TerraMath – software solutions for geological and environmental applications
Interview of Dr Robert Faber, TerraMath
Writers: Alexandra Simperler and Gerhard Goldbeck (Goldbeck Consulting)
The case is based on workflows similar to “Modelling of topography and Sedimentation along syn-sedimentary faults: WinGeol/SedTec” Faber,R.; Wagreich, M.; Austrian Journal of Earth Sciences 97(2005)60-66. “Climate as main factor controlling the sequence development of two Pleistocene alluvial fans in the Vienna Basin (eastern Austria) – a numerical modelling approach” Salcher, B.C.; Faber, R.; Wagreich, M.; Geomorphology 115 (2010) 215-227. DOI: 10.1016/j.geomorph.2009.06.030
This work has received funding via the EMMC-CSA project from the European Union‘s Horizon 2020 research and innovation programme under Grant Agreement No 723867
Reaching quantitative agreement with available experimental data, and predicting properties where data are absent, molecular and mesoscopic modelling transforms chemical engineering data science.
This minisymposium discusses virtual marketplaces and platforms by which the knowledge from multiscale modelling and simulation can be transferred to engineering practice. This requires an institutionalized collaboration between academic and industrial engineering, scientific computing, and applied mathematics, and jointly governed semantic assets to ensure the interoperability of models, numerical solvers, and databases.
Initiatives working toward this (FORCE, MarketPlace, and VIMMP) are represented at the minisymposium jointly with translators who connect method development with engineering practice.
Detailed information on the programme of the Minisymposium!!
Projects are funded within the European Union’s Horizon 2020 research and innovation programme FORCE – Grant Agreement No. 721027 VIMMP – Grant Agreement No. 760907 MarketPlace – Grant Agreement No. 760173
People, tool, process and data can be seen as the four pillars of a successful deployment of materials modelling in the industrial community. During this meeting, we would like to develop strategies with leading modelling experts in industry through discussions.
The objective of this meeting is therefore to gather a consistent number of experts from the different stakeholders interested in materials adoption/development, and provide a discussion platform for industrial requirements as compared to state-of the-art modelling techniques. The ambition of the workshop is to cover the more relevant aspects which are critical to the widespread adoption of materials modelling techniques in industry. To this, six thematic sessions are organized and will cover:
state-of-the-art modelling techniques and guidelines for further model developments;
the perspective of the European software owners/developers;
the economic impact of materials modelling on industrial innovation;
strategies for improving the two-way transfer of knowledge between academia and industry – i.e. Translators and their training requirements;
interoperability requirements and frameworks – e.g. ontologies – for integration of models and software. Finally, a special session will be dedicated to the discussion of the potential of artificial intelligence in the framework of materials modelling, with particular emphasis on the benefit for high-throughput simulations, big data and their mining – e.g. data-driven modelling – towards Industry 4.0.
Organisation, Contact & Support
This is an “Invitation-only” event. You will be contacted by the organisers. After you have confirmed your participation you will receive the registration link.
Are you interested in software commercialisation and business models relevant to the field of materials modelling? EMMC would be pleased to invite you to a workshop where you can learn from a range of experts.
Experienced Software Owners will share their successful practice with the audience and will cover open-source, non-profit and commercial business models.
The workshop is aimed in particular at PostDocs, academic research and industrial R&D group leaders or advanced PhD students who actively work on materials modelling software and codes and are looking into business models. The workshop will be a collaborative event. We expect active interactions between impulse speakers, trainers and motivated participants. Hence, we would like to invite everyone who has interest to express their experience and aim to participate and to apply for one of the 50 available places.
We know that atoms and molecules are attracted to each other simply because we know that matter condenses around us. We also know that they repel each other as molecules cannot be pushed very close to each other due to a strong repulsion. This means that in all attempts to describe inter-molecular interactions we need a wall at the short distances and a well at longer distances. A common wall-well model is the Lennard-Jones potential.
When we do ab initio modelling with nuclei and electrons the interactions between the nuclei, in the presence of the electrons, come from the first-principles and are calculated from the laws of physics. No need to think about attractions or repulsions separately. But when we go over from quantum mechanics (electronic models) to classical mechanics (atomistic models) we have to model both the sizes of the atoms and their mutual interactions. For this we have the several decades old molecular mechanical (MM) force fields (FF) which still have a strong position in all-atom (AA) molecular simulations. More sophisticated terms (polarizable, reactive, cross-terms, non-additive, three-body, etc) are being developed worldwide but most simulations are still performed with the simplest possible terms as they often are very robust and established models in classical physics. A development is in progress where machine learning techniques are applied to create accurate potential energy surfaces and force fields.
However, when we go from atomistic models to mesoscopic models we do no more have similar well-defined conceptual interaction blocks as in AA models to construct a mesoscale force field. Mesoscopic particles are generally very much softer than atoms, thus requiring much simpler and softer potentials. Nearly all the internal degrees of freedom that we have in molecules are gone except artificial bonds to connect the beads and sometimes artificial angles for three neighbouring beads. A few types of mesoscale force fields exist, but they can rather be characterized as ad hoc where some very simple objects (spheres) have been fitted to roughly reproduce some experimental data. It is not clear at all how to construct generally valid or transferable mesoscale force field. In the future, there will be an increased need for mesoscale and coarse-grained simulations as there will be a need to move towards larger and more complex soft-matter systems, simulated over longer times. But as long as we do not have accurate mesoscopic models it is difficult to couple and link these models with continuum or more macroscopic models. In materials science it may simply be better to connect atomistic models with continuum models, for example using, with finite elements and skip the third level of discrete models, namely the mesoscopic models.
We would like to initiate an informal discussion about mesoscopic discrete particle models. What is/are the best model(s) for soft-particle mesoscopic simulations according to you ? Tell us about your experiences and visions!
Kersti Hermansson & Aatto Laaksonen – Uppsala University
Every one of us who writes computer programs to solve scientific and engineering problems knows that the coding itself takes only a small part of total development time. What takes most of the time is the tedious debugging of the program before the programmer and the users are satisfied. Sometimes not all of the bugs will ever be found which may, or may not, have consequences.
There are many ways to carry out the debugging, from placing simple write statements in the code, to checking that calculated numbers are reasonable, to using debugging and optimization tools. Some programming languages are safer than others. In general syntax errors are relatively easy to find and fix already by the compiler, while, for example, logical errors may take a long time to spot as the program runs well and keeps producing results. Additional types of errors are those when we attempt to divide by zero or an undefined number while running the program.
We learn to find the errors (most of them at least) in our own ways by systematically or randomly searching for them. There exist many well-known and embarrassing errors, from computer chips being rather bad in maths to maps for GPSs not leading anywhere or rather over a cliff or satellites not starting to orbit. Long ago as a post-doctoral fellow at the IBM laboratories, a colleague of us shared a story from the lab he came from where an office mate had changed a sign in the program he had been writing for the last three months. Not as a practical joke but rather to make his life miserable in a tough and competitive environment.
By writing this blog we would like you to tell about your own experiences concerning bugs in the program. What was the most fatal or curious bug you had in your own software or in a software written by other? And how did you find it? Share your experiences with us!
Kersti Hermansson & Aatto Laaksonen – Uppsala University
This EMMC-CSA White Paper provides a basis for the standards of modelling software development and addresses areas such as method description, assumptions, accuracy and limitations; testing requirements; issue resolution; version control; user documentation and continuous support and resolution of issues.
The document is based on the work already carried out in the context of the EMMC to drive the adoption of software quality measures, and to ensure sustainable implementation of this EMMC initiative. Given the high level of sophistication of each of the developments which solve particular aspects of the multi-physics/chemistry spectrum of materials modelling, the industrial usefulness of individual achievements requires integration into larger software systems. Thus, guidelines and standards are needed, which will enable the exploitation of these codes.
The major outcome are guidelines for academic software developers creating materials modelling codes. In many cases, design decisions taken at an early stage have unforeseeable consequences for many years ahead. In this context, the white paper gives academic researchers a framework, which paves the way for successful integration and industrial deployment of materials modelling. This goal is achieved by addressing a range of topics including model descriptions and software architectures, implementation, programming languages and deployment, intellectual property and license considerations, verification, testing, validation, and robustness, organization of software development, metadata, user documentation, and support.
In version 2.0 an appendix with “Online resources to development of scientific software” has been added.