Machine learning-assisted approaches in modernized plant breeding programs

M Yoosefzadeh Najafabadi, M Hesami, M Eskandari - Genes, 2023 - mdpi.com
In the face of a growing global population, plant breeding is being used as a sustainable tool
for increasing food security. A wide range of high-throughput omics technologies have been …

Automation and machine learning augmented by large language models in a catalysis study

Y Su, X Wang, Y Ye, Y **e, Y Xu, Y Jiang, C Wang - Chemical Science, 2024 - pubs.rsc.org
Recent advancements in artificial intelligence and automation are transforming catalyst
discovery and design from traditional trial-and-error manual mode into intelligent, high …

[HTML][HTML] Superlative mechanical energy absorbing efficiency discovered through self-driving lab-human partnership

KL Snapp, B Verdier, AE Gongora, S Silverman… - Nature …, 2024 - nature.com
Energy absorbing efficiency is a key determinant of a structure's ability to provide
mechanical protection and is defined by the amount of energy that can be absorbed prior to …

Optimal experimental design: Formulations and computations

X Huan, J Jagalur, Y Marzouk - Acta Numerica, 2024 - cambridge.org
Questions of 'how best to acquire data'are essential to modelling and prediction in the
natural and social sciences, engineering applications, and beyond. Optimal experimental …

Trustworthy autonomous driving via defense-aware robust reinforcement learning against worst-case observational perturbations

X He, W Huang, C Lv - Transportation Research Part C: Emerging …, 2024 - Elsevier
Despite the substantial advancements in reinforcement learning (RL) in recent years,
ensuring trustworthiness remains a formidable challenge when applying this technology to …

ELRL-MD: a deep learning approach for myocarditis diagnosis using cardiac magnetic resonance images with ensemble and reinforcement learning integration

AMM Kasmaee, A Ataei, SV Moravvej… - Physiological …, 2024 - iopscience.iop.org
Objective. Myocarditis poses a significant health risk, often precipitated by viral infections
like coronavirus disease, and can lead to fatal cardiac complications. As a less invasive …

A survey and benchmark of high-dimensional Bayesian optimization of discrete sequences

M González-Duque, R Michael… - Advances in …, 2025 - proceedings.neurips.cc
Optimizing discrete black-box functions is key in several domains, eg protein engineering
and drug design. Due to the lack of gradient information and the need for sample efficiency …

Trustworthy machine learning-enhanced 3D concrete printing: Predicting bond strength and designing reinforcement embedment length

XR Ma, XL Wang, SZ Chen - Automation in Construction, 2024 - Elsevier
Three-dimensional concrete printing (3DCP) faces challenges in determining and ensuring
adequate bond strength between reinforcement and printed concrete. Traditional methods …

Constrained Bayesian optimization algorithms for estimating design points in structural reliability analysis

J Song, Y Cui, P Wei, MA Valdebenito… - Reliability Engineering & …, 2024 - Elsevier
Estimating the design points with high accuracy is a historical and key issue for many
reliability analysis and reliability-based design optimization methods. Indeed, it is still a …

Inverse stochastic microstructure design

AP Generale, AE Robertson, C Kelly, SR Kalidindi - Acta Materialia, 2024 - Elsevier
Abstract Inverse Microstructure Design problems are ubiquitous in materials science; for
example, property-driven microstructure design requires the inversion of a structure …