75 years after Rosalind Franklin's pioneering research, Oxford chemists describe how disordered carbon forms, atom by atom.
- 75 years after Rosalind Franklin's pioneering work, Oxford chemists have used machine learning simulations to reveal how hard carbon forms at the atomic scale.
- The simulations reveal why some carbon resists forming graphite: as a common packaging polymer (PVDC) breaks down, reactive carbon sites link together, forming five- and seven-membered rings that disrupt the formation of ordered graphite layers.
- With hard carbon emerging as a key material for sodium-ion batteries, the findings show how machine learning can unlock the atomic-scale processes behind complex materials, paving the way for more targeted material design.
In a new study published in Chemical Science, researchers from the University of Oxford's Department of Chemistry have used machine learning simulations to trace how a common polymer transforms into hard carbon, revealing the atomic-scale processes that give this technologically important material its disordered structure.
Hard carbon is an important anode material for sodium-ion batteries, which are attracting interest as economically viable alternatives to conventional lithium-ion batteries, owing to the high natural abundance and low cost of sodium. Unlike graphite, hard carbon contains curved, irregularly arranged carbon structures that resist transformation into ordered graphite, even at very high temperatures.
The origins of this unusual behaviour have puzzled scientists for decades. In the 1950s, Rosalind Franklin established the distinction between graphitizing and non-graphitizing carbons , showing that the choice of precursor plays a crucial role in determining the resulting carbon structure. However, precisely how hard carbon develops at the atomic scale has remained difficult to observe experimentally or simulate directly.
The Oxford team's study focused on polyvinylidene chloride (PVDC), a polymer that is a classic precursor of non-graphitizing carbon. To investigate the thermal decomposition of this polymer, the researchers used machine learning-driven atomistic simulations, which combine the accuracy of quantum-mechanical calculations with the computational efficiency needed to study complex chemical structures. This allowed them to follow the movement of individual atoms and observe how chemical bonds break and form as the polymer is heated, providing a detailed microscopic picture of how hard carbon develops.
The simulations reveal that removal of hydrogen and chlorine atoms from the PVDC precursor generates highly reactive carbon sites that progressively form new carbon–carbon bonds. Over time, these bonds connect the remains of the polymer chains together into an extended network.
Crucially, unlike graphite, this network contains not only six-membered carbon rings but also five- and seven-membered rings that introduce curvature. Think of a classic football: five-sided pentagons allow its otherwise hexagonal surface to curve into a sphere. Similarly, the non-hexagonal motifs in hard carbon distort the carbon network, preventing it from forming the flat, ordered layers characteristic of graphite.
The study was led by Litong Wu, building on pilot studies conducted by former students Zitong Wu and Zakariya El-Machachi, under the supervision of Professor Volker Deringer at Oxford's Department of Chemistry. Their approach demonstrates how machine learned simulations can uncover chemical pathways in complex materials that would be prohibitively expensive to study using conventional quantum-mechanical methods.
Lead author Litong Wu said: "Working on this project felt like opening a book that no one had ever read before. Out of the chaos of atoms moving at high temperatures, neat and intuitive patterns start to appear. It was a real honour to revisit the work of Rosalind Franklin, someone I've long admired. I'm equally proud that our study shows what modern computational methods, combined with today's computing power, can make possible."
Professor Deringer said: "I will just add to Litong's words how delighted I am to see multiple members of our group making this research possible: herself as a current DPhil student expertly leading the study; Zitong as a former MChem project student (whom I had first taught in her first year on the course!); and Zak as a DPhil alum who helped build the foundations for this work."
Beyond advancing our understanding of hard-carbon formation, the work promises future steps in connecting precursor chemistry to the structure and electrochemical properties of battery anodes. This prospect reflects the group's broader vision of "designing" amorphous functional materials with the help of machine learning-driven atomistic simulations.
