Software

1. ChemML:
   ChemML is a machine learning and informatics program suite for the analysis, mining, and modeling of chemical and materials data.
   https://github.com/hachmannlab/chemml,
   Authors:
   M. Haghighatlari, R. Subramanian, B. Urala Kota, G. Vishwakarma, A. Sonpal, P. H. Chen, S. Setlur, and J. Hachmann.
   Acknowledgements:
   • ChemML is based upon work supported by the U.S. National Science Foundation under grant #OAC-1751161 and #OAC-1640867.
2. Competition on Chart Data Extraction:
   A considerable amount of attention has been devoted to automatically decompose and understand the scientific charts in literature. However, there is a lack of benchmark data which can be used to compare the effectiveness of the different methods proposed so far. We organized the first edition of the Competition on Harvesting Raw Tables from Infographics (ICDAR 2019 CHART-Infographics), which we consider a major step in providing common benchmarks and tools for the chart recognition community. To provide a new common ground for evaluation, we have proposed the usage of a large-scale synthetic dataset generated from real world data sources for training and testing, and a smaller dataset based on real charts. The complex goal of data extraction from charts has been split into multiple tasks. While classification tasks are evaluated with standard metrics, we propose new metrics for the chart-specific tasks.
   Competition Webpage: https://chartinfo.github.io/
   Evaluation Tools: https://github.com/chartinfo/chartinfo.github.io
   Authors: 
   Kenny Davila, Bhargava Urala Kota, Srirangaraj Setlur, Venu Govindaraju, Christopher Tensmeyer, Sumit Shekhar, Ritwick Chaudhry
3. Metallic-Organic Framework structure: Hirshfeld Surface calculations and non-linear manifold analysis
   Metal organic frameworks (MOFs) are one of the most exciting advances in solid state materials science. They are crystalline materials assembled with metal clusters and organic linkers, which have tailorable pore sizes, pore geometries, high void fractions, and large surface areas. Those features enable a wide applications of MOFs in many fields, including gas storage, separation, catalysis, and carbon capture. As synthesizing and testing a large number of MOF is not feasible in practice, the high-throughput computational screening of the MOF database can help expedite the experimental efforts. However, typical MOF database is high-dimensional and sparse that pose the challenge of extracting the key features and trends that could guide the discovery process. To address this issue, we develop a library of MOF fingerprints based on their geometric and chemical bonding interactions. Such fingerprints are computational ready to be analyzed with various machine learning methods. Our code for vizualing an MOF database using manifold learning of Hirshfield Surfaces is provided.
   Code: https://github.com/sxz1113/ubmdi
4. Lecture Video Summarization by Extracting Handwritten Content from Whiteboards:
   Online lecture videos are a valuable resource for students across the world. The ability to find videos based on their content could make them even more useful. Methods for automatic extraction of this content reduce the amount of manual effort required to make indexing and retrieval of such videos possible. We adapt a deep learning based method for scene text detection, for the purpose of detection of handwritten text, math expressions and sketches in lecture videos.
   https://github.com/bhargavaurala/accessmath-icfhr2018,
   Authors:
   Bhargava Urala Kota, Kenny Davila, Alexander Stone, Srirangaraj Setlur and Venu Govindaraju
5. An Atlas for PFAS Chemistry
   We have developed a visualization tool to navigate  large databases associated with environmental impact of  PFAS compounds that enables one to rapidly explore systematics in structure-function relationships associated with new and emerging PFAS chemistries. The data framework maps high dimensional information associated with the SMILES approach of encoding molecular structure with functionality data including bioactivity and physicochemical property. This ‘PFAS-Map’ is a 3-dimensional unsupervised visualization tool that can automatically classify new PFAS chemistries based on current PFAS classification criteria. We provide examples on how the PFAS-Map can be utilized, including the prediction and estimation of yet unmeasured fundamental physical properties of PFAS chemistries, uncovering hierarchical characteristics in existing classification schemes, and the fusion of data from diverse sources.

   Code:  https://github.com/MatInfoUB/PFAS_Map_MaDE_UB 

Publications

• An Su and Krishna Rajan: A database framework for rapid screening of structure-function relationships in PFAS chemistry- Sci Data 8, 14 (2021). https://doi.org/10.1038/s41597-021-00798-x

• Schunder, Torsten and Yin, Dameng and Bagchi-Sen, Sharmistha and Rajan, Krishna. (2020). A spatial analysis of the development potential of rooftop and community solar energy.  Remote Sensing Applications: Society and Environment. 19 (C) 100355. Status = Deposited in NSF-PAR  doi:10.1016/j.rsase.2020.100355  ; Federal Government's License = Acknowledged. (Completed by Setlur, Srirangaraj on 08/31/2020 )  Full text    Citation details 

• Williams, Logan and Mukherjee, Arpan and Rajan, Krishna. (2020). Deep Learning Based Prediction of Perovskite Lattice Parameters from Hirshfeld Surface Fingerprints.  The Journal of Physical Chemistry Letters.  7462 to 7468. Status = Deposited in NSF-PAR  doi:10.1021/acs.jpclett.0c02201  ; Federal Government's License = Acknowledged. (Completed by Setlur, Srirangaraj on 08/31/2020 )  Full text    Citation details 

• Mukherjee, Arpan and Broderick, Scott R. and Zhang, Tianmu and Rajan, Krishna. (2019). Modularity Optimization for Fast Automated Detection of Solute Clusters in Atom Probe Tomography.  Microscopy and Microanalysis. 25 (S2) 300 to 301. Status = Deposited in NSF-PAR  doi:10.1017/S143192761900223X  ; Federal Government's License = Acknowledged. (Completed by Setlur, Srirangaraj on 08/31/2020 )  Full text    Citation details 

• Urala Kota, Bhargava and Ahmed, Saleem and Stone, Alexander and Davila, Kenny and Setlur, Srirangaraj and Govindaraju, Venu. (2019). Summarizing Lecture Videos by Key Handwritten Content Regions.  2019 International Conference on Document Analysis and Recognition Workshops (ICDARW).  13 to 18. Status = Deposited in NSF-PAR  doi:10.1109/ICDARW.2019.30058  ; Federal Government's License = Acknowledged. (Completed by Setlur, Srirangaraj on 08/31/2020 )  Full text    Citation details 

• Dongol, Ruhil and Zhang, Tianmu and Rajan, Krishna. (2020). Tracking dynamics of glass formers and modifiers via correlation maps of molecular dynamics simulation.  Journal of the American Ceramic Society. 103 (10) 5638 to 5653. Status = Deposited in NSF-PAR  doi:10.1111/jace.17280  ; Federal Government's License = Acknowledged. (Completed by Setlur, Srirangaraj on 08/31/2020 )  Full text    Citation details 

• Venugopal, Vineeth and Broderick, Scott R. and Rajan, Krishna. (2019). A picture is worth a thousand words: applying natural language processing tools for creating a quantum materials database map.  MRS Communications. 9 (4) 1134 to 1141. Status = Deposited in NSF-PAR  doi:10.1557/mrc.2019.136  ; Federal Government's License = Acknowledged. (Completed by Setlur, Srirangaraj on 08/31/2020 )  Full text    Citation details 

• Haghighatlari, Mojtaba and Vishwakarma, Gaurav and Altarawy, Doaa and Subramanian, Ramachandran and Kota, Bhargava U. and Sonpal, Aditya and Setlur, Srirangaraj and Hachmann, Johannes. (2020). ChemML : A machine learning and informatics program package for the analysis, mining, and modeling of chemical and materials data.  WIREs Computational Molecular Science.  . Status = Deposited in NSF-PAR  doi:10.1002/wcms.1458  ; Federal Government's License = Acknowledged. (Completed by Setlur, Srirangaraj on 09/01/2020 )  Full text    Citation details 

• Mukherjee, Arpan and Broderick, Scott and Rajan, Krishna. (2020). Modularity optimization for enhancing edge detection in microstructural features using 3D atomic chemical scale imaging.  Journal of Vacuum Science & Technology A. 38 (3) Article No. 033207. Status = Deposited in NSF-PAR  doi:10.1116/1.5143017  ; Federal Government's License = Acknowledged. (Completed by Setlur, Srirangaraj on 08/31/2020 )  Full text    Citation details 

• Davila, Kenny and Kota, Bhargava Urala and Setlur, Srirangaraj and Govindaraju, Venu and Tensmeyer, Christopher and Shekhar, Sumit and Chaudhry, Ritwick. (2019). ICDAR 2019 Competition on Harvesting Raw Tables from Infographics (CHART-Infographics).  2019 International Conference on Document Analysis and Recognition (ICDAR).  1594 to 1599. Status = Deposited in NSF-PAR  doi:10.1109/ICDAR.2019.00203  ; Federal Government's License = Acknowledged. (Completed by Setlur, Srirangaraj on 08/31/2020 )  Full text    Citation details 

• Xu, Fei and Davila, Kenny and Setlur, Srirangaraj and Govindaraju, Venu. (2019). Content Extraction from Lecture Video via Speaker Action Classification Based on Pose Information.  2019 International Conference on Document Analysis and Recognition (ICDAR).  1047 to 1054. Status = Deposited in NSF-PAR  doi:10.1109/ICDAR.2019.00171  ; Federal Government's License = Acknowledged. (Completed by Setlur, Srirangaraj on 08/31/2020 )  Full text    Citation details 

• Dasgupta, Aparajita and Gao, Yingjie and Broderick, Scott R. and Pitman, E. Bruce and Rajan, Krishna. (2020). Machine Learning-Aided Identification of Single Atom Alloy Catalysts.  The Journal of Physical Chemistry C. 124 (26) 14158 to 14166. Status = Deposited in NSF-PAR  doi:10.1021/acs.jpcc.0c01492  ; Federal Government's License = Acknowledged. (Completed by Setlur, Srirangaraj on 08/31/2020 )  Full text    Citation details 

• Dasgupta, Aparajita R. and Broderick, Scott U. and Mack, Connor and Kota, Bhargava and Subramanian, Ramachandran and Setlur, Srirangaraj and Govindaraju, Venu and Rajan, Krishna. (2019). Probabilistic Assessment of Glass Forming Ability Rules for Metallic Glasses Aided by Automated Analysis of Phase Diagrams.  Scientific Reports. 9 (1) . Status = Deposited in NSF-PAR  doi:10.1038/s41598-018-36224-3  ; Federal Government's License = Acknowledged. (Completed by Setlur, Srirangaraj on 09/08/2019 )  Full text    Citation details 

• Urala Kota, Bhargava and Davila, Kenny and Stone, Alexander and Setlur, Srirangaraj and Govindaraju, Venu. (2018). Automated Detection of Handwritten Whiteboard Content in Lecture Videos for Summarization.  2018 16th International Conference on Frontiers in Handwriting Recognition (ICFHR).  19 to 24. Status = Deposited in NSF-PAR  doi:10.1109/ICFHR-2018.2018.00013  ; Federal Government's License = Acknowledged. (Completed by Setlur, Srirangaraj on 09/08/2019 )  Full text    Citation details 

• Tanaka, Isao and Rajan, Krishna and Wolverton, Christopher. (2018). Data-centric science for materials innovation.  MRS Bulletin. 43 (9) 659 to 663. Status = Deposited in NSF-PAR  doi:10.1557/mrs.2018.205  ; Federal Government's License = Acknowledged. (Completed by Setlur, Srirangaraj on 09/08/2019 )  Full text    Citation details 

• Mullis, Adam S. and Broderick, Scott R. and Binnebose, Andrea M. and Peroutka-Bigus, Nathan and Bellaire, Bryan H. and Rajan, Krishna and Narasimhan, Balaji. (2019). Data Analytics Approach for Rational Design of Nanomedicines with Programmable Drug Release.  Molecular Pharmaceutics. 16 (5) 1917 to 1928. Status = Deposited in NSF-PAR  doi:10.1021/acs.molpharmaceut.8b01272  ; Federal Government's License = Acknowledged. (Completed by Setlur, Srirangaraj on 09/08/2019 )  Full text    Citation details 

• Urala Kota, Bhargava and Davila, Kenny and Stone, Alexander and Setlur, Srirangaraj and Govindaraju, Venu. (2019). Generalized framework for summarization of fixed-camera lecture videos by detecting and binarizing handwritten content.  International Journal on Document Analysis and Recognition (IJDAR).  . Status = Deposited in NSF-PAR  doi:10.1007/s10032-019-00327-y  ; Federal Government's License = Acknowledged. (Completed by Setlur, Srirangaraj on 09/08/2019 )  Full text    Citation details 

• Broderick, Scott and Zhang, Tianmu and Kota, Bhargava Urala and Subramanian, Ramachandran and Setlur, Srirangaraj and Govindaraju, Venu and Rajan, Krishna. (2018). Machine Learning for Atomic Scale Chemical and Morphological Assessment.  Microscopy and Microanalysis. 24 (S1) 526 to 527. Status = Deposited in NSF-PAR  doi:10.1017/S1431927618003124  ; Federal Government's License = Acknowledged. (Completed by Setlur, null on 08/16/2018 )  Full text    Citation details 

• Peralta, Joaquin and Broderick, Scott and Loyola, Claudia and Rajan, Krishna. (2018). Coupling of First Principles and Machine Learning for High Throughput Calculation of Evaporation Fields.  Microscopy and Microanalysis. 24 (S1) 524 to 525. Status = Deposited in NSF-PAR  doi:10.1017/S1431927618003112  ; Federal Government's License = Acknowledged. (Completed by Setlur, null on 08/16/2018 )  Full text    Citation details 

• Shen, Xiaozhou and Zhang, Tianmu and Broderick, Scott and Rajan, Krishna. (2018). Correlative Analysis of Metal Organic Framework Structures through Manifold Learning of Hirshfeld Surfaces.  Molecular Systems Design & Engineering.  . Status = Deposited in NSF-PAR  doi:10.1039/C8ME00014J  ; Federal Government's License = Acknowledged. (Completed by Setlur, null on 08/16/2018 )  Full text    Citation details 

• Kota, Bhargava Urala and Nair, Rathin Radhakrishnan and Setlur, Srirangaraj and Dasgupta, Aparajita and Broderick, Scott and Govindaraju, Venu and Rajan, Krishna. (2018). Automated Analysis of Phase Diagrams.  2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR). 02 17 - 18. Status = Deposited in NSF-PAR  doi:10.1109/ICDAR.2017.256  ; Federal Government's License = Acknowledged. (Completed by Setlur, null on 08/16/2018 )  Full text    Citation details 

• Broderick, Scott R. and Kumar, Aakash and Oni, Adedapo A. and LeBeau, James M. and Sinnott, Susan B. and Rajan, Krishna. (2018). Discovering chemical site occupancy- modulus correlations in Ni based intermetallics via statistical learning methods.  Computational Condensed Matter. 14 (C) 8 to 14. Status = Deposited in NSF-PAR  doi:10.1016/j.cocom.2017.11.001  ; Federal Government's License = Acknowledged. (Completed by Setlur, null on 08/16/2018 )  Full text    Citation details 

• K. Davila, S. Setlur, D. Doermann, B. U. Kota, and V. Govindaraju, "Chart Mining: A Survey of Methods for Automated Chart Analysis", to appear in IEEE Transactions on Pattern Analysis and Machine Intelligence - currently available for early access at: 10.1109/TPAMI.2020.2992028. Status = AWAITING_PUBLICATION.

• R. Subramanian, B. Urala Kota, S. Setlur, S. R. Broderick, V. Govindaraju, and K. Rajan, “Descriptor Development for Accelerated Materials Design: A survey.” Under revision for resubmission. Status = OTHER.

• K. Davila, B.U. Kota, S. Setlur, V. Govindaraju, C. Tensmeyer, S. Shekhar, R. Chaudhry, "ICDAR 2019 Competition on Harvesting Raw Tables from Infographics (CHART-Infographics)", 2019 15th IAPR International Conference on Document Analysis and Recognition (ICDAR), Sydney, Australia, 2019.. Status = PUBLISHED.

• F. Xu, K. Davila, S. Setlur, V. Govindaraju, "Content Extraction from Lecture Video via Speaker Action Classification based on Pose Information", 2019 15th IAPR International Conference on Document Analysis and Recognition (ICDAR), Sydney, Australia, 2019. Status = PUBLISHED.

• B.U. Kota, K. Davila, S. Ahmed, A. Stone, S. Setlur, V. Govindaraju.,"Summarizing Lecture Videos by Key Handwritten Content Regions", 8th International Workshop on Camera-Based Document Analysis & Recognition, 2019.. Status = PUBLISHED.

• A. Mukherjee, S. Broderick, K. Rajan. “Modularity Optimization for Fast Automated Detection of Solute Clusters in Atom Probe Tomography.” Submitted to Philosophical Magazine - Under review. Status = PUBLISHED.

• Venugopal, V., Broderick, S., & Rajan, K. (2019). A picture is worth a thousand words: Applying natural language processing tools for creating a quantum materials database map. MRS Communications, 9(4), 1134-1141. doi:10.1557/mrc.2019.136. Status = PUBLISHED.

• S.R. Broderick, K. Rajan. “Designing a Periodic Table for Alloy Design: Harnessing Machine Learning to Navigate a Multiscale Information Space.” – JOM : invited paper –special issue (2020). Status = AWAITING_PUBLICATION.

• Saleem Ahmed*, Kenny Davila, Srirangaraj Setlur, Venu Govindaraju, Equation Attention Relationship Network (EARN) : A Geometric Deep Metric Framework for Learning Similar Math Expression Embedding, 25th International Conference on Pattern Recognition, 2020. Status = UNDER_REVIEW.

• Bhargava Urala Kota, Kenny Davila, Alexander Stone, Srirangaraj Setlur, Venu Govindaraju, "Summarization of Academic Presentation Videos for Searching Text and Mathematical Expressions" 25th International Conference on Pattern Recognition, 2020.. Status = UNDER_REVIEW.

• Bhargava Urala Kota, Alexander Stone, Kenny Davila, Srirangaraj Setlur, Venu Govindaraju, Automated Whiteboard Lecture Video Summarization by Content Region Detection and Representation, 25th International Conference on Pattern Recognition, 2020.. Status = UNDER_REVIEW.