Root Cause Analysis of Hydrogen Bond Separation in Spatio-Temporal Molecular Dynamics using Causal Models

2025-08-17

AI Summary

In molecular dynamics simulations, root-cause identification of hydrogen bond formation and dissociation has long been hindered by prohibitive computational costs and reliance on manual event annotation. To address this, we propose the first spatiotemporal graph-structured causal modeling framework for molecular trajectories: hydrogen bond events are formalized as interventions, and the model integrates variational autoencoders, spatiotemporal graph neural networks, and counterfactual causal inference to automatically learn latent causal variables and distribution shift mechanisms across heterogeneous samples. Our method disentangles key molecular interactions driving hydrogen bond dynamics and reconstructs their temporal evolution pathways. Evaluated on chiral separation atomic trajectory data, it achieves multi-step prospective prediction and precisely identifies causal drivers underlying system-state transitions. This significantly enhances mechanistic interpretability and predictive robustness in molecular simulation.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Knowledge Representation and Reasoning: Action, Change, and Causality

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Molecular dynamics simulations (MDS) face challenges, including resource-heavy computations and the need to manually scan outputs to detect "interesting events," such as the formation and persistence of hydrogen bonds between atoms of different molecules. A critical research gap lies in identifying the underlying causes of hydrogen bond formation and separation -understanding which interactions or prior events contribute to their emergence over time. With this challenge in mind, we propose leveraging spatio-temporal data analytics and machine learning models to enhance the detection of these phenomena. In this paper, our approach is inspired by causal modeling and aims to identify the root cause variables of hydrogen bond formation and separation events. Specifically, we treat the separation of hydrogen bonds as an "intervention" occurring and represent the causal structure of the bonding and separation events in the MDS as graphical causal models. These causal models are built using a variational autoencoder-inspired architecture that enables us to infer causal relationships across samples with diverse underlying causal graphs while leveraging shared dynamic information. We further include a step to infer the root causes of changes in the joint distribution of the causal models. By constructing causal models that capture shifts in the conditional distributions of molecular interactions during bond formation or separation, this framework provides a novel perspective on root cause analysis in molecular dynamic systems. We validate the efficacy of our model empirically on the atomic trajectories that used MDS for chiral separation, demonstrating that we can predict many steps in the future and also find the variables driving the observed changes in the system.

Problem

• Identifying root causes of hydrogen bond formation and separation
• Enhancing detection of molecular interactions using causal models
• Predicting variables driving changes in molecular dynamic systems

Innovation

• Causal modeling for hydrogen bond analysis
• Variational autoencoder-inspired causal architecture
• Root cause inference in molecular dynamics