Machine learning for electrocatalyst and photocatalyst design and discovery
Electrocatalysts and photocatalysts are key to a sustainable future, generating clean fuels,
reducing the impact of global warming, and providing solutions to environmental pollution …
reducing the impact of global warming, and providing solutions to environmental pollution …
[HTML][HTML] Machine learning for a sustainable energy future
Transitioning from fossil fuels to renewable energy sources is a critical global challenge; it
demands advances—at the materials, devices and systems levels—for the efficient …
demands advances—at the materials, devices and systems levels—for the efficient …
Borates: A rich source for optical materials
M Mutailipu, KR Poeppelmeier, S Pan - Chemical Reviews, 2020 - ACS Publications
The primary goal of this review is to present a clear chemical perspective of borates in order
to stimulate and facilitate the discovery of new borate-based optical materials. These …
to stimulate and facilitate the discovery of new borate-based optical materials. These …
[HTML][HTML] Recent advances and applications of machine learning in solid-state materials science
One of the most exciting tools that have entered the material science toolbox in recent years
is machine learning. This collection of statistical methods has already proved to be capable …
is machine learning. This collection of statistical methods has already proved to be capable …
[PDF][PDF] Integrating physics-based modeling with machine learning: A survey
There is a growing consensus that solutions to complex science and engineering problems
require novel methodologies that are able to integrate traditional physics-based modeling …
require novel methodologies that are able to integrate traditional physics-based modeling …
Integrating scientific knowledge with machine learning for engineering and environmental systems
There is a growing consensus that solutions to complex science and engineering problems
require novel methodologies that are able to integrate traditional physics-based modeling …
require novel methodologies that are able to integrate traditional physics-based modeling …
Recent progress on phase engineering of nanomaterials
As a key structural parameter, phase depicts the arrangement of atoms in materials.
Normally, a nanomaterial exists in its thermodynamically stable crystal phase. With the …
Normally, a nanomaterial exists in its thermodynamically stable crystal phase. With the …
[HTML][HTML] A perspective on conventional high-temperature superconductors at high pressure: Methods and materials
Two hydrogen-rich materials, H 3 S and LaH 10, synthesized at megabar pressures, have
revolutionized the field of condensed matter physics providing the first glimpse to the …
revolutionized the field of condensed matter physics providing the first glimpse to the …
Inverse design of nanoporous crystalline reticular materials with deep generative models
Reticular frameworks are crystalline porous materials that form via the self-assembly of
molecular building blocks in different topologies, with many having desirable properties for …
molecular building blocks in different topologies, with many having desirable properties for …
[HTML][HTML] Inverse design of solid-state materials via a continuous representation
The non-serendipitous discovery of materials with targeted properties is the ultimate goal of
materials research, but to date, materials design lacks the incorporation of all available …
materials research, but to date, materials design lacks the incorporation of all available …