Learning from Failure: Predicting Electronic Structure Calculation Outcomes with Machine Learning Models

Duan, Chenru; Janet, Jon Paul; Liu, Fang; Nandy, Aditya; Kulik, Heather J.

doi:10.1021/acs.jctc.9b00057

Cited by 87Machine Learning in Materials ScienceAdvanced Chemical Physics StudiesComputational Drug Discovery Methods

Learning from Failure: Predicting Electronic Structure Calculation Outcomes with Machine Learning Models

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Duan, Chenru, Janet, Jon Paul, Liu, Fang, Nandy, Aditya, Kulik, Heather J.·2019·Journal of Chemical Theory and Computation

DOI 10.1021/acs.jctc.9b00057

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Abstract

High-throughput computational screening for chemical discovery mandates the automated and unsupervised simulation of thousands of new molecules and materials. In challenging materials spaces, such as open shell transition metal chemistry, characterization requires time-consuming first-principles simulation that often necessitates human intervention. These calculations can frequently lead to a null result, e.g., the calculation does not converge or the molecule does not stay intact during a geometry optimization. To overcome this challenge toward realizing fully automated chemical discovery in transition metal chemistry, we have developed the first machine learning models that predict the likelihood of successful simulation outcomes. We train support vector machine and artificial neural network classifiers to predict simulation outcomes (i.e., geometry optimization result and degree of deviation) for a chosen electronic structure method based on chemical composition. For these static models, we achieve an area under the curve of at least 0.95, minimizing computational time spent on non- productive simulations and therefore enabling efficient chemical space exploration. We introduce a metric of model uncertainty based on the distribution of points in the latent space to systematically improve model prediction confidence. In a complementary approach, we train a convolutional neural network classification model on simulation output electronic and geometric structure time series data. This dynamic model generalizes more readily than the static classifier by becoming more predictive as input simulation length increases. Finally, we describe approaches for using these models to enable autonomous job control in transition metal complex discovery.

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Each discrete DFT calculation requires considerable computational effort and can lead to failed outcomes if the DFT calculation of the new, hypothetical material does not converge.
“However, each discrete calculation requires considerable computational effort and can lead to failed outcomes if the DFT calculation of the new, hypothetical material does not converge (49) (Figure 2).”
From Machine Learning for the Discovery, Design, and Engineering of Materials. (2022) · cites this paper as [49]

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Representations in neural network based empirical potentials
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General Approach to Estimate Error Bars for Quantitative Structure–Activity Relationship Predictions of Molecular Activity
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Internal force corrections with machine learning for quantum mechanics/molecular mechanics simulations
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Uncertainty estimation and misclassification probability for classification models based on discriminant analysis and support vector machines
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Spin-state diversity in a series of Co( ii ) PNP pincer bromide complexes
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BASC: applying Bayesian optimization to the search for global minima on potential energy surfaces
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Computational strategies to probe CH activation in dioxo-dicopper complexes
Lan, Z., & Sharada, S. M. (2018) · Physical Chemistry Chemical Physics · doi:10.1039/c8cp05096a · Cited by 13 · see citing works

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Big-Data Science in Porous Materials: Materials Genomics and Machine Learning
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Machine Learning for Catalysis Informatics: Recent Applications and Prospects
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Autonomous Discovery in the Chemical Sciences Part I: Progress
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Roadmap on Machine learning in electronic structure
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tmQM Dataset─Quantum Geometries and Properties of 86k Transition Metal Complexes
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MOFSimplify, machine learning models with extracted stability data of three thousand metal–organic frameworks
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Machine Learning for the Discovery, Design, and Engineering of Materials.
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Large-scale comparison of 3d and 4d transition metal complexes illuminates the reduced effect of exchange on second-row spin-state energetics
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Machine learning to tame divergent density functional approximations: a new path to consensus materials design principles
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Synergistic Approach of Ultrafast Spectroscopy and Molecular Simulations in the Characterization of Intramolecular Charge Transfer in Push-Pull Molecules
Patrizi, B., Cozza, C., Pietropaolo, A., Foggi, P., & de Cumis, M. S. (2020) · Molecules · doi:10.3390/molecules25020430 · Cited by 42 · see citing works
Active Learning Exploration of Transition-Metal Complexes to Discover Method-Insensitive and Synthetically Accessible Chromophores
Duan, C., Nandy, A., Terrones, G., Kastner, D. W., & Kulik, H. J. (2022) · JACS Au · doi:10.1021/jacsau.2c00547 · Cited by 39 · see citing works
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Computational Discovery of Stable Metal–Organic Frameworks for Methane-to-Methanol Catalysis
Adamji, H., Nandy, A., Kevlishvili, I., Román‐Leshkov, Y., & Kulik, H. J. (2023) · Journal of the American Chemical Society · doi:10.1021/jacs.3c03351 · Cited by 35 · see citing works
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Semi-supervised Machine Learning Enables the Robust Detection of Multireference Character at Low Cost
Duan, C., Liu, F., Nandy, A., & Kulik, H. J. (2020) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.0c02018 · Cited by 35 · see citing works
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Quantum chemistry assisted by machine learning
Dral, P. O. (2020) · Advances in quantum chemistry · doi:10.1016/bs.aiq.2020.05.002 · Cited by 34 · see citing works
A transferable recommender approach for selecting the best density functional approximations in chemical discovery
Duan, C., Nandy, A., Meyer, R., Arunachalam, N., & Kulik, H. J. (2022) · Nature Computational Science · doi:10.1038/s43588-022-00384-0 · Cited by 31 · see citing works
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Exchange Spin Coupling from Gaussian Process Regression
Bahlke, M. P., Mogos, N., Proppe, J., & Herrmann, C. (2020) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.0c05983 · Cited by 27 · see citing works
Automated Transition Metal Catalysts Discovery and Optimisation with AI and Machine Learning
Macé, S., Xu, Y., & Nguyen, B. N. (2024) · ChemCatChem · doi:10.1002/cctc.202301475 · Cited by 27 · see citing works
Automated Construction and Optimization Combined with Machine Learning to Generate Pt(II) Methane C–H Activation Transition States
Chen, S., Nielson, T., Zalit, E., Skjelstad, B. B., Borough, B., Hirschi, W. J., Yu, S., Balcells, D., & Ess, D. H. (2021) · Topics in Catalysis · doi:10.1007/s11244-021-01506-0 · Cited by 24 · see citing works
Modeling the roles of rigidity and dopants in single-atom methane-to-methanol catalysts
Jia, H., Nandy, A., Liu, M., & Kulik, H. J. (2022) · Journal of Materials Chemistry A · doi:10.1039/d1ta08502f · Cited by 26 · see citing works
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Generative Design of Functional Metal Complexes Utilizing the Internal Knowledge and Reasoning Capability of Large Language Models
Lü, J., Song, Z., Zhao, Q., Du, Y., Cao, Y., Jia, H., & Duan, C. (2025) · Journal of the American Chemical Society · doi:10.1021/jacs.5c02097 · Cited by 36 · see citing works
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Detection of multi-reference character imbalances enables a transfer learning approach for virtual high throughput screening with coupled cluster accuracy at DFT cost
Duan, C., Chu, D. B. K., Nandy, A., & Kulik, H. J. (2022) · Chemical Science · doi:10.1039/d2sc00393g · Cited by 26 · see citing works
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Towards a machine learned thermodynamics: exploration of free energy landscapes in molecular fluids, biological systems and for gas storage and separation in metal–organic frameworks
Desgranges, C., & Delhommelle, J. (2020) · Molecular Systems Design & Engineering · doi:10.1039/d0me00134a · Cited by 22 · see citing works
Data Mining the C−C Cross‐Coupling Genome
Sawatlon, B., Wodrich, M. D., Meyer, B., Fabrizio, A., & Corminbœuf, C. (2019) · ChemCatChem · doi:10.1002/cctc.201900597 · Cited by 21 · see citing works
Representations and strategies for transferable machine learning improve model performance in chemical discovery
Harper, D. R., Nandy, A., Arunachalam, N., Duan, C., Janet, J. P., & Kulik, H. J. (2022) · The Journal of Chemical Physics · doi:10.1063/5.0082964 · Cited by 22 · see citing works
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Reducing training data needs with minimal multilevel machine learning (M3L)
Heinen, S., Khan, D., von Rudorff, G. F., Karandashev, K., Arismendi‐Arrieta, D. J., Price, A. J. A., Nandi, S., Bhowmik, A., Hermansson, K., & von Lilienfeld, O. A. (2024) · Machine Learning Science and Technology · doi:10.1088/2632-2153/ad4ae5 · Cited by 18 · see citing works
Exploiting Ligand Additivity for Transferable Machine Learning of Multireference Character across Known Transition Metal Complex Ligands
Duan, C., Ladera, A., Liu, J. C. L., Taylor, M. G., Ariyarathna, I. R., & Kulik, H. J. (2022) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.2c00468 · Cited by 18 · see citing works
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Machine Learning Models Predict Calculation Outcomes with the Transferability Necessary for Computational Catalysis
Duan, C., Nandy, A., Adamji, H., Román‐Leshkov, Y., & Kulik, H. J. (2022) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.2c00331 · Cited by 18 · see citing works
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TS‐tools: Rapid and automated localization of transition states based on a textual reaction SMILES input
Stuyver, T. (2024) · Journal of Computational Chemistry · doi:10.1002/jcc.27374 · Cited by 17 · see citing works
Computational Exploration of Codoped Fe and Ru Single-Atom Catalysts for the Oxygen Reduction Reaction
Jia, H., Duan, C., Terrones, G., Kevlishvili, I., & Kulik, H. J. (2025) · Journal of Catalysis · doi:10.1016/j.jcat.2025.116163 · Cited by 17 · see citing works
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AABBA Graph Kernel: Atom-Atom, Bond-Bond, and Bond-Atom Autocorrelations for Machine Learning
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Deep Learning-Based Modelling of Complex Photonic Crystal Slow Light Waveguides
Chen, J., Hao, R., Nasidi, I., Zhang, H., Wang, X., & Jin, S. (2023) · IEEE Journal of Selected Topics in Quantum Electronics · doi:10.1109/jstqe.2023.3305450 · Cited by 12 · see citing works
Autonome Entdeckung in den chemischen Wissenschaften, Teil I: Fortschritt
Coley, C. W., Eyke, N. S., & Jensen, K. F. (2019) · Angewandte Chemie · doi:10.1002/ange.201909987 · Cited by 12 · see citing works
Quantum Chemistry Meets Machine Learning
Fabrizio, A., Meyer, B., Fabregat, R., & Corminbœuf, C. (2019) · CHIMIA International Journal for Chemistry · doi:10.2533/chimia.2019.983 · Cited by 11 · see citing works
High-throughput computational workflow for ligand discovery in catalysis with the CSD
Short, M. A. S., Tovee, C. A., Willans, C. E., & Nguyen, B. N. (2023) · Catalysis Science & Technology · doi:10.1039/d3cy00083d · Cited by 10 · see citing works
Machine learning methods in photochemistry and photophysics
Li, J., Vacher, M., Dral, P. O., & Lopez, S. A. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-91738-4.00002-6 · Cited by 10 · see citing works
What's Left for a Computational Chemist To Do in the Age of Machine Learning?
Kulik, H. J. (2021) · Israel Journal of Chemistry · doi:10.1002/ijch.202100016 · Cited by 11 · see citing works
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Compressing physics with an autoencoder: Creating an atomic species representation to improve machine learning models in the chemical sciences
Herr, J. E., Koh, K. J., Yao, K., & Parkhill, J. (2019) · The Journal of Chemical Physics · doi:10.1063/1.5108803 · Cited by 31 · see citing works
The role of atomic-level understanding in optimizing lithium titanate oxide based anodes for lithium-ion batteries
Shahid, E., & Majid, A. (2025) · Materials Advances · doi:10.1039/d5ma01027f · Cited by 9 · see citing works
A Multitask Learning Model for Predicting Various Types of Metal–Organic Framework Stability
Zhang, S., Wang, Z., Gao, H., & Zhou, T. (2025) · Industrial & Engineering Chemistry Research · doi:10.1021/acs.iecr.5c01161 · Cited by 7 · see citing works
Bayesian Analysis Reveals the Key to Extracting Pair Potentials from Neutron Scattering Data
Shanks, B. L., Sullivan, H. W., & Hoepfner, M. P. (2024) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.4c02941 · Cited by 7 · see citing works
Ligand Many-Body Expansion as a General Approach for Accelerating Transition Metal Complex Discovery
Chu, D. B. K., Gonzalez-Narvaez, D. A., Meyer, R., Nandy, A., & Kulik, H. J. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c01728 · Cited by 6 · see citing works
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Insights into the deviation from piecewise linearity in transition metal complexes from supervised machine learning models
Cytter, Y., Nandy, A., Duan, C., & Kulik, H. J. (2023) · Physical Chemistry Chemical Physics · doi:10.1039/d3cp00258f · Cited by 5 · see citing works
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Machine learning approach to predict L -edge x-ray absorption spectra of light transition metal ion compounds
Lüder, J. (2025) · Physical review. B./Physical review. B · doi:10.1103/physrevb.111.085110 · Cited by 10 · see citing works
Time Frequency Representations and Deep Convolutional Neural Networks: A Recipe for Molecular Properties Prediction
Tchagang, A., & Valdés, J. J. (2021) · Conference proceedings - Canadian Conference on Electrical and Computer Engineering · doi:10.1109/ccece53047.2021.9569072 · Cited by 4 · see citing works
Large-scale comparison of Fe and Ru polyolefin C–H activation catalysts
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A machine learning approach to predict L-edge x-ray absorption spectra of light transition metal ion compoundsPreprint
Lüder, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.13149 · Cited by 3 · see citing works
Many-body expansion based machine learning models for octahedral transition metal complexes
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A robust solver for wavefunction-based density functional theory calculations*
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Artificial intelligence in catalysis
Rangarajan, S. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-99135-3.00002-6 · Cited by 2 · see citing works
Molecular Design Using Signal Processing and Machine Learning: Time-Frequency-like Representation and Forward DesignPreprint
Tchagang, A., Tewfik, A. H., & Valdés, J. J. (2021) · Research Square · doi:10.21203/rs.3.rs-229094/v1 · Cited by 2 · see citing works
Machine learning for sustainable development in electronics
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Representative Random Sampling of Chemical Space
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A generic model for mixture adsorption property prediction (MAPP): Toward efficient discovery of metal-organic frameworks for CO2 capture
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Authors: Duan, C., Janet, J. P., Liu, F., Nandy, A., & Kulik, H. J.
Affiliations: Massachusetts Institute of Technology (US)
Corresponding author: Kulik, Heather J.
ORCID: Duan, Chenru
Janet, Jon Paul
Liu, Fang
Nandy, Aditya
Kulik, Heather J.
Venue: Journal of Chemical Theory and Computation
Publisher: American Chemical Society (ACS)
Published: 2019-03-12
Volume: 15
Issue: 4
Pages: 2331-2345
ISSN: 1549-9618 (ISSN-L)
1549-9626
DOI: 10.1021/acs.jctc.9b00057
OpenAlex: W2921706278
Cited by: 87
References: 111
Citations by year: 2026: 5 · 2025: 7 · 2024: 11 · 2023: 7 · 2022: 14 · 2021: 10 · 2020: 20 · 2019: 12 · 2018: 1
FWCI: 5.06
Type: journal article
Language: en
Open access: green
doi.org
Licence: cc-by-nc-nd
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
doi.org/10.15223/policy-045 (stm-asf)
Locations: Journal of Chemical Theory and Computation (publishedVersion)
ChemRxiv (acceptedVersion, cc-by-nc-nd)
PubMed (publishedVersion)
DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
Royal College of Surgeons in Ireland (submittedVersion, cc-by-nc-nd)
Funders: Office of Naval Research (N00014-17-1-2956, N00014-18-1-2434)
National Science Foundation (ACI-1429830, ACI-1548562)
Defense Advanced Research Projects Agency (D18AP00039)
Topics: Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
Keywords: high-throughput computational screening, model uncertainty, convolutional neural networks, support vector machine, artificial neural networks, chemical space exploration

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Learning from Failure: Predicting Electronic Structure Calculation Outcomes with Machine Learning Models

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