Forecasting the occurrence of earthquakes, particularly of high magnitude (≥5), is a notoriously difficult problem (Nat. Comput. Sci. 3, 115; 2023). Often, scholars devote their efforts to detect and make use of possible signals that may warn of an impending earthquake — such as surface deformations prior to a shock — but observing and collecting…

Recent estimates have indicated that the median cost of researching and developing a drug is over US$1 billion per drug, with research and development taking an average of ten years. Thus, using machine learning (ML) to accelerate research timelines and reduce development costs is an appealing avenue, particularly for drug discovery centers in lower-to-middle-income countries…

Large-scale and long-time molecular dynamics (MD) simulations are important for investigating materials properties of technological relevance. One of the core elements in MD simulations is the force field model or the inter-atomic potentials, which determine the prediction accuracy, the simulation size, and the simulation duration. The recent development of machine-learning interatomic potentials (MLIPs), such as…

Pedestrian dynamics, a field that studies pedestrian movement and behavior, is gaining much importance as passenger numbers increase in many areas. Previous studies on passenger waiting behavior at railway platforms have revealed that the distribution of passengers in a space is not uniform but influenced by entryways and train stops. While these studies have been…

Recent estimates have indicated that the median cost of researching and developing a drug is over US$1 billion per drug, with research and development taking an average of ten years. Thus, using machine learning (ML) to accelerate research timelines and reduce development costs is an appealing avenue, particularly for drug discovery centers in lower-to-middle-income countries…