Thermodynamic Cartography is a theoretical method for the intelligent and systematic study of highly reactive nanoscale materials as a function of size, shape […]
MMM lab head and Office of the Chief Executive Science Leader Dr Amanda Barnard represented Australia as our Plenary Speaker at […]
HRMC is an atomistic Monte Carlo modelling code that trials atomic configurations, while fitting experimentally motivated constraints and simultaneously minimizing the […]
Many of the important property of materials, composites and aggregates of particles relate to the lack of matter; the voids. […]
A host of modules for analysing the structure and connectivity of materials and nanostructure, in detail. Each module takes as […]
The understanding of nanoparticle self-assembly and aggregation has applications in fields as diverse as nanophotonics and drug delivery in medicine. […]
Quantum mechanical calculations typically involve some trade-off between the accuracy of the method (i.e. how well the real-world chemistry and […]
The acid dissociation constant (pKa) provides a direct measure deprotonation reactions, which are fundamentally important to chemistry and biochemistry. Unfortunately, experimental measurements […]
Of course, generating and organizing results is just part of the problem. The analysis of high-throughput (HT) computational data involves encoding structural features, data […]
Quantum Monte Carlo (QMC) method is becoming increasingly popular in calculations of molecular properties because of its exclusive description of […]
Polymers are a ubiquitous part of modern life, but each application depends on the molecular structure on an individual and collective […]
The design of nanostructured materials with applications in heterogeneous catalysis is a hot topic in research. It has already been […]
Comprehensive sampling of large, detailed and heterogeneous structural configurations spaces is a daunting task; even for small data sets containing […]
Benchmarking in computational chemistry has become an arduous task. Reliable computational thermochemistry requires properties and processes to be predictable within and accuracy […]
