Putting Chemical Knowledge to Work in Machine Learning for Reactivity

Machine learning has been used to study chemical reactivity for a long time in fields such as physical organic chemistry, chemometrics and cheminformatics. Recent advances in computer science have resulted in deep neural networks that can learn directly from the molecular structure. Neural networks are a good choice when large amounts of data are available. However, many datasets in chemistry are small, and models utilizing chemical knowledge are required for good performance. Adding chemical knowledge can be achieved either by adding more information about the molecules or by adjusting the model architecture itself. The current method of choice for adding more information is descriptors based on computed quantum-chemical properties. Exciting new research directions show that it is possible to augment deep learning with such descriptors for better performance in the low-data regime. To modify the models, differentiable programming enables seamless merging of neural networks with mathematical models from chemistry and physics. The resulting methods are also more data-efficient and make better predictions for molecules that are different from the initial dataset on which they were trained. Application of these chemistry-informed machine learning methods promise to accelerate research in fields such as drug design, materials design, catalysis and reactivity

Revisiting the superaromatic stabilization energy as a local aromaticity index for excited states

The graph theory of aromaticity is a useful framework for analyzing the aromatic properties of polycyclic aromatic hydrocarbons. We present here the magnetically based superaromatic stabilization energy (m-SSE) as a local aromaticity index and validate it against its topologically based counterpart t-SSE. By comparing to DFT-computed aromaticity indices of triplet state excited polybenzenoid hydrocarbons, we find that semi-quantitative agreement can be reached by using the variable (Formula presented.) method to account for bond-length alternation. The m-SSE can further be separated into orbital and circuit contributions to gain insight into the basis of the aromatic properties of individual rings. Fully automated algorithms for calculating both m-SSE and t-SSE are now available in the COULSON package. We envision that these graph theoretical aromaticity indices will be of great use to the community to analyze the local aromaticity of excited states

Reaction profiles for quantum chemistry-computed [3 + 2] cycloaddition reactions

Bio-orthogonal click chemistry based on [3 + 2] dipolar cycloadditions has had a profound impact on the field of biochemistry and significant effort has been devoted to identify promising new candidate reactions for this purpose. To gauge whether a prospective reaction could be a suitable bio-orthogonal click reaction, information about both on- and off-target activation and reaction energies is highly valuable. Here, we use an automated workflow, based on the autodE program, to compute over 5000 reaction profiles for [3 + 2] cycloadditions involving both synthetic dipolarophiles and a set of biologically-inspired structural motifs. Based on a succinct benchmarking study, the B3LYP-D3(BJ)/def2-TZVP//B3LYP-D3(BJ)/def2-SVP level of theory was selected for the DFT calculations, and standard conditions and an (aqueous) SMD model were imposed to mimic physiological conditions. We believe that this data, as well as the presented workflow for high-throughput reaction profile computation, will be useful to screen for new bio-orthogonal reactions, as well as for the development of novel machine learning models for the prediction of chemical reactivity more broadly.Comment: 19 pages, 9 figures, journal manuscrip

Reaction profiles for quantum chemistry-computed [3 + 2] cycloaddition reactions

Bio-orthogonal click chemistry based on [3 + 2] dipolar cycloadditions has had a profound impact on the field of biochemistry and significant effort has been devoted to identify promising new candidate reactions for this purpose. To gauge whether a prospective reaction could be a suitable bio-orthogonal click reaction, information about both on- and off-target activation and reaction energies is highly valuable. Here, we use an automated workflow, based on the autodE program, to compute over 5000 reaction profiles for [3 + 2] cycloadditions involving both synthetic dipolarophiles and a set of biologically-inspired structural motifs. Based on a succinct benchmarking study, the B3LYP-D3(BJ)/def2-TZVP//B3LYP-D3(BJ)/def2-SVP level of theory was selected for the DFT calculations, and standard conditions and an (aqueous) SMD model were imposed to mimic physiological conditions. We believe that this data, as well as the presented workflow for high-throughput reaction profile computation, will be useful to screen for new bio-orthogonal reactions, as well as for the development of novel machine learning models for the prediction of chemical reactivity more broadly

Tartarus: A Benchmarking Platform for Realistic And Practical Inverse Molecular Design

The efficient exploration of chemical space to design molecules with intended properties enables the accelerated discovery of drugs, materials, and catalysts, and is one of the most important outstanding challenges in chemistry. Encouraged by the recent surge in computer power and artificial intelligence development, many algorithms have been developed to tackle this problem. However, despite the emergence of many new approaches in recent years, comparatively little progress has been made in developing realistic benchmarks that reflect the complexity of molecular design for real-world applications. In this work, we develop a set of practical benchmark tasks relying on physical simulation of molecular systems mimicking real-life molecular design problems for materials, drugs, and chemical reactions. Additionally, we demonstrate the utility and ease of use of our new benchmark set by demonstrating how to compare the performance of several well-established families of algorithms. Surprisingly, we find that model performance can strongly depend on the benchmark domain. We believe that our benchmark suite will help move the field towards more realistic molecular design benchmarks, and move the development of inverse molecular design algorithms closer to designing molecules that solve existing problems in both academia and industry alike.Comment: 29+21 pages, 6+19 figures, 6+2 table

Fast evaluation of the adsorption energy of organic molecules on metals via graph neural networks

Modeling in heterogeneous catalysis requires the extensive evaluation of the energy of molecules adsorbed on surfaces. This is done via density functional theory but for large organic molecules it requires enormous computational time, compromising the viability of the approach. Here we present GAME-Net, a graph neural network to quickly evaluate the adsorption energy. GAME-Net is trained on a well-balanced chemically diverse dataset with C1–4 molecules with functional groups including N, O, S and C6–10 aromatic rings. The model yields a mean absolute error of 0.18 eV on the test set and is 6 orders of magnitude faster than density functional theory. Applied to biomass and plastics (up to 30 heteroatoms), adsorption energies are predicted with a mean absolute error of 0.016 eV per atom. The framework represents a tool for the fast screening of catalytic materials, particularly for systems that cannot be simulated by traditional methods

Baird aromaticity in excited states and open-shell ground states

Baird aromaticity describes the stabilization of excited-state molecules due to cyclic conjugation in the same way as H\ufcckel aromaticity is used for the electronic ground state. Baird’s rule is used to make a preliminary evaluation of a ring as aromatic or antiaromatic. For quantitative evaluation, a range of computational methods has been developed and validated. These methods need to be applied with caution and expertise, ensuring that they are used on the right excited state and that the excitation is localized to the ring in question. Baird aromaticity has found applications in, for example, fulvenes and other aromatic chameleons, expanded porphyrins and ground-state triplet molecules. Further research is needed to clarify how Baird’s rule applies to singlet excited states and macrocyclic and polycyclic systems. There is also a need for more knowledge on (anti)aromaticity changes along photoreaction pathways to get a detailed understanding of how it influences photoreactivity

Influence of Aromaticity on Excited State Structure, Reactivity and Properties

This thesis describes work that could help development of new photochemical reactions and light-absorbing materials. Focus is on the chemical concept "aromaticity" which is a proven conceptual tool in developing thermal chemical reactions. It is here shown that aromaticity is also valuable for photochemistry. The influence of aromaticity is discussed in terms of structure, reactivity and properties. With regard to structure, it is found that photoexcited molecules change their structure to attain aromatic stabilization (planarize, allow through-space conjugation) or avoid antiaromatic destabilization (pucker). As for reactivity, it is found that stabilization/destabilization of reactants decrease/increase photoreactivity, in accordance with the Bell-Evans-Polanyi relationship. Two photoreactions based on excited state antiaromatic destabilization of the substrates are reported. Finally, with respect to properties, it is shown that excited state energies can be tuned by considering aromatic effects of both the electronic ground state and the electronically excited states. The fundamental research presented in this thesis forms a foundation for the development of new photochemical reactions and design of compounds for new organic electronic materials

Putting Chemical Knowledge to Work in Machine Learning for Reactivity

Machine learning has been used to study chemical reactivity for a long time in fields such as physical organic chemistry, chemometrics and cheminformatics. Recent advances in computer science have resulted in deep neural networks that can learn directly from the molecular structure. Neural networks are a good choice when large amounts of data are available. However, many datasets in chemistry are small, and models utilizing chemical knowledge are required for good performance. Adding chemical knowledge can be achieved either by adding more information about the molecules or by adjusting the model architecture itself. The current method of choice for adding more information is descriptors based on computed quantum-chemical properties. Exciting new research directions show that it is possible to augment deep learning with such descriptors for better performance in the low-data regime. To modify the models, differentiable programming enables seamless merging of neural networks with mathematical models from chemistry and physics. The resulting methods are also more data-efficient and make better predictions for molecules that are different from the initial dataset on which they were trained. Application of these chemistry-informed machine learning methods promise to accelerate research in fields such as drug design, materials design, catalysis and reactivity

Läsning av QR-koder med smarta telefoner : En undersökning om spridningen av en teknologi

Quick Response eller QR är en teknik som tillåter snabb och pålitlig optisk läsning av information som är kodad i ett rutmönster. Som läsverktyg kan man använda läsare som är speciellt tillverkade för ändamålet och numera också vanliga smartphones. Tekniken har i flera år varit framgångsrik i vissa industriella applikationer såsom lagerhållning, men det är fortfarande osäkert i vilken utsträckning den också kan vinna insteg i vardagligt användande av smartphones. Vi har genomfört en enkätundersökning bland universitetsstudenter för att uppskatta hur spridd användningen av QR-koder är och hur denna användning ser ut. För att analysera resultatet har vi använt oss av teorin om diffusion of innovations som tillhandahåller en modell för spridning av ny teknik inom ett sammanhang. Resultaten antyder försiktigtvis att kännedom om tekniken är väl spridd, men att användandet är begränsat och att det beror på brist på upplevd nytta snarare än på att tekniken skulle vara otillgänglig eller svårhanterlig.Quick Response or QR is a technology allowing fast and reliable optical reading of information encoded in a pattern of black and white squares. Reading implements include devices designed especially for this purpose as well as, in recent years, ordinary smartphones. The technology has for some time been successful in industrial applications such as storage management, but it is uncertain to what degree it might also see widespread use in everyday use of smartphones. We conduct a questionnaire-based survey targeting university students in order to estimate the extent and nature of QR usage. In analysing our findings we make use of the theory of diffusion of innovations, which provides a model for the adoption of a new technology within a given context. Our results imply that knowledge of the technology is widely dispersed, but that actual use is limited and that this is due to a lack of perceived usefulness rather than the technology as such being inaccessible or difficult to use