Cambridge University Press has published a new book co-authored by researchers from the University of Warsaw, offering both an introduction to machine learning and deep neural networks, and an overview of their applications in quantum physics and chemistry — from reinforcement learning for controlling quantum experiments to neural networks used as representations of many-body quantum states.The book appears at a time when artificial intelligence is becoming an increasingly recognized tool for scientific discovery — a development recently recognized with the Nobel Prize in Chemistry awarded for the AlphaFold tool. It serves as a timely guide for PhD students and researchers looking to apply modern machine learning methods to complex quantum problems.

Researchers in the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) have developed a machine learning framework that can predict with quantum-level accuracy how materials respond to electric fields, up to the scale of a million atoms – vastly accelerating simulations beyond quantum mechanical methods, which can model only a few hundred atoms at a time.

A microscopic enzyme could be the key to helping nitrogen fertilizers stick better to the soil and prevent run-off that causes harmful algal blooms, according to a new review article published by a Michigan State University research team.

During embryonic development, thousands of cells divide and move as one. Understanding the mechanisms that coordinate this collective behavior remains a significant challenge in biology and the physics of living systems. Researchers from UC San Diego have discovered that avian embryos control their size and shape using modular, independent physical mechanisms. This work may help develop strategies for engineering synthetic biomaterials.

Conventional string theory’s predictions are incompatible both with the observation of dark energy, which appears to be causing our universe’s expansion to speed up, and with viable theories of quantum gravity, instead predicting a vast ‘swampland’ of impossible universes. Now, a new analysis by FQxI physicist Eduardo Guendelman, of Ben-Gurion University of the Negev, in Israel, shows that an exotic subset of string models–in which the tension of strings is generated dynamically–could provide an escape route out of the string theory swampland. The analysis was reported in The European Physical Journal C, in March.

This year marks the opening of the United Nations Decade of Action for Cryospheric Sciences, an international initiative focused on the rapid changes occurring in glaciers, snow cover, ice sheets, sea ice, and permafrost and their impacts on the planet. MBARI’s advanced technology will play a critical role in this effort, providing important data about the Arctic seafloor and the Southern Ocean. MBARI research is answering fundamental questions about polar environments and other components of the cryosphere. This information can help resource managers and policymakers make decisions about the future of this important ecosystem.