Update README.md

Author: Eipgen

README.md

@@ -390,6 +390,8 @@ mlcolvar is a Python library aimed to help design data-driven collective-variabl
  This work presents a variant of an electrostatic embedding scheme that allows the embedding of arbitrary machine learned potentials trained on molecular systems in vacuo.
 - [field_schnet](https://github.com/atomistic-machine-learning/field_schnet)  
 FieldSchNet provides a deep neural network for modeling the interaction of molecules and external environments as described.
+- [FieldMACE](https://github.com/rhyan10/FieldMACE/tree/master)
+  an extension of the message-passing atomic cluster expansion (MACE) architecture that integrates the multipole expansion to model long-range interactions more effi ciently. By incorporating the multipole expansion, FieldMACE eff ectively captures environmental and long-range eff ects in both ground and excited states.
 - [MLMM](https://github.com/MALBECC/MLMM-embeddings-assessment-paper)  
   This repository contains data and software regarding the paper submited to JCIM, entitled "Assessment of embedding schemes in a hybrid machine learning/classical potentials (ML/MM) approach".
 ## Charge Model