AI-driven material discovery reveals systemic neglect of sustainable chemistry in academic databases
Original framing: “AI helps uncover overlooked 2D materials and create 11 new ones” — Phys.org
The original framing ignores how Indigenous material sciences—such as the layered clay structures used in traditional African pottery or the bamboo-based composites of Southeast Asian architecture—have long employed principles akin to MXenes. For instance, the Akan people of Ghana have used layered mineral composites in their *kente* weaving for centuries, a practice that aligns with MXene’s structural properties. The omission of these precedents reinforces the myth that Western science is the sole source of innovation, while Indigenous knowledge remains invisible even in 'disruptive' technological breakthroughs.
The AI’s 'discovery' of MXenes is not just a technological achievement but a symptom of deeper systemic failures: the academic reward system’s bias toward 'disruptive' over 'sustainable' science, the erasure of Indigenous and Global South material knowledge, and the colonial framing of innovation as solely Western.
The trickster role of AI here—exposing what was hidden—could be a turning point if institutions stop treating it as a neutral tool and instead use it to correct historical injustices. For example, by training AI on datasets that include Indigenous material sciences and marginalized researchers’ work, we could shift from a model of 'discovery' to one of *reconnection*. The solution lies not just in better algorithms but in dismantling the power structures that originally buried these materials—whether through decolonized curricula, ethics review boards, or funding reforms that prioritize sustainability over patentability. The MXene story is a microcosm of how science progresses: not through pure innovation, but through the painful correction of what was overlooked, often by those least empowered to speak.