  For decades, atomistic modeling has played a crucial role in predicting the
behavior of materials in numerous fields ranging from nanotechnology to drug
discovery. The most accurate methods in this domain are rooted in
first-principles quantum mechanical calculations such as density functional
theory (DFT). Because these methods have remained computationally prohibitive,
practitioners have traditionally focused on defining physically motivated
closed-form expressions known as empirical interatomic potentials (EIPs) that
approximately model the interactions between atoms in materials. In recent
years, neural network (NN)-based potentials trained on quantum mechanical
(DFT-labeled) data have emerged as a more accurate alternative to conventional
EIPs. However, the generalizability of these models relies heavily on the
amount of labeled training data, which is often still insufficient to generate
models suitable for general-purpose applications. In this paper, we propose two
generic strategies that take advantage of unlabeled training instances to
inject domain knowledge from conventional EIPs to NNs in order to increase
their generalizability. The first strategy, based on weakly supervised
learning, trains an auxiliary classifier on EIPs and selects the
best-performing EIP to generate energies to supplement the ground-truth DFT
energies in training the NN. The second strategy, based on transfer learning,
first pretrains the NN on a large set of easily obtainable EIP energies, and
then fine-tunes it on ground-truth DFT energies. Experimental results on three
benchmark datasets demonstrate that the first strategy improves baseline NN
performance by 5% to 51% while the second improves baseline performance by up
to 55%. Combining them further boosts performance.
