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Proton-coupled electron transfer occurs at a variety of length and time scales and often in complex environments. This Perspective summarizes a range of modeling strategies that can be used together to address remaining challenges and provide a better understanding of such reactions.
Materials design has largely expanded to multiple compositions, which requires the mixing of an increasing number of elements. In this joint Focus issue with Nature Materials, we take a closer look at the role of computational methods for guiding exploration within such vast chemical spaces.
The computational characterization of short-range order in compositionally complex materials relies on effective interatomic potentials. In this Review, challenges and opportunities in developing advanced potentials for such systems are discussed, with a focus on machine learning-based potentials.
Machine learning models have been widely applied to boost the computational efficiency of searching vast chemical space of compositionally complex materials. This Perspective summarizes the recent developments and proposes future opportunities, such as the physics-informed machine learning models.
Complex materials offer promises for exotic materials properties that enable novel applications. Nevertheless, there are numerous computational challenges for a rational design of defects in such materials, thus inspiring opportunities for developing advanced defect models.
This work involved the design of a multi-view manifold learning algorithm that capitalizes on various types of structure in high-dimensional time-series data to model dynamic signals in low dimensions. The resulting embeddings of human functional brain imaging data unveil trajectories through brain states that predict cognitive processing during diverse experimental tasks.
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.
We present a computational method to generate a single-cell-resolution model of human brain regions starting from microscopy images. The developed method has been benchmarked to reconstruct the CA1 region of a right human hippocampus, including anatomical cell organization, connectivity, and network activity.
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.
We propose a minimal and analytically tractable class of neural networks, the adaptive Ising class. By inferring the model’s parameters from resting-state brain activity recordings, we show that scale-specific oscillations and scale-free avalanches can coexist in resting brains close to a non-equilibrium critical point at the onset of self-sustained oscillations.
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.
Dr Núria López-Bigas, ICREA Research Professor and group leader in biomedical genomics at the Institute for Research in Biomedicine, discusses with Nature Computational Science about her research on cancer genomics.
A biasing potential is derived from the uncertainty of a neural network ensemble and used to modify the potential energy surface in molecular dynamics simulations and facilitate the determination of underrepresented structural regions.
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.
