Articles in 2021

The authors have developed an adaptive reinforced dynamics approach, which improves the efficiency when exploring the configurational space and free energy landscape of large biomolecules, such as proteins.

Many cities are vulnerable to disaster-related mortality and economic loss. Smart City Digital Twins can be used to facilitate disaster decision-making and influence policy, but first they must accurately capture, predict, and adapt to the city’s dynamics, including the varying pace at which changes unfold.

Defining cell identity is a fundamental task in dissecting the cellular heterogeneity in single-cell data. Here the authors developed Cepo, a method to uncover cell identity genes and enhance the retrieval of cellular identities from scRNA-seq data.

A data-driven solution of partial differential equations is developed with conditional generative adversarial networks, which could be used in both forward and inverse problems.

The accurate determination of correlation energy is a challenging task in many-electron quantum chemistry calculations, especially for metals. A recent work proposes an efficient scheme to speed up the calculation of correlation energy, reducing the computational time by up to two orders of magnitude.

A new study proposes a full-scale model of the entorhinal cortex–dentate gyrus–CA3 network, providing a conceptual overview of the computational properties of this brain network, to show that it is an efficient pattern separator.

The authors demonstrate an effective approach to lower the computing time required to accurately reach the thermodynamic limit in quantum many-body calculations. This method can be applied to solve problems in a wide range of material systems, including metals, insulators and semiconductors.

The authors present a full-scale model of the entorhinal cortex–dentate gyrus–CA3 network based on experimental data to show that fast lateral inhibition plays a key role in pattern separation.

A special prize for outstanding research in high-performance computing (HPC) applied to COVID-19-related challenges sheds light on the computational science community’s efforts to fight the ongoing global crisis.

A human mobility model that takes into account social interaction and long-term memory mechanisms is proposed, shedding more light onto the interplay between human movements and urban growth.

The study shows that a memory-aware and socially coupled human movement model can reproduce urban growth patterns at the macro level, providing a bottom-up approach to understand urban growth and to reveal its connection to human mobility behavior.

A machine learning-assisted directed evolution method is developed, combining hierarchical unsupervised clustering and supervised learning, to guide protein engineering by iteratively exploring the large mutational sequence space.

Compressing scientific data is essential to save on storage space, but doing so effectively while ensuring that the conclusions from the data are not affected remains a challenging task. A recent paper proposes a new method to identify numerical noise from floating-point atmospheric data, which can lead to a more effective compression.

Climate data are often stored at higher precision than is needed. The proposed compression automatically determines the precision from the data’s bitwise real information, removing any false information and leading to a more efficient compression.

The authors present a framework for modeling cell interactions using rigid bodies, which can used to represent cells as free moving polygons, to allow epithelial layers to smoothly interact, to model bacteria and to robustly represent membranes.

The authors propose a molecular modeling approach to simulating the galvanostatic charge–discharge process of supercapacitors under constant-potential conditions. This model can accurately predict supercapacitor dynamics when compared with experimental observations.