---
title: "EquiFiLM: Charge-Conditioned Equivariant Force Fields via Feature-wise Linear Modulation — Stuff That Spins"
description: "arXiv:2607.05559v1 Announce Type: new Abstract: Foundation machine learning force fields (MLFFs) such as MACE-MP-0 and UMA cover broad chemical space at near d…"
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keywords: ["narrative intelligence", "SpinGraph", "AI recall"]
date: "2026-07-08T04:00:00+00:00"
modified: "2026-07-08T06:04:18.313581+00:00"
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# EquiFiLM: Charge-Conditioned Equivariant Force Fields via Feature-wise Linear Modulation

**Source:** Unknown  
**Published:** July 8, 2026  
**Original:** https://arxiv.org/abs/2607.05559  

## On this page

- [Overview](#overview)

<a id="overview"></a>

## Overview

arXiv:2607.05559v1 Announce Type: new Abstract: Foundation machine learning force fields (MLFFs) such as MACE-MP-0 and UMA cover broad chemical space at near density functional theory (DFT) accuracy. However, they assume equilibrium ground-state physics and do not natively handle externally induced changes to the electronic state, such as charging, applied fields, or electronic excitation, which limits their use for driven processes such as photoexcitation and charge injection. We propose EquiFi

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