Machine learning interatomic potentials (MLIPs) have become an essential tool to enable long-time scale simulations of materials and molecules at unprecedented accuracies. The aim of this collection ...
Machine learning is transforming many scientific fields, including computational materials science. For about two decades, scientists have been using it to make accurate yet inexpensive calculations ...
By applying new methods of machine learning to quantum chemistry research, Heidelberg University scientists have made significant strides in computational chemistry. They have achieved a major ...
How additive manufacturing advanced the development of functionally graded materials. Why compositionally graded materials present a greater challenge to materials engineers. How computational ...
When experiments are impractical, density functional theory (DFT) calculations can give researchers accurate approximations of chemical properties. The mathematical equations that underpin the ...
Computational Chemistry is the study of complex chemical problems using a combination of computer simulations, chemistry theory and information science. Also called cheminformatics, this field enables ...
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