5 papers
From Feasible to Practical: Pareto-Optimal Synthesis Planning
Friedrich Hastedt, Dongda Zhang, Antonio del Rio Chanona
Current computer-aided synthesis planning (CASP) methods often treat retrosynthesis as solved once a single feasible route is identified, focusing primarily on convergence or short…
Constraint-Guided Symbolic Regression for Data-Efficient Kinetic Model Discovery
Miguel Ãngel de Carvalho Servia, Ilya Orson Sandoval, King Kuok +4
The industrialization of catalytic processes hinges on the availability of reliable kinetic models for design, optimization, and control. Traditional mechanistic models demand exte…
Reinforcement learning for efficient and robust multi-setpoint and multi-trajectory tracking in bioprocesses
Sebastián Espinel-RÃos, José L. Avalos, Ehecatl Antonio del Rio Chanona +1
Efficient and robust bioprocess control is essential for maximizing performance and adaptability in advanced biotechnological systems. In this work, we present a reinforcement-lear…
Enhancing reinforcement learning for population setpoint tracking in co-cultures
Sebastián Espinel-RÃos, Joyce Qiaoxi Mo, Dongda Zhang +2
Efficient multiple setpoint tracking can enable advanced biotechnological applications, such as maintaining desired population levels in co-cultures for optimal metabolic division…
Simplest Mechanism Builder Algorithm (SiMBA): An Automated Microkinetic Model Discovery Tool
Miguel Ãngel de Carvalho Servia, King Kuok, Hii +3
Microkinetic models are key for evaluating industrial processes' efficiency and chemicals' environmental impact. Manual construction of these models is difficult and time-consuming…