Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning
Marcel F. Langer, Alex Goeßmann, Matthias Rupp
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Abstract Computational study of molecules and materials from first principles is a cornerstone of physics, chemistry, and materials science, but limited by the cost of accurate and precise simulations. In settings involving many simulations, machine learning can reduce these costs, often by orders of magnitude, by interpolating between reference simulations. This requires representations that describe any molecule or ...
material and support interpolation. We comprehensively review and discuss current representations and relations between them. For selected state-of-the-art representations, we compare energy predictions for organic molecules, binary alloys, and Al–Ga–In sesquioxides in numerical experiments controlled for data distribution, regression method, and hyper-parameter optimization.
Results & Benchmarks
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Abstract Computational study of molecules and materials from first principles is a cornerstone of physics, chemistry, and materials science, but limited by the cost of accurate and precise simulations.
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Evidence graph: 2 refs, 1 links.
Utility signals: depth 80/100, grounding 58/100, status medium.
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Research context
138
Citations
199
References
Tasks
Interpolation (computer graphics), Computer science, Binary number, Quantum, Theoretical computer science, Computational science, Materials Science, Physical Sciences
Methods
Algorithm
Domains
Statistical physics, Materials Chemistry
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