Nicholas John Browning

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Through millions of years of evolution, Nature has accomplished the development of highly efficient and sustainable processes and the idea to understand and copy natural strategies is therefore very appealing. However, in spite of intense experimental and ...

The calculation of the electronic structure of chemical systems, necessitates computationally expensive approximations to the time-independent electronic Schrödinger equation in order to yield static properties in good agreement with experimental results. ...

The training of molecular models of quantum mechanical properties based on statistical machine learning requires large data sets which exemplify the map from chemical structure to molecular property. Intelligent a priori selection of training examples is o ...

The development of thermostable and solvent-tolerant metalloproteins is a long-sought goal for many applica- tions in synthetic biology and biotechnology. In this work, we were able to engineer a highly thermo- and organic solvent- stable metallo variant o ...

Due to their very nature, ultrafast phenomena are often accompanied by the occurrence of nonadiabatic effects. From a theoretical perspective, the treatment of nonadiabatic processes makes it necessary to go beyond the (quasi) static picture provided by th ...

Nicholas John Browning

Chat with Graph Search

Lessons from Nature: Computational Design of Biomimetic Compounds and Processes

Applications of Artificial Intelligence to Computational Chemistry

Genetic Optimization of Training Sets for Improved Machine Learning Models of Molecular Properties

A genetic algorithm based design and experimental characterization of a highly thermostable metalloprotein

Nonadiabatic effects in electronic and nuclear dynamics