From Theory to Experiment: Transformer-based Generation Enables Rapid Discovery of Novel Reactions

Deep learning methods have been proven their potential roles in the chemical field, such as reaction prediction and retrosynthesis analysis. However, the de novo generation of unreported reactions using artificial intelligence technology remains not be completely explored. Inspired by molecular generation, we proposed the task of novel reaction generation. In this work, we applied the Heck reactions to train the transformer model, state-of-art natural language process model and obtained 4717 generated reactions after sampling and processing. We then confirmed that 2253 novel Heck reactions by organizing chemists to judge the generated reactions, and adopted organic synthesis experiment to verify the feasibility of unreported reactions. In this process, it only took 15 days from Heck reaction generation to experimental verification, proving that our model learns reaction rules in-depth and can make great contributions in the novel reaction discovery.

Fast and automated identification of reactions with low barriers: the decomposition of 3-hydroperoxypropanal

We show how fast semiempirical QM methods can be used to significantly decrease the computational expense for automated reaction mechanism discovery, using two different method for generating reaction products: graph-based systematic enumeration of all possible products and the meta-dynamics approach by Grimme (J. Chem. Theory. Comput. 2019, 15, 2847). We test the two approaches on the low-barrier reactions of 3-hydroperoxypropanal, which have been studied by a large variety of reaction discovery approaches and therefore provides a good benchmark. By using PM3 and GFN2-xTB for reaction energy and barrier screening the systematic approach identifies 64 reactions (out of 27,577 possible reactions) for DFT refinement, which in turn identifies the three reactions with lowest barriers plus a previously undiscovered reaction. With optimized hyperparameters meta-dynamics followed by PM3/GFN2-xTB-based screening identifies 15 reactions for DFT refinement, which in turn identifies the three reactions with lowest barrier. The number of DFT refinements can be further reduced to as little as six for both approaches by first verifying the transition states with GFN1-xTB. The main conclusion is that the semiempirical methods are accurate and fast enough to automatically identify promising candidates for DFT refinement for the low barrier reactions of 3-hydroperoxypropanal in about 15-30 minutes using relatively modest computational resources.

Fast and Automated Identification of Reactions with Low Barriers: The Decomposition of 3-Hydroperoxypropanal

We show how fast semiempirical QM methods can be used to significantly decrease the CPU requirements for automated reaction mechanism discovery, using two different method for generating reaction products: graph-based systematic enumeration of all possible products and the meta-dynamics approach by Grimme (J. Chem. Theory. Comput. 2019, 15, 2847). We test the two approaches on the low-barrier reactions of 3-hydroperoxypropanal, which have been studied by a large variety of reaction discovery approaches and therefore provides a good benchmark. By using PM3 and GFN2-xTB for reaction energy and barrier screening the systematic approach identifies 64 reactions (out of 27,577 possible reactions) for DFT refinement, which in turn identifies the three reactions with lowest barriers plus a previously undiscovered reaction. With optimised hyperparameters meta-dynamics followed by PM3/GFN2-xTB-based screening identifies 15 reactions for DFT refinement, which in turn identifies the three reactions with lowest barrier. The number of DFT refinements can be further reduced to as little as six for both approaches by first verifying the transition states with GFN1-xTB. The main conclusion is that the semiempirical methods are accurate and fast enough to automatically identify promising candidates for DFT refinement for the low barrier reactions of 3-hydroperoxypropanal in about 15-30 minutes using relatively modest computational resources.

Unlocking the Potential of High-Throughput Experimentation for Electrochemistry with a Standardized Microscale Reactor

Organic electrochemistry has emerged as an enabling and sustainable technology in modern organic synthesis. Despite the recent renaissance of electrosynthesis, the broad adoption of electrochemistry in the synthetic community and, especially in industrial settings, has been hindered by the dearth of general, standardized platforms for high-throughput experimentation (HTE). Herein, we disclose the design of the HT<i>e^-</i>Chem, a high-throughput microscale electrochemical reactor that is compatible with existing HTE infrastructure, and enables rapid evaluation of a broad array of electrochemical reaction parameters. Utilizing the HT<i>e^-</i>Chem to accelerate reaction optimization, reaction discovery, and chemical library synthesis is illustrated using a suite of oxidative and reductive transformations under constant current, constant voltage, and electrophotochemical conditions.

Unlocking the Potential of High-Throughput Experimentation for Electrochemistry with a Standardized Microscale Reactor

Organic electrochemistry has emerged as an enabling and sustainable technology in modern organic synthesis. Despite the recent renaissance of electrosynthesis, the broad adoption of electrochemistry in the synthetic community and, especially in industrial settings, has been hindered by the dearth of general, standardized platforms for high-throughput experimentation (HTE). Herein, we disclose the design of the HT<i>e^-</i>Chem, a high-throughput microscale electrochemical reactor that is compatible with existing HTE infrastructure, and enables rapid evaluation of a broad array of electrochemical reaction parameters. Utilizing the HT<i>e^-</i>Chem to accelerate reaction optimization, reaction discovery, and chemical library synthesis is illustrated using a suite of oxidative and reductive transformations under constant current, constant voltage, and electrophotochemical conditions.

Unlocking the Potential of High-Throughput Experimentation for Electrochemistry with a Standardized Microscale Reactor

Organic electrochemistry has emerged as an enabling and sustainable technology in modern organic synthesis. Despite the recent renaissance of electrosynthesis, the broad adoption of electrochemistry in the synthetic community and, especially in industrial settings, has been hindered by the dearth of general, standardized platforms for high-throughput experimentation (HTE). Herein, we disclose the design of the HT<i>e^-</i>Chem, a high-throughput microscale electrochemical reactor that is compatible with existing HTE infrastructure, and enables rapid evaluation of a broad array of electrochemical reaction parameters. Utilizing the HT<i>e^-</i>Chem to accelerate reaction optimization, reaction discovery, and chemical library synthesis is illustrated using a suite of oxidative and reductive transformations under constant current, constant voltage, and electrophotochemical conditions.

Fast and Automated Identification of Reactions with Low Barriers: The Decomposition of 3-Hydroperoxypropanal

<div> <div> <div> <p>We show how fast semiempirical QM methods can be used to significantly decrease the CPU requirements for automated reaction mechanism discovery, using two different method for generating reaction products: graph-based systematic enumeration of all possible products and the meta-dynamics approach by Grimme (<i>J. Chem. Theory. Comput</i>. 2019, 15, 2847). We test the two approaches on the low-barrier reactions of 3-hydroperoxypropanal, which have been studied by a large variety of reaction discovery approaches and therefore provides a good benchmark. By using PM3 and GFN2-xTB for reaction energy and barrier screening the systematic approach identifies 64 reactions (out of 27,577 possible reactions) for DFT refinement, which in turn identifies the three reactions with lowest barriers plus a previously undiscovered reaction. With optimised hyperparameters meta-dynamics followed by PM3/GFN2-xTB-based screening identifies 15 reactions for DFT refinement, which in turn identifies the three reactions with lowest barrier. The number of DFT refinements can be further reduced to as little as six for both approaches by first verifying the transition states with GFN1-xTB. The main conclusion is that the semiempirical methods are accurate and fast enough to automatically identify promising candidates for DFT refinement for the low barrier reactions of 3-hydroperoxypropanal in a few hours using modest computational resources. </p> </div> </div> </div>

Fast and Automated Identification of Reactions with Low Barriers: The Decomposition of 3-Hydroperoxypropanal

<div> <div> <div> <p>We show how fast semiempirical QM methods can be used to significantly decrease the CPU requirements for automated reaction mechanism discovery, using two different method for generating reaction products: graph-based systematic enumeration of all possible products and the meta-dynamics approach by Grimme (<i>J. Chem. Theory. Comput</i>. 2019, 15, 2847). We test the two approaches on the low-barrier reactions of 3-hydroperoxypropanal, which have been studied by a large variety of reaction discovery approaches and therefore provides a good benchmark. By using PM3 and GFN2-xTB for reaction energy and barrier screening the systematic approach identifies 64 reactions (out of 27,577 possible reactions) for DFT refinement, which in turn identifies the three reactions with lowest barriers plus a previously undiscovered reaction. With optimised hyperparameters meta-dynamics followed by PM3/GFN2-xTB-based screening identifies 15 reactions for DFT refinement, which in turn identifies the three reactions with lowest barrier. The number of DFT refinements can be further reduced to as little as six for both approaches by first verifying the transition states with GFN1-xTB. The main conclusion is that the semiempirical methods are accurate and fast enough to automatically identify promising candidates for DFT refinement for the low barrier reactions of 3-hydroperoxypropanal in a few hours using modest computational resources. </p> </div> </div> </div>