Authors: Daniela E. Blanco
Source: ECS Meeting Abstracts (article, 2021-05-30)
Field: Materials Science — Materials Chemistry

Spearheading the Electrification of Chemical Manufacturing with Smart Organic Electrosynthesis Chemical innovation and new molecule development are at the heart of pharmaceutical technologies that commit to the delivery of the finest treatments for challenging diseases. Organic electrosynthesis offers multiple advantages within the industry, enabling the synthesis of new molecules and offering opportunities for more sustainable manufacturing processes through reduced waste streams, near-ambient operating conditions, and a reduced number of reaction steps. However, despite these advantages, electrochemical process development in pharma is often hindered by lengthy and complex optimization approaches, which are required to ensure the purity and selectivity necessary for successful manufacturing. In fact, developing and optimizing electrochemical reactions repeatedly requires extensive experimental campaigns to elucidate reactive behavioral trends and the effect of relevant process variables (e.g. reactant concentrations, electrode potential, temperature, pressure, flow rates) on reaction performance. Machine-learning (ML) predictive algorithms can be used to predict reaction performance, removing the need for exhaustive experimentation in reaction discovery and process development. However, most ML-enabled systems require the use of large datasets ( i.e. 100’s-1000’s of points) to deliver accurate predictions, while gathering experimental points is expensive in human and material resources. The challenge of building large datasets can be averted by developing an innovative framework around the use of supervised machine learning (SL) and active learning (AL) tools. SL models can use small datasets to predict system performance for a significantly larger number of experimental conditions. However, prediction accuracy is heavily reliant on the presence of statistically significant points in the training dataset, which can be challenging to pinpoint. To circumvent this issue, we couple SL with AL algorithms, enabling informed and smart decisions on the experimental campaign design, ultimately reducing the number of experiments required to achieve reliable predictions of reaction behavior. Addressing fundamental questions regarding the limitations of a disruptive AL-enhanced ML predictive approach, we elucidate the pathway for the design and implementation of intelligent experimental campaigns in organic electrosynthesis. The insights from this work innovate on the future of reaction engineering and process optimization, while lowering the barrier to implementation of more sustainable electrochemical processes in pharmaceuticals and other sectors of the chemical industry. Figure 1 Electrosynthesis ; Computer science ; Process (computing) ; Commit ; Machine learning ; Artificial intelligence ; Electrification ; Process engineering ; Biochemical engineering ; Industrial engineering ; Electrochemistry ; Chemistry ; Database ; Engineering ; Electrode Machine Learning in Materials Science ; Electrochemical Analysis and Applications ; Innovative Microfluidic and Catalytic Techniques Innovation