Force-field development has undergone a revolution in the past decade with the proliferation of quantum chemistry based parameterizations and the introduction of machine learning approximations of the atomistic potential energy surface. Nevertheless, transferable force-fields with broad coverage of organic chemical space remain necessary for applications in materials and chemical discovery where throughput, consistency, and computational cost are paramount. Here we introduce a force-field development framework called Topology Automated Force-Field Interactions (TAFFI) for developing transferable force-fields of varying complexity against an extensible database of quantum chemistry calculations. TAFFI formalizes the concept of atom typing and makes it the basis for generating systematic training data that maintains a one-to-one correspondence with force-field terms. This feature makes TAFFI arbitrarily extensible to new chemistries while maintaining internal consistency and transferability. As a demonstration of TAFFI, we have developed a fixed-charge force-field, TAFFI-gen, from scratch that includes coverage for common organic functional groups that is comparable to established transferable force-fields. The performance of TAFFI-gen was benchmarked against OPLS and GAFF for reproducing several experimental proper- ties of 87 organic liquids. The consistent performance of these force-fields, despite their distinct origins, validates the TAFFI framework while also providing evidence of the representability limitations of fixed-charge force-fields.
download asset main.pdf 3 MB [opens in a new tab] cloud_download
pdf : 3 MB
download asset SI.pdf 2 MB [opens in a new tab] cloud_download
pdf : 2 MB