Catalyze

From Grants to Portfolios: Index Logic for Science

How science can learn from capital markets to coordinate diversity, measure progress, and turn discovery into a public portfolio.

The Engine of Scientific Discovery: How New Methods and Tools Spark Major Breakthroughs

Abstract. How do we spark new scientific discoveries? Why do some breakthroughs seem even accidental? And most importantly, how can we accelerate them and

Open Source for Science Fund Launches to Power AI-Driven Discovery

A new multi-donor fund by Renaissance Philanthropy seeded by Biohub and Wellcome opens its first call for proposals.

Using X-Labs to Unleash AI-Driven Scientific Breakthroughs | IFP

How to adapt our science funding mechanisms to the unique infrastructure needs of large-scale AI projects

The Discovery Engine: A Framework for AI-Driven Synthesis and Navigation of Scientific Knowledge Landscapes

Scientific progress relies on the effective accumulation, synthesis, and critical evaluation of knowledge. Traditionally, the well-documented, peer reviewed publication served as the primary standard for filtering and disseminating credible findings within the scientific community. Recently, however, we are witnessing an unprecedented acceleration in research output, a veritable explosion of scientific publications across all disciplines [1]. Yet, this very abundance creates a paradox: the sheer volume threatens to overwhelm the mechanisms designed for its assimilation and synthesis. Researchers, even within highly specialized subfields, face an almost insurmountable challenge in keeping abreast of relevant developments, integrating disparate findings, and identifying the truly novel signals amidst the noise [2]. This information overload contributes to disciplinary fragmentation, hindering the cross-pollination of ideas essential for disruptive innovation [3]. Furthermore, persistent concerns regarding "reproducibility crisis" [2], predatory journals, inflation of research areas[4], growing retractions and the potential influences of bibliometrics on research direction [5] highlight systemic challenges in validating and prioritizing scientific contributions to fundamental knowledge.

The Scientific Contribution Graph: Automated Literature-based Technological Roadmapping at Scale

Sir Isaac Newton famously wrote, “If I have seen further, it is by standing on the shoulders of giants”. Scientific contributions are rarely developed in isolation, but build upon prior contributions, such as problem framings, experimental methods, and empirical findings. Understanding these prerequisite relationships is important for studying scientific progress, and for automated scientific discovery systems that must reason about which existing capabilities can be used to develop new ones (e.g. Lu et al., 2024; Jansen et al., 2025b; Baek et al., 2025).

MIRA 2026 - Modular Interoperable Research Attribution

Catalyzing Modular Interoperable Research Attribution - A workshop to design and prototype interoperable frameworks for modular research attribution. June 7-11, 2026 in Ireland.

MIRA 2026 - Modular Interoperable Research Attribution

Catalyzing Modular Interoperable Research Attribution - A workshop to design and prototype interoperable frameworks for modular research attribution. June 7-11, 2026 in Ireland.

Infinite Researchers | AI-Powered Scientific Discovery

What happens to the speed of discovery if we have infinite researchers? Explore AI experiments accelerating breakthroughs.

Cohere Labs - Catalyst Grants

Cohere Labs Catalyst Grants support academics, civic institutions & impact-driven organizations using AI and research to create real-world change.

Coordinated Research Programs — Renaissance Philanthropy – A brighter future for all through science, technology, and innovation (V2)

In many cases, ambitious R&D problems are not well-suited to individual academic labs, startups, or other existing institutions.

SciToolAgent: a knowledge-graph-driven scientific agent for multitool integration

Scientific research increasingly relies on specialized computational tools, yet effectively utilizing these tools requires substantial domain expertise. While large language models show promise in tool automation, they struggle to seamlessly integrate and orchestrate multiple tools for complex scientific workflows. Here we present SciToolAgent, a large language model-powered agent that automates hundreds of scientific tools across biology, chemistry and materials science. At its core, SciToolAgent leverages a scientific tool knowledge graph that enables intelligent tool selection and execution through graph-based retrieval-augmented generation. The agent also incorporates a comprehensive safety-checking module to ensure responsible and ethical tool usage. Extensive evaluations on a curated benchmark demonstrate that SciToolAgent outperforms existing approaches. Case studies in protein engineering, chemical reactivity prediction, chemical synthesis and metal–organic framework screening further demonstrate SciToolAgent’s capability to automate complex scientific workflows, making advanced research tools accessible to both experts and nonexperts.

A protocol for attributable modular research contribution - Matsuthoughts