activity
20222026
most citedRoom-temperature decomposition of the ethaline deep eutectic solvent

15 citations · 33 across the 7 of their papers we have counts for

collaborators

7 papers

cs.AI2026

CrystalReasoner: Reasoning and RL for Property-Conditioned Crystal Structure Generation

Yuyang Wu, Stefano Falletta, Delia McGrath +1

Generative modeling has emerged as a promising approach for crystal structure discovery. However, existing LLM-based generative models struggle with low-level atomic precision, whi…

cond-mat.mtrl-sci2025

Equivalence of charged and neutral density functional formulations for correcting the many-body self-interaction of polarons

Stefano Falletta, Jennifer Coulter, Joel B. Varley +4

The electron self-interaction problem in density functional theory affects the accurate modeling of polarons, particularly their localization and formation energy. Charged and neut…

physics.comp-ph2025

TorchSim: An efficient atomistic simulation engine in PyTorch

Orion Cohen, Janosh Riebesell, Rhys Goodall +6

We introduce TorchSim, an open-source atomistic simulation engine tailored for the Machine Learned Interatomic Potential (MLIP) era. By rewriting core atomistic simulation primitiv…

physics.chem-ph2024★ 15 cited

Room-temperature decomposition of the ethaline deep eutectic solvent

Julia H. Yang, Amanda Whai Shin Ooi, Zachary A. H. Goodwin +5

Environmentally-benign, non-toxic electrolytes with combinatorial design spaces are excellent candidates for green solvents, green leaching agents, and carbon capture sources. Here…

physics.chem-ph2024★ 14 cited

Addressing the Band Gap Problem with a Machine-Learned Exchange Functional

Kyle Bystrom, Stefano Falletta, Boris Kozinsky

The systematic underestimation of band gaps is one of the most fundamental challenges in semilocal density functional theory (DFT). In addition to hindering the application of DFT…

cond-mat.mtrl-sci2024★ 4 cited

Unified Differentiable Learning of Electric Response

Stefano Falletta, Andrea Cepellotti, Anders Johansson +4

Predicting response of materials to external stimuli is a primary objective of computational materials science. However, current methods are limited to small-scale simulations due…