Top Tweets for #machinelearningforchem
👀 Did you miss our #MachineLearningforCHEM symposium yesterday? Watch all three sessions on our YouTube playlist NOW: https://t.co/K29jW6y9C8
And thanks, again, to our co-sponsors @BUCollegeofENG @BUChemistry @BU_CAS!
24h round-up. A collection of TOP TWEETS. Enjoy!
https://t.co/YYkuaHDiBf
#AI #SelfDrivingCars #AutonomousVehicles #ConnectedCars #CV2X #V2X #CITS #ElectricVehicles #mobility #lidar #MachineLearningforCHEM
Our final panel of #MachineLearningforCHEM, moderated by Qiang Cui, answering questions about applications and reliable convergence.
Cyclic peptides have therapeutic potential. @TuftsUniversity Associate Prof. @Yu_Shan_Lin discusses how they are developing #MachineLearning models that can predict the structural ensembles of cyclic peptides in our last sesssion. #MachineLearningforCHEM
@tiwarylab @BU_Computing It's still a bit of a shocker! It'll get worse when the 2nd edition of my book has a chapter on #MachineLearningforCHEM
Which book do you think this excerpt is from? Great talk by @tiwarylab
@BU_Computing #MachineLearningforCHEM
We love when speakers keep us on our toes! @tiwarylab asks attendees to identify what book this page is from before diving into how molecular dynamics can predict how, why, and when drugs fail. #MachineLearningforCHEM
Can artificial intelligence help predict how drugs unbind, proteins fold and snowflakes form? Find out the answer in our next session from @UofMaryland Assistant Prof. @tiwarylab! #MachineLearningforCHEM
In our last #MachineLearningforCHEM session join @tiwarylab @MicheleCeriotti @Yu_Shan_Lin as they discuss using data-driven techniques to realistically simulate and understand physical structures and aid in chemical and material development. Panel moderated by Qiang Cui!
Our second panel, led by @KulisBrian, discusses shallow learning algorithms in molecular systems compared to deep learning algorithms at #MachineLearningforCHEM symposium.
A @Google Research Engineer, Bidisha Samanta, discusses how to adapt generative models and algorithms to work under the unique characteristics of molecular graphs. #MachineLearningforCHEM
How can you incorporate machine learning in physics and chemistry while still satisfying physical laws and properties that restrict these materials? Hear from @UChicago Associate Prof. @risi_kondor at Session 2 to learn more. #MachineLearningforCHEM
Join @FU_Berlin Professor @FrankNoeBerlin as they discuss building machine learning methods that attack fundamental problems in physical and chemical sciences in our next session. #MachineLearningforCHEM
Our first panel, moderated by @Chem_Diva, discussing whether #ML can help in understanding reaction mechanisms. #MachineLearningforCHEM
Grace Russell of @SnapdragonChem discusses the automatic optimization system that can be brought directly to organic reactions at our #MachineLearningforCHEM Symposium.
Join @UConn Assistant Prof. @kerrymgilmore and learn how automation can assist in overcoming physical limitations in chemical science and improve reproductivity, throughput, and outcomes. #MachineLearningforCHEM
"Iterative experimentation isn't just for optimization, it can be for information"~@cwcoley kicks off our first #MachineLearningforCHEM session!
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