  Derivation in physics, in the form of derivation reconstruction of published
results, is expensive and difficult to automate, not least because the use of
mathematics by physicists is less formal than that of mathematicians. Following
demand for informal mathematical datasets, we describe a dataset creation
method where we consider a derivation agent as a finite state machine which
exists in equational states represented by strings, where transitions can occur
through a combination of string operations that mimic mathematics, and defined
computer algebra operations. We present the novel dataset PhysAI-DS1 generated
by this method, which consists of a curated derivation of a contemporary
condensed matter physics result reconstructed using a computer algebra system.
We define an equation reconstruction task based on formulating derivation
segments as basic units of non-trivial state sequences, with the goal of
reconstructing an unknown intermediate state equivalent to one-hop inference,
extensible to the multi-hop case. We present a symbolic similarity-based
heuristic approach to solve an equation reconstruction task on the PhysAI-DS1
dataset, which employs a set of actions, a knowledge base of symbols and
equations, and a computer algebra system, to reconstruct an unknown
intermediate state within a sequence of three equational states, grouped
together as a derivation unit. Informal derivation comprehension of
contemporary results is an important step towards the comprehension and
automation of modern physics reasoners.
