A celebrated and controversial hypothesis suggests that some biological systems—parts,
aspects, or groups of them—may extract important functional benefits from operating at the …

Modern generative machine learning models are able to create realistic outputs far beyond
their training data, such as photorealistic artwork, accurate protein structures or …

At present, machine learning systems use simplified neuron models that lack the rich
nonlinear phenomena observed in biological systems, which display spatio-temporal …

Understanding the interplay of order and disorder in chaos is a central challenge in modern
quantitative science. Approximate linear representations of nonlinear dynamics have long …

We apply unsupervised machine learning techniques, mainly principal component analysis
(PCA), to compare and contrast the phase behavior and phase transitions in several …

The materials discovery process can be significantly expedited and simplified if we can learn
effectively from available knowledge and data. In the present contribution, we show that …

The field of classical stochastic processes forms a major branch of mathematics. Stochastic
processes are, of course, also very well studied in biology, chemistry, ecology, geology …

Complex systems research is becoming ever more important in both the natural and social
sciences. It is commonly implied that there is such a thing as a complex system, different …

Structural complexity of minerals is characterized using information contents of their crystal
structures calculated according to the modified Shannon formula. The crystal structure is …

The design of new materials with useful properties is becoming increasingly important.
Machine-learning tools Materials Genome Integration System Phase and Property Analysis …