Accelerating Reaction Network Explorations with Automated Reaction Template Extraction and Application

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

Accelerating Reaction Network Explorations with Automated Reaction Template Extraction and Application

Autonomously exploringchemical reaction networks withfirst-principlesmethods can generate vast data. Especially autonomous explorationswithout tight constraints risk getting trapped in regions of reactionnetworks that are not of interest. In many cases, these regions ofthe networks are only exited once fully searched. Consequently, therequired human time for analysis and computer time for data generationcan make these investigations unfeasible. Here, we show how simplereaction templates can facilitate the transfer of chemical knowledgefrom expert input or existing data into new explorations. This processsignificantly accelerates reaction network explorations and improvescost-effectiveness. We discuss the definition of the reaction templatesand their generation based on molecular graphs. The resulting simplefiltering mechanism for autonomous reaction network investigationsis exemplified with a polymerization reaction.ISSN:1549-9596ISSN:0095-2338ISSN:1520-514

Chemoton 2.0: Autonomous Exploration of Chemical Reaction Networks

Fueled by advances in hardware and algorithm design, large-scale automated explorations of chemical reaction space have become possible. Here, we present our approach to an open-source, extensible framework for explorations of chemical reaction mechanisms based on the first-principles of quantum mechanics. It is intended to facilitate reaction network explorations for diverse chemical problems with a wide range of goals such as mechanism elucidation, reaction path optimization, retrosynthetic path validation, reagent design, and microkinetic modeling. The stringent first-principles basis of all algorithms in our framework is key for the general applicability that avoids any restrictions to specific chemical systems. Such an agile framework requires multiple specialized software components of which we present three modules in this work. The key module, Chemoton, drives the exploration of reaction networks. For the exploration itself, we introduce two new algorithms for elementary-step searches that are based on Newton trajectories. The performance of these algorithms is assessed for a variety of reactions characterized by a broad chemical diversity in terms of bonding patterns and chemical elements. Chemoton successfully recovers the vast majority of these. We provide the resulting data, including large numbers of reactions that were not included in our reference set, to be used as a starting point for further explorations and for future reference.ISSN:1549-9618ISSN:1549-962

Solvation Free Energies in Subsystem Density Functional Theory

For many chemical processes the accurate description of solvent effects are vitally important. Here, we describe a hybrid ansatz for the explicit quantum mechanical description of solute-solvent and solvent-solvent interactions based on subsystem density functional theory and continuum solvation schemes. Since explicit solvent molecules may compromise the scalability of the model and transferability of the predicted solvent effect, we aim to retain both, for different solutes as well as for different solvents. The key for the transferability is the consistent subsystem decomposition of solute and solvent. The key for the scalability is the performance of subsystem DFT for increasing numbers of subsystems. We investigate molecular dynamics and stationary point sampling of solvent configurations and compare the resulting (Gibbs) free energies to experiment and theoretical methods. We can show that with our hybrid model reaction barriers and reaction energies are accurately reproduced compared to experimental data.ISSN:1549-9618ISSN:1549-962

PH-switchable ampholytic supramolecular copolymers

β-sheet-encoded anionic and cationic dendritic peptide amphiphiles form supramolecular copolymers when self-assembled in a 1:1 feed ratio of the monomers. These ampholytic materials have been designed for on-off polymerization in response to pH triggers. The cooperative supramolecular self-assembly process is switched on at a physiologically relevant pHvalue and can be switched off by increasing or decreasing the pHvalue.</p

Quantum chemical data generation as fill-in for reliability enhancement of machine-learning reaction and retrosynthesis planning

Data-driven synthesis planning has seen remarkable successes in recent years by virtue of modern approaches of artificial intelligence that efficiently exploit vast databases with experimental data on chemical reactions. However, this success story is intimately connected to the availability of existing experimental data. It may well occur in retrosynthetic and synthesis design tasks that predictions in individual steps of a reaction cascade are affected by large uncertainties. In such cases, it will, in general, not be easily possible to provide missing data from autonomously conducted experiments on demand. However, first-principles calculations can, in principle, provide missing data to enhance the confidence of an individual prediction or for model retraining. Here, we demonstrate the feasibility of such an ansatz and examine resource requirements for conducting autonomous first-principles calculations on demand.ISSN:2635-098

The subsystem quantum chemistry program Serenity

SERENITY [J Comput Chem. 2018;39:788-798] is an open-source quantum chemistry software that provides an extensive development platform focused on quantum-mechanical multilevel and embedding approaches. In this study, we give an overview over the developments done in Serenity since its original publication in 2018. This includes efficient electronic-structure methods for ground states such as multilevel domain-based local pair natural orbital coupled cluster and Moller-Plesset perturbation theory as well as the multistate frozen-density embedding quasi-diabatization method. For the description of excited states, SERENITY features various subsystem-based methods such as embedding variants of coupled time-dependent density-functional theory, approximate second-order coupled cluster theory and the second-order algebraic diagrammatic construction technique as well as GW/Bethe-Salpeter equation approaches. SERENITY's modular structure allows combining these methods with density-functional theory (DFT)-based embedding through various practical realizations and variants of subsystem DFT including frozen-density embedding, potential-reconstruction techniques and projection-based embedding.This article is categorized under:Electronic Structure Theory > Density Functional TheoryElectronic Structure Theory > Ab Initio Electronic Structure MethodsSoftware > Quantum ChemistryISSN:1759-0876ISSN:1759-088

qcserenity/serenity: Release 1.5.3

&lt;h2&gt;Release 1.5.3 (25.10.2023)&lt;/h2&gt; &lt;h3&gt;Functionalities&lt;/h3&gt; &lt;ul&gt; &lt;li&gt;Added two flavors of restricted open-shell HF and KS for the ground-state (Niklas Niemeyer)&lt;/li&gt; &lt;li&gt;Fermi-shifted Huzinaga EO Kernel for subsystem TDDFT (Niklas Niemeyer)&lt;/li&gt; &lt;li&gt;Laplace-Transform GW (Johannes Tölle, Niklas Niemeyer)&lt;/li&gt; &lt;li&gt;Renamed ReadOrbitalsTask to OrbitalsIOTask (Niklas Göllmann)&lt;/li&gt; &lt;li&gt;Added the functionality to write Turbomole files (Niklas Göllmann)&lt;/li&gt; &lt;li&gt;Added the functionality to write Molden files for both spherical and cartesian harmonics (Niklas Göllmann)&lt;/li&gt; &lt;li&gt;Added three schemes to generate complete basis function products for the Cholesky decomposition framework: Simple, First, Complete (Lars Hellmann)&lt;/li&gt; &lt;li&gt;Added the functionality to control density fitting for individual contributions (Coulomb, exchange, long-range exchange, correlation)&lt;/li&gt; &lt;/ul&gt