1/ The search for molecular conformations is a battle against exponential complexity. What if we could compress this search into manageable dimensions without losing precision? Enter tensor train optimization. 🧵 2/ Organic molecules are shape-shifters. Their 3D arrangements dictate chemical properties and applications, from drug design to spectroscopy. Yet, the combinatorial explosion of conformers makes brute force untenable. 3/ Traditional methods—molecular dynamics, Monte Carlo, and metadynamics—are effective but limited. The problem scales poorly with size and flexibility. More atoms, more chaos. 4/ Tensor train (TT) optimization offers a lifeline: turning an exponential problem into a polynomial one. Think of it as folding a multi-dimensional puzzle into solvable layers. 5/ The trick? Represent the molecule’s conformational space as a tensor—high-dimensional data—and reduce it to manageable chunks using TT algorithms. Less guesswork, more precision. 6/ Results speak. Compared to metadynamics-based approaches like CREST, TT optimization delivers faster results (up to 10x) while holding accuracy steady. It even ventures into territory that others avoid: large, flexible molecules. 7/ But the story isn't just about speed. It's about redefining what's possible—extending computational chemistry's reach to molecules previously deemed too complex. 8/ This isn't just for chemistry geeks. The philosophy applies broadly: complexity is a mirage. With the right lens (or tensor), you can simplify anything. 9/ The paper isn't just about a better algorithm; it's a framework for embracing the impossible. How many problems in life are "high-dimensional puzzles" waiting for the right reduction? 10/ If the future of science is computation, tools like tensor trains aren't just incremental—they're foundational. We aren’t just simulating molecules; we’re designing the future. Where else can "dimensionality reduction" revolutionize? 🚀 ---