Quantum Chemistry

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Thinking outside the box – beyond machine learning for quantum chemistry – CECAM

Share Flipboard Email. Helmenstine holds a Ph. She has taught science courses at the high school, college, and graduate levels. Updated August 09, Key Takeaways: Quantum Definition In chemistry and physics, quantum refers to a single packet of matter or energy.

Quantum Chemistry

In practical use, it refers to the minimum amount of energy required for a change or the minimum value of any physical property in an interaction. Quantum is the singular form of the word. 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.

What Quantum Really Means in Science

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. The most recent DFTB developers meeting was in November 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.

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].

Quantum Mechanics and the Schrodinger Equation

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. References [1] H.

Table of Contents

Senn and W. Thiel, Top. Elstner, D. Porezag, G. Jungnickel, J. Elsner, Phys. B, 58 Cui, M. Elstner, T. Frauenheim, M. Karplus et al. B Dominguez, B. Aradi, T.

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Frauenheim, V. Lutsker, T. Niehaus, J. Theory Comput.

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Koehler, G. Seifert, T. Frauenheim, Chem. Frauenheim, B Hourahine et al. A Niehaus, S. Suhai, F. Della Sala, P. Lugli et al. B, 63 Elstner, P. Hobza, T.

Submission history

Frauenheim et al. Hourahine, S. Sanna, B.

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