Topic: Bayesian optimization

Raymundo Arróyave discusses alloy discovery guided by both metallurgical knowledge and Bayesian optimization. The approach combines physical models, high-throughput CALPHAD calculations, microstructural information and experimental feedback to search chemical and processing spaces that are difficult to explore through data alone. Examples involving high-entropy and refractory alloys address multiple objectives and constraints, including feasibility, correlated properties, uncertainty and microstructural sensitivity. The seminar then connects these decision methods to the Autonomous Robotic Me

Taylor Sparks and Andrew Falkowski discuss autonomous materials laboratories with Radical AI co-founder and CEO Joseph Krause. They examine instrument integration and data-access limitations, the MATRIX vision-language model, language-model embeddings for Bayesian optimization, and how to measure the contribution of scientific intuition. Krause also explains the choice to pursue manufacturing alongside discovery, emphasizing production data and process know-how. The episode connects automated experimentation with the practical challenges of developing and scaling new alloys.

Advancing therapies for neurological disorders relies on developing efficient, personalized whole-brain models that can capture mechanistic insights and pathological features. Current challenges in creating such computational models arise from managing a considerable number of adjustable parameters. The conventional manual exploration of this parameter space is a serious limitation for personalizing models to individual subject data [1]. This work aims to address this by approaching the problem as an optimization challenge in the context of developing brain models capable of characterizing in

During smooth pursuit, the oculomotor system needs to multi-task: eye velocity is adjusted to keep the moving stimulus on the fovea; in case of target blanking or loss of foveation, saccades are performed to catch up with the target. Mathematical models can be used to implement a quantitative description of underlying processes. Thus, models can provide insight by identifying parameters related to the physiological mechanisms (like pursuit gain or retinal delay).Here, we adapted a one-dimensional oculomotor model (Marti et al. 2008) to a two-dimensional pursuit task with target blanking (see f