April 16, 2024, 4:42 a.m. | Kohei Noda, Araki Wakiuchi, Yoshihiro Hayashi, Ryo Yoshida

cs.LG updates on arXiv.org arxiv.org

arXiv:2404.08657v1 Announce Type: cross
Abstract: Recent advancements in machine learning have showcased its potential to significantly accelerate the discovery of new materials. Central to this progress is the development of rapidly computable property predictors, enabling the identification of novel materials with desired properties from vast material spaces. However, the limited availability of data resources poses a significant challenge in data-driven materials research, particularly hindering the exploration of innovative materials beyond the boundaries of existing data. While machine learning predictors are …

abstract arxiv cond-mat.mtrl-sci cond-mat.soft cs.lg development discovery enabling however identification learn machine machine learning material materials novel predictions progress property spaces through type vast

Software Engineer for AI Training Data (School Specific)

@ G2i Inc | Remote

Software Engineer for AI Training Data (Python)

@ G2i Inc | Remote

Software Engineer for AI Training Data (Tier 2)

@ G2i Inc | Remote

Data Engineer

@ Lemon.io | Remote: Europe, LATAM, Canada, UK, Asia, Oceania

Artificial Intelligence – Bioinformatic Expert

@ University of Texas Medical Branch | Galveston, TX

Lead Developer (AI)

@ Cere Network | San Francisco, US