The Conference for Machine Learning Innovation

Machine Learning Techniques for Estimating Science and Properties of Materials

Session
Join the ML Revolution!
Register until September 15:
✓ Save up to $323
✓ 3 Day Special
✓ Team discount
Register Now
Join the ML Revolution!
Register until September 15:
✓ Save up to $323
✓ 3 Day Special
✓ Team discount
Register Now
Join the ML Revolution!
Register until August 18:
✓ Pre-conference workshops for free
✓ Save up to €503
✓ 10 % Team Discount
Register Now
Join the ML Revolution!
Register until August 18:
✓ Pre-conference workshops for free
✓ Save up to €503
✓ 10 % Team Discount
Register Now
Join the ML Revolution!
Until the Conference starts:
✓ Group discount
✓ Special discount for freelancers
Register Now
Join the ML Revolution!
Until the Conference starts:
✓ Group discount
✓ Special discount for freelancers
Register Now

Since the evolution of mankind, materials have played a decisive role in the development and progression of technology and living standards. The quest for discovering existing materials with fascinating properties as well as for obtaining novel materials with specific properties has been increasing at a tremendous rate. Traditional technique of trial and error for material research is highly expensive, less efficient, and unsustainable. Moreover, experimentally obtained databases of crystalline structures along with that of physical and chemical properties of materials also continue to grow astoundingly. Therefore, to achieve the goal of methodical exploration about materials, their properties, and usages and to attain benefit from the enormous databases of material structure and properties available, material science has been turning more often towards machine learning (ML) in the past few years. ML plays an important role in accelerating the research, in prediction of structure and composition, in recognition of characterization and synthesis techniques, in evaluation of properties and devices etc. The present talk will illustrate ML techniques useful for addressing burgeoning questions as well as providing potential directions to the domain of material science. We foresee a future where the fabrication, design, characterization, application of materials is ML supported and accelerated.

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This Session belongs to the Diese Session gehört zum Programm vom SingaporeSingapore and  und BerlinBerlin program. Take me to the program of . Hier geht es zum Programm von Munich Munich .

This Session Diese Session belongs to the gehört zum Programm von SingaporeSingapore and  und BerlinBerlin program. Take me to the current program of . Hier geht es zum aktuellen Programm von Singapore Singapore , Berlin Berlin or oder Munich Munich .

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