Interpretable machine learning for knowledge generation in heterogeneous catalysis
Most applications of machine learning in heterogeneous catalysis thus far have used black-
box models to predict computable physical properties (descriptors), such as adsorption or …
box models to predict computable physical properties (descriptors), such as adsorption or …
Computational discovery of transition-metal complexes: from high-throughput screening to machine learning
Transition-metal complexes are attractive targets for the design of catalysts and functional
materials. The behavior of the metal–organic bond, while very tunable for achieving target …
materials. The behavior of the metal–organic bond, while very tunable for achieving target …
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 …
Moformer: self-supervised transformer model for metal–organic framework property prediction
Metal–organic frameworks (MOFs) are materials with a high degree of porosity that can be
used for many applications. However, the chemical space of MOFs is enormous due to the …
used for many applications. However, the chemical space of MOFs is enormous due to the …
[HTML][HTML] Understanding the diversity of the metal-organic framework ecosystem
Millions of distinct metal-organic frameworks (MOFs) can be made by combining metal
nodes and organic linkers. At present, over 90,000 MOFs have been synthesized and over …
nodes and organic linkers. At present, over 90,000 MOFs have been synthesized and over …
Big-data science in porous materials: materials genomics and machine learning
By combining metal nodes with organic linkers we can potentially synthesize millions of
possible metal–organic frameworks (MOFs). The fact that we have so many materials opens …
possible metal–organic frameworks (MOFs). The fact that we have so many materials opens …
Roadmap on machine learning in electronic structure
In recent years, we have been witnessing a paradigm shift in computational materials
science. In fact, traditional methods, mostly developed in the second half of the XXth century …
science. In fact, traditional methods, mostly developed in the second half of the XXth century …
Using machine learning and data mining to leverage community knowledge for the engineering of stable metal–organic frameworks
Although the tailored metal active sites and porous architectures of MOFs hold great promise
for engineering challenges ranging from gas separations to catalysis, a lack of …
for engineering challenges ranging from gas separations to catalysis, a lack of …
The TensorMol-0.1 model chemistry: a neural network augmented with long-range physics
Traditional force fields cannot model chemical reactivity, and suffer from low generality
without re-fitting. Neural network potentials promise to address these problems, offering …
without re-fitting. Neural network potentials promise to address these problems, offering …
Machine learning for computational heterogeneous catalysis
Big data and artificial intelligence has revolutionized science in almost every field–from
economics to physics. In the area of materials science and computational heterogeneous …
economics to physics. In the area of materials science and computational heterogeneous …