The problem: It is reliant on atom-centered partial charge, and pre-calculated parameters for its Newtonian forces. These are available for a set of 30k or so organic molecules, and most protein, lipid, and nucleic acid configurations. The problems: Making it work for arbitrary systems, and doing better than these specifically-tuned models. I want the general case, and it to work in a no-fuss way.
I am now looking into the TWA, and am interested in this class of approximation in general. I have the UI and traditional MD system mostly ready; getting ready to tackle this problem. This is a much simpler setup than the ones physicists are interested, and that the article covers, since we can focus on stable covalent-bonded systems of atoms.
I have a background in solid state physics from coursework, but I've never really used QM for almost anything outside of school.
- QuantumOptics.jl in Julia
- QuantumToolbox.jl in Julia
- qutip in python
These are all "just" nice domain specific wrappers around linear algebra and differential equation tools. They do the "silly" exponentially expensive simulation technique that works for any quantum system. If you are interested in efficient (not exponential) simulation techniques that support only a subset of all quantum dynamics try out:
- stabilizer formalism (e.g. for error correction) with QuantumClifford.jl or stim
- Gaussian quantum optics (e.g. for laser physics) with Gabs.jl
- tensor networks (e.g. for arbitrary low-rank entanglement) with ITensors.jl
