Daniel Flam-Shepherd

PhD student @UofTCompSci @VectorInst Working on machine learning for molecular design.

[Daniel Flam-Shepherd @FlamShepherd]

placing whole Amino Acids:

[Daniel Flam-Shepherd @FlamShepherd]

We built a framework for fragment-based 3D molecular design using reinforcement learning. Scales to more than 100 atoms.

Preprint: https://t.co/VgXt8Aozsu
w/ @alex_zhigalin, @A_Aspuru_Guzik

Watch the agent design a molecule by placing fragments from Imatinib:

[Daniel Flam-Shepherd @FlamShepherd]

placing OLED fragments:

[Sepp Hochreiter @HochreiterSepp]

ArXiv https://t.co/P0CRIZ3fbq: LSTMs with SMILES and SELFIES perform better at generating complex molecules than graph generative models. Confirming our own results.

[Mario Krenn @MarioKrenn6240]

Deep Learning can help solving many questions in Science. But how does it think about the world?

We analyze deep generative model's internal worldview for quantum designs: https://t.co/rxMgJXUG3D

Spearheaded by @FlamShepherd /w @ttttonywu @GuXuemei @albaclierta @A_Aspuru_Guzik

[Alan Aspuru-Guzik @A_Aspuru_Guzik]

At the interface of #machinelearning and #quantumoptics ! #generativemodels learn implicitly about entanglement and building quantum optical setups! @FlamShepherd @ttttonywu @albaclierta @MarioKrenn6240 Xuemei Gu #matterlab @UofT @UofTCompSci @CIFAR_News

https://t.co/0uspx2jKbd

[Max Welling @wellingmax]

Very nice blogpost by Fabian Fuchs on Alphafold. https://t.co/hp3biVeWSR

[Nick McGreivy @NMcGreivy]

Stellarators are fusion reactors which use an optimized twisting magnetic cage to confine 100 million ℃ plasma. Here, I use AD (JAX) to optimize the coils which produce that cage.

Like ML in the 2010s, the stellarator community seems poised to adopt AD!

https://t.co/aC1sLKyIJo