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A Database of Ultrastable MOFs Reassembled from Stable Fragments with Machine Learning Models
We separate these MOFs into their building blocks and recombine them to make a new hypothetical MOF database of over 50, 000 structures that samples orders of magnitude more connectivity nets and inorganic building blocks than prior databases.
Low-cost machine learning approach to the prediction of transition metal phosphor excited state properties
Photoactive iridium complexes are of broad interest due to their applications ranging from lighting to photocatalysis.
Active Learning Exploration of Transition Metal Complexes to Discover Method-Insensitive and Synthetically Accessible Chromophores
Transition metal chromophores with earth-abundant transition metals are an important design target for their applications in lighting and non-toxic bioimaging, but their design is challenged by the scarcity of complexes that simultaneously have optimal target absorption energies in the visible region as well as well-defined ground states.
A Transferable Recommender Approach for Selecting the Best Density Functional Approximations in Chemical Discovery
With electron density fitting and transfer learning, we build a DFA recommender that selects the DFA with the lowest expected error with respect to gold standard but cost-prohibitive coupled cluster theory in a system-specific manner.
Putting Density Functional Theory to the Test in Machine-Learning-Accelerated Materials Discovery
Accelerated discovery with machine learning (ML) has begun to provide the advances in efficiency needed to overcome the combinatorial challenge of computational materials design.
Machine learning models predict calculation outcomes with the transferability necessary for computational catalysis
Combined with model uncertainty quantification, the dynamic classifier saves more than half of the computational resources that would have been wasted on unsuccessful calculations for all reactive intermediates being considered.
Two Wrongs Can Make a Right: A Transfer Learning Approach for Chemical Discovery with Chemical Accuracy
Differences in MR character are more important than the total degree of MR character in predicting MR effect in property prediction.
Audacity of huge: overcoming challenges of data scarcity and data quality for machine learning in computational materials discovery
Machine learning (ML)-accelerated discovery requires large amounts of high-fidelity data to reveal predictive structure-property relationships.
Deciphering Cryptic Behavior in Bimetallic Transition Metal Complexes with Machine Learning
Focusing on oxidation potentials, we obtain a set of 28 experimentally characterized complexes to develop a multiple linear regression model.
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 understanding of how to improve their stability limits their use in practice.
Representations and Strategies for Transferable Machine Learning Models in Chemical Discovery
To address the common challenge of discovery in a new space where data is limited, we introduce a transfer learning approach in which we seed models trained on a large amount of data from one row of the periodic table with a small number of data points from the additional row.
