Home » Materials Modelling
Category Archives: Materials Modelling
Join peers from a wide range of backgrounds sharing an interest in materials digitalisation, from digital online marketplaces for materials modelling to interoperability and ontologies at IntOp2018, organised by the European Materials Modelling Council (EMMC) on 6-7 November in Freiburg im Breisgau, Germany.
Materials Modelling Marketplaces are next generation systems integrating tangible and intangible materials model components to support enhanced innovation. These digital marketplace take advantage of recent advances in information technologies to establish online innovation platforms to explore, learn, and create advanced materials modelling solutions covering all models and domains.
Wider interoperability across models and data is key to enhanced integration, marketplace and digitalisation. It requires agreement based on ontologies for materials, including characterisation, modelling, processing of materials as well as data, models and services.
The workshop will discuss the establishment of a digital single marketplace as innovation hub for the advancements of materials based industries. The workshop addresses both key technological and organisational human capital gaps.
The EMMC is seeking support of the entire community for the establishment of common standards for access to all online materials modelling resources including data repositories of materials properties, online modelling workflows, translation, education and training services.
The workshop will also host a kickoff session for the International Materials Ontology Interest Group led by the EMMC.
For further details and registration, see the EMMC website.
What is materials modelling good for?
This webinar examines the impact materials modelling makes, both on a macro-economic and organisational level. In particular, the wide range of impact types and mechanisms will be discussed, based on evidence from surveys and interviews with users. It will be argued that a much wider potential remit for modelling should be considered than is commonly done.
In the light of these impact mechanisms, ways of measuring and increasing impact are discussed. Setting and assessing impact levels is shown to be important, and in this context a maturity model will be introduced. Higher levels of maturity are associated with integration and optimisation and set the scene for modelling as a key factor impacting on digitalisation.
The CEN (European Committee for Standardization) Workshop Agreement CWA 17284 “Materials modelling – terminology, classification and metadata” described in an earlier post has been published. Here is the Abstract:
This CWA includes definitions of fundamental terms for the field of materials modelling and simulation. Computational materials models in this CWA are understood to be physics-based models. This CWA does not include data-based models. The definitions enable a classification of materials models. Using the entity and physics equation concepts, leads to a relatively small number of distinct materials models replacing the current situation of opacity of materials models and simulations that make the field hard to access for outsiders. This CWA also provides a systematic description and documentation of simulations including the user case, model, solver and post-processor: the “materials MOdelling DAta” (MODA). This document seeks to organize the information so that even complex simulation workflows can be conveyed more easily and key data about the models, solvers and post-processors and their implementation can be captured. A template MODA for physics-based models is described in order to guide users towards a complete documentation of material and process simulations. The CWA is based on the Review of Materials Modelling (RoMM). A MODA for data-based models can be found in the RoMM.
This document provides the basis for moving the materials modelling field up the semantic spectrum, laying the foundation for knowledge organisation achieved in many other fields (see e.g. the Osthus presentation “From Big Data to Big Analysis” given at the EMMC Workshop on Interoperability in Materials Modelling). It lays the foundation for developing and ontology of materials modelling, enabling interoperability, reasoning and knowledge extraction in the materials science domain.
Following the proposal by a group of European scientists involved in materials modelling CEN (the European Committee for Standardization) has announced a new workshop on the subject “Materials modelling terminology, classification and metadata”. It is based on many years of effort led by the European Commission and the European Materials Modelling Council (EMMC), as expressed in the Review of Materials Modelling (RoMM), which will be released in its sixth edition in January 2017. The aim is to agree on a terminology and classification of materials models and organise the description of materials modelling applications based on a system referred to as MODA (Materials Modelling Data). A common terminology in materials modelling should lead to simplified and much more efficient communication and lower the barrier to utilising materials modelling. The end result is the adoption of a CEN Workshop Agreement (CWA), a best practices document for further standardisation efforts and input for the development of a future certification scheme.
In recognition of the importance of materials modelling for industrial innovation and the strength of Europe, a new Horizon 2020 project has been funded to augment and further boost the actions of the European Materials Modelling Council (EMMC). The new European Materials Modelling Council Coordination and Support Action (EMMC-CSA) includes 15 partners and is coordinated by TU Wien.
Goldbeck Consulting is part of the EMMC management team and leads Work Package 2 on Interoperability and Integration of materials modelling.
For further information, see the EMMC-CSA Press Release.
Industry Case Studies: combining discrete and continuum modelling to address industrial R&D challenges
Materials modelling is used today by a range of industries to improve efficiency and achieve breakthroughs in the development of new and improved materials and processes. A set of four case studies has been developed by the European Materials Modelling Council which demonstrate how industrial R&D problems have been addressed by the integration of different types of materials models and what technical and technological benefits and business impacts were achieved as a result.
The case studies cover a diverse set of applications and industries, including chemical processing (Covestro), discovery of new functional materials (IMRA Europe), additive manufacturing of engine parts (MTU Aero Engines) and magnetic hard drive materials (Seagate):
- Identification of Solvents for Extractive Distillation
- Discovery of new thermoelectric materials
- Simulation of additive manufacturing of metallic components
- Integrated Recording Model for Heat Assisted Magnetic Recording (HAMR)
The case studies were compiled with the support of the EC Industrial Technologies Programme.
An article just appeared which summarizes (and includes case examples for) some important lessons for the application of computational methods in drug design. I write about it here because it includes important messages also for the materials modelling and design field. There are differences of course since materials innovation is not always about new materials design. Nevertheless some of the key points are still valid and at least worth considering. I like the ‘principle of parsimony’, and also the conclusion about the importance of good software design. Much needed in the materials field as well.
Here are some key quotes and extracts from the paper .
The value of qualitative statements. Frequently, a single new idea or a pointer in a new direction is sufficient guidance for a project team. Most project impact comes from qualitative work, from sharing an insight or a hypothesis rather than a calculated number or a priority order. The importance of this observation cannot be overrated in a field that has invested enormously in quantitative prediction methods. We believe that quantitative prediction alone is a misleading mission statement for molecular design. …
Shaping chemical space. At any given point during a project, a team’s focus is either on expanding chemical space or on narrowing it down, for different aspects of problem solving and optimization. Broadening chemical space requires methods that create new ideas within a set of constraints. ….Narrowing down chemical space can be a simple filtering process or can be based on a specific hypothesis. Within a given project context, it is important to understand whether it is required to broaden or narrow down chemical space and to choose tools and approaches accordingly. As projects progress towards candidate selection, the “amplitudes” of narrowing and broadening space typically become smaller, but the concept stays the same.
The principle of parsimony. Molecular design is a conceptual process and therefore always at risk of losing touch with reality. The scientific questions should lead to the method, and not vice versa. To achieve this, it is a helpful guiding principle to keep things as simple as possible. Choosing the simplest possible explanation and the simplest possible computational protocol leads to agility and to a better focus on the key questions at hand. …
Annotation is half the battle. … Contextual information can add value almost anywhere. A good deal of frontloading work—computational, organizational—is often required to bring data into a useful shape. Proper frontloading work can turn sophisticated queries into simple lookup processes or visualization steps. There is a significant growth potential in this area.
Staying close to experiment. One way of keeping things as simple as possible is to preferentially utilize experimental data that may support a project, wherever this is meaningful. … Rational drug design has a lot to do with clever recycling. If consistently applied, these guidelines have significant implications for the current practice of molecular design.
Let us look at some of the more problematic aspects as well. Many computational methods introduce additional parameters and thus potential sources of error that make the predictive value harder to extract. …..
What is special about molecular design is the need to build solid hypotheses and to simultaneously foster creative thinking in medicinal chemistry. If we accept this, our focus may shift from the many semi-quantitative prediction tools that we have to methods supporting this creative process. Further improvements in computational methods may then have less to do with science than with good software engineering and interface design. The tools are a just means to an end. Good science is what happens when they are appropriately employed.
 A Real-World Perspective on Molecular Design. Bernd Kuhn, Wolfgang Guba, Jérôme Hert, David W. Banner, Caterina Bissantz, Simona Maria Ceccarelli, Wolfgang Haap, Matthias Körner, Andreas Kuglstatter, Christian D. Lerner, Patrizio Mattei, Werner Neidhart, Emmanuel Pinard, Markus G. Rudolph, Tanja Schulz-Gasch, Thomas J. Woltering, and Martin Stahl
J. Med. Chem., DOI: 10.1021/acs.jmedchem.5b01875 • Publication Date (Web): 15 Feb 2016