Zobrazeno 1 - 10
of 7 126
pro vyhledávání: '"Lilienfeld"'
Autor:
Weinreich, Jan, Karandashev, Konstantin, Arrieta, Daniel Jose Arismendi, Hermansson, Kersti, von Lilienfeld, O. Anatole
We present high-quality reference data for two fundamentally important groups of molecular properties related to a compound's utility as a lithium battery electrolyte. The first one is energy changes associated with charge excitations of molecules, n
Externí odkaz:
http://arxiv.org/abs/2411.00994
Modern machine learning (ML) models of chemical and materials systems with billions of parameters require vast training datasets and considerable computational efforts. Lightweight kernel or decision tree based methods, however, can be rapidly traine
Externí odkaz:
http://arxiv.org/abs/2409.20471
We introduce the alchemical harmonic approximation (AHA) of the absolute electronic energy for charge-neutral iso-electronic diatomics at fixed interatomic distance $d_0$. To account for variations in distance, we combine AHA with this Ansatz for the
Externí odkaz:
http://arxiv.org/abs/2409.18007
Accurate quantum mechanics based predictions of property trends are so important for materials design and discovery that even inexpensive approximate methods are valuable. We use the Alchemical Integral Transform (AIT) to study multi-electron atoms,
Externí odkaz:
http://arxiv.org/abs/2406.18416
We cast the relation between the chemical composition of a solid-state material and its superconducting critical temperature (Tc) as a statistical learning problem with reduced complexity. Training of query-aware similarity-based ridge regression mod
Externí odkaz:
http://arxiv.org/abs/2406.14524
We study the applicability of the Hammett-inspired product (HIP) Ansatz to model relative substrate binding within homogenous organometallic catalysis, assigning $\sigma$ and $\rho$ to ligands and metals, respectively. Implementing an additive combin
Externí odkaz:
http://arxiv.org/abs/2405.07747
Autor:
Khan, Danish, Benali, Anouar, Kim, Scott Y. H., von Rudorff, Guido Falk, von Lilienfeld, O. Anatole
We introduce the Vector-QM24 (VQM24) dataset which comprehensively covers all possible neutral closed shell small organic and inorganic molecules and their conformers that contain up to five heavy atoms (non-hydrogen) consisting of $p$-block elements
Externí odkaz:
http://arxiv.org/abs/2405.05961
We investigate trends in the data-error scaling behavior of machine learning (ML) models trained on discrete combinatorial spaces that are prone-to-mutation, such as proteins or organic small molecules. We trained and evaluated kernel ridge regressio
Externí odkaz:
http://arxiv.org/abs/2405.05167
Atomic basis sets are widely employed within quantum mechanics based simulations of matter. We introduce a machine learning model that adapts the basis set to the local chemical environment of each atom, prior to the start of self consistent field (S
Externí odkaz:
http://arxiv.org/abs/2404.16942
Autor:
Coretti, Alessandro, Falkner, Sebastian, Weinreich, Jan, Dellago, Christoph, von Lilienfeld, O. Anatole
Publikováno v:
KIM REVIEW, Volume 2, Article 03, 2024
The paper by No\'e et al. [F. No\'e, S. Olsson, J. K\"ohler and H. Wu, Science, 365:6457 (2019)] introduced the concept of Boltzmann Generators (BGs), a deep generative model that can produce unbiased independent samples of many-body systems. They ca
Externí odkaz:
http://arxiv.org/abs/2404.16566