COVIDiagnosis-Net: Deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images.

Reaction optimization is fundamental to synthetic chemistry, from optimizing the yield of industrial processes to selecting conditions for the preparation of medicinal candidates1. Likewise, parameter optimization is omnipresent in artificial intelligence, from tuning virtual personal assistants to training social media and product recommendation systems2. Owing to the high cost associated with carrying out experiments, scientists in both areas set numerous (hyper)parameter values by evaluating only a small subset of the possible configurations. Bayesian optimization, an iterative response surface-based global optimization algorithm, has demonstrated exceptional performance in the tuning of machine learning models3. Bayesian optimization has also been recently applied in chemistry4–9; however, its application and assessment for reaction optimization in synthetic chemistry has not been investigated. Here we report the development of a framework for Bayesian reaction optimization and an open-source software tool that allows chemists to easily integrate state-of-the-art optimization algorithms into their everyday laboratory practices. We collect a large benchmark dataset for a palladium-catalysed direct arylation reaction, perform a systematic study of Bayesian optimization compared to human decision-making in reaction optimization, and apply Bayesian optimization to two real-world optimization efforts (Mitsunobu and deoxyfluorination reactions). Benchmarking is accomplished via an online game that links the decisions made by expert chemists and engineers to real experiments run in the laboratory. Our findings demonstrate that Bayesian optimization outperforms human decisionmaking in both average optimization efficiency (number of experiments) and consistency (variance of outcome against initially available data). Overall, our studies suggest that adopting Bayesian optimization methods into everyday laboratory practices could facilitate more efficient synthesis of functional chemicals by enabling better-informed, data-driven decisions about which experiments to run. Bayesian optimization is applied in chemical synthesis towards the optimization of various organic reactions and is found to outperform scientists in both average optimization efficiency and consistency.

Bayesian reaction optimization as a tool for chemical synthesis.

The increasing availability of structured but high dimensional data has opened new opportunities for optimization. One emerging and promising avenue is the exploration of unsupervised methods for projecting structured high dimensional data into low dimensional continuous representations, simplifying the optimization problem and enabling the application of traditional optimization methods. However, this line of research has been purely methodological with little connection to the needs of practitioners so far. In this paper, we study the effect of different search space design choices for performing Bayesian Optimization in high dimensional structured datasets. In particular, we analyse the influence of the dimensionality of the latent space, the role of the acquisition function and evaluate new methods to automatically define the optimization bounds in the latent space. Finally, based on experimental results using synthetic and real datasets, we provide recommendations for the practitioners.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/ail2.24

Good practices for Bayesian Optimization of high dimensional structured spaces

. The need for cost-effective support structure designs for offshore wind turbines has led to continued interest in the
development of design optimization methods. So far, almost no studies have considered the effect of uncertainty, and
hence probabilistic constraints, on the support structure design optimization problem. In this work, we present a
general methodology that implements recent developments in gradient-based design optimization, in particular the use
of analytical gradients, within the context of reliability-based design optimization methods. Gradient-based
optimization is typically more efficient and has more well-defined convergence properties than gradient-free methods,
making this the preferred paradigm for reliability-based optimization where possible. By an assumed factorization of
the uncertain response into a design-independent, probabilistic part and a design-dependent but completely
deterministic part, it is possible to computationally decouple the reliability analysis from the design
optimization. Furthermore, this decoupling makes no further assumption about the functional nature of the stochastic
response, meaning that high-fidelity surrogate modeling through Gaussian process regression of the probabilistic part
can be performed while using analytical gradient-based methods for the design optimization. We apply this methodology
to several different cases based around a uniform cantilever beam and the OC3 Monopile and different loading and
constraint scenarios. The results demonstrate the viability of the approach in terms of obtaining reliable, optimal
support structure designs and furthermore show that in practice only a limited amount of additional computational
effort is required compared to deterministic design optimization. While there are some limitations in the applied
cases, and some further refinement might be necessary for applications to high-fidelity design scenarios, the
demonstrated capabilities of the proposed methodology show that efficient reliability-based optimization for offshore
wind turbine support structures is feasible.

/pdf/reliability-based-design-optimization-of-offshore-wind-brt591im1d.pdf

J. Mockus

Papers

On the Bayes Methods for Seeking the Extremal Point