Learning (from) the Electron Density: Transferability, Conformational and Chemical Diversity

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Machine learning has provided a means to accelerate early-stage drug discovery by combining molecule generation and filtering steps in a single architecture that leverages the experience and design preferences of medicinal chemists. However, designing mach ...

Decision-making permeates every aspect of human and societal development, from individuals' daily choices to the complex decisions made by communities and institutions. Central to effective decision-making is the discipline of optimization, which seeks th ...

We present an orbital-resolved extension of the Hubbard U correction to density-functional theory (DFT). Compared to the conventional shell-averaged approach, the prediction of energetic, electronic and structural properties is strongly improved, particula ...

In light of the challenges posed by climate change and the goals of the Paris Agreement, electricity generation is shifting to a more renewable and decentralized pattern, while the operation of systems like buildings is increasingly electrified. This calls ...

Through the use of the piecewise-linearity condition of the total energy, we correct the self-interaction for the study of polarons by constructing nonempirical functionals at the semilocal level of theory. We consider two functionals, the gamma DFT and mu ...

Federated learning (FL) is a privacy-preserving collaboratively machine learning paradigm. Traditional FL requires all data owners (a.k.a. FL clients) to train the same local model. This design is not well-suited for scenarios involving data and/or system ...

Central aortic diastolic pressure decay time constant ( ) is according to the two-element Windkessel model equal to the product of total peripheral resistance (R) times total arterial compliance (C ). As such, it is related to arterial stiffness, which has ...

Machine learning (ML) enables artificial intelligent (AI) agents to learn autonomously from data obtained from their environment to perform tasks. Modern ML systems have proven to be extremely effective, reaching or even exceeding human intelligence. Altho ...

The upcoming Square Kilometre Array Observatory will produce images of neutral hydrogen distribution during the epoch of reionization by observing the corresponding 21-cm signal. However, the 21-cm signal will be subject to instrumental limitations such as ...

Molecular junctions represent a fascinating frontier in the realm of nanotechnology and are one of the smallest optoelectronic devices possible, consisting of individual molecules or a group of molecules that serve as the active element sandwiched between ...

This paper proposes a data-driven control design method for nonlinear systems that builds upon the Koopman operator framework. In particular, the Koopman operator is used to lift the nonlinear dynamics to a higher-dimensional space where the so-called obse ...

The vast amount of computational studies on electrical conduction in solid-state electrolytes is not mirrored by comparable efforts addressing thermal conduction, which has been scarcely investigated despite its relevance to thermal management and (over)he ...

Human babies have a natural desire to interact with new toys and objects, through which they learn how the world around them works, e.g., that glass shatters when dropped, but a rubber ball does not. When their predictions are proven incorrect, such as whe ...

The goal of this thesis is to propose pragmatic solutions to real challenges faced in the industry. The scope of this thesis encompasses two subjects: frequency-based structured controller synthesis for linear time-invariant (LTI) systems on one side, and ...

Learning (from) the Electron Density: Transferability, Conformational and Chemical Diversity

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Machine learning-aided generative molecular design

Reliable data-driven decision-making through optimal transport

Orbital-Resolved DFT plus U for Molecules and Solids

Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems

Nonempirical semilocal density functionals for correcting the self-interaction of polaronic states

FedSSA: Semantic Similarity-based Aggregation for Efficient Model-Heterogeneous Personalized Federated Learning

Novel theory and potential applications of central diastolic pressure decay time constant

Few-shot Learning for Efficient and Effective Machine Learning Model Adaptation

Deep learning approach for identification of H II regions during reionization in 21-cm observations - II. Foreground contamination

Plasmonically enhanced molecular junctions for investigation of atomic-scale fluctuations in self-assembled monolayers

Data-Driven Control Synthesis using Koopman Operator: A Robust Approach

Thermal conductivity of Li 3 PS 4 solid electrolytes with ab initio accuracy

Seeking the new, learning from the unexpected: Computational models of surprise and novelty in the brain

Frequency domain data-driven robust and optimal control