Papers and Code
2023
Pastrana, R., Oktay, D., Adams, R. P., Adriaenssens, S.; “JAX FDM: A differentiable solver for inverse formfinding”; International Conference on Machine Learning (ICML): 2023.  
Gleicher, M., Riveiro, M., von Landesberger, T., Deussen, O., Chang, R., Gillman, C.; “A Problem Space for Designing Visualizations”; “A Problem Space for Designing Visualizations”; IEEE Comput Graph Appl: 2023 Jul. 11. DOI: 10.1109/MCG.2023.3267213  
Friedman, D., Dieng, A. B.; “The Vendi Score: A Diversity Evaluation Metric for Machine Learning”; Transactions Mach. Learn. Res. (TMLR): 2023 Jul. 1.  
Monadjemi, S., Guo, M., Gotz, D., Garnett, R., Ottley, A.; “Human–Computer Collaboration for Visual Analytics: an Agentbased Framework”; Eurographics Conference on Visualization (EuroVis): 2023.  
Medina, E., Rycroft, C. H., Bertoldi, K.; “Nonlinear shape optimization of flexible mechanical metamaterials”; Extreme Mech. Lett.: 2023 Jun. DOI: 10.1016/j.eml.2023.102015  
Oktay, D., Mirramezani, M., Medina, E., Adams, R. P.; “Neuromechanical Autoencoders: Learning to Couple Elastic and Neural Network Nonlinearity“; International Conference on Learning Representations (ICLR): 2023.  
Weadock, N.J., Sterlin, T. C., Vigil, J. A., GoldParker, A., Smith, I. C., Ahammed, B., Krogstad, M. J., Ye, F., Coneshen, D., Gehring, P. M., Rappe, A. M., Steinruck, HG., Ertekin, E., Karunadasa, H. I., Reznik, D., Toney, M. F.; “The nature of dynamic local order in CH3NH3PbI3 and CH3NH3PbBr3”; Joule: 2023 Apr. 17. DOI: 10.1016/j.joule.2023.03.017  
Suh, A., Appleby, G., Anderson, E. W., Finelli, L., Chang, R., Cashman, D.; “Are Metrics Enough? Guidelines for Communicating and Visualizing Predictive Models to Subject Matter Experts”; IEEE Trans. Vis. Comput. Graph.: 2023 Mar. 20. DOI: 10.1109/TVCG.2023.3259341 

Nguyen, Q., Garnett,R.; “Nonmyopic Multiclass Active Search with Diminishing Returns for Diverse Discovery“; International Conference on Artificial Intelligence and Statistics (AISTATS): 2023.  
Greenberg, J., McClellan, S., Rauch, C., Zhao, X., Kelly, M., An, Y., Kunze, J., Orenstein, R., Porter, C., Meschke, V. and Toberer, E.; “Building community consensus for scientific metadata with YAMZ”; Data Intelligence: 2023 Mar. 8. DOI: 10.1162/dint_a_00211  
Zeng, W., Chen, X., Hou, Y., Shao, L., Chu, Z., Chang, R.; “SemiAutomatic Layout Adaptation for Responsive MultipleView Visualization Design“; IEEE Trans. Vis. Comput. Graph.: 2023 Jan. 30. DOI: 10.1109/TVCG.2023.3240356  
Islamov, M., Babei, H., Anderson R., Sezginel, K. B., Long, J. R., McGaughey, A. J. H., GomezGualdron, D.A., Wilmer, C. E.; “Highthroughput screening of hypothetical metalorganic frameworks for thermal conductivity”; npj Comput. Mater.: 2023 Jan. 20. DOI:10.1038/s4152402200961x 
2022
Nguyen, Q., Wu, K., Gardner, J. R., Garnett,R.; “Local Bayesian optimization via maximizing probability of descent”; Conference on Neural Information Processing Systems (NeurIPS): 2022.  
Cai, D., Adams, R. P.; “Multifidelity Monte Carlo: a pseudomarginal approach”; Conference on Neural Information Processing Systems (NeurIPS): 2022.  
An, Y., Greenberg, J., Hu, X., Kalinowski, A., Fang, X., Zhao, X., McClellan, S., UribeRomo, F., GómezGualdrón, D., Langlois, K., Furst, J., FajardoRojas, F., Ardila, K., Saikin, S., A. Harper, C., & Daniel, R.; “Exploring PreTrained Language Models to Build Knowledge Graph for MetalOrganic Frameworks (MOFs)”; IEEE BigData Conference: 2022.  
Greenberg, J., McClellan, S., Zhao, X., Kellner, E., Venator, D., Zhao, H., Shen, J., Hu, X., & An, Y.; “Materials Science Ontology Design with an AnalyticoSynthetic Facet Analysis Framework”; Metadata and Semantics Research Conference: 2022.  
Chang, R., Wang, YX., Ertekin, E.; “Towards overcoming data scarcity in materials science: unifying models and datasets with a mixture of experts framework”; npj Comput. Mater.: 2022 Nov. 18. DOI: 10.1038/s4152402200929x  
Pastrana, R., Ohlbrock, P. O., Oberbichler, T., D’Acunto, P., Parascho, S.; “Constrained FormFinding of Tension–Compression Structures using Automatic Differentiation”; CAD: 2022 Nov. 9. DOI: 10.1016/j.cad.2022.103435  
Deng, B., Zareei, A., Ding, X., Weaver, J. C., Rycroft, C. H., Bertoldi, K.; “Inverse Design of Mechanical Metamaterials with Target Nonlinear Response via a Neural Accelerated Evolution Strategy”; Adv. Mater: 2022 Oct. 13. DOI: 10.1002/adma.202270287  
Monadjemi, S., Ha, S., Nguyen, Q., Chai, H., Garnett, R., Ottley, A.; “Guided Data Discovery in Interactive Visualizations via Active Search“; IEEE VIS Conference: 2022.  
Ha, S., Monadjemi, S., Garnett, R., Ottley, A.; “A Unified Comparison of User Modeling Techniques for Predicting Data Interaction and Detecting Exploration Bias”; IEEE VIS Conference: 2022.  
Appleby, G., Espadoto, M., Chen, R., Goree, S., Telea, A. C., Anderson, E. W., Chang, R.; “HyperNP: Interactive Visual Exploration of Multidimensional Projection Hyperparameters”; Eurographics Conference on Visualization (EuroVis): 2022.  
Suh, A., Mosca, A., Robinson, S., Pham, Q., Cashman, D., Ottley, A., Chang, R.; “Inferential Tasks as an Evaluation Technique for Visualization”; Eurographics Conference on Visualization (EuroVis): 2022.  
Gathani, S., Monadjemi, S., Ottley, A., Battle, L.; “A GrammarBased Approach for Applying Visualization Taxonomies to Interaction Logs”; Eurographics Conference on Visualization (EuroVis): 2022.  
Li, J., Lopez, S. A.; “A Look Inside the Black Box of Machine Learning Photodynamics Simulations“; Acc. Chem. Res.: 2022 July 7. DOI: 10.1021/acs.accounts.2c00288  
Kumar, A., Vasileiou, S. L., Bancilhon, M., Ottley, A., Yeoh, W.; “VizXP: A Visualization Framework for Conveying Explanationsto Users in Model Reconciliation Problems“; International Conference on Automated Planning and Scheduling (ICAPS): 2022.  
Seff, A., Zhou, W., Richardson, N., Adams, R. P.; “Vitruvion: A Generative Model of Parametric CAD Sketches”; International Conference on Learning Representations (ICLR): 2022.  
Li, J., Lopez, S. A.; “Excitedstate distortions promote the photochemical 4πelectrocyclizations of fluorobenzenes via machine learning accelerated photodynamics simulations”; Chem. Eur. J.: 2022 April 26. DOI: 10.1002/chem.202200651  
Wang, L. S., Patel, S. V., Truong, E., Hu, YY., Sossina M. Haile, S. M.; “Phase Behavior and Superprotonic Conductivity in the System (1–x)CsH_{2}PO_{4} – xH_{3}PO_{4}: Discovery of OffStoichiometric α[Cs_{1–x}H_{x}]H_{2}PO_{4}”; Chem. Mater.: 2022 Feb. 11. DOI: 10.1021/acs.chemmater.1c04061  
Adrion, D. M., Lopez, S. A.; “Crossconjugation controls the stabilities and photophysical properties of heteroazoarene photoswitches”; Org. and Biomol. Chem.: 2022 Jan. 11. DOI: 10.1039/D1OB02026A  
Forte, A. E., Hanakata, P. Z., Jin, L., Zari, E., Zareei, A., Fernandes, M. C., Sumner, L., Alvarez, J., Bertoldi, K.; “Inverse Design of Inflatable Soft Membranes Through Machine Learning”; Adv. Funct. Mater.: 2022 Jan. 10. DOI: 10.1021/acs.chemmater.1c04061 
LLMProp
LLMProp is an efficiently finetuned large language model (T5 encoder) on crystals text descriptions to predict their properties. Given a text sequence that describes the crystal structure, LLMProp encodes the underlying crystal representation from its text description and output its properties such as band gap and volume.
Mixture of Experts for Materials Science
This repository contains a general framework for leveraging complementary information across different models and datasets for accurate prediction of datascarce materials properties.
Vendi Score
The Vendi Score (VS) is a metric for evaluating diversity in machine learning. VS can be interpreted as the effective number of unique elements in a sample. It increases linearly with the number of modes in the dataset. VS is highest when the items in the sample differ in many attributes, and the attributes are not correlated with each other.
Vendi Sampling
Vendi Sampling is a method for increasing the efficiency and efficacy of the exploration of molecular conformation spaces. In Vendi sampling, molecular replicas are simulated in parallel and coupled via a global statistical measure, the Vendi Score, to enhance diversity.
YAMZ
Yet Another Metadata Zoo (YAMZ) is a collaborative vocabulary application which allows individuals to define terms as well as comment and vote on them. It allows communities to develop vocabularies specific to their needs, especially to improve the quality of metadata used. Each term receives a permanent identifier in the form of an archival key (ARK) that allows users to have a stable linked data reference.
OTMapOnto Ontology Matching Tool
This repository contains the sources of the ontology matching tool, OTMapOnto, and the results of participating in the Ontology Alignment Evaluation Initiative 2021. The tool is wrapped as a Web service.
PyRAI2MD
The Python Rapid Artificial Intelligence Ab Initio Molecular Dynamics code (PyRAI2MD) code is a machine learning software that accelerates photodynamics simulations by 100,000 times. PyRAI2MD enables long timescale simulations in complex environments such as explicit solvation and for molecules of unprecedented complexity.
MultifidelityMCMC
Multifidelity Markov chain Monte Carlo (MCMC) algorithms combine models of varying fidelities in order to obtain an approximate target density with lower computational cost. We describe a class of asymptotically exact multifidelity MCMC algorithms for the setting where a sequence of models of increasing fidelity can be computed that approximates the expensive target density of interest. We take a pseudomarginal MCMC approach for multifidelity inference that utilizes a cheaper, randomizedfidelity unbiased estimator of the target fidelity constructed via random truncation of a telescoping series of the lowfidelity sequence of models.
Vitruvion
Vitruvion is a generative model of parametric CAD sketches, which constitute the basic computational building blocks of modern mechanical design. Our model, trained on realworld designs from the SketchGraphs dataset, autoregressively synthesizes sketches as sequences of primitives, with initial coordinates, and constraints that reference back to the sampled primitives. As samples from the model match the constraint graph representation used in standard CAD software, they may be directly imported, solved, and edited according to downstream design tasks.
JAX FDM
JAX FDM enables the solution of inverse formfinding problems for discrete force networks using the force density method (FDM) and gradientbased optimization. It streamlines the integration of formfinding simulations into deep learning models for machine learning research.
COMPAS CEM
Inverse design of 3D trusses via automatic differentiation. COMPAS CEM encapsulates the CEM framework into an opensource structural design tool that enables the formulation and the solution of constrained formfinding problems in plain and simple Python code.
VERDE Materials Database
The Virtual Excited State Reference for the Discovery of Electronic Materials Database. We have established this resource in line with our interest in lightresponsive πconjugated organic molecules with applications in green chemistry, organic solar cells, and organic redox flow batteries. It includes results of our active and past virtual screening studies; to date, more than 13000 density functional theory (DFT) calculations have been performed on 1500 molecules to obtain frontier molecular orbitals and photophysical properties, including excitation energies, dipole moments, and redox potentials.
Material_Recommender
Material_Recommender leverages representations extracted from language models pretrained on material science literature for material discovery and property prediction. Code still under construction.
The Institute for Data Driven Dynamical Design (ID4) is supported by the National Science Foundation through award #2118201