Knowledge-integrated machine learning for materials: lessons from gameplaying and robotics

K Hippalgaonkar, Q Li, X Wang, JW Fisher III… - Nature Reviews …, 2023 - nature.com
As materials researchers increasingly embrace machine-learning (ML) methods, it is natural
to wonder what lessons can be learned from other fields undergoing similar developments …

In pursuit of the exceptional: Research directions for machine learning in chemical and materials science

J Schrier, AJ Norquist, T Buonassisi… - Journal of the American …, 2023 - ACS Publications
Exceptional molecules and materials with one or more extraordinary properties are both
technologically valuable and fundamentally interesting, because they often involve new …

Data-Driven Machine Learning to Predict Antibacterial Activity of Cerium-Doped Nanoparticles

Y Perfecto-Avalos, DE Navarro-López… - ACS Applied Nano …, 2023 - ACS Publications
Nowadays, nanomaterials are a real alternative for controlling antibiotic-resistant bacteria.
Several nanoparticles have shown good performance in inducing bacterial death due to a …

A broad-spectrum synthetic antibiotic that does not evoke bacterial resistance

DM Heithoff, SP Mahan, L Barnes, SA Leyn… - …, 2023 - thelancet.com
Background Antimicrobial resistance (AMR) poses a critical threat to public health and
disproportionately affects the health and well-being of persons in low-income and middle …

Membrane-intercalating conjugated oligoelectrolytes

C Zhou, GWN Chia, KT Yong - Chemical Society Reviews, 2022 - pubs.rsc.org
By acting as effective biomimetics of the lipid bilayers, membrane-intercalating conjugated
oligoelectrolytes (MICOEs) can spontaneously insert themselves into both synthetic lipid …

Explainable deep-learning-assisted sweat assessment via a programmable colorimetric chip

Z Liu, J Li, J Li, T Yang, Z Zhang, H Wu, H Xu… - Analytical …, 2022 - ACS Publications
Multianalytes and individual differences of biofluids (such as blood, urine, or sweat) pose
enormous complexity and challenges to rapid, facile, high-throughput, and accurate clinical …

High-throughput screening of amorphous polymers with high intrinsic thermal conductivity via automated physical feature engineering

X Huang, S Ma, Y Wu, C Wan, CY Zhao… - Journal of Materials …, 2023 - pubs.rsc.org
The informatics algorithm-driven approach overcomes the high-cost and time-consuming
drawbacks of conventional trial-and-error procedures and enables efficient exploration of …

Exploring high thermal conductivity polymers via interpretable machine learning with physical descriptors

X Huang, S Ma, CY Zhao, H Wang, S Ju - npj Computational Materials, 2023 - nature.com
The efficient and economical exploitation of polymers with high thermal conductivity (TC) is
essential to solve the issue of heat dissipation in organic devices. Currently, the …

[HTML][HTML] AI-guided few-shot inverse design of HDP-mimicking polymers against drug-resistant bacteria

T Wu, M Zhou, J Zou, Q Chen, F Qian, J Kurths… - Nature …, 2024 - nature.com
Host defense peptide (HDP)-mimicking polymers are promising therapeutic alternatives to
antibiotics and have large-scale untapped potential. Artificial intelligence (AI) exhibits …

An anti-mycobacterial conjugated oligoelectrolyte effective against Mycobacterium abscessus

K Zhang, J Limwongyut, AS Moreland… - Science Translational …, 2024 - science.org
Infections caused by nontuberculous mycobacteria have increased more than 50% in the
past two decades and more than doubled in the elderly population. Mycobacterium …