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Tiktok/bytedance already have large ML infrastructure in place (GPU clusters), and since quantum chemistry simulation is moving towards ML based simulation, the
by chazeon 4y ago
Tiktok/bytedance already have large ML infrastructure in place (GPU clusters), and since quantum chemistry simulation is moving towards ML based simulation, they have an edge there. Open source ML simulation software such as DeePMD-kit is already seeing contributions from Tiktok employees.
- chazeon 4y agoIn case HN readers doesn’t know, ML based MD scales O(N) and traditional DFT based simulation O(N^3) where N is number of atoms. From first hand experience: what used take weeks with traditional DFT MD on CPUs are only taking few minutes with GPUs right now if you have trained a good ML potential. They achieved near DFT accuracy.
- mirker 4y agoThis type of complexity argument is also used in graphics rendering as far as I am aware. In graphics, it just has to look ok. Is there any concern for bounded error in MD?
- chazeon 4y agoWell in MD you always have to compare to experimental measurements from a macroscopic level. And you have to repeat DFT results as far as you can do DFT on it.
- 11101010001100 4y agoBut the dynamics are still classical right? You are just using a highly accurate interatomic potential.
- chazeon 4y agoThere is path-integral MD with imaginary time you can use to address quantum effects such as tunneling when you have protons in the system. But yeah, basically are still doing ionic dynamics and ignoring the electrons. The electronic effect needs to be "baked" into the ML potential when you are training the potential with DFT data.
- 11101010001100 4y agoAre there examples of this baking in when using an ML potential?
- goethes_kind 4y agoCan you point me to some papers? I'm quite curious since I do something adjacent but with fluids.
- chazeon 4y agoSee this one, for example, you could look at the method description section: Modeling liquid water by climbing up Jacob's ladder in density functional theory facilitated by using deep neural network potentials https://arxiv.org/abs/2104.14410 https://arxiv.org/abs/2104.14410 The software that are used are DeePMD-kit: https://github.com/deepmodeling/deepmd-kit https://github.com/deepmodeling/deepmd-kit the corresponding paper is https://arxiv.org/pdf/1712.03641.pdf https://arxiv.org/pdf/1712.03641.pdf
- goethes_kind 4y agoThanks. At first glance it looks very different from what I am doing, but nonetheless very interesting.
- ivalm 4y agoCan they do dft+u/strong-ish correlations?
- chazeon 4y agoI know people are exploring this front currently. Basically you could use results from DFT+U to train the potential; but I am also told that strongly correlation can now be better described by SCAN functionals better than LDA+U etc so it would be even easier then.
- Melatonic 4y agoI would guess they are planning to offer ML infrastructure as a service type stuff for chem simulation to various firms - would make sense they would need a few people in house to help build the systems and act as the "sales engineers" types who can interface with the chemistry super nerds they are trying to sell to.