Chem. Phys., 152, 074107
(2020)
Learning ab initio wavefunctions with deep neural networks
James Spencer
DeepMind, United Kingdom
TBA
Molecular electronics and spintronics as a challenge for first-principles
methods
Carmen Herrmann
University of Hamburg, Germany
Molecular conductance is measured in different experimental setups such as scanning tunneling
microscopes (STMs), molecular break junctions, and nanoparticle arrays [1,2,3]. The motivation behind
these experiments is not only studying potential reproducible nanoscale building blocks for electronics
or spintronics but also learning about molecules under unusual conditions. Using the spin degree of
freedom in such settings offers fascinating options for nanoscale functionality, and also provides new
experimental data for improving our insight into fundamental aspects of nonequilibrium physics at that
scale [3].
Compromises between accuracy and computational feasibility imply that for molecular electronics and
spintronics, a quantitative first-principles description may be elusive. We illustrate the resulting
challenges as well as successes for examples such as chiral induced spin selectivity [4,5,6,7], length-
dependent charge delocalization in molecular wires [8], and structure-property relationships for the
Kondo effect [9,10].
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Botana, J. Caram, G. Celardo, G. Cuniberti, A. Garcia-Etxarri, A. Dianat, I. Diez-Perez, Y. Guo, R. Gutierrez,
C. Herrmann, J. Hihath, S. Kale, P. Kurian, Y. Lai, T. Liu, A. Lopez, E. Medina, V. Mujica, R. Naaman, M.
Noormandipour, J. Palma, Y. Paltiel, W. Petuskey, J. Ribeiro-Silva, J. Saenz, E. Santos, M. Solyanik-Gorgone,
V. Sorger, D. Stemer, J. Ugalde, A. Valdes-Curiel, S. Varela, D. Waldeck, M. Wasielewski, P. Weiss, H.
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Programming methods for implementing coupled-cluster methods on GPUs
Jeff Hammond
NVIDIA, Finland
I will describe useful techniques for implementing coupled-cluster methods, specifically CCSD(T), on
NVIDIA GPUs, in the context of NWChem. The first demonstration will be with semidirect CCSD(T)
module, based on the Kobayashi-Rendell formulation, where we compose dozens of asynchronous
communication operations (both interprocess and host-device) with CUBLAS calls and OpenACC
kernels using streams. The net result is greater than 30 teraflop/s (FP64) on a 4 GPU node for the
Page 8 of 10GPU portion, which is ~24x times faster than the 1 CPU host execution. The second demonstration
will be in the TCE spin-orbital module, where we have implemented the bespoke triples kernels using
CUTENSOR, where we achieve 60-100% of the roofline-limited peak for 27 different tensor
contractions. We will describe how to apply CUTENSOR in other contexts in the quantum many-body
acronym soup. All of the techniques described are supported identically in C/C++ and Fortran, and in
other ways in Python. While the specific APIs are not portable, the algorithm concepts can be
implemented on any platform with equivalent features.
Tensor algorithms and software for quantum chemistry
Edgar Solomonik
University of Illinois at Urbana-Champaign, United States
TBA
Page 9 of 104. Participant list
Organizers
Grüneis, Andreas
TU Wien, Austria
Scheffler, Matthias
Fritz-Haber-Institut der Max-Planck-Gesellschaft, Germany
On-site participants
Bernart, Sarah - KIT, Germany
Cabrera, Adriana - CSIC, Spain
Del Grande, Rafael - Federal University of Rio de Janeiro, Brazil
Ellis, Ben - AWE, United Kingdom
Ethirajan, Sudheesh Kumar - University of California, Davis, United States
Herrmann, Carmen - University of Hamburg, Germany
Hummel, Felix - TU Wien, Austria
Hutter, Juerg - University of Zurich, Switzerland
Irmler, Andreas - Technical University Vienna, Austria
Kim, Jerry - NYU, United States
Kussmann, Jörg - University of Munich (LMU), Germany
Manganas, Dimitrios - MPI für Kohlenforschung, Germany
Meshhal, Moyassar - Universität Rostock, Germany
Moerman, Evgeny - NOMAD Lab Fritz Haber Institute, Germany
Nagy, Peter - Budapest University of Technology and Economics, Hungary
Neme, Natalia - University of Groningen, Netherlands
Ravindra, Pavan - University of Cambridge, United States
Shi, Benjamin - University of Cambridge, United Kingdom
Spencer, James - DeepMind, United Kingdom
Usvyat, Denis - Humboldt University, Germany
Valeev, Edward - Virginia Tech, United States
On-line participants
Avramoppilos, Aggelos - University of Thessaly, Greece
Berkelbach, Timothy - Columbia University, United States
Bishnoi, Bhupesh - National Institute of Advanced Industrial Science and Technology, Japan
Dey, Aditya - University of Rochester, United States
Goswami, Rohit - University of Iceland, Iceland
Gutti, Pavan - Vellore Institute Of Technology ( VIT), Vellore, India
Gutti, Pavan - Vellore Institute of Technology, India
Hammond, Jeff - NVIDIA, Finland
Kumar, Prashant - KU Leuven, Belgium
Menon, Surabhi - Jawaharlal Nehru Centre for Advanced Scientific Research, India
Neufeld, Verena A - Columbia University, United States
R, Rahul - IIT Kharagpur, India
Solomonik, Edgar - University of Illinois at Urbana-Champaign, United States
Ten-No, Seiichiro - Kobe University, Japan
Van Benschoten, William - University of Iowa, United States
Ye, Hong-Zhou - Columbia University, United States
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