Artificial Neural Networks (ANNs) are transforming how we understand chemical mixtures,
providing an expressive view of the chemical space and multiscale processes. Their …

Kinetic model identification relies on accurate concentration measurements and physical
constraints to limit solution multiplicity. Not having these measurements and prior knowledge …

Abstract Machine learning techniques have been widely applied in many fields of chemistry,
physics, biology, and materials science. One of the most fruitful applications is machine …

Conspectus Humans are continually bombarded with massive amounts of data. To deal with
this influx of information, we use the concept of attention in order to perceive the most …

Chemical reaction networks (CRNs), defined by sets of species and possible reactions
between them, are widely used to interrogate chemical systems. To capture increasingly …

Artificial Neural Networks (NN) are already heavily involved in methods and applications for
frequent tasks in the field of computational chemistry such as representation of potential …

Ordinary differential equations (ODEs) are extremely important in modeling dynamic
systems, such as chemical reaction networks. However, many challenges exist for building …

It is tenable to argue that nobody can predict the future with certainty, yet one can learn from
the past and make informed projections for the years ahead. In this Perspective, we …

The rise and fall of artificial neural networks is well documented in the scientific literature of
both computer science and computational chemistry. Yet almost two decades later, we are …

We present a physically-motivated topology of a deep neural network that can efficiently
infer extensive parameters (such as energy, entropy, or number of particles) of arbitrarily …