Design of New Dispersants Using Machine Learning and Visual Analytics

Artificial intelligence (AI) is an emerging technology that is revolutionizing the discovery of new materials. One key application of AI is virtual screening of chemical libraries, which enables the accelerated discovery of materials with desired properties. In this study, we developed computational models to predict the dispersancy efficiency of oil and lubricant additives, a critical property in their design that can be estimated through a quantity named blotter spot. We propose a comprehensive approach that combines machine learning techniques with visual analytics strategies in an interactive tool that supports domain experts’ decision-making. We evaluated the proposed models quantitatively and illustrated their benefits through a case study. Specifically, we analyzed a series of virtual polyisobutylene succinimide (PIBSI) molecules derived from a known reference substrate. Our best-performing probabilistic model was Bayesian Additive Regression Trees (BART), which achieved a mean absolute error of (Formula presented.) and a root mean square error of (Formula presented.), as estimated through 5-fold cross-validation. To facilitate future research, we have made the dataset, including the potential dispersants used for modeling, publicly available. Our approach can help accelerate the discovery of new oil and lubricant additives, and our interactive tool can aid domain experts in making informed decisions based on blotter spot and other key propertie

Uncertainty-based decision-making in reinforcement learning and the distributed adaptive control cognitive architecture

Treball fi de màster de: Master in Cognitive Systems and Interactive MediaDirectors: Adrián Fernández Amil, Ismael Tito FreireThis thesis explores the role of uncertainty estimation during training in Reinforce- ment Learning as a potential way of increasing sample efficiency, acting as a regu- lator between two subsystems that shape a policy: memory and stimulus-response. Memory-based subsystems are related to Episodic Reinforcement Learning, where exact snapshots or sequences of tuples generated during training are stored and then retrieved to perform the action that maximizes reward based solely on these past experiences. This way of learning is more related to how the hippocampus operates in the brain. In contrast, stimulus-response subsystems can be expressed as models that map states to actions in a model-free fashion. In humans and other animals, the dorsal striatum is responsible for making this stimulus-response mapping. However, this mapping process does not take into account the inherent uncertainty or variability of stimuli (i.e., perceptual uncertainty) in stochastic environments with partial observability and thus sometimes the optimal policy would be to rely more on the sequential feature of (model-based) memory. Several studies have shown that uncertainty plays a significant role in the decision-making process. Therefore we studied how it can arbitrate between the two systems. Concretely, we used an agent based on the Distributed Adaptive Control (DAC-ML) cognitive architecture comprising the two subsystems and an arbitration module that regulated their respective use based on the entropies of the policies. The agent was trained on a foraging task and showed dynamics that are aligned with human behaviour, where the memory-based system dominates at first, and throughout training, the stimulus-response systemslowly takes over. This research could potentially lead to more flexible and efficient Reinforcement Learning algorithms that combine different ways of learning and operating depending on the available knowledge about the environment