MARS: Machine-Learned Force Field Framework for Automated Conformational Sampling, Vibrational Spectroscopy, and Microsolvation

We present MARS, an open-source Python package for automated conformational ensemble generation, infrared spectrum prediction, and explicit microsolvation of molecular systems. Built on JAX for GPU acceleration and automatic differentiation, MARS is carried out with machine-learned force fields (MLFFs), including SO3LR and MACE, with a four-phase conformational sampling workflow combining metadynamics, multitemperature molecular dynamics, and genetic algorithms. The package provides automated vibrational mode classification using Wilson B-matrix projections and functional group identification via graph-based pattern matching. We benchmark MARS on chemically diverse systems, including alkanes, halogenated aromatics, amino acids, peptides, drug-like molecules, phosphamides, host-guest complexes, and microsolvated biomolecular models. Semiempirical quantum-chemical methods have until now provided the practical state of the art for automated conformational sampling. Our benchmarks show that modern MLFFs can improve upon this paradigm by combining greater accuracy for high-level relative conformer energies with lower computational cost and more favorable scaling with molecular size. IR benchmarks against experimental gas-phase spectra additionally show performance comparable to quantum-chemical methods, with chemically interpretable mode assignments. Applications to alanine peptides, proline/β-cyclodextrin inclusion complexes, alanine dipeptide, and 2-methoxycyclohexanone further demonstrate that MARS captures conformational reorganization, noncovalent binding motifs, solvent-induced stability shifts, and spectroscopic signatures. These results establish MARS as a transferable and accessible platform for ensemble-based molecular modeling with machine-learned potentials. MARS is freely available at github.com/tcpunilu/mars.