One of the main challenges in materials discovery is efficiently exploring the vast search
space for targeted properties as approaches that rely on trial-and-error are impractical. We …

A key aspect of the develo** field of materials informatics is optimally guiding experiments
or calculations towards parts of the relatively vast feature space where a material with …

Network biology is an interdisciplinary field bridging computational and biological sciences
that has proved pivotal in advancing the understanding of cellular functions and diseases …

The widespread integration of deep neural networks in develo** data-driven surrogate
models for high-fidelity simulations of complex physical systems highlights the critical …

In this paper, we propose a general experimental design framework for optimally guiding
new experiments or simulations in search of new materials with desired properties. The …

Significant acceleration of the future discovery of novel functional materials requires a
fundamental shift from the current materials discovery practice, which is heavily dependent …

Molecular design based on generative models, such as variational autoencoders (VAEs),
has become increasingly popular in recent years due to its efficiency for exploring high …

In many contemporary engineering problems, model uncertainty is inherent because
accurate system identification is virtually impossible owing to system complexity or lack of …

We derive robust linear filtering and experimental design for systems governed by stochastic
differential equations (SDEs) under model uncertainty. Given a model of signal and …

Plant Synthetic Biology: Quantifying the “Known Unknowns” and Discovering the “Unknown
Unknowns” | Plant Physiology | Oxford Academic Skip to Main Content Advertisement Oxford …