Machine learning workflow predicting the enantioselectivity (e.e.) of chemical reactions by Prof. Clemence Corminboeuf

nexFrontier: Novel Materials Discovery – The Marvel of Computational Science

Join us in the second edition of nexFrontier series, where we have invited speakers from NCCR MARVEL, EPFL, Fudan University and Beihang University to discuss about the new material discovery.

Machine-learning models used to search a “sea” of over 143 000 catalysts by Prof. Dr. Clemence Corminboeuf

Materials, as the basic resources of production in the physical world, have accompanied human beings going through generations of transformations in history, from wood and stone in the Stone Age to compounded fibber, plastic, and semiconductor in the modern era. Novel materials are highly demanded in an ever-advancing and the increasingly sophisticated world; however, finding the best material has been a slow, painstaking process. Researchers would often have to test hundreds and thousands of materials one by one to find the wanted properties. This process is expensive and time-consuming and laid barriers to development in most industries. Yet today, thanks to powerful big data and simulation techniques, as well as machine learning algorithms, scientists could propel innovation forward at blazing speed with findings that they have never considered.

Leveraging on the advanced informatics platform driven by big databases and high-throughput quantum simulations, NCCR MARVEL (Swiss National Centre of Competence in Research on Computational Design and Discovery of Novel Materials) is the avant-garde in new material discovery. The centre targets new materials that provide solutions to the big challenges in energy efficiency, information and communication, and pharmaceuticals. In the second edition of nexFrontier series, Professor Nicola Marzari from EPFL, director of NCCR MARVEL will share with us the great acceleration in materials discovery enabled by quantum-mechanical simulations; Prof. Clemence Corminboeuf from the Computational Chemistry division at EPFL will talk about how today’s modern computational power, quantum advancement, and machine learning have enabled and sped up large-scale data analyses especially in the field of computational chemistry. Then, their Chinese counterpart, Prof. Xingao Gong, Academician of the Chinese Academy of Science, Computational Condensed Matter Physicist, and Professor of Fudan University will talk about Computational design of new energy materials from the perspective of Physical Sciences; then Prof. Peng Wang from Beihang University whose research focuses on uncertainty quantification will show us the potential of computational science in searching effective drug products such as pharmaceutical microspheres.

Event Rundown

16:00-16:03           Introduction

Lefei Chen, Junior Project Manager of Academic Relations, Swissnex in China

16:03-16:15           The Great Acceleration in Materials Discovery

Prof. Dr. Nicola Marzari, Chair of Theory and Simulation of Materials at EPFL, Director of NCCR MARVEL, Excellence Chair at the University of Bremen


Prof. Dr. Xingao Gong, Computational Condensed Matter Physicist, Academician of the Chinese Academy of Sciences, Professor and Doctoral supervisor of Fudan University


16:27-16:39           Computational Chemistry in the Age of Big Data

Prof. Dr. Clemence Corminboeuf, Scientist, Professor in theoretical chemistry at EPFL, member of NCCR MARVEL


16:39-16:51           Computational Science and its Potential: An Example in Search of Effective Drug Product Design

Prof. Dr. Peng Wang (Koby), Professor of uncertainty quantification at Beihang University, Co-principal investigator for the Chinese Material Genome project

16:51-17:10           Q&A


In collaboration with


The NCCR MARVEL is a centre on Computational Design and Discovery of Novel Materials created by the Swiss National Science Foundation in May 2014. MARVEL targets the accelerated design and discovery of novel materials, via a materials’ informatics platform of database-driven high-throughput quantum simulations, powered by advanced electronic-structure capabilities, for predictive accuracy innovative sampling methods to explore configuration/composition space application of big-data concepts to computational materials science. The search is focused on materials for energy harvesting, storage, and conversion, materials for information-and-communication technologies, and organic crystals/pharmaceuticals. Codes, data and workflows of the project are disseminated through the Materials Cloud platform and the Quantum Mobile virtual machine, both powered by the materials’ informatics framework AiiDA.


nexFrontier series is the latest webinar series at Swissnex in China which spotlights cutting-edge research in Switzerland and China. With a particular focus on NCCRs and top-class research labs in China, this series hopes to introduce the most pioneering research advancements in Switzerland and China to an enthusiastic audience and promotes potential exchange and collaboration between the two labs or institutions.


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