Towards accelerating physical discovery via non-interactive and interactive multi-fidelity Bayesian Optimization: Current challenges and future opportunities

arXiv:2402.13402v1 Announce Type: new
Abstract: Both computational and experimental material discovery bring forth the challenge of exploring multidimensional and often non-differentiable parameter spaces, such as phase diagrams of Hamiltonians with multiple interactions, composition spaces of combinatorial libraries, processing spaces, and molecular embedding spaces. Often these systems are expensive or time-consuming to evaluate a single instance, and hence classical approaches based on exhaustive grid or random search are too data intensive. This resulted in strong interest towards active learning methods such …

abstract arxiv bayesian challenge challenges computational cs.lg current diagrams differentiable discovery embedding experimental fidelity future interactions interactive libraries material multidimensional multiple opportunities optimization processing spaces type via