Accelerating Reaction Network Explorations with Automated Reaction Template Extraction and Application

Abstract

Autonomously exploring chemical reaction networks with first-principles methods can generate vast data. Especially autonomous explorations without tight constraints risk getting trapped in regions of reaction networks that are not of interest. In many cases, these regions of the networks are only exited once fully searched. Consequently, the required human time for analysis and computer time for data generation can make these investigations unfeasible. Here, we show how simple reaction templates can facilitate the transfer of chemical knowledge from expert input or existing data into new explorations. This process significantly accelerates reaction network explorations and improves cost-effectiveness. We discuss the definition of the reaction templates and their generation based on molecular graphs. The resulting simple filtering mechanism for autonomous reaction network investigations is exemplified with a polymerization reaction.

A human-machine interface for automatic exploration of chemical reaction networks

“…Kinetic modeling can even be exploited for taming the combinatorial explosion of reactive events 12 , 69 . The number of reactive sites may also be controlled by various heuristic rules, such as first-principles heuristics that exploit properties of the wavefunction or electron density 17 , 70 , 71 , graph-based rules in combination with known reactivity 72 , or electronegativity-based polarization rules, where, for example, hydrogen is considered active when bound to oxygen 60 , 69 .…”

“…The modular infrastructure of SCINE in general, SCINE CHEMOTON in particular, and of our STEERING WHEEL algorithm form a suitable basis for further extensions of individual parts of automated workflows, such as more advanced Network Expansion Steps (e.g., reaction trials featuring multiple electronic structure models 31 , systematic network refinement with more accurate electronic structure methods 68 or with automated microsolvation approaches 178 – 181 , or more exhaustive conformer generation 174 , 182 – 184 ). Moreover, inclusion of Selection Steps that do not rely on human input, such as general heuristics derived from first principles 71 , results from existing explorations 72 , machine learning 185 , path information 12 , or kinetic simulations 69 , 186 – 192 is straightforward and will further enhance the capabilities of the STEERING WHEEL, which has been implemented into our graphical user interface SCINE HERON, which is available free of charge and open source.…”

Exploring Chemical Reaction Space with Machine Learning Models: Representation and Feature Perspective

“…For example, to sample more TS states in a limited amount of time, the temperature is adjusted for the TS exploration, and Monte Carlo simulated annealing is also applied to get the next stage of the reaction by accelerating the conformation changes. Other methods including applying templates for iteration and employing techniques to effectively explore the landscape for identifying TS without screening the entire space are also developed. One of the most popular software packages for using the idea of CRN to predict mechanisms is the Reaction Mechanism Generator (RMG) developed by the Green group from MIT. , By utilizing a template elementary reaction database and thermochemical and kinetic parameters, RMG can generate reaction mechanisms for multiple species and predict the kinetics properties. , …”

Exploring Chemical Space Using Ab Initio Hyperreactor Dynamics

“…The common principle of all first-principles chemical reaction space exploration methods is very simple: exhaustively investigate the mechanistic paths available to a set of initial compounds by simulating the system’s dynamics on its potential energy surface (PES) and construct the corresponding reaction network connecting all found intermediates and products by adjacent reaction paths . While first attempts to explore the vastness of the chemical reaction space by computational means relied on reducing the multidimensional problem of chemical transformations to a two-dimensional matrix representation paired with heuristic concepts, , recent methodology − leverages the power of available data science approaches, such as machine learning and neural networks, − as well as efficiently exploits modern computer hardware. − …”