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| 1 | Active learning for accelerated design of layered materials显示文摘Hetero-structures made from vertically stacked monolayers of transition metal dichalcogenides hold great potential for optoelectronic and thermoelectric devices.Discovery of the optimal layered material for specific applications necessitates the estimation of key material properties,such as electronic band structure and thermal transport coefficients.However,screening of material properties via brute force ab initio calculations of the entire material structure space exceeds the limits of current computing resources.Moreover,the functional dependence of material properties on the structures is often complicated,making simplistic statistical procedures for prediction difficult to employ without large amounts of data collection.Here,we present a Gaussian process regression model,which predicts material properties of an input hetero-structure,as well as an active learning model based on Bayesian optimization,which can efficiently discover the optimal hetero-structure using a minimal number of ab initio calculations.The electronic band gap,conduction/valence band dispersions,and thermoelectric performance are used as representative material properties for prediction and optimization.The Materials Project platform is used for electronic structure computation,while the BoltzTraP code is used to compute thermoelectric properties.Bayesian optimization is shown to significantly reduce the computational cost of discovering the optimal structure when compared with finding an optimal structure by building a regression model to predict material properties.The models can be used for predictions with respect to any material property and our software,including data preparation code based on the Python Materials Genomics(PyMatGen)library as well as python-based machine learning code,is available open source. | Lindsay Bassman Pankaj Rajak Rajiv K.Kalia Aiichiro Nakano Fei Sha Jifeng Sun David J.Singh Muratahan Aykol Patrick Huck Kristin Persson Priya Vashishta | 2018 | npj Computational Materials2018，，1： | 10 |
| 2 | High-throughput predictions of metal-organic framework electronic properties:theoretical challenges,graph neural networks,and data exploration显示文摘With the goal of accelerating the design and discovery of metal–organic frameworks(MOFs)for electronic,optoelectronic,and energy storage applications,we present a dataset of predicted electronic structure properties for thousands of MOFs carried out using multiple density functional approximations.Compared to more accurate hybrid functionals,we find that the widely used PBE generalized gradient approximation(GGA)functional severely underpredicts MOF band gaps in a largely systematic manner for semi-conductors and insulators without magnetic character.However,an even larger and less predictable disparity in the band gap prediction is present for MOFs with open-shell 3d transition metal cations.With regards to partial atomic charges,we find that different density functional approximations predict similar charges overall,although hybrid functionals tend to shift electron density away from the metal centers and onto the ligand environments compared to the GGA point of reference.Much more significant differences in partial atomic charges are observed when comparing different charge partitioning schemes.We conclude by using the dataset of computed MOF properties to train machine-learning models that can rapidly predict MOF band gaps for all four density functional approximations considered in this work,paving the way for future high-throughput screening studies.To encourage exploration and reuse of the theoretical calculations presented in this work,the curated data is made publicly available via an interactive and user-friendly web application on the Materials Project. | Andrew S.Rosen Victor Fung Patrick Huck Cody T.O’Donnell Matthew K.Horton Donald G.Truhlar Kristin A.Persson Justin M.Notestein Randall Q.Snurr | 2022 | npj Computational Materials2022，，1： | 1 |
| 3 | Enabling materials informatics for ^(29)Si solid-state NMR of crystalline materials显示文摘Nuclear magnetic resonance(NMR)spectroscopy is a powerful tool for obtaining precise information about the local bonding of materials,but difficult to interpret without a well-vetted dataset of reference spectra.The ability to predict NMR parameters and connect them to three-dimensional local environments is critical for understanding more complex,long-range interactions. | He Sun Shyam Dwaraknath Handong Ling Xiaohui Qu Patrick Huck Kristin A.Persson Sophia E.Hayes | 2020 | npj Computational Materials2020，，1： | 0 |
| 4 | Author Correction:Active learning for accelerated design of layered materials显示文摘Since the publication of this work,Lindsay Bassman Oftelie has changed their name from Lindsay Bassman.This has now been amended. | Lindsay Bassman Oftelie Pankaj Rajak Rajiv KKalia Aiichiro Nakano Fei Sha Jifeng Sun David JSingh Muratahan Aykol Patrick Huck Kristin Persson Priya Vashishta | 2022 | npj Computational Materials2022，，1： | 0 |
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