Carbonitride Metal Navigator

Descripción del proyecto

Liberar el potencial de los carbonitruros metálicos

Los carbonitruros metálicos, compuestos que combinan metales terrestres con carbono y nitrógeno, presentan notables propiedades mecánicas, electrónicas, catalíticas y superconductoras con aplicaciones potenciales en energías limpias, catálisis y tecnologías cuánticas. Han permanecido infraexplorados debido al coste y a la vez que supone utilizar los métodos convencionales de la teoría del funcional de la densidad (TFD) para explorar sistemáticamente este vasto espacio químico. Con el apoyo de las acciones Marie Skłodowska-Curie, el proyecto CarMeN pretende alcanzar una precisión cercana a la TFD a una fracción del coste computacional. Los potenciales aprendidos automáticamente a partir de datos de la TFD de alta calidad se combinarán con la predicción de estructuras cristalinas para permitir la búsqueda sistemática de compuestos de carbonitruro metálico estables y metaestables con propiedades deseables. Se validará la viabilidad experimental de los candidatos predichos.

Objetivo

The CarMeN (Carbonitride Metal Navigator) project aims to revolutionize the discovery of functional materials by combining machine learning (ML) with crystal structure prediction (CSP) to accelerate exploration of the vast and underexplored space of metal carbonitrides (MCNs). MCNs, composed of earth-abundant metals with carbon and nitrogen, exhibit exceptional mechanical, electronic, catalytic, and superconducting properties, making them highly relevant for sustainable energy, catalysis, quantum technologies, and advanced manufacturing. Despite this promise, only a small fraction of MCN compounds has been studied due to the limitations of conventional density functional theory (DFT) methods.

The project overcomes these bottlenecks by developing machine-learned potentials (MLPs) trained on high-quality DFT data, achieving near-DFT accuracy at orders-of-magnitude lower cost. These MLPs will enable high-throughput CSP, systematically mapping stable and metastable MCN phases while incorporating multi-objective optimization to identify compounds with technologically desirable properties. Predicted structures will be rigorously validated through phonon stability checks, property calculations (mechanical, electronic, catalytic, and energetic), and synthetic pathway analysis, ensuring experimental feasibility.

The methodology is structured across five work packages: (i) project and data management under FAIR principles, (ii) MLP development, (iii) ML-accelerated CSP, (iv) property evaluation and synthesis-informed validation, and (v) dissemination and open science practices. By uniting interdisciplinary advances in machine learning, computational chemistry, and materials science, CarMeN will establish a scalable discovery paradigm that bridges theoretical prediction and practical application. Ultimately, the project seeks to deliver sustainable, high-performance materials while advancing open, reproducible practices in computational materials research.