Feel free to email Rachel for a PDF copy of any of these! If you are interested in Rachel’s earlier work prior to founding the ACME group, check out her Google Scholar page!
Wang, X., Loli, J. A., Ulissi, Z. W., de Boer, M. P., Webler, B. A., & Kurchin, R. C. (2025). Constraint Active Search in Process Window Optimization for Powder Feed Directed Energy Deposition. Integrating Materials and Manufacturing Innovation.
@article{cas_immi,
title = {Constraint Active Search in Process Window Optimization for Powder Feed Directed Energy Deposition},
author = {Wang, Xiaoxiao and Loli, Jose A. and Ulissi, Zachary W. and de Boer, Maarten P. and Webler, Bryan A. and Kurchin, Rachel C.},
journal = {Integrating Materials and Manufacturing Innovation},
doi = {10.1007/s40192-025-00393-7},
year = {2025}
}
Optimizing process parameters for directed energy deposition is crucial to achieve high-quality printed parts. However, this optimization process often entails significant time and cost investments. An initial investigation into the process window can be conducted through the examination of single tracks. In this work, we investigate the utility of constraint active search (CAS) to efficiently identify process window that yield 4340 low-alloy steel single tracks with desired geometrical features. The effectiveness of the CAS method was assessed through experiments with physical and interpolated measurement. Fifty single tracks from randomly sampled process parameter combinations with different power, scan velocity, and laser spot size and ten single tracks from CAS-generated parameters were produced and analyzed. The results demonstrate that our search method outperforms random search, with 80% of parameter sets identified as desirable compared to only 4% in the case of random search. Moreover, an interpolated ground truth in input spaces of various dimensionalities was built in order to assess repeatability without excessive experimental cost. The results indicate that the CAS achieves higher precision compared to grid search and random search, especially in higher-dimensional process parameter spaces.
Diehl, P., Soneson, C., Kurchin, R. C., Mounce, R., & Katz, D. S. (2025). The Journal of Open Source Software (JOSS): Bringing Open-Source Software Practices to the Scholarly Publishing Community for Authors, Reviewers, Editors, and Publishers. Journal of Librarianship and Scholarly Communication, 12(2), eP18285.
@article{joss_jlsc,
title = {The Journal of Open Source Software (JOSS): Bringing Open-Source Software Practices to the Scholarly Publishing Community for Authors, Reviewers, Editors, and Publishers},
author = {Diehl, Patrick and Soneson, Charlotte and Kurchin, Rachel C. and Mounce, Ross and Katz, Daniel S.},
journal = {Journal of Librarianship and Scholarly Communication},
volume = {12},
issue = {2},
pages = {eP18285},
doi = {10.31274/jlsc.18285},
year = {2025}
}
Introduction: Open-source software (OSS) is a critical component of open science, but contributions to the OSS ecosystem are systematically undervalued in the current academic system. The Journal of Open Source Software (JOSS) contributes to addressing this by providing a venue (that is itself free and diamond OA and all open-source, built in a layered structure using widely available elements/services of the scholarly publishing ecosystem) for publishing OSS, run in the style of open-source software itself. One element of JOSS is that it uses open peer review in a collaborative, iterative format, unlike most publishers. Additionally, all the parts of JOSS, from the reviews to the papers to the software that is the subject of the papers to the software that the journal runs, are open.
Background: We describe JOSS’s history and its peer review process using an editorial bot, and present statistics gathered from JOSS’s public review history on GitHub showing an increasing number of peer reviewed papers each year. We discuss the new JOSSCast and use it as a data source to understand reasons why interviewed authors decided to publish in JOSS.
Discussion and Outlook: JOSS’s process differs significantly from traditional journals, which has impeded JOSS’s inclusion in indexing services such as Web of Science. In turn, this discourages researchers within certain academic systems, such as Italy’s, which emphasize the importance of Web of Science and/or Scopus indexing for grant applications and promotions. JOSS is a fully diamond open access journal with a cost of around US$5 per paper for the 401 papers published in 2023. The scalability of running JOSS with volunteers and financing JOSS with grants and donations is discussed.
Timmins, A., & Kurchin, R. C. (2024). Addressing accuracy by prescribing precision: Bayesian error estimation of point defect energetics. Journal of Applied Physics, 136(9), 095701.
@article{beefdefects,
title = {Addressing accuracy by prescribing precision: Bayesian error estimation of point defect energetics},
author = {Timmins, Andrew and Kurchin, Rachel C.},
journal = {Journal of Applied Physics},
volume = {136},
issue = {9},
pages = {095701},
doi = {10.1063/5.0211543},
year = {2024}
}
With density functional theory (DFT), it is possible to calculate the formation energy of charged point defects and in turn to predict a range of experimentally relevant quantities, such as defect concentrations, charge transition levels, or recombination rates. While prior efforts have led to marked improvements in the accuracy of such calculations, comparatively modest effort has been directed at quantifying their uncertainties. However, in the broader DFT research space, the development of Bayesian Error Estimation Functionals (BEEF) has enabled uncertainty quantification (UQ) for other properties. In this paper, we investigate the utility of BEEF as a tool for UQ of defect formation energies. We build a pipeline for propagating BEEF energies through a formation-energy calculation and test it on intrinsic defects in several materials systems spanning a variety of chemistries, bandgaps, and crystal structures, comparing to prior published results where available. We also assess the impact of aligning to a deep-level transition rather than to the VBM (valence band maximum). We observe negligible dependence of the estimated uncertainty upon a supercell size, though the relationship may be obfuscated by the fact that finite-size corrections cannot be computed separately for each member of the BEEF ensemble. Additionally, we find an increase in estimated uncertainty with respect to the absolute charge of a defect and the relaxation around the defect site without deep-level alignment, but this trend is absent when the alignment is applied. While further investigation is warranted, our results suggest that BEEF could be a useful method for UQ in defect calculations.
Tang, J., Jiang, K., Tseng, P.-S., Kurchin, R. C., Porter, L. M., & Davis, R. F. (2024). Thermal stability and phase transformation of α-, κ(ε)-, and γ-Ga2O3 films under different ambient conditions. Applied Physics Letters, 125(9), 092104.
@article{ga2o3stability,
title = {Thermal stability and phase transformation of α-, κ(ε)-, and γ-Ga2O3 films under different ambient conditions},
author = {Tang, Jingyu and Jiang, Kunyao and Tseng, Po-Sen and Kurchin, Rachel C. and Porter, Lisa M. and Davis, Robert F.},
journal = {Applied Physics Letters},
volume = {125},
issue = {9},
pages = {092104},
doi = {10.1063/5.0214500},
year = {2024}
}
Phase transitions in metastable α-, κ(ε)-, and γ-Ga2O3 films to thermodynamically stable β-Ga2O3 during annealing in air, N2, and vacuum have been systematically investigated via in situ high-temperature x-ray diffraction (HT-XRD) and scanning electron microscopy (SEM). These respective polymorphs exhibited thermal stability to ∼471–525 °C, ∼773–825 °C, and ∼490–575 °C before transforming into β-Ga2O3, across all tested ambient conditions. Particular crystallographic orientation relationships were observed before and after the phase transitions, i.e., (0001) α-Ga2O3 → (-201) β-Ga2O3, (001) κ(ε)-Ga2O3 → (310) and (-201) β-Ga2O3, and (100) γ-Ga2O3 → (100) β-Ga2O3. The phase transition of α-Ga2O3 to β-Ga2O3 resulted in catastrophic damage to the film and upheaval of the surface. The respective primary and possibly secondary causes of this damage are the +8.6% volume expansion and the dual displacive and reconstructive transformations that occur during this transition. The κ(ε)- and γ-Ga2O3 films converted to β-Ga2O3 via singular reconstructive transformations with small changes in volume and unchanged surface microstructures.
Wang, X., Musielewicz, J., Tran, R., Ethirajan, S. K., Fu, X., Mera, H., Kitchin, J. R., Kurchin, R., & Ulissi, Z. W. (2024). Generalization of Graph-Based Active Learning Relaxation Strategies Across Materials. Machine Learning: Science and Technology, 5(2), 025018.
@article{wang2024generalization,
title = {Generalization of Graph-Based Active Learning Relaxation Strategies Across Materials},
author = {Wang, Xiaoxiao and Musielewicz, Joseph and Tran, Richard and Ethirajan, Sudheesh Kumar and Fu, Xiaoyan and Mera, Hilda and Kitchin, John R and Kurchin, Rachel and Ulissi, Zachary W},
journal = {Machine Learning: Science and Technology},
volume = {5},
issue = {2},
pages = {025018},
doi = {10.1088/2632-2153/ad37f0},
year = {2024}
}
Although density functional theory (DFT) has aided in accelerating the discovery of new materials, such calculations are computationally expensive, especially for high-throughput efforts. This has prompted an explosion in exploration of machine learning (ML) assisted techniques to improve the computational efficiency of DFT. In this study, we present a comprehensive investigation of the broader application of Finetuna, an active learning framework to accelerate structural relaxation in DFT with prior information from Open Catalyst Project pretrained graph neural networks. We explore the challenges associated with out-of-domain systems: alcohol (C>2) on metal surfaces as larger adsorbates, metal oxides with spin polarization, and three-dimensional (3D) structures like zeolites and metal organic frameworks. By pre-training ML models on large datasets and fine-tuning the model along the simulation, we demonstrate the framework’s ability to conduct relaxations with fewer DFT calculations. Depending on the similarity of the test systems to the training systems, a more conservative querying strategy is applied. Our best-performing Finetuna strategy reduces the number of DFT single-point calculations by 80% for alcohols and 3D structures, and 42% for oxide systems.
Kurchin, R. C. (2024). Using Bayesian parameter estimation to learn more from data without black boxes. Nature Reviews Physics, 1–3.
@article{kurchin2024using,
title = {Using Bayesian parameter estimation to learn more from data without black boxes},
author = {Kurchin, Rachel C},
journal = {Nature Reviews Physics},
pages = {1--3},
year = {2024},
publisher = {Nature Publishing Group UK London},
doi = {10.1038/s42254-024-00698-0}
}
In an age of expensive experiments and hype around new data-driven methods, researchers understandably want to ensure they are gleaning as much insight from their data as possible. Rachel C. Kurchin argues that there is still plenty to be learned from older approaches without turning to black boxes.