Error-Controlled Exploration of Chemical Reaction Networks with Gaussian Processes.
Gregor N. Simm,Markus Reiher +1 more
TLDR
A new approach is presented that allows for the systematic, problem-oriented, and rolling improvement of quantum chemical results through the application of Gaussian processes, due to its Bayesian nature.Abstract:
For a theoretical understanding of the reactivity of complex chemical systems, relative energies of stationary points on potential energy hypersurfaces need to be calculated to high accuracy. Due to the large number of intermediates present in all but the simplest chemical processes, approximate quantum chemical methods are required that allow for fast evaluations of the relative energies but at the expense of accuracy. Despite the plethora of benchmark studies, the accuracy of a quantum chemical method is often difficult to assess. Moreover, a significant improvement of a method’s accuracy (e.g., through reparameterization or systematic model extension) is rarely possible. Here, we present a new approach that allows for the systematic, problem-oriented, and rolling improvement of quantum chemical results through the application of Gaussian processes. Due to its Bayesian nature, reliable error estimates are provided for each prediction. A reference method of high accuracy can be employed if the uncertaint...read more
Citations
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Automated exploration of the low-energy chemical space with fast quantum chemical methods
TL;DR: An efficient scheme for the in silico sampling for parts of the molecular chemical space by semiempirical tight-binding methods combined with a meta-dynamics driven search algorithm is proposed and discussed, opening many possible applications in modern computational chemistry and drug discovery.
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Machine learning for interatomic potential models
TL;DR: An overview of three emerging approaches to developing machine-learned interatomic potential models that have not been extensively discussed in existing reviews: moment tensor potentials, message-passing networks, and symbolic regression are included.
Journal ArticleDOI
Quantum Machine Learning in Chemical Compound Space.
TL;DR: The case is made for quantum machine learning: An inductive molecular modeling approach which can be applied to quantum chemistry problems.
Journal ArticleDOI
A quantitative uncertainty metric controls error in neural network-driven chemical discovery
TL;DR: In this paper, the authors introduce the distance to available data in the latent space of a neural network ML model as a low-cost, quantitative uncertainty metric that works for both inorganic and organic chemistry.
Journal ArticleDOI
Exploration of Reaction Pathways and Chemical Transformation Networks
TL;DR: The different algorithmic approaches for the investigation of chemical reaction networks differ in their application range, the level of completeness of the exploration, and the amount of heuristics and human intervention required.
References
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