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1. Composition Based Oxidation State Prediction of Materials Using Deep Learning Language Models

   https://doi.org/10.1002/advs.202301011  

   Fu, Nihang ; Hu, Jeffrey ; Feng, Ying ; Morrison, Gregory ; Loye, Hans‐Conrad zur ; Hu, Jianjun ( August 2023 , Advanced Science)

   Oxidation states (OS) are the charges on atoms due to electrons gained or lost upon applying an ionic approximation to their bonds. As a fundamental property, OS has been widely used in charge‐neutrality verification, crystal structure determination, and reaction estimation. Currently, only heuristic rules exist for guessing the oxidation states of a given compound with many exceptions. Recent work has developed machine learning models based on heuristic structural features for predicting the oxidation states of metal ions. However, composition‐based oxidation state prediction still remains elusive so far, which has significant implications for the discovery of new materials for which the structures have not been determined. This work proposes a novel deep learning‐based BERT transformer language model BERTOS for predicting the oxidation states for all elements of inorganic compounds given only their chemical composition. This model achieves 96.82% accuracy for all‐element oxidation states prediction benchmarked on the cleaned ICSD dataset and achieves 97.61% accuracy for oxide materials. It is also demonstrated how it can be used to conduct large‐scale screening of hypothetical material compositions for materials discovery.

    

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2. Prediction of seebeck coefficient for compounds without restriction to fixed stoichiometry: A machine learning approach

   https://doi.org/10.1002/jcc.25067  

   Furmanchuk, Al'ona ; Saal, James E. ; Doak, Jeff W. ; Olson, Gregory B. ; Choudhary, Alok ; Agrawal, Ankit ( September 2017 , Journal of Computational Chemistry)

   The regression model‐based tool is developed for predicting the Seebeck coefficient of crystalline materials in the temperature range from 300 K to 1000 K. The tool accounts for the single crystal versus polycrystalline nature of the compound, the production method, and properties of the constituent elements in the chemical formula. We introduce new descriptive features of crystalline materials relevant for the prediction the Seebeck coefficient. To address off‐stoichiometry in materials, the predictive tool is trained on a mix of stoichiometric and nonstoichiometric materials. The tool is implemented into a web application (http://info.eecs.northwestern.edu/SeebeckCoefficientPredictor) to assist field scientists in the discovery of novel thermoelectric materials. © 2017 Wiley Periodicals, Inc.

    

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3. Formation energy prediction of crystalline compounds using deep convolutional network learning on voxel image representation

   https://doi.org/10.1038/s43246-023-00433-9  

   Davariashtiyani, Ali ; Kadkhodaei, Sara ( December 2023 , Communications Materials)

   Emerging machine-learned models have enabled efficient and accurate prediction of compound formation energy, with the most prevalent models relying on graph structures for representing crystalline materials. Here, we introduce an alternative approach based on sparse voxel images of crystals. By developing a sophisticated network architecture, we showcase the ability to learn the underlying features of structural and chemical arrangements in inorganic compounds from visual image representations, subsequently correlating these features with the compounds’ formation energy. Our model achieves accurate formation energy prediction by utilizing skip connections in a deep convolutional network and incorporating augmentation of rotated crystal samples during training, performing on par with state-of-the-art methods. By adopting visual images as an alternative representation for crystal compounds and harnessing the capabilities of deep convolutional networks, this study extends the frontier of machine learning for accelerated materials discovery and optimization. In a comprehensive evaluation, we analyse the predicted convex hulls for 3115 binary systems and introduce error metrics beyond formation energy error. This evaluation offers valuable insights into the impact of formation energy error on the performance of the predicted convex hulls.

    

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4. Topological representations of crystalline compounds for the machine-learning prediction of materials properties

   https://doi.org/10.1038/s41524-021-00493-w  

   Jiang, Yi ; Chen, Dong ; Chen, Xin ; Li, Tangyi ; Wei, Guo-Wei ; Pan, Feng ( December 2021 , npj Computational Materials)

   null (Ed.)

   Abstract Accurate theoretical predictions of desired properties of materials play an important role in materials research and development. Machine learning (ML) can accelerate the materials design by building a model from input data. For complex datasets, such as those of crystalline compounds, a vital issue is how to construct low-dimensional representations for input crystal structures with chemical insights. In this work, we introduce an algebraic topology-based method, called atom-specific persistent homology (ASPH), as a unique representation of crystal structures. The ASPH can capture both pairwise and many-body interactions and reveal the topology-property relationship of a group of atoms at various scales. Combined with composition-based attributes, ASPH-based ML model provides a highly accurate prediction of the formation energy calculated by density functional theory (DFT). After training with more than 30,000 different structure types and compositions, our model achieves a mean absolute error of 61 meV/atom in cross-validation, which outperforms previous work such as Voronoi tessellations and Coulomb matrix method using the same ML algorithm and datasets. Our results indicate that the proposed topology-based method provides a powerful computational tool for predicting materials properties compared to previous works. 

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5. A generative artificial intelligence framework based on a molecular diffusion model for the design of metal-organic frameworks for carbon capture

   https://doi.org/10.1038/s42004-023-01090-2  

   Park, Hyun ; Yan, Xiaoli ; Zhu, Ruijie ; Huerta, Eliu A. ; Chaudhuri, Santanu ; Cooper, Donny ; Foster, Ian ; Tajkhorshid, Emad ( February 2024 , Communications Chemistry)

   Metal-organic frameworks (MOFs) exhibit great promise for CO₂capture. However, finding the best performing materials poses computational and experimental grand challenges in view of the vast chemical space of potential building blocks. Here, we introduce GHP-MOFassemble, a generative artificial intelligence (AI), high performance framework for the rational and accelerated design of MOFs with high CO₂adsorption capacity and synthesizable linkers. GHP-MOFassemble generates novel linkers, assembled with one of three pre-selected metal nodes (Cu paddlewheel, Zn paddlewheel, Zn tetramer) into MOFs in a primitive cubic topology. GHP-MOFassemble screens and validates AI-generated MOFs for uniqueness, synthesizability, structural validity, uses molecular dynamics simulations to study their stability and chemical consistency, and crystal graph neural networks and Grand Canonical Monte Carlo simulations to quantify their CO₂adsorption capacities. We present the top six AI-generated MOFs with CO₂capacities greater than 2m mol g⁻¹, i.e., higher than 96.9% of structures in the hypothetical MOF dataset.

    

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