The journal of physical chemistry letters
Sep 9, 2022
The use of machine learning (ML) is exploding in materials science as a result of its high predictive performance of material properties. Tremendous trainable parameters are required to build an outperforming predictive model, which makes it impossib...
The journal of physical chemistry letters
Aug 18, 2022
Machine-learning force fields have become increasingly popular because of their balance of accuracy and speed. However, a significant limitation is the use of element-specific features, leading to poor scalability with the number of elements. This wo...
The journal of physical chemistry letters
Aug 5, 2022
Two-dimensional (2D) spectroscopy encodes molecular properties and dynamics into expansive spectral data sets. Translating these data into meaningful chemical insights is challenging because of the many ways chemical properties can influence the spec...
The journal of physical chemistry letters
Jun 21, 2022
Infrared (IR) spectra contain detailed and extensive information about the chemical composition and bonding environment in a sample. However, this information is difficult to extract from complex heterogeneous systems because of overlapping absorptio...
The journal of physical chemistry letters
May 24, 2022
Δ-machine learning, or the hierarchical construction scheme, is a highly cost-effective method, as only a small number of high-level energies are required to improve a potential energy surface (PES) fit to a large number of low-level points. However...
The journal of physical chemistry letters
May 11, 2022
Using a deep neuronal network (DNN) model trained on the large ANI-1 data set of small organic molecules, we propose a transferable density-free many-body dispersion (DNN-MBD) model. The DNN strategy bypasses the explicit Hirshfeld partitioning of th...
The journal of physical chemistry letters
May 6, 2022
molecular dynamics (AIMD) is an established method for revealing the reactive dynamics of complex systems. However, the high computational cost of AIMD restricts the explorable length and time scales. Here, we develop a fundamentally different appro...
The journal of physical chemistry letters
Apr 28, 2022
Machine learning (ML) is believed to have enabled a paradigm shift in materials research, and in practice, ML has demonstrated its power in speeding up the cost-efficient discovery of new materials and autonomizing materials laboratories. In this Per...
The journal of physical chemistry letters
Apr 22, 2022
Memristors are candidate devices for constructing artificial neurons, synapses, and computational networks for brainlike information processing and sensory-motor autonomous systems. However, the dynamics of natural neurons and synapses are challengin...
The journal of physical chemistry letters
Dec 23, 2021
Inspired by the brain, future computation depends on creating a neuromorphic device that is energy-efficient for information processing and capable of sensing and learning. The current computation-chip platform is not capable of self-power and neurom...