Tag Archives: workshop

Actively Learning Materials Science Workshop 2023

27.2.-3.3.2023 Helsinki/Espoo, Finland

Highly concentrated participants in one of the in-depth tutorial sessions.

From 27th to 3rd March 2023 the Actively Learning Materials Science workshop was held at Aalto University in Helsinki/Espoo, Finland. This workshop welcomed 81 in-person participants from 10 countries (and many more among the 50+ online participants), also comprising 12 invited members among lecturers, teaching assistants, organizers and technical helpers. The event was sponsored jointly by CECAM, the Psi-k organization, Aalto University, and the Finnish Center for Artificial Intelligence, with talk and poster prizes sponsored by Wiley.

The workshop was dedicated to active learning (AL) algorithms, i.e. algorithms where machine learning datasets are collected on-the-fly in the search for optimal solutions. Paradigmatic examples in this area include (but are not limited to) Active Learning methods, Reinforcement Learning protocols, and Bayesian Optimization approaches. In the tutorials, talks and poster presentations, the participants showcased how AL enables to tackle outstanding problems in the optimal design of experiments, efficient traversal of complicated search spaces for electronic structure simulations and high throughput screening.

A key strength of AL techniques lies in the automated manner in which the machine learning model selects the data to include into the dataset via acquisition strategies. The requested data points can then be evaluated via computation or experiment and included into the model iteratively, until the optimal solution converges. The resulting compact, maximally informative datasets make AL particularly suitable for applications where data is scarce or data acquisition expensive. In this way, AL has helped accelerate materials discovery  away from big-data and free of human bias. Despite recent successes, future applications of AL on experimental data are slow, given that key data infrastructure is still lacking. Working with multiple objectives, or multidimensional data remains challenging. Novel method development across the research field is needed to advance AL techniques and associated frameworks in materials research.

Actively Learning Materials Science (AL4MS) focussed on two key objectives, both from a pedagogical (first part of the event) as well as from an advanced perspective (second part of the event): 1) How could data infrastructures and AL algorithm development advance experimental materials discovery? 2) How could we combine multiple channels of information in the same AL model? Continue reading Actively Learning Materials Science Workshop 2023

10th ABINIT International Developer Workshop – Part 2

10th ABINIT International Developer Workshop – Part 2
May 16-19, 2022 Guidel-Plages, France

Event website
The complete list of participants can be found here.

General presentation

The ABINIT developer workshops form a series of events, crucial for the community of ABINIT developers, organized every two years. A unique occasion for most developers to acquire or maintain a global view of the project and stay up to date with the latest capabilities, planned developments, and overall strategy. The developer workshop is always an opportunity to invite external researchers, from other codes and communities, to exchange best practices and expertise…

This workshop was the second part of the 10th ABINIT developer workshop. It was held from 16th to 19th May 2022 in Guidel-Plages (Brittany, France).

Because of the COVID19 pandemic situation, in June 2021, the meeting occurred in a fully remote version, with only remote presentations and some group discussions. We missed several important parts of the workshop : small group discussions, thematic discussions, informal discussions, hackathons, etc. Many of the participants emphasized the need to meet again in person when the health situation permits it.

In May 2022 we organized the second part of the workshop, with a smaller number of participants, mostly based on the missing ingredients above, plus a few invited presentations. It was a complementary and entirely live/offline event, consisting of discussions, round tables and hackathons. The physical presence of developers was a requirement to have efficient round tables and informal discussions.

The workshop was mainly dedicated to implementations and decision making by the developers:

Every morning we had a session of hackathons. Divided into small groups, we worked on the ABINIT package : coding, improving the documentation, creating tutorials, interfacing the code with other software, etc. Each developer chose projects and hackathons according to his/her specific expertise in the project.

During two afternoons, we met collectively to discuss and consider the future of the code: future scientific themes, dissemination and the visibility, user experience improvement.

A third afternoon was dedicated to invited speakers’ presentations. The speakers were chosen because of their involvement in projects external/complementary to ABINIT. Continue reading 10th ABINIT International Developer Workshop – Part 2

Report on the “ML-IP 2021” workshop (Young and Early-career Researchers’ Tutorial on Machine Learning Interatomic Potentials)

Machine learning potentials have now established themselves as a method of choice in many atomistic simulation projects. This tutorial workshop was aimed at young and early-career researchers who are interested in using machine learning potentials in their work, but are unsure of where to start or of how feasible the proposed application would be.

While the field continues to produce new theoretical and methodological advances, there is now a large class of systems that can be treated with existing, established methods. The main issues now for new researchers entering the field are, first, choosing between the many different machine learning methods (and correspondingly many software packages) available, and second, learning about simulation workflows and best practices that are often undocumented, unwritten “common knowledge”.

The workshop was designed with two main aims: First, to give these researchers a solid introduction in the basic scientific techniques of designing, fitting, and validating a machine learning potential for a new system. Second, to provide a platform for young researchers interested in using machine learning potentials in their work to connect to those involved in developing methods for machine learning potentials, in order to accelerate the adoption of machine learning techniques in the wider atomistic simulation community. Continue reading Report on the “ML-IP 2021” workshop (Young and Early-career Researchers’ Tutorial on Machine Learning Interatomic Potentials)