Some programming languages, like Rust and Zig, classify many errors as expected. Others, like JavaScript and Python, classify them as unexpected. For example, when you parse JSON in Go, the compiler makes you handle the error; not so in Ruby. I tend to prefer stricter compilers for production software and looser languages for scripts and prototypes, in part because of their philosophy about errors. (The Rustaceans among you probably notice that this whole post is very similar to Rust’s error philosophy.)
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That’s a 13× throughput increase from one type hint.
Machine learning interatomic potentials like MACE (NeurIPS 2022) have reached a point where they rival density functional theory in accuracy while running orders of magnitude faster. But using them still requires Python scripting, command-line fluency, and environment setup that shuts out a large number of researchers, especially those with accessibility needs, those in under-resourced labs, or students encountering computational chemistry for the first time.