Publications associées à Surrogate Based Genetic Algorithm Method for Efficient Identification of Low-Energy Peptide Structures

Publications associées (15)

Inverse Geometric Locomotion

Numerous tasks in robotics and character animation involve solving combinations of inverse kinematics and motion planning problems that require the precise design of pose sequences to achieve desired motion objectives. Accounting for the complex interplay ...

2025

A Study on Quantum Graph Neural Networks Applied to Molecular Physics

Su Yeon Chang

This paper introduces a novel architecture for Quantum Graph Neural Networks, which is significantly different from previous approaches found in the literature. The proposed approach produces similar outcomes with respect to previous models but with fewer ...

IOP Publishing Ltd2025

Introduction and Overview: Chapter 1 of the Special Issue: on the Path to Tokamak Burning Plasma Operation

Ambrogio Fasoli

The International Tokamak Physics Activity (ITPA) has developed a comprehensive overview of the results of its coordinated R&D activities in fusion physics and diagnostics implemented during the past two decades with the aim of developing an improved physi ...

IOP Publishing Ltd2025

Structure and Interactions in 1:2 K+–Crown Ether Complexes via Gas-Phase Cold UV and IR Spectroscopy

Thomas Rizzo

We obtained gas-phase UV and IR spectra of 1:2 potassium ion-crown ether (CE) complexes (benzo-15-crown-5 (B15C5), benzo-18-crown-6 (B18C6), dibenzo-18-crown-6 (DB18C6), and 18-crown-6 (18C6)) at approximately 10 K and determined the structure and intermol ...

American Chemical Society (ACS)2025

Resonant inelastic X-ray scattering tools to count 5 f electrons of actinides and probe bond covalency

Marinella Mazzanti

The actinides possess a complex electronic structure, making their chemical and physical properties among the least understood in the periodic table. Advanced spectroscopic tools, able to obtain deep insights into the electronic structure and binding prope ...

NATURE PORTFOLIO2025

Dimensionless Group-Driven Ensemble Neural Networks for Robust Design Optimisation in Engineering

Jürg Alexander Schiffmann, Soheyl Massoudi

This work aims to develop a scalable surrogate modelling framework for robust multidisciplinary design optimisation (RMDO) in complex engineering systems. Focusing on gas-bearing supported rotors, it addresses the need to maintain system performance under ...

Oxford University Press (OUP)2025

Visualizing hot carrier dynamics by nonlinear optical spectroscopy at the atomic length scale

Klaus Kern

Probing and manipulating the spatiotemporal dynamics of hot carriers in nanoscale metals is crucial to a plethora of applications ranging from nonlinear nanophotonics to single-molecule photochemistry. The direct investigation of these highly non-equilibri ...

Springer Science and Business Media LLC2025

Constructing multicomponent cluster expansions with machine-learning and chemical embedding

Yann Lorris Müller, Anirudh Raju Natarajan

Cluster expansions are commonly employed as surrogate models to link the electronic structure of an alloy to its finite-temperature properties. Using cluster expansions to model materials with several alloying elements is challenging due to a rapid increas ...

NATURE PORTFOLIO2025

Physics-Enhanced Graph Neural Networks for Soft Sensing in Industrial Internet of Things

Olga Fink, Keivan Faghih Niresi

The Industrial Internet of Things (IIoT) is reshaping manufacturing, industrial processes, and infrastructure management. By fostering new levels of automation, efficiency, and predictive maintenance, IIoT is transforming traditional industries into intell ...

2024

Atomic-level structure of the amorphous drug atuliflapon via NMR crystallography

David Lyndon Emsley, Manuel Cordova, Albert Hofstetter, Pierrick Berruyer, Federico Maria Paruzzo, Bruno Simões de Almeida, Martins Balodis, Daria Torodii, Jacob Brian Holmes

We determine the complete atomic-level structure of the amorphous form of the drug atuliflapon, a 5-lipooxygenase activating protein (FLAP) inhibitor, via chemical-shift-driven NMR crystallography. The ensemble of preferred structures allows us to identify ...

2024

Mapping orthorhombic domains with geometrical phase analysis in rare-earth nickelate heterostructures

Duncan Thomas Lindsay Alexander, Bernat Mundet

Most perovskite oxides belong to the Pbnm space group, composed of an anisotropic unit cell, A-site antipolar displacements, and oxygen octahedral tilts. Mapping the orientation of the orthorhombic unit cell in epitaxial heterostructures that consist of at ...

Aip Publishing2024

The Nobel Prize in Chemistry: past, present, and future of AI in biology

Luciano Andres Abriata

The work by Hassabis and Jumper on protein structure prediction together with Baker's supremacy in de novo protein design set the stage for a future where AI not only deciphers biology at the atomic level but also designs new molecules for biotechnology, m ...

NATURE PORTFOLIO2024

Machine Learning for Screening Small Molecules as Passivation Materials for Enhanced Perovskite Solar Cells

Mohammad Khaja Nazeeruddin, Bin Ding, Xianfu Zhang

Utilization of small molecules as passivation materials for perovskite solar cells (PSCs) has gained significant attention recently, with hundreds of small molecules demonstrating passivation effects. In this study, a high-accuracy machine learning model i ...

Wiley-VCH Verlag Gmbh2024

Investigation of Interface Interactions Between Monolayer MoS2 and Metals: Implications on Strain and Surface Roughness

Klaus Kern

Achieving a low contact resistance has been an important issue in the design of two-dimensional (2D) semiconductor-metal interfaces. The metal contact resistance is dominated by interfacial interactions. Here, we systematically investigate 2D semiconductor ...

2024

Cross-code verification of non-neutral ABL and single wind turbine wake modelling in LES

Fernando Porté Agel, Marwa Souaiby

Large Eddy Simulations (LES) of atmospheric boundary layer (ABL) flow with the actuator disc (AD) for turbine modelling is a widely used method of simulating wind farm flows. Hence, it is important to understand the requirements for achieving a good compar ...

IOP PUBLISHING LTD2023