MS Force Field Applications (MS FF Applications) includes access to Schrödinger’s widely-used OPLS4 and new OPLS5 force field, as well as all the Schrödinger machine learning force fields (MLFFs) for diverse property prediction workflows. Schrödinger is committed to advancing innovations in force fields to help you achieve more accurate, reliable modeling outcomes.

What’s New: 

• OPLS5: Includes an explicit treatment of polarizability via the addition of Drude oscillators that enables accurate modeling of cation-pi interactions and more accurate treatment of hydrogen bonding to charged systems.
• MPNICE: Machine learning force fields, also known as machine learning interatomic potentials, represent an intermediate between classical force fields and DFT, maintaining the linear scaling of the former while approaching the accuracy of the latter. Message Passing Network with Iterative Charge Equilibration (MPNICE) is an MLFF architecture developed by Schrödinger for which multiple pre-trained models covering 89 elements are available, and which explicitly incorporates equilibrated atomic charges and long range electrostatics.
• Fine-tuning: While architectures like MPNICE and UMA offer exceptional generalizability, achieving high-fidelity accuracy for specialized systems often requires tailoring the model through quantum mechanical data. MS FF Applications provides an intuitive, automated interface for fine-tuning MPNICE models using DFT calculations from Jaguar or Quantum Espresso. This enables the development of custom, application-specific MLFFs that integrate seamlessly across the full range of materials science workflows and solutions.
• MPNICE embeddings for 3D ML models: The internal layers of MLFFs encapsulate a sophisticated, dense mapping of chemical space and 3D atomic configurations. By leveraging these latent representations, MPNICE embeddings allow you to build machine learning models directly from 3D coordinates. This enables the creation of high-fidelity predictive models across organic, inorganic, and hybrid systems – spanning both periodic and non-periodic structures.