Portfolio-Based Constrained Multi-Objective Bayesian Optimization for Materials Design
This story is from 2026-09-18. It is preserved in the archive; the latest stories are on the live feed.
arXiv:2609.19550v1 Announce Type: cross Abstract: Materials discovery and design campaigns can be formulated as constrained multi-objective Bayesian optimization (CMOBO) problems, within which each experimental decision negotiates between two coupled but competing goals: discovering feasible candid…
Read the full story at arXiv stat.ML ↗
Timeline · 1 report
- 2026-09-18 04:00 · arXiv stat.ML
Portfolio-Based Constrained Multi-Objective Bayesian Optimization for Materials Design
More stories
- Anthropic says Claude 'leads' 26 percent of its AI R&D work — Engadget
- Novo Nordisk Will Use Anthropic’s Claude for Drug Research — Wall Street Journal Technology
- Introducing Kimi K3 on Amazon Bedrock — AWS Machine Learning Blog
- Optimizing agent system prompts with Amazon Bedrock AgentCore — AWS Machine Learning Blog
- Introducing Amazon SageMaker HyperPod Inference Gateway — AWS Machine Learning Blog
- Introducing Astra for Law — OpenAI News
- Gemini Hacked Three Companies in First Known Breakout by Google’s AI — Wall Street Journal Technology
- OpenAI reveals cases of ‘concerning’ AI behaviour as it announces new disclosure system — The Guardian AI
Get the daily brief of stories like this at 6:30 every morning →