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Thinking outside the box - beyond machine learning for quantum chemistry

Location : CECAM-DE-MM1P
October 7, 2019 – October 11, 2019

The field of machine learning (ML) is already making rapid and tremendous impact at the interfaces of the traditional disciplines of Chemistry, Physics, Biology and materials science. Its ability to use existing examples to rapidly make meaningful predictions in new cases offers a new way to screen wide ranges of structures and to estimate the results of highly accurate methods at much reduced cost. However, there are several issues which require careful thought in deploying these tools. Firstly, reproducibility in the training of models is a current topic of active debate receiving substantial attention and within the last year calls for more physical based approaches are beginning to appear. Then issues of the explainability and explicability of the predictions also matter, particularly with some of the more powerful ML methods. Finally there are problems with additivity to models: learning new cases tends to overwrite existing expertise and predicting properties and responses outside of the original model are not usually possible.
A counterpoint to these methods is the experiences of the past 20 years with approximate quantum mechanical methods [1], which now represent an essential part of computational tools for a solid atomistic understanding of a broad range of physical, chemical and biological problems for both large and challenging systems. These methods are parameterized, but can provide a clear physical understanding of complex structures and processes. Additionally, they can readily be extended to calculate properties and systems outside of their original parameters and fitting sets. However, this commonly comes at the cost of substantial Human effort to parameterize and test these models, providing substantial opportunities for ML.

DFTB
The DFTB approach provides modular components within other academic and/or commercial software products, including DFTB+[19], ADF [20], ATK [21], DeMon [22], Gaussian [23] and Materials Studio [24], and several MM-force fields tools, eg. CHARMM [25]. This considerably enhances the spreading of the method to potential applicants in both academic settings and in the R&D of industrial companies. Overviews of some of the range of DFTB developments and extensions in the species issues of the Journal of Physical Chemistry A 111, Number 26 (2007) and Physica Status Solidi b 249, Issue 2 (2012).
The most recent DFTB developers meeting was in November 2016 to report and discuss the present status of DFTB developments in the different software products and to join forces for further improvements in accuracy, parameterization of new systems and extensions of functionality.
Trends in Machine Learning
The Journal of Chemical Physics has recently invited a special issue on “Data-enabled theoretical chemistry” which provides a comprehensive contemporary view on the field with over 40 contributions from leading scientists actively working on the integration of modern machine learning techniques into quantum chemistry [26]. The issue was motivated by preceding successes in the field such as the systematic fitting of potential energies for molecular dynamics simulations or vibrational spectroscopy [27,28]. As also reviewed recently [29], laws of Physics have been rediscovered with ML [30], atomization energies and other electronic ground-state properties of organic molecules can now be predicted with hybrid DFT accuracy [31], and clusters can be identified [32] and compounds mapped [33]. ML can also be used to discover new molecules [34] or crystals [35], and even new reactions [36]. Various properties and systems have been studied with ML, including electrons [37], chemical potentials [38], ionic forces [39], or NMR shifts [40]. By now, neural networks and Gaussian processes have demonstrably surpassed DFT accuracy when it comes to the prediction of electronic ground-state properties of organic materials [41]. Efforts to further improve and assess ML models for their application throughout compositional space are ongoing [42]. When it comes to the improvement of well established QM methods, however, ML based investigations, such as in Refs. [43], are sparse.

 

References

References
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[3] Q. Cui, M. Elstner, T. Frauenheim, M. Karplus et al., J. Phys. Chem. B 105 (2001) 569.
[4] A. Dominguez, B. Aradi, T. Frauenheim, V. Lutsker, T. A. Niehaus, J. Chem. Theory Comput. 9 (2013) 4901.
[5] C. Koehler, G. Seifert, T. Frauenheim, Chem. Phys. 309 (2005) 23.
[6] C. Köhler, Th. Frauenheim, B Hourahine et al., J. Phys. Chem. A 111 (2007) 5622.
[7] T. A. Niehaus, S. Suhai, F. Della Sala, P. Lugli et al., Phys. Rev. B, 63 (2001) 085108.
[8] T. A. Niehaus, J. Mol. Str. THEOCHEM, 914 (2009) 38.
[9] M. Elstner, P. Hobza, T. Frauenheim et al., J. Chem. Phys., 114 (2001) 5149.
[10] B. Hourahine, S. Sanna, B. Aradi, C. Koehler, T.A. Niehaus, T. Frauenheim, J. Phys. Chem. A 111 (2007) 5671. 
[11] JG. Hou, X. Zhu and Q. Cui. Chem. Theory Comput. 6 (2010) 2303.
[12] J. Reimers, G. Solomon, A. Gagliardi, et.al., J. Phys. Chem. A 111 (2007) 5692.
[13] A. Dominguez, B. Aradi, T. Frauenheim, V. Lutsker, T. A. Niehaus, J. Chem. Theory Comput. 9 (2013) 4901.
[14] M. Wahiduzzaman, A. F. Oliveira, P. Philipsen, L. Zhechkov, E. van Lenthe, H. A. Witek, T. Heine. J. Chem. Theory Comput. 9 (2013) 4006.
[15] J. M. Knaup, B. Hourahine and Th. Frauenheim J. Phys. Chem. A 111 (2007) 5637; M. Gaus, C.-P. Chou, H. Witek, M. Elstner J. Phys. Chem. A 113 (2009) 11866; Z. Bodrog, B. Aradi and T. Frauenheim J. Chem. Theory Comput. 7 (2011) 2654; M. Doemer, E. Liberatore, J. M. Knaup, I. Tavernelli, U. Rothlisberger Molecular Physics 111 (2013) 3595; M. P. Lourenço, M. C. da Silva, A. F. Oliveira, M. C. Quintão, H. A. Duarte Theoretical Chem. Accounts 135 (2016) 11; C.-P. Chou, Y. Nishimura, C.-C. Fan, G. Mazur, S. Irle, H. A. Witek J. Chem. Theory Comput. 12 (2016) 53.
[16] J. J. Kranz, M. Kubillus, R. Ramakrishnan, O. A. von Lilienfeld , and M. Elstner J. Chem. Theory Comput. 14 (2018) 2341.
[17] A. W. Huran, C. Steigemann, T. Frauenheim, B. Aradi, and M. A. L. Marques J. Chem. Theory Comput. 14 (2018) 2947.
[18] L. Shen abd W. Yang J. Chem. Theory Comput. 14 (2018) 1442.
[19] https://www.dftb.org
[20] https://www.scm.com/product/adf/ 
[21] http://www.quantumwise.com/documents/tutorials/ATK-11.8/DFTB/index.html/
[22] http://demon-nano.ups-tlse.fr/
[23] http://www.gaussian.com/g_tech/g_ur/k_dftb.htm
[24] http://accelrys.com/products/materials-studio/quantum-and-catalysis-software.html
[25] http://www.charmm.org/documentation/c37b1/sccdftb.html
[26] J. Chem. Phys, volume 148, issue 24 (2018).
[27] J. Behler and M. Parrinello, Phys. Rev. Lett. 98 (207) 146401.
[28] A. P. Bartok, M. C. Payne, R. Kondor, and G. Csanyi, Phys. Rev. Lett. 104 (2010)
136403. 
[29] O. A. von Lilienfeld, Angew. Chem. Int. Ed. 57 (2018) 4164.
[30] M. Schmidt and H. Lipson, Science 324 (2009) 81.
[31] M. Rupp, A. Tkatchenko, K.-R. Mueller, and O. A. von Lilienfeld, Phys. Rev. Lett. 108 (2012) 058301; G. Montavon, M. Rupp, V. Gobre, A. Vazquez-Mayagoitia, K. Hansen, A. Tkatchenko, K-R. Mueller, O. A. von Lilienfeld, New J. Phys. 15 (2013) 095003.
[32] A. Rodriguez and A. Laio, Science 344 (2014) 1492.
[33] S. De, F. Musil, T. Ingram, C. Baldauf, and M. Ceriotti, J. Cheminf. 9 (2017) 6.
[34] E. O. Pyzer-Knapp, K. Li, and A. Aspuru-Guzik, Adv. Fun. Mat. 25 (2015) 6495.
[35] F. A. Faber, A. Lindmaa, O. A. von Lilienfeld, and R. Armiento, Phys. Rev. Lett. 117
(2016) 135502.
[36] P. Raccuglia, K. C. Elbert, P. D. F. Adler, C. Falk, M. B. Wenny, A. Mollo, M. Zeller, S. A. Friedler, J. Schrier, and A. J. Norquist, Nature 533 (2016) 73.
[37] G. Carleo and M. Troyer, Science 355 (2017) 602.
[38] K. T. Schutt, F. Arbabzadah, S. Chmiela, K. R. Muller, and A. Tkatchenko, Nat. Commun. 8 (2017) 13890.
[39] S. Chmiela, A. Tkatchenko, H. E. Sauceda, I. Poltavsky, K. T. Schutt, and K.-R. Muller,
Sci. Adv. 3 (2017) e1603015.
[40] M. Rupp, R. Ramakrishnan, O. A. von Lilienfeld, J. Phys. Chem. Lett. 6 3309 (2015); F.
M. Paruzzo, A. Hofstetter, F. Musil, S. De, M. Ceriotti, L. Emsley,
https://arxiv.org/abs/1805.11541 (2018).
[41] F. A. Faber, L. Hutchison, B. Huang, J. Gilmer, S. S. Schoenholz, G. E. Dahl, O. Vinyals, S. Kearnes, P. F. Riley, and O. A. von Lilienfeld, J. Chem. Theory Comput., 13 (2017)
5255.
[42] B. Huang and O. A. von Lilienfeld, arXiv preprint arXiv:1707.04146 (2017); F. Faber, A. Christensen, B. Huang, O. A. von Lilienfeld, J. Chem. Phys. 148 (2018) 241717; K. T. Schuett, H. E. Sauceda, P.-J. Kindermans, A. Tkatchenko, and K.-R. Mueller, J. Chem. Phys. 148 (2018) 241722.
[43] R. Ramakrishnan, P. O. Dral, M. Rupp, O. A. von Lilienfeld, J. Chem. Theory Comput. 11 (2015) 2087; P. Dral, O. A. von Lilienfeld, W. Thiel, J. Chem. Theory Comput. 11 (2015)
2120; T. Bereau, R. A. DiStasio, A. Tkatchenko, O. A. von Lilienfeld, J. Chem. Phys. 148
(2018) 241706.

List of confirmed speakers

China

Guanhua Chen (Hongkong University, Hongkong)
Chiyung Yam (Beijing Computational Science Research Center)

Denmark
Bjork Hammer (Aarhus University)

Finland
Patrick Rinke (Aalto University, Helsinki)

France
Maxime Van den Bossche (Sorbonne University, Paris)

Germany
Tristian Bereau (Max Planck Institute for Polymer Research, Mainz)
Franco Bonafé (Max Planck Institute for the Structure and Dynamics of Matter, Hamburg)
Julian Gebhardt (Fraunhofer Institute for Mechanics of Materials, Freiburg)
Stefan Grimme (University of Bonn)
Roland Mitric (University of Wuerzburg)
Karsten Reuter (Munich University of Technology)
Gotthard Seifert (Technical University of Dresden)

Japan
Hiromi Nakai (Waseda University, Shinjuku)

Luxembourg
Alexandre Tkatchenko (University of Luxembourg)

Sweden
Rickard Armiento (Lynköping University)
Jolla Kullgren (University of Uppsala)

Switzerland
Anatole von Lilienfeld (University of Basel)
Ursula Roethlisberger (Swiss Federal Institute of Technology, Lausanne)

United Kingdom
Ben Hourahine (University of Strathclyde, Glasgow)
Stefano Leoni (Universtiy of Cardiff)

United States
Kipton Barros (Los Alamos National Laboratory)
Volker W. Blum (Duke University, Durham)
Qiang Cui (Boston University)
Nir Goldman (Lawrence Livermore National Laboratory)
Stephan Irle (Oak Ridge National Laboratory)
Olexandre Isayev (University of North Caroline, Chapel Hill)
Jacek Jakowski (Oak Ridge National Laboratory)
Ben T. Nebgen (Los Alamos National Laboratory)
Anders M. N. Niklasson (Los Alamos National Laboratory)
Sergei Tretiak (Los Alamos National Laboratory)
Weitao Yang (Duke University, Durham)
David Yaron (Carnegie Mellon University Pittsburgh)

 

outbox_program

Conference site:  House of Science, Downtown

Monday, October 7th 2019 (Radisson Blu Hotel Bremen)
18:00 21:00 Registration
Tuesday, October 8th 2019 (House of Science Bremen, Downtown)
08:00 08:50 Registration
08:50 09:00 Opening and welcome, Thomas Frauenheim
Session: Machine Learning for Complex Quantum Systems
Chair: Sheng Meng
09:00 09:40 Anatole von Lilienfeld, University of Basel, Switzerland Quantum machine learning
09:40 10:20 Tristan Bereau, Max Planck Institute for Polymer Research, Mainz, Germany Modeling intermolecular interactions with physics and ML
10:20 10:50 Coffee Break
10:50 11:30 Karsten Reuter, Munich University of Technology, Germany Knowledge-based approaches is catalysis and energy modelling
11:30 12:10 Hiromi Nakai, Waseda University, Shinjuku, Japan Semi-local machine-learned kinetic energy density functional with third-order gradients of electron density
12:10 13:50 Lunch Break (Restaurant Q1) and Coffee
13:50 14:30 Guanhua Chen, University of Hong Kong, China Deep learnt exchange-correlation potential
Session: Machine Learning for Structure Prediction
Chair: Thomas Niehaus
14:30 15:10 Bjørk Hammer, Aarhus University, Denmark Speeding up atomistic structure search with machine learning
15:10 15:50 Rickard Armiento, Lynköping University, Sweden Machine learning for materials stability
15:50 16:20 Coffee Break
16:20 17:00 Stefano Leoni, University of Cardiff, UK ML in multiple timescale molecular dynamics simulations
17:00 17:40 Jacek Jakowski, Oak Ridge National Laboratory, Tennessee, USA Directed transformations of nanomaterials and beam-matter interactions

19:00

21:30

 

  Welcome Reception (Bremen Town Hall)

Wednesday, October 9th 2019 (House of Science Bremen, Downtown)

Session:

 

Machine Learning for DFTB repulsive interactions (I)

 

 

 

 

Chair: Malte Schüler

08:30

09:10

 

Maxime Van den Bossche, Sorbonne University, Paris, France
Accelerating global optimization searches with an adaptive DFTB parametrization scheme

09:10

09:50

 

David Yaron, Carnegie Mellon University, Pittsburgh,
Pennsylvania, USA
A DFTB layer for deep learning of electronic hamiltonians

09:50

10:30

 

Nir Goldman, Lawrence Livermore National Laboratory,
California, USA
Combining ML approaches with DFTB for simulations of reactive
materials

10:30

11:00

 

Coffee Break

11:00

11:40

 

Benjamin Hourahine, University of Strathclyde, Glasgow, UK
Learning around the DFTB model

11:40

12:20

 

Stefan Grimme, University of Bonn, Germany
New tight-binding quantum chemistry methods

12:20

14:00

 

Lunch Break (Restaurant Q1) and Coffee

Session:

 

Machine Learning and MD

 

 

 

 

Chair: Gabriela Penazzi

14:00

14:40

 

Kipton Barros, Los Alamos National Laboratory, New Mexico, USA Advances in machine learned potentials for molecular dynamics simulation

14:40

15:20

 

Weitao Yang, Duke University Durham, North Carolina, USA
Machine learning in simulations and force fields with quantum mechanics/molecular mechanics and in DFT

15:20

16:00

 

Stephan Irle, Oak Ridge National Laboratory, Tennessee, USA
Neural network corrected DFTB/MD simulations of long-timescale
self-assembly and transport processes

16:00

16:30

 

Coffee Break

16:30

17:10

 

Roland Mitric, University of Wuerzburg, Germany
Simulation of light-induced nonadiabatic dynamics in molecular aggregates

17:10

17:50

 

Franco Bonafé, Max Planck Institute for the Structure and Dynamics
of Matter, Hamburg, Germany
Simulations of impulsive vibrational spectra using Ehrenfest real-time TDDFTB

19:00

22:30

 

Conference Dinner (Restaurant Juergenshof)

Thursday, October 10th 2019 (House of Science Bremen, Downtown)

 

Session:

 

Machine Learning for Quantum Chemistry & Electronic Structure

 

 

 

 

 

Chair: Alessandro Pecchia

 

08:30

09:10

 

Benjamin Nebgen, Los Alamos National Laboratory, New Mexico, USA
Hückel theory resurrected: dynamic parameterization of effective hamiltonians using deep learning

 

09:1010:50 
Volker Blum, Duke University, Durham, North Carolina, USA
The ELSI infrastructure
 
 

09:50

10:30

 

Ursula Roethlisberger, Swiss Federal Institute of Technology,
Lausanne, Switzerland
Computational Chemistry Meets Artificial Intelligence

 

10:30

11:00

 

Coffee Break

 

11:00

11:40

 

Julian Gebhardt, Fraunhofer Institute for Mechanics of Materials, Freiburg, Germany
Big data approach for next level hybrid perovskite solar cells

 

11:40

12:20

 

Alexandre Tkatchenko, University of Luxembourg, Luxembourg
Towards exact molecular dynamics simulations with quantum chemistry and machine learning

 

12:20

14:00

 

Lunch Break (Restaurant Q1) and Coffee

 

Session:

 

Machine Learning for DFTB repulsive interactions (II)

 

 

 

 

 

Chair: Cristopher Camacho

 

14:00

14:40

 

Chiyung Yam, Beijing Computational Science Research Center, China
Theoretical investigation of current-induced light emission in scanning tunneling microscopy molecular junctions

 

14:40

15:20

 

Qiang Cui, Boston University, Massachusetts, USA
Improvement of DFTB model for condensed phase simulations

 

15:20

16:00

 

Jolla Kullgren, University of Uppsala, Sweden
Physically constrained splines – a step towards transferable repulsive potentials for SCC-DFTB

 

17:20

 

 

 

Poster Mounting

 

17:30

20:30

 

Poster Session, Catering Buffet (House of Science)

 

Friday, October 11th 2019 (House of Science Bremen, Downtown)

 

Session:

ML for electronic and spectroscopic properties

 

 

 

 

 

Chair: Balint Aradi

 

08:30

09:10

 

Patrick Rinke, Aalto University, Helsinki, Finland
ARTIST: artificial intelligence for spectroscopy

 

09:10

09:50

 

Olexandre Isayev, University of North Carolina, Chapel Hill, USA
Accurate and transferable multitask prediction of chemical properties with an atoms-in-molecule neural network

 

09:50

10:30

 

Anders Niklasson, Los Alamos National Laboratory, New Mexico, USA
Graph-based linear scaling electronic structure theory

 

10:30

11:00

 

Coffee Break

 

11:00

11:40

 

Sergei Tretiak, Los Alamos National Laboratory, New Mexico, USA
Multiple cloning and polaritons in excited state non-adiabatic molecular dynamics

 

11:40

12:20

 

Gotthard Seifert, Technical University of Dresden, Germany
Bridging scales in materials simulations – quantum versus
classical simulations

 

12:20

12:25

 

Closing words: Thomas Frauenheim

 

12:25

 

 

 

Departure

 

To apply please click here