Research articles

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 microscopic moiré spin model that enables the description of moiré magnetic exchange interactions via a sliding-mapping method is proposed. The twist-angle and substrate-influenced magnetic phase diagram addresses disagreements between theories and experiments.

A deep learning ab initio method for studying magnetic materials is developed, reducing the computational cost and opening opportunities to predict the electronic properties of magnetic superstructures, such as magnetic skyrmions.

A method to compute the quantum harmonic free energy contributions in large materials and biomolecular simulations at a reasonable cost is proposed, making quantum mechanical estimates of thermodynamic quantities possible for complex systems.

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.

An approach, CorALS, is proposed to enable the construction and analysis of large-scale correlation networks for high-dimensional biological data as an open-source framework in Python.

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.

A computational method is proposed to generate the full-scale dataset of the tridimensional position and connectivity of neurons in the CA1 region of the human hippocampus starting from high-resolution microscopy images and experimental data.

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.

Data visualization is widely used in science, but interpreting such visualizations is prone to error. Here a dynamic visualization is introduced for capturing more information and improving the reliability of visual interpretations.

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.

A computational workflow centered on probabilistic machine learning is developed to reconstruct the energy dispersion from photoemission band-mapping data. The workflow uncovers previously inaccessible momentum-space structural information at scale.

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.