The choice of simulation methods in computational materials science is driven by a fundamental trade-off: bridging large time-and length-scales with highly accurate …
BACKGROUND Optical spectrometry is one of the most powerful and widely used characterization tools in scientific and industrial research. Benchtop laboratory spectrometer …
Best practices in machine learning for chemistry | Nature Chemistry Skip to main content Thank you for visiting nature.com. You are using a browser version with limited support for …
The development of machine learning models for electrocatalysts requires a broad set of training data to enable their use across a wide variety of materials. One class of materials …
Transition-metal complexes are attractive targets for the design of catalysts and functional materials. The behavior of the metal–organic bond, while very tunable for achieving target …
M Meuwly - Chemical Reviews, 2021 - ACS Publications
Machine learning (ML) techniques applied to chemical reactions have a long history. The present contribution discusses applications ranging from small molecule reaction dynamics …
One of the most exciting tools that have entered the material science toolbox in recent years is machine learning. This collection of statistical methods has already proved to be capable …
COVID-19 has resulted in huge numbers of infections and deaths worldwide and brought the most severe disruptions to societies and economies since the Great Depression …
Density functional theory (DFT) plays a pivotal role in chemical and materials science because of its relatively high predictive power, applicability, versatility, and computational …