The DP5 Probability, Quantification and Visualisation of Structural Uncertainty in Single Molecules

Whenever a new molecule is made, a chemist will justify the proposed structure by analysing the NMR spectra. The widely-used DP4 algorithm will choose the best match from a series of possibilities, but draws no conclusions from a single candidate structure. Here we present the DP5 probability, a step-change in the quantification of molecular uncertainty: given one structure and one 13C NMR spectra, DP5 gives the probability of the structure being correct. We show the DP5 probability can rapidly differentiate between structure proposals indistinguishable by NMR to an expert chemist. We also show in a number of challenging examples the DP5 probability may prevent incorrect structures being published and later reassigned. DP5 will prove extremely valuable in fields such as discovery-driven automated chemical synthesis and drug development. Alongside the DP4-AI package, DP5 can help guide synthetic chemists when resolving the most subtle structural uncertainty. The DP5 system is available at https://github.com/Goodman-lab/DP5.

The DP5 Probability, Quantification and Visualisation of Structural Uncertainty in Single Molecules

Whenever a new molecule is made, a chemist will justify the proposed structure by analysing the NMR spectra. The widely-used DP4 algorithm will choose the best match from a series of possibilities, but draws no conclusions from a single candidate structure. Here we present the DP5 probability, a step-change in the quantification of molecular uncertainty: given one structure and one 13C NMR spectra, DP5 gives the probability of the structure being correct. We show the DP5 probability can rapidly differentiate between structure proposals indistinguishable by NMR to an expert chemist. We also show in a number of challenging examples the DP5 probability may prevent incorrect structures being published and later reassigned. DP5 will prove extremely valuable in fields such as discovery-driven automated chemical synthesis and drug development. Alongside the DP4-AI package, DP5 can help guide synthetic chemists when resolving the most subtle structural uncertainty. The DP5 system is available at https://github.com/Goodman-lab/DP5.

Computational Design of Modular Robots Based on Genetic Algorithm and Reinforcement Learning

Designing novel robots that can cope with a specific task is a challenging problem because of the enormous design space that involves both morphological structures and control mechanisms. To this end, we present a computational method for automating the design of modular robots. Our method employs a genetic algorithm to evolve robotic structures as an outer optimization, and it applies a reinforcement learning algorithm to each candidate structure to train its behavior and evaluate its potential learning ability as an inner optimization. The size of the design space is reduced significantly by evolving only the robotic structure and by performing behavioral optimization using a separate training algorithm compared to that when both the structure and behavior are evolved simultaneously. Mutual dependence between evolution and learning is achieved by regarding the mean cumulative rewards of a candidate structure in the reinforcement learning as its fitness in the genetic algorithm. Therefore, our method searches for prospective robotic structures that can potentially lead to near-optimal behaviors if trained sufficiently. We demonstrate the usefulness of our method through several effective design results that were automatically generated in the process of experimenting with actual modular robotics kit.

Synthesis of 3,15-Disuccinate-12-Coumarin Substituted Andrographolide Derivatives and Their Antiplatelet Aggregation Activities In Vitro

In order to develop a series of novel compounds with antiplatelet aggregation activities, 3,15-disuccinate-12-coumarin substituted derivatives were designed and synthesized based on the natural product andrographolide. In vitro antiplatelet aggregation activities were evaluated by the turbidimetric method with thrombin, adenosine diphosphate (ADP), arachidonic acid (AA), and collagen as inducers. The biological evaluation revealed that compound 11k showed significant inhibition activity for thrombin, AA, and collagen-induced platelet aggregation at the same time and exhibited a dose-dependent behavior. Compound 11c showed the highest antiplatelet aggregation activity induced by ADP. Most of the derivatives had no significant cytotoxicity. Therefore, our work proved that 3,15-disuccinate-12-coumarin substituted andrographolide derivatives had the potential to become a novel candidate structure for antiplatelet aggregation and deserved further study.

New possible candidate structure for phase IV of solid hydrogen

As a whole, the vibron frequencies of the Ama2 structure agree better with the experimental results compared with the Pc structure.

Design, Synthesis and Evaluation of Novel Trichloromethyl Dichlorophenyl Triazole Derivatives as Potential Safener

The dominance of safener can unite with herbicides acquiring the efficient protection of crop and qualifying control of weeds in agricultural fields. In order to solve the crop toxicity problem and exploit the novel potential safener for fenoxaprop-P-ethyl herbicide, a series of trichloromethyl dichlorobenzene triazole derivatives were designed and synthesized by the principle of active subunit combination. A total of 21 novel substituted trichloromethyl dichlorobenzene triazole compounds were synthesized by substituted aminophenol and amino alcohol derivatives as the starting materials, using cyclization and acylation. All the compounds were unambiguously characterized by IR, 1H-NMR, 13C-NMR, and HRMS. A greenhouse bioassay indicated that most of the title compounds could protect wheat from injury caused by fenoxaprop-P-ethyl at varying degrees, in which compound 5o exhibited excellent safener activity at a concentration of 10 μmol/L and was superior to the commercialized compound fenchlorazole. A structure–activity relationship for the novel compounds was determined, which demonstrated that those compounds containing benzoxazine groups showed better activity than that of oxazole-substituted compounds. Introducing a benzoxazine fragment and electron-donating group to specific positions could improve or maintain the safener activity for wheat against attack by the herbicide fenoxaprop-P-ethyl. A molecular docking model suggested that a potential mechanism between 5o and fenoxaprop-P-ethyl is associated with the detoxication of the herbicide. Results from the present work revealed that compound 5o exhibited good crop safener activities toward wheat and could be a promising candidate structure for further research on wheat protection.

Dense Wire Mesh as a High-Efficiency Solar Volumetric Absorber

Heating a gas to over 1,000°C with concentrated sunlight can enable advanced high-performance applications such as solar-driven combined cycles and solar thermo-chemical processes. Solar receivers using volumetric porous absorbers are intended to produce the ‘volumetric effect’ leading to reduced heat loss and high absorber efficiency. However, experiments on volumetric absorbers have not shown this effect, and the absorbers’ efficiency is usually in the range of 70–80% rather than the desirable range of over 90%. Several porous structure geometries, including the well-known ceramic honeycomb and ceramic foam, were investigated with a numerical model. The results show that even optimal configurations still fall short of the desired range of absorber efficiency. A new candidate structure, a dense wire mesh, was investigated and compared to the conventional absorbers. The volumetric convection coefficient was also measured experimentally to provide validation of the single report found in the literature for this structure. An attractive solution with high efficiency of 90% was found for a dense wire mesh with pore diameter of 1 mm and porosity of 0.83. This geometry seems then a promising candidate for future volumetric absorbers.