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Perfecting liquid-state theories with machine intelligence
Recent years have seen a significant increase in the use of machine intelligence for
predicting the electronic structure, molecular force fields, and physicochemical properties of …
predicting the electronic structure, molecular force fields, and physicochemical properties of …
Why neural functionals suit statistical mechanics
We describe recent progress in the statistical mechanical description of many-body systems
via machine learning combined with concepts from density functional theory and many-body …
via machine learning combined with concepts from density functional theory and many-body …
Perspective: How to overcome dynamical density functional theory
We argue in favour of develo** a comprehensive dynamical theory for rationalizing,
predicting, designing, and machine learning nonequilibrium phenomena that occur in soft …
predicting, designing, and machine learning nonequilibrium phenomena that occur in soft …
Hyperdensity functional theory of soft matter
We present a scheme for investigating arbitrary thermal observables in spatially
inhomogeneous equilibrium many-body systems. Extending the grand canonical ensemble …
inhomogeneous equilibrium many-body systems. Extending the grand canonical ensemble …
Ab initio uncertainty quantification in scattering analysis of microscopy
Estimating parameters from data is a fundamental problem in physics, customarily done by
minimizing a loss function between a model and observed statistics. In scattering-based …
minimizing a loss function between a model and observed statistics. In scattering-based …
Probabilistic forecast of nonlinear dynamical systems with uncertainty quantification
Data-driven modeling is useful for reconstructing nonlinear dynamical systems when the
underlying process is unknown or too expensive to compute. Having reliable uncertainty …
underlying process is unknown or too expensive to compute. Having reliable uncertainty …
Machine learning approaches to classical density functional theory
In this chapter, we discuss recent advances and new opportunities through methods of
machine learning for the field of classical density functional theory, dealing with the …
machine learning for the field of classical density functional theory, dealing with the …
High-dimensional operator learning for molecular density functional theory
Classical density functional theory (cDFT) provides a systematic approach to predict the
structure and thermodynamic properties of chemical systems through the single-molecule …
structure and thermodynamic properties of chemical systems through the single-molecule …
Metadensity functional theory for classical fluids: Extracting the pair potential
The excess free energy functional of classical density functional theory depends upon the
type of fluid model, specifically on the choice of (pair) potential, is unknown in general, and …
type of fluid model, specifically on the choice of (pair) potential, is unknown in general, and …
Physics-informed Gaussian process regression of in operando capacitance for carbon supercapacitors
Amorphous porous carbons are one of the most popular electrode materials for energy
storage owing to their high electrical conductivity, large specific surface area and low …
storage owing to their high electrical conductivity, large specific surface area and low …