Reviews & Analysis

Physical computing, particularly photonic computing, offers a promising alternative by directly encoding data in physical quantities, enabling efficient probabilistic computing. This Perspective discusses the challenges and opportunities in photonic probabilistic computing and its applications in artificial intelligence.

A recent study assesses bias in artificial intelligence (AI)-generated medical language to find differences in age, sex, and ethnicity. An optimization technique is proposed to improve fairness without sacrificing performance.

The inverse design of functional crystalline materials via generative models is a rapidly growing field, but one that faces challenges in representation and generation architectures. This Perspective systematically examines these limitations and explores strategies for future improvement.

A comprehensive open-source benchmarking suite is presented. It can be used to evaluate the performance and functionality of various quantum software development kits for manipulating and compiling quantum circuits.

This Perspective highlights the vital role of physics-based modeling in computational enzyme engineering, exploring key advances, challenges and future steps. By integrating machine learning, these approaches can enhance each other, unlocking the full potential of enzyme design and discovery.

Inspired by the morphologies of xeric plant leaves, we have developed biomimetic liquid crystal elastomer bilayers that can bend, spiral and twist. These adaptive shape morphing structures can twist to improve water collection efficiency and wind resistance, suggesting their potential application in adaptive water collection and directional transportation.

The continuous drive for efficiency in high-performance computing has led to the development of new frameworks aimed at optimizing large-scale simulations. One such advancement is dynamic block activation, a method designed to significantly accelerate continuum models while making full use of modern computing architectures that combine central processing units and graphics processing units.

We introduce free-energy machine (FEM), an efficient and general method for solving combinatorial optimization problems. FEM combines free-energy minimization from statistical physics with gradient-based optimization techniques in machine learning and utilizes parallel computation, outperforming state-of-the-art algorithms and showcasing the synergy of merging statistical physics with machine learning.

We propose a diversity-aware population modeling framework using Bayesian multilevel regression and post-stratification to quantify sociodemographic disparities in cognitive development. Our approach improved subgroup estimates, guiding targeted public health strategies and addressing biases in traditional models to support more equitable decision-making.

Predicting stable crystal structures for complex systems that involve multiple elements or a large number of atoms presents a formidable challenge in computational materials science. A recent study presents an efficient crystal-structure search method for this task, utilizing symmetry and graph theory.

Identifying promising synthesis targets and designing routes to their synthesis is a grand challenge in chemistry and materials science. Recent work employing machine learning in combination with traditional approaches is opening new ways to address this truly Herculean task.

Identifying pleiotropic associations for rare variants in multi-ethnic biobank-scale whole-genome sequencing data poses considerable challenges. This study introduced MultiSTAAR as a scalable and robust multi-trait rare variant analysis framework designed for both coding and noncoding regions by integrating multiple variant functional annotations and leveraging multivariate modeling across diverse phenotypes.

We present Spatial Modeling Algorithms for Reactions and Transport (SMART), a software package that simulates spatiotemporally detailed biochemical reaction networks within realistic cellular and subcellular geometries. This paper highlights the use of SMART in several biological test cases including cellular mechanotransduction, calcium signaling in neurons and cardiomyocytes, and adenosine triphosphate synthesis.

A recent study proposes a data structure that addresses crucial challenges related to storage and computation of large genome databases.

A recent study demonstrates through numerical simulations that implementing large language models based on sparse mixture-of-experts architectures on 3D in-memory computing technologies can substantially reduce energy consumption.

By combining several probabilistic AI algorithms, a recent study demonstrates experimentally that the inherent noise and variation in memristor nanodevices can be exploited as features for energy-efficient on-chip learning.

To achieve an advanced neuromorphic computing system with brain-like energy efficiency and generalization capabilities, we propose a hardware–software co-design of in-memory reservoir computing. This co-design integrates a liquid state machine-based encoder with artificial neural network projections on a hybrid analog–digital system, demonstrating zero-shot learning for multimodal event data.

We present Morpho, an extensible programmable environment that uses finite elements for shape optimization in soft matter. Given an energy functional that incorporates physical boundaries and effects such as elasticity and electromagnetism, together with additional constraints to be satisfied, Morpho predicts the optimized shape and structure adopted by the material.

An extensive audit of large language models reveals that numerous models mirror the ‘us versus them’ thinking seen in human behavior. These social prejudices are likely captured from the biased contents of the training data.

Today’s high-performance computing systems are nearing an ability to simulate the human brain at scale. This presents a new challenge: going forward, will the bigger challenge be the brain’s size or its complexity?