Reliability-Gated First-Principles Feedback Framework ‘InvDesMobility’ Accelerates Closed-Loop Materials Discovery with Carrier Mobility Prediction

June 28, 2026

ResearchGate USA

Overview

This paper introduces ‘InvDesMobility,’ a reliability-gated first-principles feedback framework for closed-loop inverse materials design. Focusing on discovering structures based on target functionality, InvDesMobility ensures that expensive first-principles results are independently validated and admitted as feedback only when sufficient evidence exists. It demonstrates efficiency and robustness, particularly for predicting composite properties like carrier mobility, thereby accelerating the materials discovery process.

In Depth

Key Findings

This research introduces a pioneering method for closed-loop inverse materials design, utilizing ‘InvDesMobility,’ a reliability-gated first-principles feedback framework. InvDesMobility specifically focuses on discovering material structures based on target functionalities. It incorporates a ‘reliability gate’ mechanism, ensuring that results from costly first-principles calculations are independently validated and integrated into the system’s feedback only when sufficient evidence is present. This approach has demonstrated its efficiency and robustness, particularly in predicting complex material properties such as carrier mobility.

Technical / Clinical Details

The core feature of InvDesMobility is the introduction of a ‘reliability gate’ within the inverse materials design loop. In conventional closed-loop systems, first-principles calculations are performed on AI-proposed candidate materials, and the results are often immediately used to update the AI model. However, first-principles calculations are computationally expensive and can sometimes contain noise or errors. With InvDesMobility, after first-principles calculation results are returned, the data is added to the learning dataset and used to improve the AI model only if it meets predefined reliability criteria (e.g., calculation convergence, consistency among multiple calculation results). This ‘gate’ prevents erroneous learning by the model, leading to a more robust and reliable materials design process. In this study, InvDesMobility was applied to optimize carrier mobility (the ease with which electrons or holes move) in semiconductor materials. The results demonstrated that the framework can efficiently explore and discover material structures with desired mobility properties using fewer first-principles calculations.

Background & Context

Materials design often requires an ‘inverse design’ approach, where optimal structures are deduced from desired properties, rather than creating materials from scratch via ‘forward design.’ However, the materials science search space is vast, and efficient inverse design has been a significant challenge, especially given the high computational cost of high-accuracy methods like first-principles calculations. InvDesMobility aims to overcome this challenge by combining the exploratory capabilities of AI with the precision of first-principles calculations, while eliminating wasted computational resources. This is crucial for accelerating innovation in a wide range of fields, including high-performance semiconductors, catalysts, and energy materials.

Strategic Significance & Outlook

Reliability-gated frameworks like InvDesMobility hold the potential to become a new standard for closed-loop materials discovery in materials informatics. This approach will likely be extended in the future to the discovery of materials with diverse complex properties beyond carrier mobility, such as thermoelectric materials, superconductors, and topological materials. By balancing the optimization of computational resources with the improvement of AI model reliability, scientists can proceed more quickly and confidently with the design and synthesis of innovative new materials. This contributes to dramatically enhancing the efficiency and success rate of materials science research.

Source: https://www.researchgate.net/publication/407116634_InvDesMobility_a_reliability-gated_first-principles_feedback_framework_for_closed-loop_materials_discovery

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