1
|
O’Connor D, Bier I, Tom R, Hiszpanski AM, Steele BA, Marom N. Ab Initio Crystal Structure Prediction of the Energetic Materials LLM-105, RDX, and HMX. CRYSTAL GROWTH & DESIGN 2023; 23:6275-6289. [PMID: 38173900 PMCID: PMC10763925 DOI: 10.1021/acs.cgd.3c00027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Revised: 08/02/2023] [Indexed: 01/05/2024]
Abstract
Crystal structure prediction (CSP) is performed for the energetic materials (EMs) LLM-105 and α-RDX, as well as the α and β conformational polymorphs of 1,3,5,7-tetranitro-1,3,5,7-tetraazacyclooctane (HMX), using the genetic algorithm (GA) code, GAtor, and its associated random structure generator, Genarris. Genarris and GAtor successfully generate the experimental structures of all targets. GAtor's symmetric crossover scheme, where the space group symmetries of parent structures are treated as genes inherited by offspring, is found to be particularly effective. However, conducting several GA runs with different settings is still important for achieving diverse samplings of the potential energy surface. For LLM-105 and α-RDX, the experimental structure is ranked as the most stable, with all of the dispersion-inclusive density functional theory (DFT) methods used here. For HMX, the α form was persistently ranked as more stable than the β form, in contrast to experimental observations, even when correcting for vibrational contributions and thermal expansion. This may be attributed to insufficient accuracy of dispersion-inclusive DFT methods or to kinetic effects not considered here. In general, the ranking of some putative structures is found to be sensitive to the choice of the DFT functional and the dispersion method. For LLM-105, GAtor generates a putative structure with a layered packing motif, which is desirable thanks to its correlation with low sensitivity. Our results demonstrate that CSP is a useful tool for studying the ubiquitous polymorphism of EMs and shows promise of becoming an integral part of the EM development pipeline.
Collapse
Affiliation(s)
- Dana O’Connor
- Department
of Materials Science and Engineering, Carnegie
Mellon University, Pittsburgh, Pennsylvania 15213, United States
| | - Imanuel Bier
- Department
of Materials Science and Engineering, Carnegie
Mellon University, Pittsburgh, Pennsylvania 15213, United States
| | - Rithwik Tom
- Department
of Physics, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
| | - Anna M. Hiszpanski
- Materials
Science Division, Lawrence Livermore National
Laboratory, Livermore, California 94550, United States
| | - Brad A. Steele
- Materials
Science Division, Lawrence Livermore National
Laboratory, Livermore, California 94550, United States
| | - Noa Marom
- Department
of Materials Science and Engineering, Carnegie
Mellon University, Pittsburgh, Pennsylvania 15213, United States
- Department
of Physics, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
- Department
of Chemistry, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
| |
Collapse
|
2
|
Kilgour M, Rogal J, Tuckerman M. Geometric Deep Learning for Molecular Crystal Structure Prediction. J Chem Theory Comput 2023. [PMID: 37053511 PMCID: PMC10373482 DOI: 10.1021/acs.jctc.3c00031] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/15/2023]
Abstract
We develop and test new machine learning strategies for accelerating molecular crystal structure ranking and crystal property prediction using tools from geometric deep learning on molecular graphs. Leveraging developments in graph-based learning and the availability of large molecular crystal data sets, we train models for density prediction and stability ranking which are accurate, fast to evaluate, and applicable to molecules of widely varying size and composition. Our density prediction model, MolXtalNet-D, achieves state-of-the-art performance, with lower than 2% mean absolute error on a large and diverse test data set. Our crystal ranking tool, MolXtalNet-S, correctly discriminates experimental samples from synthetically generated fakes and is further validated through analysis of the submissions to the Cambridge Structural Database Blind Tests 5 and 6. Our new tools are computationally cheap and flexible enough to be deployed within an existing crystal structure prediction pipeline both to reduce the search space and score/filter crystal structure candidates.
Collapse
Affiliation(s)
- Michael Kilgour
- Department of Chemistry, New York University, New York, New York 10003, United States
| | - Jutta Rogal
- Department of Chemistry, New York University, New York, New York 10003, United States
- Fachbereich Physik, Freie Universität Berlin, 14195 Berlin, Germany
| | - Mark Tuckerman
- Department of Chemistry, New York University, New York, New York 10003, United States
- Courant Institute of Mathematical Sciences, New York University, New York, New York 10012, United States
- NYU-ECNU Center for Computational Chemistry at NYU Shanghai, 3663 Zhongshan Rd. North, Shanghai 200062, China
- Simons Center for Computational Physical Chemistry at New York University, New York, New York 10003, United States
| |
Collapse
|
3
|
Tom R, Gao S, Yang Y, Zhao K, Bier I, Buchanan EA, Zaykov A, Havlas Z, Michl J, Marom N. Inverse Design of Tetracene Polymorphs with Enhanced Singlet Fission Performance by Property-Based Genetic Algorithm Optimization. CHEMISTRY OF MATERIALS : A PUBLICATION OF THE AMERICAN CHEMICAL SOCIETY 2023; 35:1373-1386. [PMID: 36999121 PMCID: PMC10042130 DOI: 10.1021/acs.chemmater.2c03444] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 01/06/2023] [Indexed: 06/19/2023]
Abstract
The efficiency of solar cells may be improved by using singlet fission (SF), in which one singlet exciton splits into two triplet excitons. SF occurs in molecular crystals. A molecule may crystallize in more than one form, a phenomenon known as polymorphism. Crystal structure may affect SF performance. In the common form of tetracene, SF is experimentally known to be slightly endoergic. A second, metastable polymorph of tetracene has been found to exhibit better SF performance. Here, we conduct inverse design of the crystal packing of tetracene using a genetic algorithm (GA) with a fitness function tailored to simultaneously optimize the SF rate and the lattice energy. The property-based GA successfully generates more structures predicted to have higher SF rates and provides insight into packing motifs associated with improved SF performance. We find a putative polymorph predicted to have superior SF performance to the two forms of tetracene, whose structures have been determined experimentally. The putative structure has a lattice energy within 1.5 kJ/mol of the most stable common form of tetracene.
Collapse
Affiliation(s)
- Rithwik Tom
- Department
of Physics, Carnegie Mellon University, Pittsburgh, Pennsylvania15213, United States
| | - Siyu Gao
- Department
of Materials Science and Engineering, Carnegie
Mellon University, Pittsburgh, Pennsylvania15213, United States
| | - Yi Yang
- Department
of Materials Science and Engineering, Carnegie
Mellon University, Pittsburgh, Pennsylvania15213, United States
| | - Kaiji Zhao
- Department
of Materials Science and Engineering, Carnegie
Mellon University, Pittsburgh, Pennsylvania15213, United States
| | - Imanuel Bier
- Department
of Materials Science and Engineering, Carnegie
Mellon University, Pittsburgh, Pennsylvania15213, United States
| | - Eric A. Buchanan
- Department
of Chemistry, University of Colorado, Boulder, Colorado80309, United States
| | - Alexandr Zaykov
- Institute
of Organic Chemistry and Biochemistry, Czech
Academy of Sciences, 16610Prague 6, Czech
Republic
- Department
of Physical Chemistry, University of Chemistry
and Technology, 166 28Prague 6, Czech Republic
| | - Zdeněk Havlas
- Institute
of Organic Chemistry and Biochemistry, Czech
Academy of Sciences, 16610Prague 6, Czech
Republic
| | - Josef Michl
- Department
of Chemistry, University of Colorado, Boulder, Colorado80309, United States
- Institute
of Organic Chemistry and Biochemistry, Czech
Academy of Sciences, 16610Prague 6, Czech
Republic
| | - Noa Marom
- Department
of Physics, Carnegie Mellon University, Pittsburgh, Pennsylvania15213, United States
- Department
of Materials Science and Engineering, Carnegie
Mellon University, Pittsburgh, Pennsylvania15213, United States
- Department
of Chemistry, Carnegie Mellon University, Pittsburgh, Pennsylvania15213, United States
| |
Collapse
|
4
|
Moayedpour S, Dardzinski D, Yang S, Hwang A, Marom N. Structure prediction of epitaxial inorganic interfaces by lattice and surface matching with Ogre. J Chem Phys 2021; 155:034111. [PMID: 34293896 DOI: 10.1063/5.0051343] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
We present a new version of the Ogre open source Python package with the capability to perform structure prediction of epitaxial inorganic interfaces by lattice and surface matching. In the lattice matching step, a scan over combinations of substrate and film Miller indices is performed to identify the domain-matched interfaces with the lowest mismatch. Subsequently, surface matching is conducted by Bayesian optimization to find the optimal interfacial distance and in-plane registry between the substrate and the film. For the objective function, a geometric score function is proposed based on the overlap and empty space between atomic spheres at the interface. The score function reproduces the results of density functional theory (DFT) at a fraction of the computational cost. The optimized interfaces are pre-ranked using a score function based on the similarity of the atomic environment at the interface to the bulk environment. Final ranking of the top candidate structures is performed with DFT. Ogre streamlines DFT calculations of interface energies and electronic properties by automating the construction of interface models. The application of Ogre is demonstrated for two interfaces of interest for quantum computing and spintronics, Al/InAs and Fe/InSb.
Collapse
Affiliation(s)
- Saeed Moayedpour
- Department of Chemistry, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
| | - Derek Dardzinski
- Department of Materials Science and Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
| | - Shuyang Yang
- Department of Materials Science and Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
| | - Andrea Hwang
- Department of Materials Science and Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
| | - Noa Marom
- Department of Chemistry, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
| |
Collapse
|
5
|
Nguyen P, Loveland D, Kim JT, Karande P, Hiszpanski AM, Han TYJ. Predicting Energetics Materials' Crystalline Density from Chemical Structure by Machine Learning. J Chem Inf Model 2021; 61:2147-2158. [PMID: 33899482 DOI: 10.1021/acs.jcim.0c01318] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
To expedite new molecular compound development, a long-sought goal within the chemistry community has been to predict molecules' bulk properties of interest a priori to synthesis from a chemical structure alone. In this work, we demonstrate that machine learning methods can indeed be used to directly learn the relationship between chemical structures and bulk crystalline properties of molecules, even in the absence of any crystal structure information or quantum mechanical calculations. We focus specifically on a class of organic compounds categorized as energetic materials called high explosives (HE) and predicting their crystalline density. An ongoing challenge within the chemistry machine learning community is deciding how best to featurize molecules as inputs into machine learning models-whether expert handcrafted features or learned molecular representations via graph-based neural network models-yield better results and why. We evaluate both types of representations in combination with a number of machine learning models to predict the crystalline densities of HE-like molecules curated from the Cambridge Structural Database, and we report the performance and pros and cons of our methods. Our message passing neural network (MPNN) based models with learned molecular representations generally perform best, outperforming current state-of-the-art methods at predicting crystalline density and performing well even when testing on a data set not representative of the training data. However, these models are traditionally considered black boxes and less easily interpretable. To address this common challenge, we also provide a comparison analysis between our MPNN-based model and models with fixed feature representations that provides insights as to what features are learned by the MPNN to accurately predict density.
Collapse
Affiliation(s)
- Phan Nguyen
- Computational Engineering Division, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - Donald Loveland
- Materials Science Division, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - Joanne T Kim
- Computing Scholar Program, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - Piyush Karande
- Computational Engineering Division, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - Anna M Hiszpanski
- Materials Science Division, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - T Yong-Jin Han
- Materials Science Division, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| |
Collapse
|
6
|
Bier I, O'Connor D, Hsieh YT, Wen W, Hiszpanski AM, Han TYJ, Marom N. Crystal structure prediction of energetic materials and a twisted arene with Genarris and GAtor. CrystEngComm 2021. [DOI: 10.1039/d1ce00745a] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
A molecular crystal structure prediction workflow, based on the random structure generator, Genarris, and the genetic algorithm (GA), GAtor, is successfully applied to two energetic materials and a chiral arene.
Collapse
Affiliation(s)
- Imanuel Bier
- Department of Materials Science and Engineering, Carnegie Mellon University, Pittsburgh, PA, 15213, USA
| | - Dana O'Connor
- Department of Materials Science and Engineering, Carnegie Mellon University, Pittsburgh, PA, 15213, USA
| | - Yun-Ting Hsieh
- Department of Materials Science and Engineering, Carnegie Mellon University, Pittsburgh, PA, 15213, USA
| | - Wen Wen
- Department of Chemistry, Carnegie Mellon University, Pittsburgh, PA, 15213, USA
| | - Anna M. Hiszpanski
- Materials Science Division, Lawrence Livermore National Laboratory, Livermore, CA, 94550, USA
| | - T. Yong-Jin Han
- Materials Science Division, Lawrence Livermore National Laboratory, Livermore, CA, 94550, USA
| | - Noa Marom
- Department of Materials Science and Engineering, Carnegie Mellon University, Pittsburgh, PA, 15213, USA
- Department of Chemistry, Carnegie Mellon University, Pittsburgh, PA, 15213, USA
- Department of Physics, Carnegie Mellon University, Pittsburgh, PA, 15213, USA
| |
Collapse
|