MMM2018

Daniel Urban

(Fraunhofer IWM, Germany)

Tilmann Hickel

(MPIE, Germany)

Marc Asta

(UC Berkeley, USA)

Minoru Otani

(AIST, Japan)

This symposium covers innovative high-throughput and materials-informatics approaches for the discovery and design of novel materials with targeted functional properties. The paradigm of a theory-guided data-driven materials research, which is based on the innovative methodologies of the modern information society, is currently extending the traditional means of material science, which are based on fundamental principles and empirical wisdom from experiment, theory, or simulation. The challenge is to use recent developments in the fields of data mining, machine learning, and artificial intelligence for the identification of structure-composition-property relationships in the high-dimensional materials data space.

Recent success cases which illustrate the synergy of condensed-matter physics and big-data informatics include combinatorial high-throughput screenings with first-principles approaches such as density-functional theory and machine-learning approaches that exploit internet repositories of materials data. The material systems studied cover ionic or electronic conductors for electrochemical energy converters, permanent magnet materials without supply-risky rare-earth elements, and cost-efficient high-performance materials for consumer-electronics devices. The wider applicability of data-driven and physics-informed strategies for the search for novel materials is foreseen and shall be encouraged by this symposium.

The symposium intends to gather scientists who employ numerical simulations or combinatorial experiments for the high-throughput screening of materials and/or make use of machine learning approaches for analyzing large amounts of materials data in order to extract the underlying physics. Submissions of contributions are solicited, which deal with developments of computational or experimental high-throughput techniques for accumulating, analyzing, interpreting, storing, and sharing fundamental knowledge about materials in efficient ways in order to employ the respective “big data” for the design of novel materials for multiple functions on or across several length scales. Contributions may range, and preferably bridge, from knowledge-driven research to application-oriented development.

• "Machine Learning and Materials Discovery"
• By Gus Hart, Brigham Young University, USA

• "Finding the needle in the haystack: Materials discovery through high-throughput ab initio computing and data mining"
• By Geoffroy Hautier, Université Louvain, Belgium

• "Novel two-dimensional materials: Materials discovery, data provenance, and workflow reproducibility"
• By Nicola Marzari, EPFL Lausanne, Switzerland

• "Computational exploration of strong permanent magnet compounds"
• By Takashi Miyake, AIST & NIMS, Japan

• "Exploration of large ab initio data spaces to design structural materials with superior mechanical properties"
• By Joerg Neugebauer, MPI fuer Eisenforschung

• "Data-Driven Discovery of new materials"
• By Isao Tanaka, Kyoto University, Japan

• "Using Machine-Learning to Create Predictive Material Property Models"
• By Chris. Wolverton, Northwestern University, USA