output
20162026
most citedEffects of Cooling Rate on Structural Relaxation in Amorphous Drugs: Elastically Collective Nonlinear Langevin Equation Theory and Machine Learning Study

32 citations

10 papers

eess.SY2026

Assessing Maintenance of Medium Voltage Cable Networks Under Time-Varying Loading

Jochen Lorenz Cremer

The electrification and ongoing energy transition lead to systematic changes in electricity loading and variability in power systems. Distribution systems were designed for regular…

cs.LG2026

Differentiable Thermodynamic Phase-Equilibria for Machine Learning

Karim K. Ben Hicham, Moreno Ascani, Jan G. Rittig +1

Accurate prediction of phase equilibria remains a central challenge in chemical engineering. Physics-consistent machine learning methods that incorporate thermodynamic structure in…

math.OC2025

Accelerating Deterministic Global Optimization via GPU-parallel Interval Arithmetic

Hongzhen Zhang, Tim Kerkenhoff, Neil Kichler +4

Spatial Branch and Bound (B&B) algorithms are widely used for solving nonconvex problems to global optimality, yet they remain computationally expensive. Though some works have bee…

cond-mat.mtrl-sci2025

Exceptional thermoelectric properties in NaTlSb enabled by quasi-1D band structure

Øven A. Grimenes, Ole M. Løvvik, Kristian Berland

Materials with reduced dimensionality offer beneficial density-of-states (DOS) profiles for thermoelectric energy conversion, but can be impractical in realistic devices. Encouragi…

math.DS2024★ 1 cited

Nontrivial Kron Reduction for Power Grid Dynamics Modeling

Laurent Pagnier, Robin Delabays, Melvyn Tyloo

The Kron reduction is used in power grid modeling when the analysis can -- supposedly -- be restricted to a subset of nodes. Typically, when one is interested in the phases' dynami…

cs.LG2024★ 7 cited

Graph Reinforcement Learning for Power Grids: A Comprehensive Survey

Mohamed Hassouna, Clara Holzhüter, Pawel Lytaev +3

The increasing share of renewable energy and distributed electricity generation requires the development of deep learning approaches to address the lack of flexibility inherent in…

cond-mat.soft2020★ 9 cited

Coupling between structural relaxation and diffusion in glass-forming liquids under pressure variation

Anh D. Phan, Kajetan Koperwas, Marian Paluch +1

We theoretically investigate structural relaxation and activated diffusion of glass-forming liquids at different pressures using both the Elastically Collective Nonlinear Langevin…

cond-mat.soft2019★ 32 cited

Effects of Cooling Rate on Structural Relaxation in Amorphous Drugs: Elastically Collective Nonlinear Langevin Equation Theory and Machine Learning Study

Anh D. Phan, Katsunori Wakabayashi, Marian Paluch +1

Theoretical approaches are formulated to investigate the molecular mobility under various cooling rates of amorphous drugs. We describe the structural relaxation of a tagged molecu…

cs.LG2017★ 18 cited

Comparative Performance Analysis of Neural Networks Architectures on H2O Platform for Various Activation Functions

Yuriy Kochura, Sergii Stirenko, Yuri Gordienko

Deep learning (deep structured learning, hierarchi- cal learning or deep machine learning) is a branch of machine learning based on a set of algorithms that attempt to model high-…

cond-mat.dis-nn2016

Anderson localization and saturable nonlinearity in one-dimensional disordered lattices

Ba Phi Nguyen, Kihong Kim

We investigate numerically the propagation and the Anderson localization of plane waves in a one-dimensional lattice chain, where disorder and saturable nonlinearity are simultaneo…