14 papers · ranked by Valyu relevance
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
Solving optimization problems, especially for nonlinear and constrained systems, is a challenge. Decades of specialized algorithms have been developed for general and special cases of root finding, minimization (including constraints), for parameter estimation, and mapping connected spaces. These approaches typically…
Connor Taylor, Kobi Felton, Daniel Wigh, Mohammed Jeraal + 4 more
Functionalization of C–H bonds is a key challenge in medicinal chemistry, particularly for fragment-based drug discovery (FBDD) where such transformations need to be executed in the presence of polar functionality necessary for fragment-protein binding. New technologies such as high-throughput experimentation and…
Yannick Ureel, Maarten R. Dobbelaere, Yi Ouyang, Kevin De Ras + 3 more
By combining machine learning with design of experiments, so-called active machine learning, more efficient and cheaper research can be conducted. Machine learning algorithms are more flexible, and are better at investigating the processes spanning all length scales of chemical engineering. While the active machine…
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…
Ramsey Issa, Robert Sorenson, Taylor D. Sparks
The discovery of new dental materials is typically a slow process due to high-dimensionality of the formulation space as well as the multiple competing objectives which must be optimized for a given application. Here, we lay out a strategy using active learning and Bayesian optimization that has led to the discovery of…
Owen Madin, Michael Shirts
Dispersion-repulsion interactions, commonly represented in atomistic force fields by the Lennard-Jones (LJ) potential, play an important role in the accuracy of molecular simulations. Training the force field parameters used in the LJ potential is challenging, generally requiring adjustment based on simulations of…
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…
Yifan Wu, Aron Walsh, Alex Ganose
What is the minimum number of experiments, or calculations, required to find an optimal solution? Relevant chemical problems range from identifying a compound with target functionality within a given phase space to controlling materials synthesis and device fabrication conditions. A common feature in this application…
Etinosa Osaro, Yamil Colón
The application of machine learning (ML) techniques in materials science has revolutionized the pace and scope of materials research and design. In the case of metal-organic frameworks (MOFs), a promising class of materials due to their tunable properties and versatile applications in gas adsorption and separation, ML…
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Developing generalizable machine learning models with minimal data remains a central challenge in materials informatics. Effective models can significantly reduce costly computational simulations and time-intensive experimentation by providing reliable predictions of material properties. In this work, we investigate…
Sterling Baird, Jason R. Hall, Taylor D. Sparks
Would you rather search for a line inside a cube or a point inside a square? Physics-based simulations and wet-lab experiments often have symmetries (degeneracies) that allow reducing problem dimensionality or search space, but constraining these degeneracies is often unsupported or difficult to implement in many…
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Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…
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…