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YOUNG RESEARCHER’S WORKSHOP ON MACHINE LEARNING FOR MATERIALS 2022 09-13 May 2022, Trieste (IT) – Report

State of the Art and Workshop Objectives

Data-driven methods have emerged as a novel paradigm to advance materials discovery over the past decade. Machine learning potentials (MLPs) enable the sampling of  trajectories with the same accuracy of high-level electronic structure methods but at a fraction of their cost. MLPs have been established as a means to rationalize puzzles previously unapproachable by atomistic simulations. Elsewhere, the chemical and physical properties of large chemical spaces are now screened in a high-throughput fashion by leveraging artificial intelligence methods, materials simulations, and automation protocols. The screening is not only viable for the case of known structures, but generative models can now autonomously generate previously-unseen, and tailored, molecules and crystals structures with a target property. Machine learning (ML) methods therefore serve as formidable surrogates to accelerate expensive computational screening, but also to guide experimental screening and extract knowledge from data gathered via high-throughput or from literature. Furthermore, the advances in the theoretical understanding of how machine learning algorithms work is demystifying and surpassing the vision of data-driven approaches as magic black-boxes.

This event built upon the state-of-the-art in the field of machine learning for materials in two ways. Firstly, it helped instruct the next generation of young researchers on the latest advancements in methods and applications of AI for material discovery through didactic lectures and hands-on tutorials. Secondly, the workshop promoted a discussion on the implications of the latest advancements in data-driven methods on the different sub-areas of Materials discovery, bringing together experts of different fields in the world of machine learning for materials and promoting cross-contamination of ideas and techniques.

Outcomes

The introductory part of the workshop paved the way to an overview of supervised and unsupervised methods which well represented the state of the art in machine learning for materials. A number of tutorials on publicly available open source and documented codes were offered to participants.

Many discussions during the workshop focused on the design of atomic descriptors for supervised tasks in materials science. Two techniques were highlighted by various speakers as top performers: atom-density (e.g., atomic cluster expansion (ACE)) representation, and equivariant learnt representations via message-passing networks (MPE(3)N). Both methods efficiently encode information about local atomic environments and allow for very accurate learning of atomic or structural properties (e.g., forces, energies, etc.). A discussion of a unified theory to reconcile the dichotomy between ACE and MPE(3)N was a recurring trend across invited and contributed speakers. State-of-the-art Pareto fronts of prediction speed-accuracy, and an analysis on the memory and time requirements for training were also often reported.

A second set of common themes and techniques related to the use of generative models. Their application ranged across disciplines: SMILES-based short-term memory recurrent neural networks were used to design drugs; cartesian coordinates and autoencoders were utilized to unbiasedly obtain equivariant representation for quantitative structure-activity relationships; classical descriptors and variational autoencoders were adopted to map states during dynamics and/or glass phenotypes.

A third set of common themes related to the use of machine learning methods to accelerate the first principles screening of material stability. The application of this method ranges from energy materials (e.g., perovskites) to molecular crystals (e.g., drugs) and leveraged uncertainty-driven methods to iteratively and accurately chart convex-hulls and establish thermodynamically stable phases.

A final class of major scientific points of discussion encompasses a broader spectrum of topics which enables to bridge complexity gaps between data models and experiments, as so to establish rational design paradigms from structure-property relationships. A heterogeneous list of techniques debated includes (but is not limited to): machine learning potentials for fast-and-accurate simulation of complex dynamics; transfer learning to derive universal predictors which work well across different chemistries; experimental characterization and manipulation of materials via data-driven optimization.

Overall, the need for cross-contamination of expertises emerged as a strong and resonant topic throughout the conference. During panel discussions, presentations, and face-to-face interactions, participants expressed the need to escape scientific bubbles and gather information about techniques, applications, and developments in fields adjacent to their own research. In this regard the presence of leaders in atomistic modeling, computer science, machine learning, experimentalists, and industry representatives enabled an interdisciplinary exchange of perspectives and experiences.

Our workshop was indeed specifically designed to address the needs for multi-disciplinary cross-contamination, and we received resounding feedback about how such an effort was successful. All (to our knowledge, and according to a currently ongoing survey) participants to the workshop, be it an invited speaker, an online attendee, a poster presenter, or a young researcher that attended their first conference on the topic, was largely positive about the structure, topics, and organization of the event.

Finally, all talks, tutorials, and panel discussions that took place during the workshop have been recorded and uploaded on Youtube, the ICTP website, and the conference’s website, thus making high-quality scientific content available to anyone.

Dissemination 

The workshop allowed researchers in adjacent fields to meet each other, learn about the most recent advances of their colleagues, and network in a scientifically fertile environment. Moreover, the presence of an introductory school in the workshop allowed for young researchers that are new to the field to learn about potential applications of machine learning technologies to their area of interest, and to better appreciate the advancements presented by invited leading researchers during the “workshop” part of the conference. From the networking perspective, the event north star was indeed to enable the cross-fertilization of research networks, promoting the encounter and collaboration between domain experts.

From the computational perspective, tutorials used Google Colabs seamlessly. All the codes discussed were open-source, and all tutorials presented during the workshop are available on the conference’s website for anyone to follow.

The computational expense associated with the machine learning for materials codes described is mostly related to data-generation. In this case data from the literature were utilized. A discussion on how to develop accessible and efficient machine learning codes which do not necessarily necessitate expensive computational architectures (e.g., GPU highly parallel facilities) has been put forward. Similarly, a reflection on the need to push open-science (open code, open data, etc.) to ensure the democratization of the field has been discussed. While there exist open repositories and robust generation routine for computational data (e.g., Materials Cloud, IoChemBD, NOMAD, Materials Project, AFLOW ), a discussion on how to promote the creation of FAIR compliant routines and databases for experiment-related data and codes was initiated

Scientific, Technologic, and Societal Impact

The discovery of novel materials for catalytic applications, energy storage, diagnostics, and therapeutics is one of the key ways in which the goal of a sustainable and equitable development can be reached (see also UN Sustainable Development Goals (SDGs) )

The recent years have seen a surge in the development and application of machine learning technologies in materials science. The initial results are extremely promising, with applications ranging from the design of novel catalysts for CO2 reduction to the exploration of the chemical space of energy materials for novel Lithium-free batteries. While giant steps have been made within the design of algorithms and the understanding of the theoretical backbone of machine learning in materials and chemical science, widespread and large scale applications are just starting to bloom and to have a real-world impact, allowing, e.g., for the discovery of novel stable materials or the design of never-seen-before catalysts or drugs.

This workshop pushed forward research in these critical fields by providing both a way to spread research advancements to young researchers, and a way to initiate collaboration between widely renowned scientists of different fields. This workshop further equipped the next generation of scientists (in academia and industry alike) with skills in tackling the complex problems related to high-performance materials design.

The presence of industry representatives (Roche, Bayer, Microsoft Research, AIndo) on one hand offered an overview of possible career pathways to participants. From an alternative perspective, the state-of-the-art methods and achievements obtained by our community were promoted to these R&D teams.

Final Report Scientific Meeting

Molecular Simulation 2022: past, present and future

Event website: https://bricabrac.fisica.unimo.it/ErcMlk80/

The meeting took place on 25th to 29th June 2022 in Erice (Italy), at the Villa San Giovanni complex, previously a clerical summer residence but now used for conferences and cultural manifestations.

This event brought together old and new friends to discuss state of the art methods and current challenges in molecular simulations, reflect on many years of development and applications, and reflect on the future of the field. The meeting enabled scientists from different generations to meet in an atmosphere that combined excellence and open discussions and paved the way for new scientific perspectives and collaborations. There were 29 speakers coming from all over the world, and over 125 participants in total (full capacity of Erice site that had some restrictions in place due to the Covid pandemic). The program was composed of 9 sessions each with 3 to 4 speakers, and there were several poster sessions. The meeting was also an occasion to celebrate Prof Mike Klein’s 80th birthday, the numerous important and remarkable contributions Mike has made to chemistry, biophysics, materials science, and, in particular, the field of computer simulation. Speakers highlighted in their talks recent advances in modelling and simulation in biophysics, biochemistry, material science, chemistry and physics.

Eight PhD students were awarded prizes among the 64 posters presented in the meeting. The prizes were contributed by the MDPI publisher, CECAM and the RSC.

We thank Psi-k for their generous support in making this a successful meeting.

Full program & Invited Speakers

Program and Timetable

SCIENTIFIC REPORT ON THE PSI-K WORKSHOP: “ATOMIC SCALE MATERIALS MICROSCOPY: THEORY MEETS EXPERIMENT”

Psi-k workshop on
“Atomic scale materials microscopy: theory meets experiment”
National Railway Museum, York (UK)
26-28 June 2017

Summary:

Atomic scale materials characterization is now one of the major drivers of technological innovation in areas such as nanoelectronics, catalysis, medicine, clean energy generation and energy storage. This can in a large part be attributed to advances in electron and scanning probe microscopies, which are now able to provide atomically resolved structural, chemical and electronic characterization of a wide range of functional materials. However, the types of systems relevant to applications, which include surfaces, interfaces, nanocrystals and two-dimensional materials, are complex and interpreting experimental images and spectra is often extremely challenging. On the other hand, parallel advances in theoretical approaches means that theory can often offer invaluable guidance. These approaches include first principles methods for structure prediction, simulation of scanning probe and electron microscopy images, and prediction of various spectroscopic signatures (e.g. EELS and STS). Some of the most impressive examples of this kind of research in recent years have combined complementary theoretical and experimental approaches in a synergistic way to unravel the complex structure of materials. This type of integrated approach is increasingly being recognised as critical to advanced materials research and development by both industry and research funders.

It was in this context that the Psi-k workshop: “Atomic scale materials microscopy: theory meets experiment” was held between the 26th and 28th of June 2017 at the National Railway Museum in York (UK). The scientific focus was on the application and development of first principles methods that, in synergy with advanced microscopy techniques (e.g. TEM, EELS, STM, AFM), can help to unravel the structure and properties of materials at the atomic scale. Open to both experts and newcomers the aim was to provide a rounded overview of emerging methods and challenges in the field, and provide an opportunity for in-depth discussion and exchange of ideas. Continue reading SCIENTIFIC REPORT ON THE PSI-K WORKSHOP: “ATOMIC SCALE MATERIALS MICROSCOPY: THEORY MEETS EXPERIMENT”

Richard Martin’s ‘Electronic Structure’

martin

The study of the electronic structure of materials is at a momentous stage, with the emergence of computational methods and theoretical approaches. Many properties of materials can now be determined directly from the fundamental equations for the electrons, providing insights into critical problems in physics, chemistry, and materials science. This book provides a unified exposition of the basic theory and methods of electronic structure, together with instructive examples of practical computational methods and real-world applications. Appropriate for both graduate students and practising scientists, this book describes the approach most widely used today, density functional theory, with emphasis upon understanding the ideas, practical methods and limitations. Many references are provided to original papers, pertinent reviews, and widely available books. Included in each chapter is a short list of the most relevant references and a set of exercises that reveal salient points and challenge the reader.

Highly recommended!