Automated High-Throughput Screening of Organic Crystal Structures Using Population-Based Sampling Methods and Open-Source Software

Основные понятия

This paper introduces HTOCSP, an open-source software package that automates the prediction and screening of small organic molecule crystal structures using population-based sampling methods and existing open-source molecular modeling tools.

Аннотация

• Bibliographic Information: Zhu, Q., & Hattori, S. (2024). Automated High-throughput Organic Crystal Structure Prediction via Population-based Sampling. arXiv preprint arXiv:2408.08843v2.

• Research Objective: This paper introduces a new open-source software package, High-throughput Organic Crystal Structure Prediction (HTOCSP), for predicting and screening crystal structures of small organic molecules. The authors aim to address the limitations of existing commercial software and the lack of open-source tools specifically designed for high-throughput organic crystal structure prediction.

• Methodology: HTOCSP integrates several existing open-source infrastructures in molecular modeling, including RDKit, AmberTools, PyXtal, and CHARMM. The software utilizes a six-step workflow: (1) Molecular analysis using SMILES strings, (2) Force field generation (GAFF or OpenFF), (3) Symmetry-constrained structure calculation (GULP or CHARMM), (4) Crystal structure generation using PyXtal, (5) Population-based sampling methods (Stochastic Width-First, Stochastic Depth-First, and Deterministic Quasi-random), and (6) Objective function evaluation (lattice energy or similarity to experimental PXRD data). The authors benchmark HTOCSP on a dataset of 100 experimentally reported crystals, comparing the efficiency of different sampling strategies and force field options.

• Key Findings: The benchmark results demonstrate the effectiveness of HTOCSP in predicting crystal structures, with varying success rates depending on the complexity of the energy landscape. The authors categorize the tested systems into four tiers based on the sampling success rate, highlighting the factors that influence the difficulty of crystal structure prediction. They also find that the choice of sampling strategy significantly impacts the success rate, with Depth-First Sampling proving more effective for systems with wide meta-basins and Width-First Sampling performing better for those with narrow meta-basins.

• Main Conclusions: HTOCSP provides a valuable open-source tool for high-throughput organic crystal structure prediction, enabling researchers to explore a wide range of potential crystal forms efficiently. The authors emphasize the importance of choosing appropriate sampling strategies based on the expected complexity of the energy landscape.

• Significance: The development of HTOCSP addresses a critical gap in the field of organic crystal structure prediction by providing an accessible and efficient open-source platform. This tool has the potential to accelerate the discovery and development of new organic materials with tailored properties in various applications, including pharmaceuticals, organic electronics, and molecular materials.

• Limitations and Future Research: The authors acknowledge the need for further improvements, such as developing more efficient sampling strategies, incorporating machine learning techniques to predict cell parameters, implementing robust post-analysis tools for structure ranking, and enabling iterative force field optimization. Future research will focus on addressing these limitations to enhance the reliability and efficiency of HTOCSP.

Статистика

The benchmark test was performed on a set of 100 experimentally reported crystals.
The systems were divided into four tiers based on the average success rate (SR) from four different sampling strategies.
Tier I (SR > 0.5%): 56 systems considered relatively easy CSP challenges.
Tier II (0.05% < SR < 0.5%): 33 systems presenting relatively moderate CSP challenges.
Tier III (0.001% < SR < 0.05%): 6 systems requiring over 100k structural samples.
Tier IV (SR < 0.001%): 4 examples representing the most challenging CSP cases.
A minimum population size of 256 was used in each generation.
The maximum number of generations was set to 500.
The calculation could be terminated earlier if at least 10 matched structures were found.
In WFS runs, the fraction of mutation was set to 0.4.

Цитаты

"To promote the open-source activity in organic CSP, we have developed an open source code High-throughput Organic Crystal Structure Prediction (HTOCSP) that allows the automated prediction of organic crystal structures with a minimal input, by leveraging several existing open-source infrastructures in molecular modeling."
"In practical CSP, we recommend a minimum population size of 256 in each generation."
"Clearly, there is a trade-off between using WFS and DFS. In the future, more in-depth studies will be conducted to develop a predictive model that infers CSP complexity and further improves the success rate by enhancing sampling methods."

Ключевые выводы из

Automated High-throughput Organic Crystal Structure Prediction via Population-based Sampling

by Qiang Zhu, S... в arxiv.org 10-22-2024

https://arxiv.org/pdf/2408.08843.pdf

Дополнительные вопросы

How might the integration of machine learning techniques further enhance the efficiency and accuracy of crystal structure prediction in HTOCSP, beyond predicting cell parameters?

Machine learning (ML) presents a versatile toolset with the potential to significantly enhance both the efficiency and accuracy of crystal structure prediction (CSP) within the HTOCSP framework, extending beyond the prediction of cell parameters. Here are several promising avenues:

Predicting Energy Landscapes and Meta-basin Shapes: ML models can be trained on existing CSP datasets, encompassing molecular structures, crystallographic information, and calculated energy landscapes. By learning from these patterns, ML can predict the likely shape and characteristics of meta-basins for new molecules. This information can guide the sampling algorithms, focusing efforts on regions of the energy landscape more likely to harbor the target structures, thus enhancing sampling efficiency.

Guiding Mutation and Crossover Operations: In population-based optimization algorithms like genetic algorithms, ML can play a crucial role in guiding the mutation and crossover operations. By analyzing successful mutations and crossovers from past CSP runs, ML models can learn to propose more promising structural modifications, leading to faster convergence and potentially discovering novel crystal packing motifs.

Classifying Crystal Structures and Identifying Promising Candidates: ML can be employed to classify generated crystal structures based on their likelihood of being experimentally realizable. By training on features such as energy rankings, structural descriptors, and comparisons to known polymorphs, ML can help prioritize structures for further refinement with more accurate but computationally expensive methods like DFT, optimizing resource allocation.

Learning Force Field Corrections: ML models can be trained to learn the systematic errors associated with the chosen force field. By analyzing discrepancies between force field predictions and higher-level calculations or experimental data, ML can develop corrective terms or potentials, improving the accuracy of energy rankings and structure prediction, especially at non-standard conditions.

Accelerating Structure Relaxation: ML potentials, like ANI and MACE, have shown promise in accelerating structure relaxation while maintaining reasonable accuracy. Integrating these ML potentials within the HTOCSP workflow can significantly speed up the geometry optimization steps, enabling the exploration of a larger number of candidate structures within a given time frame.

By strategically integrating these ML-driven approaches, HTOCSP can evolve into a more powerful and autonomous CSP platform, accelerating the discovery of novel organic materials.

Could the limitations of force fields in accurately ranking polymorph energies, particularly at non-standard conditions, be mitigated by incorporating experimental data or more accurate computational methods during the sampling or post-analysis stages?

Yes, the limitations of force fields in accurately ranking polymorph energies, especially at non-standard conditions, can be significantly mitigated by incorporating experimental data or more accurate computational methods during both the sampling and post-analysis stages of CSP. Here's how:
During Sampling:

Experimentally Derived Constraints: If available, experimental data like powder X-ray diffraction (PXRD) patterns, melting points, or solubility data can be incorporated as constraints or objectives during the sampling process. This guides the search towards structures consistent with the experimental observations, reducing reliance solely on force field energy rankings.

Multi-Level Sampling: A tiered approach can be employed, where initial sampling is performed using a computationally efficient force field. Subsequently, a subset of promising candidates can be selected for further refinement and energy evaluation using more accurate but computationally demanding methods like DFT or hybrid QM/MM methods.

During Post-Analysis:

Energy Re-ranking with Higher-Level Methods: The initial set of candidate structures generated using force fields can be re-ranked based on single-point energy calculations using more accurate methods like DFT, improving the identification of the most stable polymorphs.

Lattice Energy Corrections: Develop and apply corrections to the force field lattice energies based on higher-level calculations or experimental data. This can involve training machine learning models to capture systematic errors in the force field or using thermodynamic models to account for temperature and pressure effects.

Free Energy Calculations: Go beyond static lattice energies and perform free energy calculations, such as lattice phonon calculations or molecular dynamics simulations, to account for entropic contributions to polymorph stability, which are crucial at non-standard conditions.

Ensemble Analysis: Instead of focusing solely on the lowest energy structure, analyze ensembles of low-energy structures. This provides a more comprehensive picture of the potential polymorph landscape and can reveal structures that might be missed by relying solely on force field rankings.

By strategically incorporating these approaches, the accuracy of polymorph energy rankings can be significantly improved, leading to more reliable CSP predictions, even at non-standard conditions.

What are the broader implications of accessible and efficient open-source tools like HTOCSP for scientific research and development beyond the field of crystal structure prediction?

Accessible and efficient open-source tools like HTOCSP hold significant implications that extend far beyond the immediate field of crystal structure prediction, impacting various domains of scientific research and development:

Democratization of Materials Science: Open-source tools level the playing field by providing researchers, regardless of their institution's resources, with access to powerful computational tools. This fosters collaboration, accelerates scientific discovery, and promotes innovation in materials design and development.

Accelerated Materials Discovery: By automating and streamlining complex computational workflows, HTOCSP enables high-throughput screening of vast chemical spaces. This accelerates the identification of promising candidates for various applications, including pharmaceuticals, organic electronics, and energy materials.

Data-Driven Materials Design: Open-source tools facilitate the generation and sharing of large datasets, crucial for training machine learning models. These models can then be used to predict material properties, optimize synthesis conditions, and guide the discovery of novel materials with tailored properties.

Reproducibility and Transparency: Open-source code promotes transparency and reproducibility in scientific research. Researchers can readily scrutinize, modify, and build upon existing code, ensuring the reliability and validity of scientific findings.

Education and Training: Open-source tools serve as valuable educational resources, allowing students and early-career researchers to gain hands-on experience with cutting-edge computational techniques, fostering the next generation of scientists.

Cross-Disciplinary Applications: The underlying principles and algorithms employed in HTOCSP can be adapted and applied to other fields facing similar challenges in structure prediction and optimization, such as protein folding, drug design, and catalyst discovery.

Economic Benefits: Open-source tools reduce the financial barriers to entry for smaller companies and startups, fostering innovation and competition in the development of new technologies and products.

In conclusion, open-source tools like HTOCSP are instrumental in driving progress across various scientific disciplines. By making computational tools more accessible, efficient, and transparent, they empower researchers to tackle complex scientific challenges, ultimately leading to technological advancements and societal benefits.