Revolutionizing drug formulation development: the increasing impact of machine learning
Over the past few years, the adoption of machine learning (ML) techniques has rapidly
expanded across many fields of research including formulation science. At the same time …
expanded across many fields of research including formulation science. At the same time …
[HTML][HTML] Battery safety: Machine learning-based prognostics
Lithium-ion batteries play a pivotal role in a wide range of applications, from electronic
devices to large-scale electrified transportation systems and grid-scale energy storage …
devices to large-scale electrified transportation systems and grid-scale energy storage …
In pursuit of the exceptional: Research directions for machine learning in chemical and materials science
Exceptional molecules and materials with one or more extraordinary properties are both
technologically valuable and fundamentally interesting, because they often involve new …
technologically valuable and fundamentally interesting, because they often involve new …
Artificial-intelligence-led revolution of construction materials: From molecules to Industry 4.0
Industry 4.0 promotes the transformation of manufacturing industry to intelligence, which
demands advances in materials, devices, and systems of the construction industry …
demands advances in materials, devices, and systems of the construction industry …
[HTML][HTML] Synthesis, properties, applications, 3D printing and machine learning of graphene quantum dots in polymer nanocomposites
This comprehensive review discusses the recent progress in synthesis, properties,
applications, 3D printing and machine learning of graphene quantum dots (GQDs) in …
applications, 3D printing and machine learning of graphene quantum dots (GQDs) in …
From prediction to design: recent advances in machine learning for the study of 2D materials
H He, Y Wang, Y Qi, Z Xu, Y Li, Y Wang - Nano Energy, 2023 - Elsevier
Although data-driven approaches have made significant strides in various scientific fields,
there has been a lack of systematic summaries and discussions on their application in 2D …
there has been a lack of systematic summaries and discussions on their application in 2D …
Process-structure multi-objective inverse optimisation for additive manufacturing of lattice structures using a physics-enhanced data-driven method
K Shi, D Gu, H Liu, Y Chen, K Lin - Virtual and Physical Prototyping, 2023 - Taylor & Francis
Additive manufacturing (AM) has become a practical solution for fabricating lightweight and
high-strength metallic lattice structures. The inverse optimisation of process-structure …
high-strength metallic lattice structures. The inverse optimisation of process-structure …
Machine learning guided hydrothermal synthesis of thermochromic VO2 nanoparticles
Y Chen, H Ji, M Lu, B Liu, Y Zhao, Y Ou, Y Wang… - Ceramics …, 2023 - Elsevier
Vanadium dioxide (VO 2) is a promising material for energy-saving smart windows due to its
reversible metal-to-insulator transition near room temperature, concomitantly with a …
reversible metal-to-insulator transition near room temperature, concomitantly with a …
Machine Learning Assisted Analysis, Prediction, and Fabrication of High‐Efficiency CZTSSe Thin Film Solar Cells
The Earth‐abundant element‐based Cu2ZnSn (S, Se) 4 (CZTSSe) absorber is considered
as a promising material for thin‐film solar cells (TFSCs). The current record power …
as a promising material for thin‐film solar cells (TFSCs). The current record power …
What is missing in autonomous discovery: open challenges for the community
Self-driving labs (SDLs) leverage combinations of artificial intelligence, automation, and
advanced computing to accelerate scientific discovery. The promise of this field has given …
advanced computing to accelerate scientific discovery. The promise of this field has given …