Data-driven strategies for fragment library design

Statistical inference of molecular-network properties

Machine learning with filters derived from random-matrix theory

Multiscale modelling of organic semiconductors and electronic devices

Develop, optimize, deploy machine-learning pipelines

Machine-learning library for molecular and materials modelling

Random-matrix frameworks for statistical modelling

Toolkit for charge- and energy-transport simulations

... started off as a library for atomistic and molecular convolutional descriptors and kernels for machine learning. Next to the successful SOAP descriptor and variants thereof, it implements nonlinear filtering strategies, graph kernels and attribution routines. The library has a C++ core with python bindings, allowing for fast execution and simple extension.
Visit SOAP++ on GitHub

... is a python toolkit for the design and deployment of random-matrix frameworks for machine learning. The core of the library implements filters and transformations informed by random-matrix theory. Models are flexibly constructed as pipelines acting on a state object that records data input and output.
Visit librmt on GitHub

... (or: Versatile Object-oriented Toolkit for Coarse-graining Applications) started as a C++ software suite for post-processing of molecular-dynamics data. Its two major components are VOTCA-CSG (a library for systematic coarse-graining of particle systems, V. Rühle et al.) and VOTCA-CTP (a library for charge- and energy-transport simulations), recently joined by VOTCA-XTP (an extension for quantum-chemical calculations developed in the group of B. Baumeier).
Visit VOTCA on GitHub

Monte-Carlo, molecular dynamics and a little bit of machine learning

Computer simulation methods in chemistry and physics
This a course on how to solve the many-body problem of statistical mechanics using molecular-dynamics and Monte-Carlo simulations, taught in collaboration with Dr Rosana Collepardo. It is directed at 4th-year students of the Natural Sciences Tripos.
Download slides (Part I)
Download slides (Part II) - Coming soon

Copyright 2019 | Carl Poelking