6 papers
Position: Align AI to Our Aspirations, Not Our Flaws
Nikita Kazeev, Bui Nhat Huyen Phan
We argue that aligning AI to aggregated human preferences is the wrong target. With current technology, one can train AIs to share the values of a Silicon Valley techno-optimist, a…
Composable Crystals: Controllable Materials Discovery via Concept Learning
Nian Liu, Yuwei Zeng, Ryoji Kubo +7
De novo crystal generation, a central task in materials discovery, aims to generate crystals that are simultaneously valid, stable, unique, and novel. Existing methods mainly rely…
Crys-JEPA: Accelerating Crystal Discovery via Embedding Screening and Generative Refinement
Nian Liu, Nikita Kazeev, Stephen Gregory Dale +8
De novo crystal generation seeks to discover materials that are not merely realistic, but also stable and novel. However, most existing generative models are trained to maximize th…
Generative design of inorganic materials
Jose Recatala-Gomez, Haiwen Dai, Zhu Ruiming +9
Materials discovery is fundamental to advance next-generation technologies as well as for sustainable and circular economy. Beyond computational screening, generative models are ef…
AI4X Roadmap: Artificial Intelligence for the advancement of scientific pursuit and its future directions
Stephen G. Dale, Nikita Kazeev, Alastair J. A. Price +65
Artificial intelligence and machine learning are reshaping how we approach scientific discovery, not by replacing established methods but by extending what researchers can probe, p…
Wyckoff Transformer: Generation of Symmetric Crystals
Nikita Kazeev, Wei Nong, Ignat Romanov +4
Crystal symmetry plays a fundamental role in determining its physical, chemical, and electronic properties such as electrical and thermal conductivity, optical and polarization beh…