Articles

A theoretical framework for quantum neural network (QNN) overparametrization, a phase transition in loss landscape complexity, is established. The precise characterization of the critical number of parameters offered is expected to impact QNN design.

This study presents an ab initio approach for the real-time charge carrier quantum dynamics in the momentum space, which is computationally more efficient than conventional real-space non-adiabatic molecular dynamics method. The method is applied to study hot carrier dynamics in graphene, which provides insights about the phonon-specific relaxation mechanism.

Kirigami is an ancient art form that is now increasingly studied and applied in science and technology. This work presents an additive approach for the computational design of kirigami and two fabrication strategies for its physical instantiation.

A disease space is constructed from clinical records by embedding all diseases and considering a patient’s space coordinates as a measure of their health state. This measure was associated with 108 genetic loci, on which models were built to predict various morbidities.

Mental conflict has been regarded as subjective instead of quantitative. This study developed a data-driven method to decode temporal dynamics of conflict between reward and curiosity, which can elucidate mechanisms of irrational decision-making.

This study proposes a diffusion model, ProteinSGM, for the design of novel protein folds. The designed proteins are diverse, experimentally stable and structurally consistent with predicted models

GAME-Net is a graph deep learning model trained with small molecules containing a wide set of functional groups for predicting the adsorption energy of closed-shell organic molecules on metal surfaces, avoiding expensive density functional theory simulations.

NeCLAS is a machine learning pipeline that can accurately and efficiently predict nanoscale interactions, which has broad applications in biological processes and material properties.

A machine learning algorithm speeds up the sampling of rare assembly events, discovers their mechanisms, extrapolates them across chemical and thermodynamic space, and condenses the learned assembly mechanisms into a human-interpretable form.

A manifold learning method called T-PHATE is developed for high-dimensional time-series data. T-PHATE is applied to brain data (functional magnetic resonance imaging) where it faithfully denoises signals and unveils latent brain-state trajectories which correspond with cognitive processing.

The study shows that scale-specific oscillations and scale-free neuronal avalanches in resting brains co-exist in the simplest model of an adaptive neural network close to a non-equilibrium critical point at the onset of self-sustained oscillations.

A biasing energy derived from the uncertainty of a neural network ensemble modifies the potential energy surface in molecular dynamics simulations to rapidly discover under-represented structural regions that meaningfully augment the training data set.

A topological data analysis-driven machine learning model for guiding protein engineering is proposed, complementing protein sequence and structure embeddings when navigating the fitness landscape.

The concept of evolving scattering networks is proposed for material design in wave physics. The concept has the potential to enable network-based material classification, microstructure screening and the design of stealthy hyperuniformity with superdense phases.

A generative deep learning model of molecular structure is combined with supervised deep learning models of molecular properties to achieve high-throughput (multi-)property-driven design of organic molecules.

A method that infers gene networks and rate parameters directly from single-molecule fluorescence in situ hybridization RNA snapshot data is proposed and demonstrated on synthetic and real data, providing insights on data from S. cerevisiae and E. coli.

A framework is presented to extrapolate the range of behaviors for influenza antibodies. Using this basis set of behaviors, the collective action of multiple antibodies can be teased apart to describe the individual antibodies within.

Design choices for dimensionality reduction on calcium imaging recordings are systematically evaluated, and a method called calcium imaging linear dynamical system (CILDS) is proposed for performing deconvolution and dimensionality reduction jointly.

This work provides a physics-based theoretical framework for accurate protein–ligand binding affinity estimation based on molecular dynamics simulations, enhanced sampling, non-parametric reweighting and the orientation quaternion formalism.

A hybrid functional (CF22D) with higher across-the-board accuracy for chemistry than most existing non-doubly hybrid functionals is presented by using a large database and a performance-triggered iterative supervised training method.