Unsupervised learning methods for molecular simulation data
Unsupervised learning is becoming an essential tool to analyze the increasingly large
amounts of data produced by atomistic and molecular simulations, in material science, solid …
amounts of data produced by atomistic and molecular simulations, in material science, solid …
Dilute alloys based on Au, Ag, or Cu for efficient catalysis: from synthesis to active sites
The development of new catalyst materials for energy-efficient chemical synthesis is critical
as over 80% of industrial processes rely on catalysts, with many of the most energy-intensive …
as over 80% of industrial processes rely on catalysts, with many of the most energy-intensive …
Transition metal nanoparticles as nanocatalysts for Suzuki, Heck and Sonogashira cross-coupling reactions
Transition metal (TM) catalyzed cross-coupling reactions are the utmost versatile and
reliable methods for the production of many industrially important fine chemicals. The …
reliable methods for the production of many industrially important fine chemicals. The …
Hierarchical materials from high information content macromolecular building blocks: construction, dynamic interventions, and prediction
Hierarchical materials that exhibit order over multiple length scales are ubiquitous in nature.
Because hierarchy gives rise to unique properties and functions, many have sought …
Because hierarchy gives rise to unique properties and functions, many have sought …
Theory of anisotropic metal nanostructures
KA Fichthorn - Chemical Reviews, 2023 - ACS Publications
A significant challenge in the development of functional materials is understanding the
growth and transformations of anisotropic colloidal metal nanocrystals. Theory and …
growth and transformations of anisotropic colloidal metal nanocrystals. Theory and …
Uncertainty estimation for molecular dynamics and sampling
Machine-learning models have emerged as a very effective strategy to sidestep time-
consuming electronic-structure calculations, enabling accurate simulations of greater size …
consuming electronic-structure calculations, enabling accurate simulations of greater size …
Machine learning accelerates quantum mechanics predictions of molecular crystals
Quantum mechanics (QM) approaches (DFT, MP2, CCSD (T), etc.) play an important role in
calculating molecules and crystals with a high accuracy and acceptable efficiency. In recent …
calculating molecules and crystals with a high accuracy and acceptable efficiency. In recent …
[HTML][HTML] Deep learning for UV absorption spectra with SchNarc: First steps toward transferability in chemical compound space
Machine learning (ML) has shown to advance the research field of quantum chemistry in
almost any possible direction and has also recently been applied to investigate the …
almost any possible direction and has also recently been applied to investigate the …
Discovery and prediction capabilities in metal-based nanomaterials: An overview of the application of machine learning techniques and some recent advances
The application of machine learning (ML) techniques to metal-based nanomaterials has
contributed greatly to understanding the interaction of nanoparticles, properties prediction …
contributed greatly to understanding the interaction of nanoparticles, properties prediction …
Machine learning for accurate force calculations in molecular dynamics simulations
The computationally expensive nature of ab initio molecular dynamics simulations severely
limits its ability to simulate large system sizes and long time scales, both of which are …
limits its ability to simulate large system sizes and long time scales, both of which are …