Those who hope to extend the frontier of science and solve challenging problems are coming together in new ways never seen before — around an intelligent closed loop of literature understanding, computational simulation, and experimental validation, bringing AI for literature analysis, computation, and AI for experimental science together in one open community.
Read → Compute → Experiment: AI for literature analysis and AI for experimental science, across the research pipeline
The integration of machine learning and physical modeling is changing the paradigm of scientific research; today, AI is reshaping the entire workflow from hypothesis to experimental validation. This calls for a new infrastructure—new platforms for collaboration, new coding frameworks, new data processing schemes, and new ways of using the computing power. It also calls for a new culture—the culture of working together closely for the benefit of all, of free exchange and sharing of knowledge and tools, of respect and appreciation of each other's work, and of the pursuit of harmony among diversity.
