Materials discovery with extreme properties via reinforcement learning-guided combinatorial chemistry.

The goal of most materials discovery is to discover materials that are superior to those currently known. Fundamentally, this is close to extrapolation, which is a weak point for most machine learning models that learn the probability distribution of data. Herein, we develop reinforcement learning-guided combinatorial chemistry, which is a rule-based molecular designer driven by trained policy for selecting subsequent molecular fragments to get a target molecule. Since our model has the potential to generate all possible molecular structures that can be obtained from combinations of molecular fragments, unknown molecules with superior properties can be discovered. We theoretically and empirically demonstrate that our model is more suitable for discovering better compounds than probability distribution-learning models. In an experiment aimed at discovering molecules that hit seven extreme target properties, our model discovered 1315 of all target-hitting molecules and 7629 of five target-hitting molecules out of 100 000 trials, whereas the probability distribution-learning models failed. Moreover, it has been confirmed that every molecule generated under the binding rules of molecular fragments is 100% chemically valid. To illustrate the performance in actual problems, we also demonstrate that our models work well on two practical applications: discovering protein docking molecules and HIV inhibitors.

Generative Chemical Transformer: Neural Machine Learning of Molecular Geometric Structures from Chemical Language via Attention.

Discovering new materials better suited to specific purposes is an important issue in improving the quality of human life. Here, a neural network that creates molecules that meet some desired multiple target conditions based on a deep understanding of chemical language is proposed (generative chemical Transformer, GCT). The attention mechanism in GCT allows a deeper understanding of molecular structures beyond the limitations of chemical language itself which cause semantic discontinuity by paying attention to characters sparsely. The significance of language models for inverse molecular design problems is investigated by quantitatively evaluating the quality of the generated molecules. GCT generates highly realistic chemical strings that satisfy both chemical and linguistic grammar rules. Molecules parsed from the generated strings simultaneously satisfy the multiple target properties and vary for a single condition set. These advances will contribute to improving the quality of human life by accelerating the process of desired material discovery.

In silico discovery of active, stable, CO-tolerant and cost-effective electrocatalysts for hydrogen evolution and oxidation.

Various databases of density functional theory (DFT) calculations for materials and adsorption properties are currently available. Using the Materials Project and GASpy databases of material stability and binding energies (H* and CO*), respectively, we evaluate multiple aspects of catalysts to discover active, stable, CO-tolerant, and cost-effective hydrogen evolution and oxidation catalysts. Finally, we suggest a few candidate materials for future experimental validations. We highlight that the stability analysis is easily obtainable but provides invaluable information to assess thermodynamic and electrochemical stability, bridging the gap between simulations and experiments. Furthermore, it reduces the number of expensive DFT calculations required to predict catalytic activities of surfaces by filtering out unstable materials.

Catalyst-electrolyte interface chemistry for electrochemical CO2 reduction.

The electrochemical reduction of CO2 stores intermittent renewable energy in valuable raw materials, such as chemicals and transportation fuels, while minimizing carbon emissions and promoting carbon-neutral cycles. Recent technoeconomic reports suggested economically feasible target products of CO2 electroreduction and the relative influence of key performance parameters such as faradaic efficiency (FE), current density, and overpotential in the practical industrial-scale applications. Furthermore, fundamental factors, such as available reaction pathways, shared intermediates, competing hydrogen evolution reaction, scaling relations of the intermediate binding energies, and CO2 mass transport limitations, should be considered in relation to the electrochemical CO2 reduction performance. Intensive research efforts have been devoted to designing and developing advanced electrocatalysts and improving mechanistic understanding. More recently, the research focus was extended to the catalyst environment, because the interfacial region can delicately modulate the catalytic activity and provide effective solutions to challenges that were not fully addressed in the material development studies. Herein, we discuss the importance of catalyst-electrolyte interfaces in improving key operational parameters based on kinetic equations. Furthermore, we extensively review previous studies on controlling organic modulators, electrolyte ions, electrode structures, as well as the three-phase boundary at the catalyst-electrolyte interface. The interfacial region modulates the electrocatalytic properties via electronic modification, intermediate stabilization, proton delivery regulation, catalyst structure modification, reactant concentration control, and mass transport regulation. We discuss the current understanding of the catalyst-electrolyte interface and its effect on the CO2 electroreduction activity.

General technoeconomic analysis for electrochemical coproduction coupling carbon dioxide reduction with organic oxidation.

Electrochemical processes coupling carbon dioxide reduction reactions with organic oxidation reactions are promising techniques for producing clean chemicals and utilizing renewable energy. However, assessments of the economics of the coupling technology remain questionable due to diverse product combinations and significant process design variability. Here, we report a technoeconomic analysis of electrochemical carbon dioxide reduction reaction-organic oxidation reaction coproduction via conceptual process design and thereby propose potential economic combinations. We first develop a fully automated process synthesis framework to guide process simulations, which are then employed to predict the levelized costs of chemicals. We then identify the global sensitivity of current density, Faraday efficiency, and overpotential across 295 electrochemical coproduction processes to both understand and predict the levelized costs of chemicals at various technology levels. The analysis highlights the promise that coupling the carbon dioxide reduction reaction with the value-added organic oxidation reaction can secure significant economic feasibility.

Risk-based underground pipeline safety management considering corrosion effect.

Due to the long term usage and irregular maintenance for corrosion checks, catastrophic accidents have been increasing in underground pipelines. In this study, a new safety management methodology of underground pipeline, risk-based pipeline management, is introduced reflecting corrosion effect. First, principle of the risk-based pipeline management is presented compared with an original method, qualitative measure. It is distinguished from the qualitative measure by reflecting societal risk and corrosion in safety management of underground pipeline. And then, it is applied to an existing underground propylene pipeline in Ulsan Industrial Complex, South Korea. The consequence analysis is based on real information, and the frequency analysis reflects degree of corrosion. For calculation of corrosion rate, direct current voltage gradient (DCVG) and close interval potential survey (CIPS) are conducted. As a result of applying the risk-based pipeline management, risk integral is reduced by 56.8% compared to the qualitative measure. Finally, sensitivity analysis is conducted on variables, which affect the risk of the pipeline. This study would contribute to introduce quantitative measure to pipeline management and increase safety of pipeline.