Direction, evidence grade, and study type are AI-generated labels (gpt-5-mini), not human-verified. Syntheses are LLM-written. "Tensions" are machine-detected candidates, not confirmed contradictions. A research-acceleration tool, not peer review.
How this is built →
1Distinct papers
4Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
103343437
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Lausanne (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Innovation: 1 paper
- Productivity: 1 paper
Claim outcomes
- Organizational Efficiency: 1 paper
- Other: 1 paper
- Error Rate: 1 paper
- Output Quality: 1 paper
- Task Completion Time: 1 paper
- Firm Productivity: 1 paper
- Research Productivity: 1 paper
- Training Effectiveness: 1 paper
Papers in the Semantic Scholar view
Latest stored Semantic Scholar author observations only. Citation counts below are from the same provider and are not combined with other services.
Scroll the table horizontally to see every column.
| Paper | Author evidence | Date | Provider citations |
|---|---|---|---|
| A unified Bayesian-surrogate framework cuts costly quantum-chemistry evaluations by about tenfold without losing accuracy, speeding up minima and saddle-point searches on potential-energy surfaces. The method—implemented in Rust and combining derivative-aware GPs, inverse-distance kernels, optimal-transport sampling, and trust-region controls—lowers compute and capital costs for computational discovery workflows.arxiv | Lausanne provider id |
2026-03-11 | 0 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 0 cumulative citations. This is a coverage summary, not an author score or h-index.