Harnessing the power of machine learning for carbon capture, utilisation, and storage (CCUS)–a state-of-the-art review
Carbon capture, utilisation and storage (CCUS) will play a critical role in future
decarbonisation efforts to meet the Paris Agreement targets and mitigate the worst effects of …
decarbonisation efforts to meet the Paris Agreement targets and mitigate the worst effects of …
Material evolution with nanotechnology, nanoarchitectonics, and materials informatics: what will be the next paradigm shift in nanoporous materials?
Materials science and chemistry have played a central and significant role in advancing
society. With the shift toward sustainable living, it is anticipated that the development of …
society. With the shift toward sustainable living, it is anticipated that the development of …
A multi-modal pre-training transformer for universal transfer learning in metal–organic frameworks
Metal–organic frameworks (MOFs) are a class of crystalline porous materials that exhibit a
vast chemical space owing to their tunable molecular building blocks with diverse …
vast chemical space owing to their tunable molecular building blocks with diverse …
The role of machine learning in the understanding and design of materials
Developing algorithmic approaches for the rational design and discovery of materials can
enable us to systematically find novel materials, which can have huge technological and …
enable us to systematically find novel materials, which can have huge technological and …
Data quantity governance for machine learning in materials science
Y Liu, Z Yang, X Zou, S Ma, D Liu… - National Science …, 2023 - academic.oup.com
Data-driven machine learning (ML) is widely employed in the analysis of materials structure–
activity relationships, performance optimization and materials design due to its superior …
activity relationships, performance optimization and materials design due to its superior …
Machine learning meets with metal organic frameworks for gas storage and separation
The acceleration in design of new metal organic frameworks (MOFs) has led scientists to
focus on high-throughput computational screening (HTCS) methods to quickly assess the …
focus on high-throughput computational screening (HTCS) methods to quickly assess the …
Transfer learning for solvation free energies: From quantum chemistry to experiments
FH Vermeire, WH Green - Chemical Engineering Journal, 2021 - Elsevier
Data scarcity, bias, and experimental noise are all frequently encountered problems in the
application of deep learning to chemical and material science disciplines. Transfer learning …
application of deep learning to chemical and material science disciplines. Transfer learning …
Two-dimensional energy histograms as features for machine learning to predict adsorption in diverse nanoporous materials
A major obstacle for machine learning (ML) in chemical science is the lack of physically
informed feature representations that provide both accurate prediction and easy …
informed feature representations that provide both accurate prediction and easy …
From bulk effective mass to 2D carrier mobility accurate prediction via adversarial transfer learning
X Chen, S Lu, Q Chen, Q Zhou, J Wang - nature communications, 2024 - nature.com
Data scarcity is one of the critical bottlenecks to utilizing machine learning in material
discovery. Transfer learning can use existing big data to assist property prediction on small …
discovery. Transfer learning can use existing big data to assist property prediction on small …
Machine learning accelerates the investigation of targeted MOFs: performance prediction, rational design and intelligent synthesis
Metal-organic frameworks (MOFs) are a new class of nanoporous materials that are widely
used in various emerging fields due to their large specific surface area, high porosity and …
used in various emerging fields due to their large specific surface area, high porosity and …