Sort by
Refine Your Search
-
through molecular dynamics, simulations, and benchmarks Active Learning in Configurational and Chemical Spaces Integrate uncertainty-aware MLFFs into active learning frameworks Explore automated dataset
-
datasets across broad chemical space Evaluate models through molecular dynamics, simulations, and benchmarks Active Learning in Configurational and Chemical Spaces Integrate uncertainty-aware MLFFs
-
, medicine, nursing and midwifery. Research at the department integrates basic and translational, molecular and computational approaches with a focus on cancer biology, as well as clinical research projects in
-
background in computational and/or mechanistic modelling of physical, chemical, or biological systems (e.g., PBPK, molecular dynamics) Experience with cheminformatics libraries and chemical datasets is an
-
SD- 26053 PHD IN ULTRA-FAST MACHINE-LEARNING INTERATOMIC POTENTIALS FOR NANOINDENTATION OF TIC MA...
dynamics (MD) simulations of different materials families composed of Ti and C. Titanium carbides, for example, exhibit exceptional hardness, high melting point, wear and abrasion resistance, and many other
-
apply ultra-fast machine-learning interatomic potentials (UFPs, Xie et al., npj Comput. Mater., 2023, 10.1038/s41524-023-01092-7 ) for long, multi-million-atom molecular dynamics (MD) simulations