scholarly journals Energy landscapes for machine learning

2017 ◽  
Vol 19 (20) ◽  
pp. 12585-12603 ◽  
Author(s):  
Andrew J. Ballard ◽  
Ritankar Das ◽  
Stefano Martiniani ◽  
Dhagash Mehta ◽  
Levent Sagun ◽  
...  
Keyword(s):  
Machine Learning ◽  
Energy Landscapes ◽  
Molecular Science

The energy landscapes framework developed in molecular science provides new insight in the field of machine learning.

scholarly journals Quantum Chemistry Meets Machine Learning

2019 ◽  
Vol 73 (12) ◽  
pp. 983-989 ◽  
Author(s):  
Alberto Fabrizio ◽  
Benjamin Meyer ◽  
Raimon Fabregat ◽  
Clemence Corminboeuf
Keyword(s):  
Machine Learning ◽  
Statistical Learning ◽  
Quantum Chemical ◽  
Large Scale ◽  
Organic Molecules ◽  
Chemical Properties ◽  
Learning Approaches ◽  
Energy Landscapes ◽  
Free Energy Landscapes ◽  
Large Scale Screening

In this account, we demonstrate how statistical learning approaches can be leveraged across a range of different quantum chemical areas to transform the scaling, nature, and complexity of the problems that we are tackling. Selected examples illustrate the power brought by kernel-based approaches in the large-scale screening of homogeneous catalysis, the prediction of fundamental quantum chemical properties and the free-energy landscapes of flexible organic molecules. While certainly non-exhaustive, these examples provide an intriguing glimpse into our own research efforts.

The Interplay among Molecular Structures, Crystal Symmetries and Lattice Energy Landscapes Revealed by Unsupervised Machine Learning: A Closer Look at Pyrrole Azaphenacenes

2019 ◽  
Author(s):  
Jack Yang ◽  
Nathan Li ◽  
Sean Li
Keyword(s):  
Crystal Structure ◽  
Machine Learning ◽  
Crystal Engineering ◽  
Crystal Packing ◽  
Similarity Index ◽  
Lattice Energy ◽  
Molecular Structures ◽  
Machine Learning Techniques ◽  
Specific Reference ◽  
Energy Landscapes

The ability to perform large-scale crystal structure predictions (CSP) have significantly advanced the synthesis of functional molecular solids by designs. In our recent work [Chem. Mater., 30, 4361 (2018)], we demonstrated our latest developments in organic CSPs by screening a set of 28 pyrrole azaphenacene isomers which led to one new molecule with higher thermodynamic stability and carrier mobilities in its crystalline form, compared to the one reported experimentally. Hereby, using the lattice energy landscapes for pyrrole azaphenacenes as examples, we applied machine-learning techniques to statistically reveal in more details, on how molecular symmetry and Z' values translate to the crystal packing landscapes, which in terms affect the coverage of landscape through quasi-random crystal structure samplings. A recurring theme in crystal engineering is to identify the probabilities of targeting isostructures to a specific reference crystal upon chemical functionalisations. For this, we propose here a global similarity index in conjunction with the Energy-Density Isostructurality (EDI) map to analyse the lattice energy landscapes for halogen substituted pyrrole azaphenacenes. A continue effort in the field is to accelerate CSPs for sampling a much wider chemical space for high-throughput material screenings, we propose a potential solution to this challenge drawn upon this study. Our work will hopefully stimulate the crystal engineering community in adapting a more statistically-oriented approach in understanding crystal packing of organic molecules in the age of digitisation.

Machine learning of ab-initio energy landscapes for crystal structure predictions

2019 ◽  
Vol 158 ◽  
pp. 414-419 ◽  
Author(s):  
Shreyas Honrao ◽  
Bryan E. Anthonio ◽  
Rohit Ramanathan ◽  
Joshua J. Gabriel ◽  
Richard G. Hennig
Keyword(s):  
Crystal Structure ◽  
Machine Learning ◽  
Ab Initio ◽  
Energy Landscapes

scholarly journals Energy landscapes for a machine learning application to series data

2016 ◽  
Vol 144 (12) ◽  
pp. 124119 ◽  
Author(s):  
Andrew J. Ballard ◽  
Jacob D. Stevenson ◽  
Ritankar Das ◽  
David J. Wales
Keyword(s):  
Machine Learning ◽  
Series Data ◽  
Energy Landscapes

Augmenting machine learning of energy landscapes with local structural information

2020 ◽  
Vol 128 (8) ◽  
pp. 085101
Author(s):  
Shreyas J. Honrao ◽  
Stephen R. Xie ◽  
Richard G. Hennig
Keyword(s):  
Machine Learning ◽  
Structural Information ◽  
Energy Landscapes

The interplay among molecular structures, crystal symmetries and lattice energy landscapes revealed using unsupervised machine learning: a closer look at pyrrole azaphenacenes

CrystEngComm ◽  
2019 ◽  
Vol 21 (41) ◽  
pp. 6173-6185 ◽  
Author(s):  
Jack Yang ◽  
Nathan Li ◽  
Sean Li
Keyword(s):  
Machine Learning ◽  
Lattice Energy ◽  
Molecular Structures ◽  
Energy Landscapes ◽  
Unsupervised Machine Learning

Using unsupervised machine learning and CSPs to help crystallographers better understand how crystallizations are affected by molecular structures.

The Interplay among Molecular Structures, Crystal Symmetries and Lattice Energy Landscapes Revealed by Unsupervised Machine Learning: A Closer Look at Pyrrole Azaphenacenes

2019 ◽  
Author(s):  
Jack Yang ◽  
Nathan Li ◽  
Sean Li
Keyword(s):  
Crystal Structure ◽  
Machine Learning ◽  
Crystal Engineering ◽  
Crystal Packing ◽  
Similarity Index ◽  
Lattice Energy ◽  
Molecular Structures ◽  
Machine Learning Techniques ◽  
Specific Reference ◽  
Energy Landscapes

The ability to perform large-scale crystal structure predictions (CSP) have significantly advanced the synthesis of functional molecular solids by designs. In our recent work [Chem. Mater., 30, 4361 (2018)], we demonstrated our latest developments in organic CSPs by screening a set of 28 pyrrole azaphenacene isomers which led to one new molecule with higher thermodynamic stability and carrier mobilities in its crystalline form, compared to the one reported experimentally. Hereby, using the lattice energy landscapes for pyrrole azaphenacenes as examples, we applied machine-learning techniques to statistically reveal in more details, on how molecular symmetry and Z' values translate to the crystal packing landscapes, which in terms affect the coverage of landscape through quasi-random crystal structure samplings. A recurring theme in crystal engineering is to identify the probabilities of targeting isostructures to a specific reference crystal upon chemical functionalisations. For this, we propose here a global similarity index in conjunction with the Energy-Density Isostructurality (EDI) map to analyse the lattice energy landscapes for halogen substituted pyrrole azaphenacenes. A continue effort in the field is to accelerate CSPs for sampling a much wider chemical space for high-throughput material screenings, we propose a potential solution to this challenge drawn upon this study. Our work will hopefully stimulate the crystal engineering community in adapting a more statistically-oriented approach in understanding crystal packing of organic molecules in the age of digitisation.

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