Exploration of Reaction Pathways and Chemical Transformation Networks

For the investigation of chemical reaction networks, the identification of all relevant intermediates and elementary reactions is mandatory. Many algorithmic approaches exist that perform explorations efficiently and automatedly. These approaches differ in their application range, the level of completeness of the exploration, as well as the amount of heuristics and human intervention required. Here, we describe and compare the different approaches based on these criteria. Future directions leveraging the strengths of chemical heuristics, human interaction, and physical rigor are discussed.Comment: 48 pages, 4 figure

Recent Developments in Process Digitalisation for Advanced Nanomaterial Syntheses

Digitalisation and industry 4.0 are set to profoundly change the way chemical and materials discovery and development work. The integration of multiple enabling technologies such as flow synthesis, automation, analytics, and real-time reaction control lead to highly efficient, productive, data-driven discovery and synthetic protocols. For instance, the development of flow chemistry enables the fine control and automation of process parameters such as flow rates, temperature, and pressure, which inherently enhances process efficiency. Flow chemistry presents a more sustainable means of manufacturing in terms of waste minimisation, as it enables the integration of synthetic processes with downstream processing. Furthermore, it allows the integration of analytical techniques to provide in situ process monitoring of large amounts of process and product data. The application of Artificial Intelligence (AI) and/or Machine Learning (ML) techniques allows rapid decision making that can optimise existing processes, and it has also been applied in the discovery of novel materials, synthetic pathways and chemicals. All this is contributing to an effective digitalisation of chemical and material synthetic processes from the laboratory to large-scale industrial deployment. This paper presents recent developments in the effective digitalisation of chemical synthetic processes which integrates continuous flow synthesis, analytics and artificial intelligence technologies. Specifically, this paper illustrates the emerging trend of process digitalisation through the advanced syntheses of materials with catalytic, optical and optoelectronic applications

Mechanism Deduction from Noisy Chemical Reaction Networks

We introduce KiNetX, a fully automated meta-algorithm for the kinetic analysis of complex chemical reaction networks derived from semi-accurate but efficient electronic structure calculations. It is designed to (i) accelerate the automated exploration of such networks, and (ii) cope with model-inherent errors in electronic structure calculations on elementary reaction steps. We developed and implemented KiNetX to possess three features. First, KiNetX evaluates the kinetic relevance of every species in a (yet incomplete) reaction network to confine the search for new elementary reaction steps only to those species that are considered possibly relevant. Second, KiNetX identifies and eliminates all kinetically irrelevant species and elementary reactions to reduce a complex network graph to a comprehensible mechanism. Third, KiNetX estimates the sensitivity of species concentrations toward changes in individual rate constants (derived from relative free energies), which allows us to systematically select the most efficient electronic structure model for each elementary reaction given a predefined accuracy. The novelty of KiNetX consists in the rigorous propagation of correlated free-energy uncertainty through all steps of our kinetic analyis. To examine the performance of KiNetX, we developed AutoNetGen. It semirandomly generates chemistry-mimicking reaction networks by encoding chemical logic into their underlying graph structure. AutoNetGen allows us to consider a vast number of distinct chemistry-like scenarios and, hence, to discuss assess the importance of rigorous uncertainty propagation in a statistical context. Our results reveal that KiNetX reliably supports the deduction of product ratios, dominant reaction pathways, and possibly other network properties from semi-accurate electronic structure data.Comment: 36 pages, 4 figures, 2 table

Retrosynthetic reaction prediction using neural sequence-to-sequence models

We describe a fully data driven model that learns to perform a retrosynthetic reaction prediction task, which is treated as a sequence-to-sequence mapping problem. The end-to-end trained model has an encoder-decoder architecture that consists of two recurrent neural networks, which has previously shown great success in solving other sequence-to-sequence prediction tasks such as machine translation. The model is trained on 50,000 experimental reaction examples from the United States patent literature, which span 10 broad reaction types that are commonly used by medicinal chemists. We find that our model performs comparably with a rule-based expert system baseline model, and also overcomes certain limitations associated with rule-based expert systems and with any machine learning approach that contains a rule-based expert system component. Our model provides an important first step towards solving the challenging problem of computational retrosynthetic analysis

Designing materials for electrochemical carbon dioxide recycling

Electrochemical carbon dioxide recycling provides an attractive approach to synthesizing fuels and chemical feedstocks using renewable energy. On the path to deploying this technology, basic and applied scientific hurdles remain. Integrating catalytic design with mechanistic understanding yields scientific insights and progresses the technology towards industrial relevance. Catalysts must be able to generate valuable carbon-based products with better selectivity, lower overpotentials and improved current densities with extended operation. Here, we describe progress and identify mechanistic questions and performance metrics for catalysts that can enable carbon-neutral renewable energy storage and utilization

Software Tools and Approaches for Compound Identification of LC-MS/MS Data in Metabolomics.

The annotation of small molecules remains a major challenge in untargeted mass spectrometry-based metabolomics. We here critically discuss structured elucidation approaches and software that are designed to help during the annotation of unknown compounds. Only by elucidating unknown metabolites first is it possible to biologically interpret complex systems, to map compounds to pathways and to create reliable predictive metabolic models for translational and clinical research. These strategies include the construction and quality of tandem mass spectral databases such as the coalition of MassBank repositories and investigations of MS/MS matching confidence. We present in silico fragmentation tools such as MS-FINDER, CFM-ID, MetFrag, ChemDistiller and CSI:FingerID that can annotate compounds from existing structure databases and that have been used in the CASMI (critical assessment of small molecule identification) contests. Furthermore, the use of retention time models from liquid chromatography and the utility of collision cross-section modelling from ion mobility experiments are covered. Workflows and published examples of successfully annotated unknown compounds are included