Intro

In my first (real) podcast episode, I talk with Corin and Ari Wagen, two brothers who I met through my writing. They are building something super cool: a molecular simulation company called Rowan (which recently got into the Nat Friedman AI grant program). We discuss neural network potentials (NNP’s), whether dynamics are useful at all, the role of computational chemistry in drug design, what the future of the field looks like for molecular simulation, and a lot more.

If you work in molecular simulation, I recommend trying out their tool at rowansci.com. I’m not a chemist and cannot vouch for the tool personally, but I can vouch for how much I’d trust Corin and Ari to build something useful. Not a paid sponsorship, not anything I have an investment in, etc, etc, I just genuinely want their startup to succeed.

If you're confused by this episode, check out the 'Jargon Explanation' on the Substack post: https://www.owlposting.com/i/152329408/jargon-explanation

Transcript of this episode (contains links to all referenced organizations and papers)

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Timestamps:

00:00 Introduction

01:19 Divide between classical and quantum simulation

03:48 What are NNP's actually learning?

06:02 What will NNP's fail on?

08:08 Short range and long range interactions in NNP's

10:23 Emergent behavior in NNP's

16:58 Enhanced sampling

18:16 Cultural distinctions in NNP's for life-sciences and material sciences

21:13 Gap between simulation and real-life

36:18 Benchmarking in NNP's

41:49 Is molecular dynamics actually useful?

53:14 Solvent effects

55:17 Quantum effects in large biomolecules

57:03 The legacy of DESRES and Anton

01:02:27 Unique value add of simulation data

01:06:34 NNP's in material science

01:13:57 The road to building NNP's

01:21:13 Building the SolidWorks of molecular simulation

01:30:05 Simulation workflows

01:41:06 The role of computational chemistry

01:44:06 The future of NNP's

01:51:23 Selling to scientists

02:01:41 What would you spend 200 million on?