arXiv subjects

Ingvars Vitenburgs

Publications and source records attributed to Ingvars Vitenburgs.

3 recordsLinked to original sources

arXiv2026

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models

Machine-learned interatomic potentials (MLIPs) have had a profound impact on molecular modelling in recent years, promising to resolve the long-standing tension between the scale and accuracy of simulations. There has been a proliferation of new models and designs, and recently the paradigm of ``foundational'' MLIPs has become prevalent. Broadly speaking, foundation models are trained on large diverse datasets and promise to work well for new systems with minimal updates required. However, in such a new and fast moving field, there are many unanswered questions. In this article, we set out to articulate and explore what we see as the most important among these questions. We start by developing a working definition for foundational MLIPs and use this definition to frame the subsequent open questions. Despite the rapid progress in the field of MLIP models, we believe that these are fundamental questions which will continue to define cutting edge research in MLIPs in the years to come.

cond-mat.mtrl-sci↗

arXiv2026

Hydrodynamic Backflow for Easing the Fermion Sign in Finite-Temperature Electron Path Integral Simulations

Some notable technology systems, such as high-temperature superconductors and materials for controlled nuclear fusion, require an accurate description of finite-temperature quantum matter. Stochastic path integral methods are finite-temperature and numerically exact, but scale poorly with system size due the notorious Fermion sign problem. To somewhat mitigate this, we use a hydrodynamical backflow coordinate transformation. Our first approach was a continuous normalizing flow machine learning optimisation. We found this to roughly halve the statistical uncertainty at medium sign severity. Numerical issues challenged training effectively. Thus, a semi-analytic analogue was developed to estimate the optimal parameters. We do this by using a derived expression dependent on a Bosonic observable. Hence, the calculation of these values does not have a sign problem. The resulting backflow transformations reduce the problem by multiple orders of magnitude in the specific case of a harmonically trapped, two-dimensional, electron gas at finite-temperature. The total energy of the system agrees with previous, backflow untransformed, studies and we calculate energies for up to 32 electrons. The limiting factor is found to be, primarily, the $O(N^3)$ calculation of the Jacobian, stemming from the coordinate transformation of the backflow. A more thorough implementation may further improve this scaling. Even without this, a route for simulating electron systems at currently unreachable regimes is obtained.

cond-mat.str-el↗

arXiv2024

Extended Coupled Cluster approach to Twisted Graphene Layers

A study of correlation effects in twisted bilayer graphene, using the extended coupled cluster method, is presented. This approach considers both self-consistent mean-field and beyond mean-field contributions, and can describe phase transitions in such strongly correlated systems, without further inputs or assumptions. Detailed expressions and a suitable implementation for the method are developed. Combining modern tensor contraction techniques with singular value decomposition, the correlation effects are successfully described in a qualitative manner, including contributions from the short-range and long-range parts of the Coulomb interaction. The superconducting gap is found to be maximal at a twist angle of $\theta_c = 1.00 \deg$ with a roughly equal combination of s-wave and f-wave components. Using BCS theory, the size of the gap corresponds to a critical temperature value of $T_\text{c}^\text{BCS} = 0.5$K. This matches qualitatively with experimental data. Within the limitation of the numerical truncations used, a novel candidate for the mechanism behind superconductive phases in twisted bilayer graphene is proposed.

cond-mat.str-el↗