Computational science articles from across Nature Portfolio

An improved optimization algorithm enables the training of large-scale neural quantum states in which the enormous number of neuron connections capture the intricate complexity of quantum many-body wavefunctions. This advance leads to unprecedented accuracy in paradigmatic quantum models, opening up new avenues for simulating and understanding complex quantum phenomena.

Discovering the governing laws of dynamics from data, relevant to biology and social science, remains challenging for systems exhibiting stochasticity. The authors propose an approach to infer the stochastic differential equations of complex systems from experimental data.

Efficient heat dissipation is crucial for electronics. Interfacial thermal resistance (ITR) poses considerable challenges that require innovative solutions. Machine learning approaches could enhance ITR predictions by analysing large datasets to guide the development of inorganic, amorphous and 2D materials for advanced thermal management in next-generation electronic devices.

An improved optimization algorithm enables the training of large-scale neural quantum states in which the enormous number of neuron connections capture the intricate complexity of quantum many-body wavefunctions. This advance leads to unprecedented accuracy in paradigmatic quantum models, opening up new avenues for simulating and understanding complex quantum phenomena.

Software is much more than just code. It is time to confront the complexity of licenses, uses, governance, infrastructure and other facets of software in science. Their influence is ubiquitous yet overlooked.

Nature Computational Science wants to publish your computational social science research.