Abstract Physics-Informed Neural Networks (PINN) are neural networks (NNs) that encode
model equations, like Partial Differential Equations (PDE), as a component of the neural …

Self-driving laboratories (SDLs) promise an accelerated application of the scientific method.
Through the automation of experimental workflows, along with autonomous experimental …

By encoding time series as a string of numerical digits, we can frame time series forecasting
as next-token prediction in text. Develo** this approach, we find that large language …

Despite great progress in simulating multiphysics problems using the numerical
discretization of partial differential equations (PDEs), one still cannot seamlessly incorporate …

Most machine learning algorithms are configured by a set of hyperparameters whose values
must be carefully chosen and which often considerably impact performance. To avoid a time …

The imputation of missing values in time series has many applications in healthcare and
finance. While autoregressive models are natural candidates for time series imputation …

Reaction optimization is fundamental to synthetic chemistry, from optimizing the yield of
industrial processes to selecting conditions for the preparation of medicinal candidates …

Machine learning models are poised to make a transformative impact on chemical sciences
by dramatically accelerating computational algorithms and amplifying insights available from …

The past two decades have seen a major expansion in the availability, size, and precision of
time-domain data sets in astronomy. Owing to their unique combination of flexibility …

Global machine learning force fields, with the capacity to capture collective interactions in
molecular systems, now scale up to a few dozen atoms due to considerable growth of model …