Materials chemistry has been one of the fastest growing areas in chemistry in the past decade. This is driven by materials discovery for renewable energy, clean environment, and biomedical applications. Understanding structure-property relationships is fundamental to the chemistry of materials and key to realizing materials functions. Advances in theoretical understanding, algorithms, computational power, big data, artificial intelligence and machine learning are enabling computational tools to play an increasing role in materials discovery, development and optimization. For example, recently developed data mining techniques and machine learning approaches enable the virtual synthesis of novel materials, with their properties being predicted on a computer before ever being synthesized in a laboratory.
This workshop aims to bring together computational scientists working on focused topics of materials chemistry to exchange ideas and to stimulate discussion. The 2021 workshop will concentrate on machine learning in materials chemistry: from data to descriptors, predictions and insights; from materials informatics and high-throughput screening to virtual synthesis; from neural network potentials to exploration of potential-energy surface and structure prediction; from catalysis to energy storage. This workshop will provide a unique opportunity for the participants to broaden their view and deepen their understanding of machine learning and its broader impacts in materials and chemistry.
We wish to ensure an intimate workshop setting, with no more than 20 to 25 participants. If you are interested in attending, but have not received an invitation, please contact the workshop organizer before registering.
TSRC is about expanding the frontiers of science, exploring new ideas, and building collaborations. The workshop schedule will allow for substantial unstructured time for participants to talk and think.
De-en Jiang, Department of Chemistry, University of California, Riverside
Graeme Henkelman, Department of Chemistry, University of Texas at Austin
Richard Hennig, Department of Materials Science and Engineering, University of Florida, Gainesville
Jeffrey Greeley, School of Chemical Engineering, Purdue University
With a workshop organizer's approval, students/post docs/lab members/retired senior scientists can register for $50 if they are not participating as a presenter. Please register at the normal rate and send an email to Sara Friedberg (firstname.lastname@example.org) to let her know that you would like to participate at that rate. When you email her, please include the name of the workshop and the name of the workshop organizer who approved that participation rate. Thank you!
|Ai, Qianxiang||Fordham University|
|Aspuru-Guzik, Alan||University of Toronto|
|Chen, Xiaoyu||Colorado School of Mines|
|Cook, Cameron||University of California Riverside|
|Csanyi, Gabor||University of Cambridge|
|Das, Abhishek||Facebook AI Research|
|Fung, Victor||Oak Ridge National Laboratory|
|Galli, Giulia||University of Chicago|
|Heras Domingo, Javier||Carnegie Mellon University|
|Kharabadze, Saba||Binghamton University|
|Khot, Aditi||Purdue University|
|Kolmogorov, Alexey||Binghamton University|
|Kwon, Hyuna||University of California, Riverside|
|Lee, Seung Eun||Fordham University|
|Rupp, Matthias||University of Konstanz|
|Sandoval, Ernesto||Binghamton University|
|Savoie, Brett||Purdue University|
|Sparks, Taylor||University of Utah|
|Thummuru, Dhileep Nagi Reddy||University of california Riverside|
|Wang, Xiaoyu||University at Buffalo|
|Wei, Liu Tsung||Colorado School of Mines|
|Xin, Hongliang||Virginia Polytechnic Institute and State University|
|Zurek, Eva||University at Buffalo, SUNY|