Efficient Generation of Stable Planar Cages for Chemistry

Barth, Dominique; David, Olivier; Quessette, Franck; Reinhard, Vincent; Strozecki, Yann; Vial, Sandrine

doi:10.1007/978-3-319-20086-6_18

Cited by 11 publications

(7 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, mathematicians have developed an algorithm for generating maps of 'stable planar cages', where they did not restrict themselves to 2-component systems and generated >400 000 unique maps. 51 The majority of these maps are too complex to be feasible with organic chemistry and thus our limited number of plausible topologies is more practical. However, the topologies we discuss can still be used to describe the underlying connectivity in a multicomponent system and indeed, multiple components may be an attractive design approach for the synthesis of some of the lower symmetry topologies.…”

Section: Enumeration Of Organic Cage Topologiesmentioning

confidence: 99%

Topological landscapes of porous organic cages

et al. 2017

View full text Add to dashboard Cite

We define a nomenclature for the classification of porous organic cage molecules, enumerating the 20 most probable topologies, 12 of which have been synthetically realised to date. We then discuss the computational challenges encountered when trying to predict the most likely topological outcomes from dynamic covalent chemistry (DCC) reactions of organic building blocks. This allows us to explore the extent to which comparing the internal energies of possible reaction outcomes is successful in predicting the topology for a series of 10 different building block combinations.

show abstract

Section: Enumeration Of Organic Cage Topologiesmentioning

confidence: 99%

Topological landscapes of porous organic cages

et al. 2017

View full text Add to dashboard Cite

show abstract

“…From a technical point of view, a typical way to uniquely define equivalent patterns would be to consider only the minimum equivalent pattern, using a lexicographic order for example, 58 each time a pattern is identified. Such an approach becomes quickly computationally expensive, especially since pattern detection is constantly performed in a UCT+DoE run.…”

Section: ) Of the Chosen Geometry Gmentioning

confidence: 99%

Replacing Chemical Intuition by Machine Learning: a Mixed Design of Experiments - Reinforcement Learning Approach to the Construction of Training Sets for Model Hamiltonians

Staub

Steinmann

2021

Preprint

View full text Add to dashboard Cite

Model Hamiltonians based on the so-called cluster expansion (CE), which consist of a linear fit of parameters corresponding to geometric patterns, provide an efficient and rigorous means to quickly evaluate the energy of diverse arrangements of adsorbate mixtures on reactive surfaces as typically relevant for heterogeneous catalysis. However, establishing the model Hamiltonian is a tedious task, requiring the construction and optimization of many geometries. Today, most of these geometries are constructed by hand, based on chemical intuition or random choices. Hence, the quality of the training set is unlikely to be optimal and its construction is not reproducible. Herein, we propose a reformulation of the construction of the training set as a strategy-based game, aiming at an efficient exploration of the relevant patterns constituting the model Hamiltonian. Based on this reformulation, we exploit a typical active learning solution for machine-learning such a strategy game: an upper confidence tree (UCT) based framework. However, in contrast to standard games, evaluating the true score is computationally expensive, as it requires a costly geometry optimization. Hence, we augment the UCT with a pre-exploration step inspired by the variance-based Design of Experiments (DoE) methods. This novel mixed UCT+DoE framework allows to automatically construct a well adapted training set, minimizing computational cost and user-intervention. As a proof of principle, we apply our UCT+DoE approach on the CO oxidation reaction on Pd(111), for which a relevant model Hamiltonian has been established previously. The results demonstrate the effectiveness of the custom built UCT and its significant benefits on a DoE-based approach.

show abstract

“…Easier to obtain or to predict than 3D-graphs, 2D-graphs already carry information on the structure, the functional properties and even the 3D shape of the molecules they model. Examples include the classification of similar molecules according to their topology [25,26], the prediction of patterns in biological molecules [27], the prediction of the 3D structure of small molecules [28], etc. Molecular graphs are also commonly used in supervised machine learning algorithms, the framework of graph-based models for molecules is indeed naturally suited for carrying out predictions in message-passing neural network schemes.…”

Section: Introductionmentioning

confidence: 99%

Algorithmic graph theory, reinforcement learning and game theory in MD simulations: from 3D-structures to topological 2D-MolGraphs and backwards

Bougueroua

Bricage

Aboulfath

et al. 2022

Preprint

View full text Add to dashboard Cite

This paper reviews graph theory-based methods that were recently developed in our group for post-processing molecular dynamics trajectories. We show that the use of algorithmic graph theory not only provides a direct and fast methodology to identify conformers sampled over time but also allows to follow the interconversions between the conformers through graphs of transitions in time. Examples of gas phase molecules and inhomogeneous aqueous solid interfaces are presented to demonstrate the power of topological 2D graphs and their versatility for post-processing molecular dynamics trajectories. An even more complex challenge is to predict 3D structures from topological 2D graphs. Our first attempts to tackle such a challenge are presented with the development of game theory and reinforcement learning methods for predicting the 3D structure of a gas-phase peptide.

show abstract

Efficient Generation of Stable Planar Cages for Chemistry

Cited by 11 publications

References 17 publications

Topological landscapes of porous organic cages

Topological landscapes of porous organic cages

Replacing Chemical Intuition by Machine Learning: a Mixed Design of Experiments - Reinforcement Learning Approach to the Construction of Training Sets for Model Hamiltonians

Algorithmic graph theory, reinforcement learning and game theory in MD simulations: from 3D-structures to topological 2D-MolGraphs and backwards

Contact Info

Product

Resources

About