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Materials Theory Group

 

Ephemeral Data Derived Potentials

The ddp package contains a suite of tools to construct and test data derived interatomic potentials. They were originally designed to be used with the airss first principles structure prediction package. Ab initio random structure searching (AIRSS) can be used to generate data, and exploit the generated ddp potentials to potentially accelerate searches. They are referred to as ephemeral data derived potentials as they can quickly be custom-built for a particular set of structure searching parameters, discarded and regenerated as those parameters change. The methodology is introduced in Pickard, Ephemeral data derived potentials for random structure search, 2022EDDP version 0.2, which extends the use of EDDPs to high-accuracy molecular and lattice dynamics, is presented in Salzbrenner et al., Developments and further applications of ephemeral data derived potentials, 2023.

Prerequisites

The airss package should be installed, as well as the source for the nn package, before installing the ddp and repose packages. Specifically, the SPGLIB library built by airss is reused by the repose package. Various command line tools, such as gnu parallel are used. The grace plotting package is supported. gfortran 9.3 or above is recommended, but ifort is supported.

Obtaining the eddp packages

The eddp packages (ddp, repose and nn) are released under the GPL2 licence, as is the airss package. You can download the full source code for all the packages below. Note that you will require a Unix-like environment in order to build the code. Windows users can install a Linux subsystem if required.

Download current versions of airss, ddp, repose and nn

Getting started

The airss, ddp, repose, and nn packages are compiled and installed using make && make install, and if successful you should add {airss,ddp,repose}/bin to your path. The ddp-batch suite of scripts enables convenient training of EDDPs on batch queueing systems.

Next steps

The README file for the ddp package provides some sample data for generating potentials, as well a instructions for perfoming an iterative fit.

Contactairss@msm.cam.ac.uk

Specific versions of EDDP:

EDDP Version 0.2

EDDP Version 0.1

Latest news

Welcome to Will Galloway and Dr. JiuYang Shi !

25 November 2025

Will Galloway has joined the MTG as a PhD student in October. Dr. JiuYang Shi will collaborate with MTG, he is a postdoc in Prof. Angelos Michaelides.

AIRAPT Conference in Matsuyama (Japan)

28 September 2025

Dr. Ryuhei Sato (Assistant Professor at Tokyo University) and Dr. Maélie Caussé attended the 2025 AIRAPT Conference.

Dr. Ruyhei Sato and Kim Kamsma visiting MTG. And Welcome to Stefano Racioppi !

1 September 2025

Dr. Ruyhei Sato from Tokyo University and Tim Kamsam (PhD student in Utrecht University in Theoretical Physics and Mathematics) visited Material Theory Group this summer for few months ! Picture of the MTG (from left to...

Chris Pickard on the American Scientist podcast

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MTG group leader Chris Pickard was featured on the American Scientist podcast this month. Access the podcast using this link to hear Chris discuss a range of topics, from future research directions of the group, to pondering...

CS2PA Workshop in Poitiers (France)

20 June 2025

The first edition CS2PA workshop, a series of hands-on tutorials on Crystal Structure Prediction & Machine-learned potentials was held in Poitiers University (France), (see program of CS2PA ) MTG leader Chris Pickard and...

Congratulations Pascal Salzbrenner !

10 June 2025

Dr. Pascal Salzbrenner completed his PhD on High-throughput Ab Initio Phase Diagram Prediction in 2025.

Successful Gordon Research Conference on Materials at High Pressure

27 September 2024

The Materials Theory Group was well represented at the Gordon Research at High Pressure Conference in Holderness, New Hampshire, from 14-19 July. This year, the conference was chaired by our group leader, Prof. Chris Pickard...