Search · four archives
Search · four archives
13 papers · ranked by Valyu relevance
Andre KY Low, Flore Mekki-Berrada, Aleksandr Ostudin, Jiaxun Xie + 7 more
The development of automated high-throughput experimental platforms has enabled fast sampling of high-dimensional decision spaces. To reach target properties efficiently, these platforms are increasingly paired with intelligent experimental design. When solving optimization problems, Bayesian-based optimizers are often…
Jiyizhe Zhang, Daria Semochkina, Naoto Sugisawa, David Woods + 1 more
Multi-objective Bayesian optimization (MOBO) has shown to be a promising tool for reaction development. However, noise is usually unavoidable during experiments and makes it challenging to find reliable solutions. In this study, we focus on finding a set of optimal reaction conditions using multi-objective Euclidian…
Pablo Quijano Velasco, Kedar Hippalgaonkar, Balamurugan Ramalingam
The discovery of optimal conditions of chemical reactions is a labor-intensive, time-consuming task that requires exploring a high-dimensional parametric space. Historically the optimization of chemical reactions has been performed by manual experimentation guided by human intuition and Design of Experiments where one…
Authors not listed
Optimizing the synthesis conditions of advanced materials is challenging, especially when outcomes are subject to inherent experimental uncertainties. Bayesian optimization is a popular tool for accelerating materials discovery, but its standard risk-neutral framework overlooks the variability of outcomes under…
Ken Jenewein, Luca Torresi, Navid Haghmoradi, Attila Kormányos + 2 more
Experimental catalyst optimization is plagued by slow and laborious efforts. Finding innovative materials is key to advancing research areas for sustainable energy conversion, such as electrocatalysis. Artificial intelligence (AI)-guided optimization bears great potential to autonomously learn from data and plan new…
Riley Hickman, Priyansh Parakh, Austin Cheng, Qianxiang Ai + 3 more
Experiment planning algorithms are a required component of autonomous platforms for scientific discovery. Selecting a suitable optimization algorithm for a novel application is an important yet difficult choice a researcher has to make based on past empirical performance on similar tasks. To facilitate the evaluation…
Tatsuya Yoshizawa, Shoichi Ishida, Tomohiro Sato, Masateru Ohta + 2 more
Molecular design using data-driven generative models has emerged as a promising technology, impacting various fields such as drug discovery and the development of functional materials. However, this approach is often susceptible to optimization failure due to reward hacking, where prediction models fail to accurately…
Kobi Felton, Jan Rittig, Alexei Lapkin
In the fine chemicals industry, reaction screening and optimisation are essential to development of new products. However, this screening can be extremely time and labor intensive, especially when intuition is used. Machine learning offers a solution through iterative suggestions of new experiments based on past…
Authors not listed
Designing efficient photoreactors remains challenging due to the complex interplay of light transport phenomena, shaped by reflection, scattering and absorption processes. Here, we introduce a workflow that integrates ray-tracing digital twins with multi-objective Bayesian optimization to autonomously design…
Robert Arbon, Yanchen Zhu, Antonia S. J. S. Mey
Markov state models (MSM) are a popular statistical method for analyzing the conformational dynamics of proteins, including protein folding. With all statistical and machine learning (ML) models choices must be made about the modeling pipeline that cannot be directly learned from the data. These choices, or…
Authors not listed
This article presents an overview about the state of the art in the development of structured packings for distillation applications. The focus is on highlighting different approaches including heuristic development cycles, the development of new packing structures, 3D-printing as tool for manufacturing, and…
Riley Hickman, Matteo Aldeghi, Alán Aspuru-Guzik
Model-based optimization strategies, such as Bayesian optimization (BO), have been deployed across the natural sciences in design and discovery campaigns due to their sample efficiency and flexibility. The combination of such strategies with automated laboratory equipment and/or high-performance computing in a…
Tobias Seidel, Lena-Marie Ränger, Thomas Grützner, Michael Bortz
In this work we present a new approach that we use to simulate and optimize multiple dividing wall columns at the same time. Instead of considering all model equations as constraints and all process variables as optimization variables in a large and highly nonlinear optimization problem we only incorporate a subset of…