Abstract

Machine Learning (ML) is revolutionizing how scientists predict, synthesize, and validate new materials from super-strong high-entropy alloys to next-generation battery cathodes. Ensuring that this progress remains fair, transparent, and environmentally responsible is therefore central to the future of sustainable materials R&D. This paper examines the normative foundations guiding the accountable creation and deployment of ML models. Because ML rapidly shapes healthcare, finance, and public policy, upholding applications that advance transparency and social benefit is crucial. We articulate ten core principles: accuracy, bias, accessibility, security, privacy, transparency, accountability, human oversight, sustainability, and harm avoidance and demonstrate pathways to incorporate them so that ML systems strengthen social well-being rather than undermine it. Blending theoretical perspectives with real-world illustrations, we propose actionable best practices that foster trust and responsible progress in ML while sketching out emerging research frontiers.

Keywords:Machine Learning; Computational materials science; Materials informatics; Bias; Transparency; Data privacy; Sustainability; Human centric AI