About

Luke Achenie is a professor in the Department of Chemical Engineering at Virginia Tech. He holds a Ph.D. and an M.A.M. from Carnegie Mellon University, an M.S. from Northwestern University, and a B.S. from the Massachusetts Institute of Technology. His research interests include multi-scale molecular modeling using agent-based approaches, machine learning, blood-brain barrier modeling, pharmacokinetic modeling, uncertainty analysis, process design, and mathematical programming. He is based in Goodwin Hall at Virginia Tech, located at 635 Prices Fork Road, Blacksburg, VA 24061.

Research topics

• Computer Science
• Political Science
• Artificial Intelligence
• Knowledge management
• Chemistry
• Engineering management
• Engineering
• Data science
• Materials science
• Business

Selected publications

• Incorporating Mechanistic Insights into Structure–Property Modeling of Metal–Organic Frameworks for <i>H</i>₂ and <i>CH</i>₄ Adsorption: A CGCNN Approach

  Industrial & Engineering Chemistry Research · 2025-02-10 · 4 citations

  articleOpen accessCorresponding

  In this paper, we introduce a novel approach for understanding gas adsorption mechanisms in Metal–Organic Frameworks (MOFs) by combining a tailored Crystal Graph Convolutional Neural Network (AG-CGCNN) with game theory through Shapley Additive Explanations (SHAP). Our technique identifies the root causes of suboptimal performance in crystal structures through the analysis of softmax probabilities of performance outcomes, alongside the marginal contributions of crystalline and geometric features to these outcomes. Specifically, our approach discerns whether poor performance stems from inefficient atom–atom interactions indicating suboptimal gas molecule capture or from the subpar distribution of geometric characteristics, including surface area, pore limiting diameter (PLD), or largest cavity diameter (LCD). Additionally, the modeling framework adopted in this study clearly captures how intrinsic atomic features facilitate excess adsorption while volumetric spatial characteristics account for bulk absorption in complex porous MOF networks. Furthermore, we have introduced an innovative technique to quantitatively evaluate the amount of both excess gas absorbed within site interactions and bulk gas within the void spaces in a single MOF unit cell using a modified CGCNN regression model and SHAP. Additionally, we highlight the excellent performance of three 100 K-trained AG-CGCNN and demonstrate how its architecture can be used to determine the efficiency of a MOF gas capture system.

  PublisherDOI

• Examining generalizability of AI models for catalysis

  Journal of Catalysis · 2025-06-07 · 4 citations

  article
  PublisherDOI

• Decoding the Stability of Transition-Metal Alloys with Theory-infused Deep Learning

  ArXiv.org · 2025-06-03

  preprintOpen access

  We introduce an interpretable deep learning framework that predicts the cohesive energy of transition-metal alloys (TMAs) by embedding cohesion theory within graph neural networks (GNNs). Beyond accurate prediction of cohesive energy, a key indicator of thermodynamic stability, the model offers mechanistic insights by disentangling energy contributions into physically meaningful components. These data-driven interpretations reveal periodic trends and stability principles governing transition metals. We apply the model to single-atom alloys (SAAs) to assess their thermodynamic resilience against two destabilizing processes: agglomeration (adatom clustering) and segregation (migration into the subsurface). Our analysis shows that these phenomena are governed by distinct physical factors-agglomeration is primarily influenced by localized d-orbital coupling, while segregation is dictated by delocalized effects such as wavefunction renormalization. This model thus serves as an explainable AI tool for understanding and guiding the design of stable TMAs, with implications for catalysis and materials discovery.

  PublisherOA PDFDOI

• Digital twins for health: a scoping review

  npj Digital Medicine · 2024 · 533 citations

  • Computer Science
  • Political Science
  • Business

  The use of digital twins (DTs) has proliferated across various fields and industries, with a recent surge in the healthcare sector. The concept of digital twin for health (DT4H) holds great promise to revolutionize the entire healthcare system, including management and delivery, disease treatment and prevention, and health well-being maintenance, ultimately improving human life. The rapid growth of big data and continuous advancement in data science (DS) and artificial intelligence (AI) have the potential to significantly expedite DT research and development by providing scientific expertise, essential data, and robust cybertechnology infrastructure. Although various DT initiatives have been underway in the industry, government, and military, DT4H is still in its early stages. This paper presents an overview of the current applications of DTs in healthcare, examines consortium research centers and their limitations, and surveys the current landscape of emerging research and development opportunities in healthcare. We envision the emergence of a collaborative global effort among stakeholders to enhance healthcare and improve the quality of life for millions of individuals worldwide through pioneering research and development in the realm of DT technology.

  PublisherOA PDFDOI

• Unifying theory of electronic descriptors of metal surfaces upon perturbation

  Physical review. B./Physical review. B · 2024-09-16 · 5 citations

  articleOpen access

  We present a unifying theory for predicting electronic descriptors (e.g., the $d$-band center ${\ensuremath{\epsilon}}_{d}$) of transition and noble metal surfaces by interpretable deep learning. Distinct from black-box machine learning models, fundamental insights into underlying physical processes can be obtained without sacrificing prediction accuracy. In addition to the charge transfer, strain, and ligand effects in the conventional wisdom, we identified orbital resonance in $d$-electron hopping as a crucial factor that modulates the shape of the $d$-state distribution, thereby shifting ${\ensuremath{\epsilon}}_{d}$ of a $d$-metal site upon perturbation. Our findings reveal the promise of machine learning in advancing domain knowledge, paving the way toward theory-guided, data-driven design of materials beyond brute-force screening.

  PublisherOA PDFDOI

• Explainable AI for optimizing oxygen reduction on Pt monolayer core–shell catalysts

  Electrochemical Science Advances · 2024-03-11 · 8 citations

  articleOpen access

  Abstract As a subfield of artificial intelligence (AI), machine learning (ML) has emerged as a versatile tool in accelerating catalytic materials discovery because of its ability to find complex patterns in high‐dimensional data. While the intricacy of cutting‐edge ML models, such as deep learning, makes them powerful, it also renders decision‐making processes challenging to explain. Recent advances in explainable AI technologies, which aim to make the inner workings of ML models understandable to humans, have considerably increased our capacity to gain insights from data. In this study, taking the oxygen reduction reaction (ORR) on {111}‐oriented Pt monolayer core–shell catalysts as an example, we show how the recently developed theory‐infused neural network (TinNet) algorithm enables a rapid search for optimal site motifs with the chemisorption energy of hydroxyl (OH) as a single descriptor, revealing the underlying physical factors that govern the variations in site reactivity. By exploring a broad design space of Pt monolayer core–shell alloys ( candidates) that were generated from thermodynamically stable bulk structures in existing material databases, we identified novel alloy systems along with previously known catalysts in the goldilocks zone of reactivity properties. SHAP (SHapley Additive exPlanations) analysis reveals the important role of adsorbate resonance energies that originate from ‐band interactions in chemical bonding at metal surfaces. Extracting physical insights into surface reactivity with explainable AI opens up new design pathways for optimizing catalytic performance beyond active sites.

  PublisherOA PDFDOI

• Origin of unique electronic structures of single-atom alloys unraveled by interpretable deep learning

  The Journal of Chemical Physics · 2024-10-22 · 9 citations

  article

  We uncover the origin of unique electronic structures of single-atom alloys (SAAs) by interpretable deep learning. The approach integrates tight-binding moment theory with graph neural networks to accurately describe the local electronic structure of transition and noble metal sites upon perturbation. We emphasize the complex interplay of interatomic orbital coupling and on-site orbital resonance, which shapes the d-band characteristics of an active site, shedding light on the origin of free-atom-like d-states that are often observed in SAAs involving d10 metal hosts. This theory-infused neural network approach significantly enhances our understanding of the electronic properties of single-site catalytic materials beyond traditional theories.

  PublisherDOI

• Retraction of “A Perovskite Solar Cell with Enhanced Light Stability and High Photovoltaic Conversion Efficiencies”

  ACS Sustainable Chemistry & Engineering · 2024-06-20

  articleOpen accessSenior author

  ADVERTISEMENT RETURN TO ARTICLES ASAPPREVRetractionNEXTORIGINAL ARTICLEThis notice is a retraction.Retraction of "A Perovskite Solar Cell with Enhanced Light Stability and High Photovoltaic Conversion Efficiencies"Quan Yang*Quan YangMore by Quan Yanghttps://orcid.org/0000-0002-4130-2625, Riccardo DettoriRiccardo DettoriMore by Riccardo Dettori, and Luke E. K. AchenieLuke E. K. AchenieMore by Luke E. K. AchenieCite this: ACS Sustainable Chem. Eng. 2024, XXXX, XXX, XXX-XXXPublication Date (Web):June 20, 2024Publication History Received11 June 2024Published online20 June 2024https://pubs.acs.org/doi/10.1021/acssuschemeng.4c04799https://doi.org/10.1021/acssuschemeng.4c04799retractionACS Publications© 2024 American Chemical Society. This publication is available under these Terms of Use. Request reuse permissions This publication is free to access through this site. Learn MoreArticle Views-Altmetric-Citations-LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail PDF (554 KB) Get e-Alertsclose Get e-Alerts

  PublisherOA PDFDOI

• JARVIS-Leaderboard: a large scale benchmark of materials design methods

  npj Computational Materials · 2024-05-07 · 48 citations

  articleOpen access

  Abstract Lack of rigorous reproducibility and validation are significant hurdles for scientific development across many fields. Materials science, in particular, encompasses a variety of experimental and theoretical approaches that require careful benchmarking. Leaderboard efforts have been developed previously to mitigate these issues. However, a comprehensive comparison and benchmarking on an integrated platform with multiple data modalities with perfect and defect materials data is still lacking. This work introduces JARVIS-Leaderboard, an open-source and community-driven platform that facilitates benchmarking and enhances reproducibility. The platform allows users to set up benchmarks with custom tasks and enables contributions in the form of dataset, code, and meta-data submissions. We cover the following materials design categories: Artificial Intelligence (AI), Electronic Structure (ES), Force-fields (FF), Quantum Computation (QC), and Experiments (EXP). For AI, we cover several types of input data, including atomic structures, atomistic images, spectra, and text. For ES, we consider multiple ES approaches, software packages, pseudopotentials, materials, and properties, comparing results to experiment. For FF, we compare multiple approaches for material property predictions. For QC, we benchmark Hamiltonian simulations using various quantum algorithms and circuits. Finally, for experiments, we use the inter-laboratory approach to establish benchmarks. There are 1281 contributions to 274 benchmarks using 152 methods with more than 8 million data points, and the leaderboard is continuously expanding. The JARVIS-Leaderboard is available at the website: https://pages.nist.gov/jarvis_leaderboard/

  PublisherOA PDFDOI

• Unraveling Reactivity Origin of Oxygen Reduction at High-Entropy Alloy Electrocatalysts with a Computational and Data-Driven Approach

  The Journal of Physical Chemistry C · 2024-06-29 · 23 citations

  articleOpen access

  High-entropy alloys (HEAs), characterized as compositionally complex solid solutions with five or more metal elements, have emerged as a novel class of catalytic materials with unique attributes. Because of the remarkable diversity of multielement sites or site ensembles stabilized by configurational entropy, human exploration of the multidimensional design space of HEAs presents a formidable challenge, necessitating an efficient, computational and data-driven strategy over traditional trial-and-error experimentation or physics-based modeling. Leveraging deep learning interatomic potentials for large-scale molecular simulations and pretrained machine learning models of surface reactivity, our approach effectively rationalizes the enhanced activity of a previously synthesized PdCuPtNiCo HEA nanoparticle system for electrochemical oxygen reduction, as corroborated by experimental observations. We contend that this framework deepens our fundamental understanding of the surface reactivity of high-entropy materials and fosters the accelerated development and synthesis of monodisperse HEA nanoparticles as a versatile material platform for catalyzing sustainable chemical and energy transformations.

  PublisherDOI

Recent grants

• Collaborative Research: Large-Scale Optimization Strategies for Design Under Uncertainty

  NSF · $246k · 2005–2009

Frequent coauthors

• Hongliang Xin

  Virginia Tech

  15 shared

• G.M. Ostrovsky

  Kazan State Technological University

  15 shared

• Arunprakash T. Karunanithi

  University of Colorado Denver

  13 shared

• Rafiqul Gani

  12 shared

• Shih‐Han Wang

  Virginia Tech

  11 shared

• Quan Yang

  11 shared

• Manish Sinha

  11 shared

• Ivan Datskov

  8 shared