scholarly journals Machine Learning Accelerates the Discovery of Design Rules and Exceptions in Stable Metal–Oxo Intermediate Formation

ACS Catalysis ◽  
2019 ◽  
Vol 9 (9) ◽  
pp. 8243-8255 ◽  
Author(s):  
Aditya Nandy ◽  
Jiazhou Zhu ◽  
Jon Paul Janet ◽  
Chenru Duan ◽  
Rachel B. Getman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intermediate Formation ◽  
Design Rules ◽  
Rules And Exceptions

scholarly journals Machine Learning Accelerates the Discovery of Design Rules and Exceptions in Stable Metal-Oxo Intermediate Formation

2019 ◽  
Author(s):  
Aditya Nandy ◽  
Jiazhou Zhu ◽  
Jon Paul Janet ◽  
Chenru Duan ◽  
Rachel Getman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Spin State ◽  
Density Functional ◽  
Intermediate Formation ◽  
Catalyst Design ◽  
Structure Property ◽  
Design Rules ◽  
Energy Prediction ◽  
Formation Energies ◽  
Rules And Exceptions

<p>Metal-oxo moieties are important catalytic intermediates in the selective partial oxidation of hydrocarbons and in water splitting. Stable metal-oxo species have reactive properties that vary depending on the spin state of the metal, complicating the development of structure-property relationships. To overcome these challenges, we train the first machine learning (ML) models capable of predicting metal-oxo formation energies across a range of first-row metals, oxidation states, and spin states. Using connectivity-only features tailored for inorganic chemistry as inputs to kernel ridge regression or artificial neural network ML models, we achieve good mean absolute errors (4-5 kcal/mol) on set-aside test data across a range of ligand orientations. Analysis of feature importance for oxo formation energy prediction reveals the dominance of non-local, electronic ligand properties in contrast to other transition metal complex properties (e.g., spin-state or ionization potential). We enumerate the theoretical catalyst space with an ANN, revealing both expected trends in oxo formation energetics, such as destabilization of the metal-oxo species with increasing <i>d</i>-filling, as well as exceptions, such as weak correlations with indicators of oxidative stability of the metal in the resting state or unexpected spin-state dependence in reactivity. We carry out uncertainty aware evolutionary optimization using the ANN to explore a > 37,000 candidate catalyst space. New metal and oxidation state combinations are uncovered and validated with density functional theory (DFT), including counter-intuitive oxo-formation energies for oxidatively stable complexes. This approach doubles the density of confirmed DFT leads in originally sparsely populated regions of property space, highlighting the potential of ML-model-driven discovery to uncover catalyst design rules and exceptions.</p>

scholarly journals Machine Learning Accelerates the Discovery of Design Rules and Exceptions in Stable Metal-Oxo Intermediate Formation

2019 ◽  
Author(s):  
Aditya Nandy ◽  
Jiazhou Zhu ◽  
Jon Paul Janet ◽  
Chenru Duan ◽  
Rachel Getman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Spin State ◽  
Density Functional ◽  
Intermediate Formation ◽  
Catalyst Design ◽  
Structure Property ◽  
Design Rules ◽  
Energy Prediction ◽  
Formation Energies ◽  
Rules And Exceptions

<p>Metal-oxo moieties are important catalytic intermediates in the selective partial oxidation of hydrocarbons and in water splitting. Stable metal-oxo species have reactive properties that vary depending on the spin state of the metal, complicating the development of structure-property relationships. To overcome these challenges, we train the first machine learning (ML) models capable of predicting metal-oxo formation energies across a range of first-row metals, oxidation states, and spin states. Using connectivity-only features tailored for inorganic chemistry as inputs to kernel ridge regression or artificial neural network ML models, we achieve good mean absolute errors (4-5 kcal/mol) on set-aside test data across a range of ligand orientations. Analysis of feature importance for oxo formation energy prediction reveals the dominance of non-local, electronic ligand properties in contrast to other transition metal complex properties (e.g., spin-state or ionization potential). We enumerate the theoretical catalyst space with an ANN, revealing both expected trends in oxo formation energetics, such as destabilization of the metal-oxo species with increasing <i>d</i>-filling, as well as exceptions, such as weak correlations with indicators of oxidative stability of the metal in the resting state or unexpected spin-state dependence in reactivity. We carry out uncertainty aware evolutionary optimization using the ANN to explore a > 37,000 candidate catalyst space. New metal and oxidation state combinations are uncovered and validated with density functional theory (DFT), including counter-intuitive oxo-formation energies for oxidatively stable complexes. This approach doubles the density of confirmed DFT leads in originally sparsely populated regions of property space, highlighting the potential of ML-model-driven discovery to uncover catalyst design rules and exceptions.</p>

scholarly journals Exploring the rules of chimeric antigen receptor phenotypic output using combinatorial signaling motif libraries and machine learning

2022 ◽  
Author(s):  
Kyle G Daniels ◽  
Shangying Wang ◽  
Milos S Simic ◽  
Hersh K Bhargava ◽  
Sara Capponi ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Chimeric Antigen Receptor ◽  
Antigen Receptor ◽  
Building Blocks ◽  
Cell Fates ◽  
Design Rules ◽  
Binding Motifs ◽  
The Neural Network ◽  
Tumor Killing

Chimeric antigen receptor (CAR) costimulatory domains steer the phenotypic output of therapeutic T cells. In most cases these domains are derived from native immune receptors, composed of signaling motif combinations selected by evolution. To explore if non-natural combinations of signaling motifs could drive novel cell fates of interest, we constructed a library of CARs containing ~2,300 synthetic costimulatory domains, built from combinations of 13 peptide signaling motifs. The library produced CARs driving diverse fate outputs, which were sensitive motif combinations and configurations. Neural networks trained to decode the combinatorial grammar of CAR signaling motifs allowed extraction of key design rules. For example, the non-native combination of TRAF- and PLCg1-binding motifs was found to simultaneously enhance cytotoxicity and stemness, a clinically desirable phenotype associated with effective and durable tumor killing. The neural network accurately predicts that addition of PLCg1-binding motifs improves this phenotype when combined with TRAF-binding motifs, but not when combined with other immune signaling motifs (e.g. PI3K- or Grb2- binding motifs). This work shows how libraries built from the minimal building blocks of signaling, combined with machine learning, can efficiently guide engineering of receptors with desired phenotypes.

scholarly journals Sequencing Enabling Design and Learning in Synthetic Biology

2020 ◽  
Author(s):  
Pierre-Aurelien Gilliot ◽  
Thomas E. Gorochowski
Keyword(s):  
Machine Learning ◽  
Synthetic Biology ◽  
De Novo ◽  
Data Sets ◽  
New Wave ◽  
Design Rules ◽  
Biological Design ◽  
Sequencing Technologies ◽  
Dna And Rna

The ability to read and quantify nucleic acids such as DNA and RNA using sequencing technologies has revolutionized our understanding of life. With the emergence of synthetic biology, these tools are now being put to work in new ways - enabling de novo biological design. Here, we show how sequencing is supporting the creation of a new wave of biological parts and systems, as well as providing the vast data sets needed for the machine learning of design rules for predictive bioengineering. However, we believe this is only the tip of the iceberg and end by providing an outlook on recent advances that will likely broaden the role of sequencing in synthetic biology and its deployment in real-world environments.

scholarly journals Bim Machine Learning and Design Rules to Improve the Assembly Time in Steel Construction Projects

2021 ◽  
Vol 14 (1) ◽  
pp. 288
Author(s):  
Mathieu Fokwa Soh ◽  
David Bigras ◽  
Daniel Barbeau ◽  
Sylvie Doré ◽  
Daniel Forgues
Keyword(s):  
Machine Learning ◽  
Construction Projects ◽  
Steel Structure ◽  
Information Modeling ◽  
Practical Case ◽  
Design Rules ◽  
Steel Construction ◽  
Building Information ◽  
The Cost

Integrating the knowledge and experience of fabrication during the design phase can help reduce the cost and duration of steel construction projects. Building Information Modeling (BIM) are technologies and processes that reduce the cost and duration of construction projects by integrating parametric digital models as support of information. These models can contain information about the performance of previous projects and allow a classification by linear regression of design criteria with a high impact on the duration of the fabrication. This paper proposes a quantitative approach that applies linear regressions on previous projects’ BIM models to identify some design rules and production improvement points. A case study applied on 55,444 BIM models of steel joists validates this approach. This case study shows that the camber, the weight of the structure, and its reinforced elements greatly influence the fabrication time of the joists. The approach developed in this article is a practical case where machine learning and BIM models are used rather than interviews with professionals to identify knowledge related to a given steel structure fabrication system.

scholarly journals Machine learning to tame divergent density functional approximations: a new path to consensus materials design principles

2021 ◽  
Vol 12 (39) ◽  
pp. 13021-13036
Author(s):  
Chenru Duan ◽  
Shuxin Chen ◽  
Michael G. Taylor ◽  
Fang Liu ◽  
Heather J. Kulik
Keyword(s):  
Machine Learning ◽  
Density Functional ◽  
Universal Design ◽  
Materials Design ◽  
Design Principles ◽  
Feature Analysis ◽  
Design Rules

Machine learning (ML)-based feature analysis reveals universal design rules regardless of density functional choices. Using the consensus among multiple functionals, we identify robust lead complexes in ML-accelerated chemical discovery.

scholarly journals Combining machine learning and high-throughput experimentation to discover photocatalytically active organic molecules

2021 ◽  
Author(s):  
Xiaobo Li ◽  
Phillip M. Maffettone ◽  
Yu Che ◽  
Tao Liu ◽  
Linjiang Chen ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Throughput ◽  
Organic Molecules ◽  
Chemical Compounds ◽  
Structure Property ◽  
Design Rules ◽  
Useful Components ◽  
High Throughput Experimentation ◽  
Unique Chemical

Light-absorbing organic molecules are useful components in photocatalysts, but it is difficult to formulate reliable structure-property design rules. More than 100 million unique chemical compounds are documented in the PubChem...

Machine Learning of Design Rules: Methodology and Case Study

1994 ◽  
Vol 8 (3) ◽  
pp. 286-308 ◽  
Author(s):  
Tomasz Arciszewski ◽  
Eric Bloedorn ◽  
Ryszard S. Michalski ◽  
Mohamad Mustafa ◽  
Janusz Wnek
Keyword(s):  
Machine Learning ◽  
Design Rules
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