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Workshop: Artificial Intelligence for Materials Science (AIMS)
July 12-14, 2022 (Virtual)
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The Materials Genome Initiative (MGI) promises to expedite materials discovery through high-through computation and experiment. Application of Artificial-intelligence (AI) tools such as machine-learning, deep-learning and various optimization techniques are critical to achieving such a goal. Some of the key areas of applications in employing AI techniques to materials are: developing well-curated and diverse datasets, choosing effective representation for materials, inverse materials design, integrating autonomous experiments and theory, and choosing appropriate algorithm/work-flow. The idea of including physics-based models in the AI framework is also fascinating. Lastly, uncertainty quantification in AI based predictions for material properties and issues related to building infrastructure for disseminating AI knowledge are of immense importance for making AI based investigation of materials successful. This workshop is intended to cover all the above-mentioned challenges. To make the workshop as effective as possible we plan to mainly focus on inorganic solid-state materials, but are not limited by it. NOTE: As the maximum number of registrations for AIMS2022 workshop has been reached, we provide a tentative waiting list form for interested participants.
Workshop topics
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bookmark_borderDataset and tools for employing AI for materials
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bookmark_borderIntegrating experiments with AI techniques
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bookmark_borderGraph neural network
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bookmark_borderComparison of AI techniques for materials
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bookmark_borderChallenges applying AI to materials
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bookmark_borderUncertainty quantification and building trust in AI predictions.
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bookmark_borderGenerative modeling
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bookmark_borderUsing AI to develop classical force-fields
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bookmark_borderNatural language processing
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bookmark_borderUsing AI to develop classical force-fields
Confirmed speakers
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bookmark_borderSimon Billinge (Columbia University)
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bookmark_borderShyue Ping Ong (UC San Diego)
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bookmark_border Heather Kulik (MIT)
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bookmark_borderTian Xie (Microsoft)
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bookmark_border Chris Rackauckas (MIT)
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bookmark_border Ekin Dogus Cubuk (Google).
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bookmark_borderTim Mueller (Johns Hopkins)
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bookmark_borderJorg Behler (University of Goettingen)
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bookmark_borderJacqueline Cole (Cambridge University)
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bookmark_borderGeoffrey Hautier (Dartmouth)
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bookmark_border Trevor David Rhone (RPI)
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bookmark_border Wei Chen (Northwestern University)
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bookmark_border Keith Butler (UKRI)
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bookmark_borderKedar Hippalgaonkar (NTU/A*STAR)
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bookmark_borderBoris Kozinsky (Harvard University)
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bookmark_borderAdama Tandia (Corning Inc.)
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bookmark_borderMaria Chen (ANL)
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bookmark_borderRama Vasudevan (ORNL)
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bookmark_border Zachary Ulsisi (CMU)
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bookmark_border Raymundo Arroyave (TAMU)
Commitee members

perm_identity Material Scientist
business NIST
location_on Gaithersburg, Maryland

perm_identity Material Scientist
business NIST
location_on Gaithersburg, Maryland

perm_identity Material Scientist
business NIST
location_on Gaithersburg, Maryland

perm_identity Material Scientist
business NIST
location_on Gaithersburg, Maryland

perm_identity Material Scientist
business NIST
location_on Gaithersburg, Maryland

perm_identity Material Scientist
business NIST
location_on Gaithersburg, Maryland
External co-organizer: Mahesh Neupane (mahesh.r.neupane.civ@army.mil)
JARVIS in numbers
JARVIS - FF
1471
Datasets
Feb. 25, 2019
JARVIS - DFT
65286
Datasets
Feb. 25, 2021
JARVIS - ML
111783
Datasets
Feb. 25, 2019