Reviews & Analysis

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?

By developing an efficient spin symmetry penalty, a recent study has substantially accelerated the calculation of accurate energies with correct spin states in variational Monte Carlo for both ground and excited states of quantum many-particle systems.

A recent study proposes DeepBlock, a deep learning-based approach for generating ligands with targeted properties, such as low toxicity and high affinity with the given target. This approach outperforms existing methods in the field while maintaining synthetic accessibility and drug-likeness.

A recent study has modeled and quantified the expected rise in electronic waste due to the increasing deployment of generative artificial intelligence.

A recent study introduces a series of approaches that predict protein fitness and stability after the introduction of mutations. The work focuses on combining different data and pre-training to overcome data scarcity.

A method is introduced to compute provable bounds on noise-free quantum expectation values from noisy samples, promising potential applications in quantum optimization and machine learning.

Active machine learning is employed in academia and industry to support drug discovery. A recent study unravels the factors that influence a deep learning models’ ability to guide iterative discovery.

A recent study proposes a strategy for the prediction of genetic perturbation outcomes by breaking it down into three subtasks: identifying differentially expressed genes, determining expression change directions, and estimating gene expression magnitudes.

A deep learning algorithm is presented to classify single-particle tracking trajectories into theoretical models of anomalous diffusion and detect if the trajectory is related to a model not originally found within the training dataset.

A highly efficient reconstruction method has been developed for the direct computation of Hamiltonian matrices in the atomic orbital basis from density functional theory calculations originally performed in the plane wave basis. This enables machine learning calculations of electronic structures on a large scale, which are otherwise not feasible with standard methods, and thus fills a methodological gap in terms of accessible length scales.

A recent study proposes an approach that integrates unspliced and spliced mRNA count data by leveraging shared biophysical states across cells, offering a more interpretable and consistent framework for determining cell clusters based on transcriptional kinetics.

A computational model is proposed to provide a better understanding of human altruism, highlighting the role of multiple motives that influence altruistic behaviors.

By developing a machine learning framework, a recent study substantially accelerates the calculation of electron–phonon coupling, making it computationally feasible to predict and understand a range of important physical phenomena, including electronic transport, hot-carrier relaxation, and superconductivity in complex materials.

According to a recent study, a small network consisting of four leaky integrate-and-fire neurons can reproduce the behavior of a single Hodgkin–Huxley neuron, thereby bridging the gap between endogenous and exogenous complexity.

To address the challenge of pretraining foundational models with large datasets, a multi-task approach is proposed, thus helping to overcome the data scarcity problem in biomedical imaging.

A recent study proposes a computational method for the design of free-form metamaterials systems. The method simplifies the design process by avoiding the use of anisotropic materials that are usually required for the conventional methods. The method can be applied in designing both two-dimensional and three-dimensional metamaterials that are subject to multiple physical fields.

A method leverages protein structural data to predict T-cell receptor–peptide interactions for unseen peptide epitopes, which can be particularly useful for applications in cancer immunotherapy, autoimmunity studies, and vaccine design.

A recent study shows that, by leveraging nonlinear optical processes in disordered media, photonic processors can transform high-dimensional machine-learning data, using nonlinear functions that are otherwise challenging for digital electronic processors to compute.

A two-stage learning algorithm is proposed to directly uncover the symbolic representation of rules for skill acquisition from large-scale training log data.

CASTLE, a deep learning approach, extracts interpretable discrete representations from single-cell chromatin accessibility data, enabling accurate cell type identification, effective data integration, and quantitative insights into gene regulatory mechanisms.