Grand Challenge Publications and Presentations

Using Machine Learning and Molecular Mechanics Calculations for Biological and Chemical Weapons Countermeasures

Heesung, S., Allen, J.E., and Bennet, W.F.D. (2024). “Enhancing Docking Accuracy with PECAN2, a 3D Atomic Neural Network Trained without Co-Complex Crystal Structures.” Machine Learning and Knowledge Extraction, 6(1), 642-657. doi: 10.3390/make6010030

Bennett, W. F. D., Bernardi, A., Ozturk, T. N., et al. (2024). “ezAlign: A Tool for Converting Coarse-Grained Molecular Dynamics Structures to Atomistic Resolution for Multiscale Modeling.” Molecules, 29(15), 3557. doi: 10.3390/molecules29153557

Jones, D., Zhang, X., Bennion, B.J., et al. (2024). “HDBind: Encoding of Molecular Structure with Hyperdimensional Binary Representations.” Scientific Reports, 14, 29025. doi: 10.1038/s41598-024-80009-w

Molecular Dynamics Simulations of Strength of Complex Metal Alloys

Bertin, N., Bulatov, V.V., and Zhou, Z. (2024). “Learning Dislocation Dynamics Mobility Laws from Large-Scale MD Simulations.” npj Computational Materials, 10, 192. doi: 10.1038/s41524-024-01378-4

Bertin, N., Cai, W., Aubry, S., et al. (2024). “Enhanced Mobility of Dislocation Network Nodes and its Effect on Dislocation Multiplication and Strain Hardening.” Acta Materialia, 271, 119884. doi: 10.1016/j.actamat.2024.119884

Bertin, N., Bulatov, V., and Zhou, F. (2024). “Graph Neural Network-Based Discrete Dislocation Dynamics.” Schöntal Symposium on Dislocation-Based Plasticity, 2024.

Bertin, N., Bulatov, V., and Zhou, F. (2024). “Learning Dislocation Dynamics with Graph Neural Networks.” IMSI Workshop on Data Sciences for Mesoscale and Macroscale Materials Models, 2024.

Bogenschutz, Peter

Ultra-High Resolution Climate Simulations for California

Zhang, J., Bogenschutz, P., Tang, Q., et al. (2024). “Leveraging Regional Mesh Refinement to Simulate Future Climate Projections for California Using the Simplified Convection-Permitting E3SM Atmosphere Model Version 0.” Geoscientific Model Development, 17(9), 3687-3731. doi: 10.5194/gmd-17-3687-2024

Zhang, J., Bogenschutz, P., Taylor, M., et al. (2024). "Pushing the Simplified Convection Permitting E3SM Atmosphere Model to 100m by Leveraging Regional Mesh Refinement Over the California Bay Area.” American Geophysical Union Meeting.

Correa, Alfredo

Nonperturbative Studies of Functional Materials Under Nonequilibrium Conditions - Phase II

Mo, M., Tamm, A., Metsanurk, E., et al. (2024). “Direct Observation of Strong Momentum-Dependent Electron-Phonon Coupling in a Metal.” Science Advances, 10(11). doi: 10.1126/sciadv.adk9051

Enhancing Subscale Modeling of Kinetics in High-Energy-Density Plasmas with Deep Learning

Rusby, D.R., Kemp, A.J., Wilks, S.C., et al. (2024). “Review and Meta-Analysis of Electron Temperatures from High-Intensity Laser–Solid Interactions.” Physics of Plasmas, 31(4), 040503. doi: 10.1063/5.0197279

Mariscal, D.A., Djordjevic, B.Z, Anirudh, R., et al. (2024). “Toward Machine-Learning-Assisted PW-Class High-Repetition-Rate Experiments with Solid Targets.” Physics of Plasmas, 31(7), 073105. doi: 10.1063/5.0190553

Draeger, Erik

Tracking Cancer Metastasis Using Optimized Ensembles of Densely-Packed Moving-Window Simulations

Tanade, C., Khan, N.S., Rakestraw, E., et al. (2024). “Establishing the Longitudinal Hemodynamic Mapping Framework for Wearable-Driven Coronary Digital Twins.” npj Digital Medicine, 7, 236. doi: 10.1038/s41746-024-01216-3

Martin, A., Liu, G., Joo, B., et al. (2024). “Designing a GPU-Accelerated Communication Layer for Efficient Fluid-Structure Interaction Computations on Heterogeneous Systems.” International Conference for High Performance Computing, Networking, Storage and Analysis, 1-20. doi: 10.1109/SC41406.2024.00099

Frolov, Timofey

Cracking in Additively Manufactured Tungsten

Hatton, P., Perez, D., Frolov, T., et al. (2024). “He Bubble-Induced Phase Transformation of W Grain Boundaries Revealed by Accelerated Molecular Dynamics.” Acta Materialia, 269, 119821. doi: 10.1016/j.actamat.2024.119821

Winter, I.S. and Frolov, T. (2024). “Phase Pattern Formation in Grain Boundaries.” Physical Review Letters, 132, 186204. doi: 10.1103/PhysRevLett.132.186204

Chen, E., Heo, T.W., Wood, B.C., et al. (2024). “Grand Canonically Optimized Grain Boundary Phases in Hexagonal Close-Packed Titanium.” Nature Communications, 15, 7049. doi: 10.1038/s41467-024-51330-9

Devulapalli, V., Chen, E., Brink, T., et al. (2024). “Topological Grain Boundary Segregation Transitions.” Science, 386 (6720), 420-424. doi: 10.1126/science.adq4147

Ingolfsson, Helgi

Ft Pathogen Countermeasure Design Using Machine Learning and Membrane Modeling

Bennett, W.F.D., Bernardi, A., Ozturk, T.N., et al. (2024). “ezAlign: A Tool for Converting Coarse-Grained Molecular Dynamics Structures to Atomistic Resolution for Multiscale Modeling.” Molecules, 29(15), 3557. doi: 10.3390/molecules29153557

Ozturk, T.N., König, M., Carpenter, T.S., et al. (2024). “Chapter Seven - Building Complex Membranes with Martini 3.” Methods in Enzymology, 701, 237-285. doi: 10.1016/bs.mie.2024.03.010

Ji, Kaihua

Three-Dimensional Phase-Field Modeling of Microstructural Pattern Formation during Rapid Alloy Solidification

Invited talk: Ji, K. (2024). “Quantitative Phase-Field Modeling of Rapid Alloy Solidification in Three Dimensions.” Computational Materials Science and Engineering, Gordon Research Seminar.

Predicting Reactivity of Multicomponent Polymeric Materials for Carbon Capture and Conversion

Marple, M., Li, S., Hunter-Sellars, E., et al. (2025). “Detecting Reactive Products in Carbon Capture Polymers with Chemical Shift Anisotropy and Machine Learning.” The Journal of Physical Chemistry, 129(5), 2701-2712. doi: 10.1021/acs.jpcc.4c07459

Li, S., Guta, Y., Calegari Andrade, M.F., et al. (2024). “Competing Kinetic Consequences of CO2 on the Oxidative Degradation of Branched Poly(ethylenimine). Journal of the American Chemical Society, 146(41), 28201-28213. doi: 10.1021/jacs.4c08126

Pham, Anh

Probing the Origin of Electrocatalyst Instability in Hydrogen Production Systems

Chen, B., Rowberg, A.J.E, Pham, T.A., et al. (2024). “Reactivity of Sulfur Vacancy-Rich MoS2 to Water Dissociation.” J. Phys. Chem. C, 128(25), 10379-10387. doi: 10.1021/acs.jpcc.4c01677

Kwon, H., Calegari Andrade, M.F., Ardo, S., et al. (2024). “Confinement Effects on Proton Transfer in TiO2 Nanopores from Deep Potential Molecular Dynamics Simulations.” ACS Appl. Mater. Interfaces 16(24), 31687-31695. doi: 10.1021/acsami.4c02339

Calegari Andrade, M.F., Aluru, N.R., and Pham, T.A. (2024). “Non-Linear Effects of Hydrophobic Confinement on the Electronic Structures and Dielectric Response of Water.” Journal of Physical Chemistry Lett. 15(26), 6872-6879. doi: 10.1021/acs.jpclett.4c01242

Pottier, Loïc

Machine Learning Driven Multiscale Simulations of RAS/RAF Activation

Oppelstrup, T., Stanton, L.G., Tempkin, J.O. B., et al. (2024). “Anisotropic Interactions for Continuum Modeling of Protein–Membrane Systems.” The Journal of Chemical Physics, 161(24), 244908. doi: 10.1063/5.0237408

Devarajan, H., Lumsden, I., Wang, C., et al. (2024). “DYAD: Locality-Aware Data Management for Accelerating Deep Learning Training.” 2024 IEEE 36th International Symposium on Computer Architecture and High-Performance Computing (SBAC-PAD), 13-24. doi: 10.1109/SBAC-PAD63648.2024.00010

Georgouli, K., Stephany, R.R., Tempkin, J.O., et al. (2024). “Generating Protein Structures for Pathway Discovery Using Deep Learning.” J. Chem. Theory Comput., 20(20), 8795-8806. doi: 10.1021/acs.jctc.4c00816

Shrestha, R., Carpenter, T.S., Van, Q.N., et al. (2024). “Membrane Lipids Drive Formation of KRAS4b-RAF1 RBDCRD Nanoclusters on the Membrane.” Communications Biology, 242. doi: 10.1038/s42003-024-05916-0

Riedel, William

Transport and Coupling of Short-Pulse Laser-Driven Particle Beams in the Fast Ignition Approach to Inertial Fusion Energy

Kemp, A.J., Wilks, S.C., Tabak, M. (2024). “Laser-to-Proton Conversion Efficiency Studies for Proton Fast Ignition.” Phys. Plasma, 31(4), 042709. doi:  10.1063/5.0191531

Verriere, Marc

High-Fidelity Fission Simulations for Nucleosynthesis

Bjelcic, A., and Schunck. N. (2025). “Computing the QRPA Level Density with the Finite Amplitude Method.” Computer Physics Communications, 306, 109387. doi: 10.1016/j.cpc.2024.109387

Li, T., and Schunck, N. (2024). “Numerical Convergence of Electromagnetic Responses with the Finite-Amplitude Method.” EPJ Web of Conferences, 292, 10001. doi: 10.1051/epjconf/202429210001

Li, T., Schunck, N., and Grosskopf, M. (2024). “Multipole Responses in Fissioning Nuclei and Their Uncertainties.” Physical Review C, 110, 034317. doi: 10.1103/PhysRevC.110.034317

Zurek, L., Bogner, S.K., Furnstahl, R.J., et al. (2024). “Optimized Nuclear Energy Density Functionals Including Long-Range Pion Contributions.” Physical Review C, 109, 014319. doi: 10.1103/PhysRevC.109.014319

Schunck, N., Verriere, M., Siwach, P., et al. (2024). “Microscopic Theory of Fission.” 5th Gogny Conference on Nuclear Structure and Reactions, 2024.

Verriere, M., and Schunck, N., (2024). “Fully Microscopic Description of Fission with Three Degrees of Freedom.” 5th Gogny Conference on Nuclear Structure and Reactions, 2024.

Verriere, M., and Schunck, N., (2024). “Fully Microscopic Description of Fission with Three Degrees of Freedom.” 3rd edition of the FIESTA School and Workshop, 2024.

Schunck, N., Verriere, M., Siwach, P., et al. (2024). “LLNL FPY Modeling and Evaluation.” CSWEG, BNL, 2024

Siwach, P., Verriere, M., and Schunck, N., (2024). “Impact of Collective Correlations on Fission Fragments Properties.” APS Division of Nuclear Physics Meeting, 2024.

Schunck, N., Verriere, M., Siwach, P., et al. (2024). “Predicting Initial Conditions of Fission Fragments.” 30th Nuclear Physics Workshop, 2024.

Gravitational Waves from Violent Dark Matter Transitions in a Supercomputer

Ayyar, V., Matsumoto, N., Meyer, A., et al. (2024). “Finite Temperature Transition in Hyper Stealth Dark Matter using Möbius Domain Wall Fermions.” Proceedings of the 41st International Symposium on Lattice Field Theory. doi: 10.48550/arXiv.2502.00331

Weitzner, Stephen

Taming the Unregulated Growth of Cathode-Electrolyte Interphases in Lithium-Ion Batteries

Kim, K., Adelstein, N., Dive, A., et al. (2024). “Probing Degradation at Solid-State Battery Interfaces Using Machine-Learning Interatomic Potential.” Energy Storage Materials, 73, 103842. doi: 10.1016/j.ensm.2024.103842

High Performance Computing-Aided Drug Discovery

Maciag, A.E., Stice, J., Wang, B., et al. (2024). “Abstract ND07: BBO-8520, a first-in-class, direct inhibitor of KRASG12C (ON), locks GTP-bound KRASG12C in the state 1 conformation resulting in rapid and complete blockade of effector binding.” Cancer Research, 84(7). doi: 10.1158/1538-7445.AM2024-ND07

Beltran, P., Dhirendra, S., Xu, R., et al. (2024). “Abstract RF02-02: BBO-10203, a first-in-class, orally bioavailable, selective covalent small molecule that inhibits RAS-driven PI3Kalpha activity without affecting glucose metabolism.” Cancer Research, 84(9). doi: 10.1158/1538-7445.SABCS23-RF02-02

Simanshu, D., Xu, R., Stice, J., et al. (2024). “9 Oral: BBO-10203, an orally bioavailable small molecule that disrupts the RAS-PI3Kα interaction leading to pAKT and tumor growth inhibition in models of breast, lung and colorectal cancer.” European Journal of Cancer, 211. doi: 10.1016/j.ejca.2024.114538

Morstein, J., Bowcut, V., Fernando, M., et al. (2024). “Targeting Ras-, Rho-, and Rab-family GTPases via a conserved cryptic pocket.” Cell, 187(22), 6379-6392.e6317. doi: 10.1016/j.cell.2024.08.017

Maciag, A.E., Stice, J.P., Wang, B., et al. (2025). “Discovery of BBO-8520, a First-In-Class Direct and Covalent Dual Inhibitor of GTP-Bound (ON) and GDP-Bound (OFF) KRASG12C.” Cancer Discovery. doi: 10.1158/2159-8290.CD-24-0840

A Phase 1a/1b Open-Label Study Evaluating the Safety, Tolerability, Pharmacokinetics, and Efficacy of BBO-8520 in Subjects with Advanced KRASG12C Mutant Non-Small Cell Lung Cancer - the ONKORAS-101 Study: Clinical trial. (2024). BridgeBio Oncology Therapeutics (TheRas. Inc.)

A Phase 1a/1b Study of the PI3Kα:RAS Breaker BBO-10203 in Subjects With Advanced Solid Tumors (The BREAKER-101 Study): Clinical trial. (2024). BridgeBio Oncology Therapeutics (TheRas, Inc.)

Heesung S, Allen J.E., and Bennet W.F.D. (2024). “Enhancing Docking Accuracy with PECAN2, a 3D Atomic Neural Network Trained without Co-Complex Crystal Structures.” Machine Learning & Knowledge Extraction, 6(1), 642 – 657. doi: 10.3390/make6010030

Stevenson, G.A., Kirshner D., Bennion B.J., et al. (2023). “Clustering Protein Binding Pockets and Identifying Potential Drug Interactions: A Novel Ligand-Based Featurization Method.” Journal of Chemical Information and Modeling, 63(21), 6655 – 6666. doi: 10.1021/acs.jcim.3c00722

Direct Comparison of Metal Strength Between Quantum Accurate MD Simulations and High-Strain Rate Experiments

Bertin, N., Carson, R., Bulatov, V. V., et al. (2023). “Crystal Plasticity Model of BCC Metals from Large-Scale MD Simulations.” Acta Materialia, 260, 119336. doi: 10.1016/j.actamat.2023.119336

Bertin. N., Cai, W., Aubry, S., et al. (2023). “Cross-Scale Modeling of Metal Plasticity: Dislocation Dynamics Insights from Large-Scale MD Simulations.” Proceedings of the 2023 Cairo Symposium on the Physics of Metal Plasticity.

Borges-Araújo, L., Borges-Araújo, A., Ozturk, T.N., et al. (2023). "Martini 3 Coarse-Grained Force Field for Cholesterol." Journal of Chemical Theory and Computation, 19(20), 7387 - 7404. doi:  10.1021/acs.jctc.3c00547

Carpenter, T.S. (2023). “Complex, Convoluted, Yet Consistent: Protein Induced Membrane Remodeling Around GPCRs.” Proceedings from the Biophysical Society Annual Meeting.

Carpenter, T. S. (2023). “What’s lipids got to do with it? What’s lipids but a second-hand emulsion?”​ University of Oxford.

Djordjević, B.Z., Kim, J., Wilks, S.C., et al. (2023). “Transfer Learning and Multi-Fidelity Modeling of Laser-Driven Particle Acceleration.” Physics of Plasmas, 30(4). doi: 10.1063/5.0139285

Djordjević, B.Z., Bremer, P.T., Williams, G.T., et al. (2023). “Application of Machine Learning to High-Repetition-Rate Laser-Plasma Physics on the Path to Inertial Fusion Energy.” Proceedings of the Sinteza 2023 - International Scientific Conference on Information Technology, Computer Science, and Data Science. doi: 10.15308/Sinteza-2023-2-8

Draeger, Erik

Martin, A., Liu, G., Ladd, W., et al. (2023). “Performance Evaluation of Heterogeneous GPU Programming Frameworks for Hemodynamic Simulations.” Proceedings of the SC ’23 Workshops of the International Conference on High Performance Computing, Network, Storage, and Analysis: ACM, 1126-1137. doi: 10.1145/3624062.3624188

Valero-Lara, P., Vetter, J., Gounley, J., et al. (2023). “Moment Representation of Regularized Lattice Boltzmann Methods on NVIDIA and AMD GPUs.” Proceedings of the SC23 Workshops of the International Conference on High Performance Computing, Network, Storage, and Analysis: ACM, 1697-1704. doi: 10.1145/3624062.3624250

Tanade, C., Rakestraw, E., Ladd, W., et al. (2023). “Cloud Computing to Enable Wearable-Driven Longitudinal Hemodynamic Maps.” Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis: ACM, 1-14. doi: 10.1145/3581784.3607101.

Roychowdhury, S., Mahmud, S., Martin, A., et al. (2023). “Enhancing Adaptive Physics Refinement Simulations Through the Addition of Realistic Red Blood Cell Counts.” Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis: ACM, 1-13. doi: 10.1145/3581784.3607105

Yousef, A.Z., Draeger, E.W., and Randles, A. (2023). “Low-Cost Post Hoc Reconstruction of HPC Simulations at Full Resolution.” 2023 IEEE 13th Symposium on Large Data Analysis and Visualization: IEEE, 17-21. doi: 10.1109/LDAV60332.2023.00009

Roychowdhury, S., Draeger, E.W., and Randles, A. (2023). “Establishing Metrics to Quantify Spatial Similarity in Spherical and Red Blood Cell Distributions.” Journal of Computational Science, 71, 102060. doi: 10.1016/j.jocs.2023.102060

Ladd, W., Jensen, C., Madhurima, V., et al. (2023). “Optimizing Cloud Computing Resource Usage for Hemodynamic Simulation.” 2023 IEEE International Parallel and Distributed Processing Symposium: IEEE, 568-578. doi: 10.1109/IPDPS54959.2023.00063

Computing Atomic Nuclei: Exploring the Universe Through a Nuclear Lens

Kravvaris, K., Navratil, P., Quaglioni, S., et al. (2023). "Ab Initio Informed Evaluation of the Radiative Capture of Protons on 7Be." Physics Letters B, 845, 138156. doi: 10.1016/j.physletb.2023.138156

Hebborn, C., Nunes, F.M., Potel, G., et al. (2023). "Optical Potentials for the Rare-Isotope Beam Era", Journal of Physics G: Nuclear and Particle Physics, 50(6), 060501. doi: 10.1088/1361-6471/acc348

Navratil, P., Kravvaris, K., Gysbers, P., et al. (2023). "Ab Initio Investigations of A=8 Nuclei: α–α Scattering, Deformation in 8He, Radiative Capture of Protons on 7Be and 7Li and the X17 Boson." Journal of Physics: Conference Series, 2586, 012062. doi: 10.1088/1742-6596/2586/1/012062

Kravvaris, K. (2023). "Three-Nucleon Force Effects and Uncertainties in Ab Initio Nuclear Reactions." APS Division of Nuclear Physics Meeting.

Kravvaris, K. (2023) "Ab Initio Calculations of Nuclear Reactions." University of Notre Dame, Department of Physics Nuclear Seminar.

Quaglioni, S. (2023). "Near-Term Quantum Simulation of Nuclear Dynamics." The 25th European Conference on Few-Body Problems in Physics.

Quaglioni, S. (2023). "Report From the Nuclear Structure, Reactions and Astrophysics Town Hall." APS Meeting.

Li, S., Calegari Andrade, M.F., Varni, A.J., et al. (2023). “Enhanced Hydrogen Bonding via Epoxide-Functionalization Restricts Mobility in Poly(ethylenimine) for CO₂ Capture.” Chemical Communications, 72(59) 10737 – 10740. doi: 10.1039/D3CC02702C

Govindarajan, N., Lin, T.Y., Roy, T., et al. (2023). “Coupling Microkinetics with Continuum Transport Models to Understand Electrochemical CO₂ Reduction in Flow Reactors.” PRX Energy, 2(3). doi: 10.1103/PRXEnergy.2.033010

Rodgers, A.J., Krischer, L., Afanasiev, M., et al. (2024). “Adjoint Waveform Tomography for Crustal and Upper Mantle Structure of the Middle East and Southwest Asia for Improved Waveform Simulations Using Openly Available Broadband Data.” Bulletin of the Seismological Society of America. doi: 10.1785/0120230248

Doody, C., Rodgers, A., Afanasiev, M., Boehm, C., et al. (2023). “Comparing Adjoint Waveform Tomography Models of California and Nevada Using Different Starting Models.” Journal of Geophysical Research: Solid Earth, 128(5). doi: 10.1029/2023JB026463

Doody, C., Rodgers, A., Afanasiev, M., et al. (2023). “CANVAS: An Adjoint Waveform Tomography Model of California and Nevada.” Journal of Geophysical Research: Solid Earth, 128(12). doi: 10.1029/2023JB027583

Chiang, A. (2023). “Regional Moment Tensor Inversion Using a Three-Dimensional Earth Model and its Application to the Western United States.” Berkeley Seismology Laboratory, University of California, Berkeley.

Doody, C. (2023). “Exploring the Earth Beneath our Feet: Seismic Modelling of California. University of California, Berkeley.

Doody, C., Rodgers, A., Afanasiev, M., et al. (2023). “The California-Nevada Adjoint Simulations (CANVAS) Model. Poster presentation, Southern California Earthquake Center Annual Meeting, 2023.

Doody, C., Rodgers, A., Afanasiev, M., et al. (2023). The California-Nevada Adjoint Simulations (CANVAS) Model. American Geophysical Union Fall Meeting.

Rodgers, A. (2023). “Adjoint Waveform Tomography Models for Improved Moment Tensor Inversions with Three-Dimensional Greens Functions and Sparse Regional Networks.” Air Force Technical Applications Center.

Rodgers, A. (2023). “Adjoint Waveform Tomography of the Crust and Upper Mantle Structure of the Western United States for Improved Seismic Simulations and Source Characterization.” University of Utah, Department of Geophysics.

Rodgers, A., Chiang, A., Simmons, N., et al. (2023). “High-Performance Computing for Improved 3D Seismic Models and Regional Distance Moment Tensors.” Comprehensive Nuclear Test Ban Treaty Organization, Workshop on High-Performance Computing, 2023.

Rodgers, A., Chiang, A. Simmons, N. (2023). “Adjoint Waveform Tomography Models for Improved Moment Tensor Inversions with Three-Dimensional Greens Functions and Sparse Regional Networks.” Comprehensive Nuclear Test Ban Treaty Organization, Science and Technology Conference, 2023.

Rodgers, A. and Fichtner, A. (2023). “Adjoint Waveform Tomography for Next Generation Waveform-Based Explosion Monitoring and Forensic Seismology.” American Geophysical Union Fall Meeting. 

High-fidelity Fission Simulations for Nucleosynthesis

Schunck, N., Verriere, M., Potel Aguilar, G., et al. (2023). “Microscopic Calculation of Fission Product Yields for Odd-Mass Nuclei.” Physical Review C, 107(4). doi: 10.1103/PhysRevC.107.044312

Appelquist, T., Brower, R.C., Cushman, K.K., et al. (2023). “Hidden Conformal Symmetry from the Lattice.” Physical Review D, 108(9). doi: 10.1103/PhysRevD.108.L091505

Pateloudis, S., Bergner, G., Hanada, M., et al. (2023). “Precision Test of Gauge/Gravity Duality in D0-Brane Matrix Model at Low Temperature.” Journal of High Energy Physics, 2023(71). doi: 10.1007/JHEP03(2023)071

Bulava, J., Hanlon, A.D., Horz, B., et al. (2023). “Elastic Nucleon-Pion Scattering at mπ = 200 MeV from Lattice QCD.” Nuclear Physics B, 987, 116105. doi: 10.1016/j.nuclphysb.2023.116105

Sun, W., Kim, N., Ebrahim, A.M, et al. (2024). “Coupled Experimental–Theoretical Characterization of a Carbon Electrode in Vanadium Redox Flow Batteries using X-ray Absorption Spectroscopy.” ASC Applied Materials & Interfaces, 16(17), 8791 – 8801. doi: 10.1021/acsami.3c17049

Sharma, A.K., Pei, J., Yang, Y., et al. (2024). “Revealing the Mechanism of Action of a First-in-Class Covalent Inhibitor of KRASG12C(ON) and Other Functional Properties of Oncogenic KRAS by ³¹P NMR.”   Journal of Biological Chemistry, 300(2), 105650. doi: 10.1016/j.jbc.2024.105650

Bennett, W.F.D., Fox, S.J., Sun, D., et al. (2022). “Bacterial Membranes Are More Perturbed by the Asymmetric Versus Symmetric Loading of Amphiphilic Molecules.” Membranes, 12(4). doi: 10.3390/membranes12040350

Ahn, D.H., Zhang, X., Mast, J., et al. (2022). “Scalable Composition and Analysis Techniques for Massive Scientific Workflows.” IEEE 18th International Conference on e-Science, 2022.

Djordjević, B.Z., Kim, J., Wilks, S.C., et al. (2022). “Transfer Learning and Multi-Fidelity Modeling of Laser-Driven Particle Acceleration.” APS DPP Annual Meeting, 2022.

Grace, E.S., Djordjević, B.Z., Guang, Z., et al. (2022). “Single-Shot Measurements of Pulse-Front Tilt in Intense ps Laser Pulses and its Effect on Accelerated Electron and Ion Beam Characteristics.” AIP Publishing, 93(12). doi: 10.1063/5.0101803

Swanson, K.K., Mariscal, D.A., Djordjević, B.Z., et al. (2022). “Applications of Machine Learning to a Compact Magnetic Spectrometer for High Repetition Rate, Laser-Driven Particle Acceleration.” AIP Publishing, 93(10). doi: 10.1063/5.0101857

Draeger, Erik

Roychowdhury, S., Draeger, E.W., and Randles, A. (2022). “Establishing Metrics to Quantify Underlying Structure in Vascular Red Blood Cell Distributions.” Computational Science – ICCS 2022, 13350. D. Groen, C. de Mulatier, M. Paszynski, V. V. Krzhizhanovskaya, J. J. Dongarra, and P. M. A. Sloot (Eds.), Lecture Notes in Computer Science, 13350, 79-91. Springer, Cham. doi: 10.1007/978-3-031-08751-6_7 (Best Paper Award)

Puleri, D.F., Roychowdhury, S., Balogh, P., et al. (2022). “High Performance Adaptive Physics Refinement to Enable Large-Scale Tracking of Cancer Cell Trajectory.” 2022 IEEE International Conference on Cluster Computing: IEEE, 230-242. doi: 10.1109/CLUSTER51413.2022.00036

Computing Atomic Nuclei: Exploring the Universe Through a Nuclear Lens

Hebborn, C., Hupin, G., Kravvaris, K., et al. (2022). “Ab Initio Prediction of the 4He(d,γ)6Li Big Bang Radiative Capture.” Physical Review Letters, 129(4). doi: 10.1103/PhysRevLett.129.042503

Hlophe, L., Kravvaris, K., Quaglioni, S. (2022). “Quantifying Uncertainties Due to Irreducible Three-Body Forces in Deuteron-nucleus Reactions.” Physical Review C, 107(1). doi: 10.1103/PhysRevC.107.014315

Atkinson, M.C., Navrátil, P., Hupin, G., et al. (2022). “Ab Initio Calculation of the β Decay from 11Be to a 10Be+p Resonance.” Physical Review C, 105(5). doi: 10.1103/PhysRevC.105.054316

High-Fidelity Fission Simulations

 Ney, E.M., Engel, J., Schunck, N. (2022). “Two-Body Weak Currents in Heavy Nuclei.” Physical Review C, 105(3). doi: 10.1103/PhysRevC.105.034349

Navarro Perez, R., Schunck, N. (2022). “Controlling Extrapolations of Nuclear Properties with Feature Selection.” Physics Letters B, 833. doi: 10.1016/j.physletb.2022.137336

Marević, P., Schunck, N., Ney, E.M., et al. (2022). “Axially-Deformed Solution of the Skyrme-Hartree-Fock-Bogoliubov Equations Using the Transformed Harmonic Oscillator Basis (IV) HFBTHO (v4.0): A New Version of the Program.” Computer Physics Communications, 276. doi: 10.1016/j.cpc.2022.108367

Schunck, N., Regnier, D. (2022). “Theory of Nuclear Fission.” Progress in Particle and Nuclear Physics, 125. doi: 10.1016/j.ppnp.2022.103963

Meyer, A., Berkowitz, E., Bouchard, C., et al. (2022). “Nucleon Axial Form Factor from Domain Wall on HISQ.” Proceedings of the 38th International Symposium on Lattice Field Theory. doi:  10.22323/1.396.0081

Bergner, G., Bodendorfer, N., Hanada, M., et al. (2022). “Confinement/Deconfinement Transition in the D0-Brane Matrix Model - A Signature of M-Theory?” Journal of High Energy Physics, 2022(96). doi: 10.1007/JHEP05(2022)096

Applequist, T., Brower, R.C., Cushman, K.K., et al. (2022). “Goldstone Boson Scattering with a Light Composite Scalar.” Physical Review D, 105(3). doi: 10.1103/PhysRevD.105.034505

Revealing RAS/RAF Activation through Massive Multiphysics Simulations

Ingólfsson, H.I., Neale, C., Carpenter, T.S., et al. (2022). “Machine Learning-Driven Multiscale Modeling Reveals Lipid-Dependent Dynamics of RAS Signaling Proteins.” Proceedings of National Academy of Sciences (PNAS), 119(1). doi: 10.1073/pnas.2113297119

Nguyen, K., Lopez, C.A., Neale, C., et al. (2022). “Exploring CRD Mobility During RAS/RAF Engagement at the Membrane.” Biophysical Journal, 121(19). doi: 10.1016/j.bpj.2022.06.035

Bhatia, H., Thiagarajan, J.J., Anirudh, R., et al. (2022). “A Biology-Informed Similarity Metric for Simulated Patches of Human Cell Membrane.” Machine Learning: Science and Technology, 3(3). doi: 10.1088/2632-2153/ac8523

Lopez, C.A., Zhang, X., Aydin, F., et al. (2022). “Asynchronous Reciprocal Coupling of Martini 2.2 Coarse-Grained and CHARMM36 All-Atom Simulations in an Automated Multiscale Framework.” Journal of Chemical Theory and Computation, 18(8), 5025 - 5045. doi: 10.1021/acs.jctc.2c00168

Accelerated Materials Simulations with Graph Neural Networks

Bertin, N., Zhou, F. (2023). “Accelerating discrete dislocation dynamics simulations with graph neural networks.” Computational Physics, 487. doi: 10.1016/j.jcp.2023.112180

Draeger, Erik

Gounley, J., Vardhan, M., Draeger, E.W., et al. (2021). “Propagation Pattern for Moment Representation of the Lattice Boltzmann Method.” IEEE Transactions on Parallel and Distributed Systems, 33(3), 642-653. doi: 10.1109/TPDS.2021.3098456

Balogh, P., Gounley, J., Roychowdhury, S., et al. (2021). “A Data-Driven Approach to Modeling Cancer Cell Mechanics During Microcirculatory Transport.” Scientific Reports, 11, 15232. doi: 10.1038/s41598-021-94445-5.

Kraus, R.G., Hemley, R.J., Ali, S.J., et al. (2022). “Measuring the Melting Curve of Iron at Super-Earth Core Conditions.” Science, 375(6577), 202 – 205. doi: 10.1126/science.abm1472 

Winter, I.S., Rudd, R.E., Oppelstrup T., et al. (2022). "Nucleation of Grain Boundary Phases." Physical Review Letters, 128(3). doi: 10.1103/PhysRevLett.128.035701

Frolov, T., Bertin, N., Oppelstrup, T. (2022). "Modeling Grain Boundary Mediated Plasticity with Massively Parallel Atomistic Simulations." The Minerals, Metals, & Materials Society, TMS 2022.

High-Fidelity Fission Simulations

Verriere, M., Schunck, N., Regnier, D. (2021). “Microscopic Calculation of Fission Product Yields with Particle-Number Projection.” Physical Review C, 103(5). doi: 10.1103/PhysRevC.103.054602

Marević, P., Schunck, N., Randrup, J., et al. (2021). “Angular Momentum of Fission Fragments from Microscopic Theory.” Physical Review C, 104(2). doi: 10.1103/PhysRevC.104.L021601

The Emergence of Nuclear and Dark Matter Structure and Interactions

 Miller, N., Bradley, G., Clark, M.A., et al. (2021). “The Hyperon Spectrum from Lattice QCD.” Proceedings of the 38th International Symposium on Lattice Field Theory, Lattice, 2021.

Meyer, A.S., Berkowitz, E., Bouchard, C., et al. (2021). “Nucleon Axial Form Factor from Domain Wall on HISQ.” Proceedings of the 38th International Symposium on Lattice Field Theory, Lattice, 2021.

Miller, N., Carpenter, L., Berkowitz, E., et al. (2021). “Scale Setting the Möbius Domain Wall Fermion on Gradient-Flowed HISQ Action Using the Omega Baryon Mass and the Gradient-Flow Scales t0 and w0.” Physical Review D, 103(5). doi: 10.1103/PhysRevD.103.054511

Brower, R.C., Cushman, K., Fleming, G.T., et al. (2021). “Stealth Dark Matter Confinement Transition and Gravitational Waves.” Physical Review D, 103(1).  doi: 10.1103/PhysRevD.103.014505 

Hörz, B., Howarth, D., Rinaldi, E., et al. (2021). “Two-Nucleon S-Wave Interactions at the SU(3) Flavor - Symmetric Point with Mud ~ ms: a First Lattice QCD Calculation with the Stochastic Laplacian Heaviside Method.” Physical Review C, 103(1). doi: 10.1103/PhysRevC.103.014003

Appelquist, T., Brower, R.C., Cushman, K.K., et al. (2021). “Near-Conformal Dynamics in a Chirally Broken System.” Physical Review D, 103(1). doi: 10.1103/PhysRevD.103.014504

Charity, R.J., Webb, T.B., Elson, J.M., et al. (2021). “Using Spin Alignment of Inelastically Excited Nuclei in Fast Beams to Assign Spins: The Spectroscopy of 13O as a Test Case.” Physical Review C, 104(2). doi: 10.1103/PhysRevC.104.024325

Novario, S., Gysbers, P., Engel, E., et al. (2021). “Coupled-Cluster Calculations of Neutrinoless Double-β Decay in 48Ca.” Physical Review Letters, 126(18). doi: 10.1103/PhysRevLett.126.182502

Draeger, Erik

Jensen, C., Ghorbannia, A., Urick, D., et al. (2024) “Predicting Downstream Aneurysmal Degeneration Following Type A Dissection Repair with Computational Fluid Dynamics.” Circulation, 150, no. Suppl 1.doi: 10.1161/circ.150.suppl_1.4117345

Vardhan, M., Gounley, J., Chen, E.C., et al. (2021). “Non-Invasive Characterization of Complex Coronary Lesions.” Scientific Reports, 11, 8145. doi: 10.1038/s41598-021-86360-6

Randles, A., Wirsching, H.G., Dean, J.A. et al. (2021). “Computational Modelling of Perivascular-Niche Dynamics for the Optimization of Treatment Schedules for Glioblastoma.” Nat Biomed Eng, 5, 346-359. doi: 10.1038/s41551-021-00710-3

Feiger, B., Gounley, J., Adler, D., et al. (2020). “Accelerating Massively Parallel Hemodynamic Models of Coarctation of the Aorta Using Neural Networks.” Scientific Reports, 10, 9508. doi: 10.1038/s41598-020-66225-0.

Feiger, B., Kochar, A., Gounley, J., et al. (2020). “Determining the Impacts of Venoarterial Extracorporeal Membrane Oxygenation on Cerebral Oxygenation Using a One-Dimensional Blood Flow Simulator.” Journal of Biomechanics, 104, 109707. doi: 10.1016/j.jbiomech.2020.109707

Bennett, W.F.D., He, S., Bilodeau, C., et al. (2020). “Predicting Small Molecule Transfer Free Energies by Combining Molecular Dynamics Simulations and Deep Learning.” Journal of Chemical Information and Modeling, 60(11), 5375 - 5381. doi: 10.1021/acs.jcim.0c00318

Sun, D., He, S., Bennett, W.F.D., et al. (2020). “Atomistic Characterization of Gramicidin Channel Formation.” Journal of Chemical Theory and Computation, 17(1), 7 – 12. doi: 10.1021/acs.jctc.0c00989

Sun, D., Peyear, T.A., Bennett, W.F.D., et al. (2020). “Assessing the Perturbing Effects of Drugs on Lipid Bilayers Using Gramicidin Channel-Based in Silico and In Vitro Assays.” Journal of Medical Chemistry, 63(20), 11809 – 11818. doi: 10.1021/acs.jmedchem.0c00958

Blumer M., Harris, S., Li, M., et al. (2020). “Simulations of Asymmetric Membranes Illustrate Cooperative Leaflet Coupling and Lipid Adaptability.” Frontiers in Cell Developmental Biology, 8. doi: 10.3389/fcell.2020.00575

Kemp, A.J., Wilks, S.C. (2020). “Direct Electron Acceleration in Multi-Kilojoule, Multi-Picosecond Laser Pulses.” Physics of Plasmas, 27(10). doi: 10.1063/5.0007159

First Principles Predictions of Phase Transitions in Magnetic Materials

Malone, F.D., Zhang, S., Morales, M. (2020). “Accelerating Auxiliary-Field Quantum Monte Carlo Simulations of Solids with Graphical Processing Units.” Journal of Chemical Theory and Computation, 16(7), 4286 – 4297. doi: 10.1021/acs.jctc.0c00262

Lee, J., Malone, F.D., Morales, M. (2020). “Utilizing Essential Symmetry Breaking in Auxiliary-Field Quantum Monte Carlo: Application to the Spin Gaps of the C36 Fullerene and an Iron Porphyrin Model Complex.” Journal of Chemical Theory and Computation, 16(5), 3019 – 3027. doi: 10.1021/acs.jctc.0c00055

Lee, J., Malone, F.D., Reichman, D. (2020). “The Performance of Phaseless Auxiliary-Field Quantum Monte Carlo on the Ground State Electronic Energy of Benzene.”  The Journal of Chemical Physics, 153(12). doi: 10.1063/5.0024835 

Malone, F.D., Benali, A., Morales, M., et al. (2020). “Systematic Comparison and Cross-Validation of Fixed-Node Diffusion Monte Carlo and Phaseless Auxiliary-Field Quantum Monte Carlo in Solids.” Physical Review B, 102(16). doi: 10.1103/PhysRevB.102.161104

Morales, M.A., and Malone, F.D. (2020). "Accelerating the Convergence of Auxiliary-Field Quantum Monte Carlo in Solids with Optimized Gaussian Basis Sets." The Journal of Chemical Physics, 153(19), 194111. doi: 10.1063/5.0025390

Nuclear Fission and Nucleosynthesis

Marević, P., Schunck, N. (2020). “Fission of 240Pu with Symmetry-Restored Density Functional Theory.” Physical Review Letters, 125(10). doi: 10.1103/PhysRevLett.125.102504

Schunck, N., O’Neal, J., Grosskopf, M., et al. (2020). “Calibration of Energy Density Functionals with Deformed Nuclei.” Journal of Physics G: Nuclear and Particle Physics, 47(7). doi: 10.1088/1361-6471/ab8745

Miller, N., Monge-Camacho, H., Cheng Chang, C., et al. (2020). “FK/Fπ from Mobius Domain-Wall Fermions Solved on Gradient-Flowed HISQ Ensembles.” Physical Review D, 102(3). doi: 10.1103/PhysRevD.102.034507

Kravvaris, K., Quinlan, K.R., Quaglioni, S., et al. (2020). “Quantifying Uncertainties in Neutron-α Scattering with Chiral Nucleon-Nucleon and Three-Nucleon Forces.” Physical Review C, 102(2). doi: 10.1103/PhysRevC.102.024616

Holland, E.T., Wendt, K.A., Konstantinos, K., et al. (2020). “Optimal Control for the Quantum Simulation of Nuclear Dynamics.” Physical Review A, 101(6). doi: 10.1103/PhysRevA.101.062307

Ingólfsson, H.I., Bhatia, H., Zeppelin, T., et al. (2020). “Capturing Biologically Complex Tissue-Specific Membranes at Different Levels of Compositional Complexity.” Journal of Physical Chemistry B, 124(36), 7819 – 7829. doi: 10.1021/acs.jpcb.0c03368   

Zhang, X., Sundram, S., Oppelstrup, T., et al. (2020). “ddcMD: A Fully GPU-Accelerated Molecular Dynamics Program for the Martini Force Field.” Journal of Chemical Physics, 153(4). doi: 10.1063/5.0014500

Goswami, D., Chen, D., Yang, Y., et al. (2020). “Membrane Interactions of the G-Domain and the HVR of KRAS4b Defines Its Unique Diffusion Behavior.” e-Life 9: e47654. doi: 10.7554/eLife.47654 

Roychowdhury, S., Gounley, J., and Randles, A. (2020). “Evaluating the Influence of Hemorheological Parameters on Circulating Tumor Cell Trajectory and Simulation Time.” Proceedings of the Platform for Advanced Scientific Computing Conference: ACM, 4, 1-10. doi: 10.1145/3394277.3401848

Ames, J., Puleri, D.F., Balogh, P., et al. (2020). “Multi-GPU Immersed Boundary Method Hemodynamics Simulations.” Journal of Computational Science, 44, 101153. doi: 10.1016/j.jocs.2020.101153

Vardhan, M., Gounley, J., Hegele, L., et al. (2019) “Moment representation in the Lattice Boltzmann Method on Massively Parallel Hardware.” Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis: ACM, 34, 1-21. doi: 10.1145/3295500.3356204

Herschlag, G., Gounley, J., Roychowdhury, S., et al. (2019) “Multi-Physics Simulations of Particle Tracking in Arterial Geometries with a Scalable Moving Window Algorithm.” 2019 IEEE International Conference on Cluster Computing: IEEE. doi: 10.1109/CLUSTER.2019.8891041

Gounley, J., Draeger, E.W., and Randles, A. (2019). “Immersed Boundary Method Halo Exchange in a Hemodynamics Application.” Computational Science – ICCS 2019, 11536, J. M. F. Rodrigues, P. J. S. Cardoso, J. Monteiro, R. Lam, V. V. Krzhizhanovskaya, M. H. Lees, J. J. Dongarra, and P. M. A. Sloot, Eds., in Lecture Notes in Computer Science, 11536. Cham: Springer International Publishing, 2019, 441-455. doi: 10.1007/978-3-030-22734-0_32

Gounley, J., Draeger, E.W., Oppelstrup, T., et al. (2019). “Computing the Ankle-Brachial Index with Parallel Computational Fluid Dynamics.” Journal of Biomechanics, 82, 28-37. doi: 10.1016/j.jbiomech.2018.10.007

Ames J., Rizzi, S., Insley, J., et al. (2019). “Low-Overhead in Situ Visualization Using Halo Replay.” 2019 IEEE 9th Symposium on Large Data Analysis and Visualization: IEEE, 16-26. doi: 10.1109/LDAV48142.2019.8944265

Higginson, D. P., Holod, I., Link, A. (2020). “A Corrected Method for Coulomb Scattering in Arbitrarily Weighted Particle-In-Cell Plasma Simulations.” Journal of Computational Physics, 413. doi: 10.1016/j.jcp.2020.109450

Higginson, D.P., Amendt, P., Meezan, N., et al. (2019). “Hybrid Particle-In-Cell Simulations of Laser-Driven Plasma Interpenetration, Heating, and Entrainment.” Physics of Plasmas, 26. doi: 10.1063/1.5110512

Higginson, D.P., Link, A., Schmidt, A. (2019). “A Pairwise Nuclear Fusion Algorithm for Weighted Particle-In-Cell Plasma Simulations.” Journal of Computational Physics, 388. doi: 10.1016/j.jcp.2019.03.020

Higginson, D.P., Ross, J.S., Ryutov, D.D., et al. (2019). “Kinetic Effects on Neutron Generation in Moderately Collisional Interpenetrating Plasma Flows.” Physics of Plasmas, 26, 012113. doi: 10.1063/1.5048386

Sun, D., Peyear, T.A., Bennett, W.F.D., et al. (2019). “Molecular Mechanism for Gramicidin Dimerization and Dissociation in Bilayers of Different Thickness.” Biophysical Journal, 117(10), 1831 – 1844. doi: 10.1016/j.bpj.2019.09.044

Guillaume, H., Quaglioni, S., Navrátil, P. (2019). “Ab Initio Predictions for Polarized Deuterium-Tritium Thermonuclear Fusion.” Nature Communications, 10, 351. doi: 10.1038/s41467-018-08052-6

Gysbers, P., Hagen, G., Holt, J.D., et al. (2019). “Discrepancy Between Experimental and Theoretical β-Decay Rates Resolved from First Principles.” Nature Physics, 15, 428 – 431. doi: 10.1038/s41567-019-0450-7

Gazit, D., Quaglioni, S., Navrátil, P. (2019).” Erratum: Three-Nucleon Low-Energy Constants from the Consistency of Interactions and Currents in Chiral Effective Field Theory.” Physical Review Letters, 103. doi: 10.1103/PhysRevLett.122.029901

Bonaccorso, A., Cappuzzello, F., Carbone, D., et al. (2019). “Application of an ab Initio S Matrix to Data Analysis of Transfer Reactions to the Continuum Populating 11Be.” Physical Review C, 100(2). doi: 10.1103/PhysRevC.100.024617

Regnier, D., Dubray, N., Schunck, N. (2019). “From Asymmetric to Symmetric Fission in the Fermium Isotopes Within the Time-Dependent Generator-Coordinate-Method Formalism.” Physical Review C, 99(2). doi: 10.1103/PhysRevC.99.024611

Matheson, Z., Giuliani S.A., Nazarewicz, W., et al. (2019). “Cluster Radioactivity of 294118Og176.” Physical Review C, 99(4). doi: 10.1103/PhysRevC.99.041304 

Fiuza, F., Swadling, G.F., Grassi, A., et al. (2020). “Electron Acceleration in Laboratory-Produced Turbulent Collisionless Shocks.” Nature Physics, 16, 916 – 920. doi: 10.1038/s41567-020-0919-4 

Joint Design of Advanced Computing Solutions for Cancer

Bhatia, H., Ingólfsson, H.I., Carpenter, T.S., et al. (2019). “MemSurfer: A Tool for Robust Computation and Characterization of Curved Membranes.” Journal of Chemical Theory and Computation, 15(11), 6411 – 6421. doi: 10.1021/acs.jctc.9b00453

Zhang, X, et al. (2019). "A Massively Parallel Infrastructure for Adaptive Multiscale Simulations: Modeling RAS Initiation Pathway for Cancer." Proceedings of The International Conference for High Performance Computing, SC19.

Zhang, M., Peyear, T.A., Patmanidis, I., et al. (2018). “Fluorinated Alcohols' Effects on Lipid Bilayer Properties.” Biophysical Journal, 115(4), 679 – 689. doi: 10.1016/j.bpj.2018.07.010

Bulgac, A., Forbes, M.M., Jin, S., et al. (2018). “Minimal Nuclear Energy Density Functional.” Physical Review C, 97(4). doi: 10.1103/PhysRevC.97.044313

Navarro Perez, R., Schunck, N., Dyhdalo, A., et al. (2018). ”Microscopically Based Energy Density Functionals for Nuclei Using the Density Matrix Expansion. II. Full Optimization and Validation.” Physical Review C, 97(5). doi: 10.1103/PhysRevC.97.054304

Regnier, D., Dubray, M., Verrière, M., et al. (2018). "FELIX-2.0: New version of the Finite Element Solver for the Time Dependent Generator Coordinate Method with the Gaussian Overlap Approximation."  Computer Physics Communications, 225, 180 - 191. doi: 10.1016/j.cpc.2017.12.007

Chang, C.C., Nicholson, A.N., Rinaldi, E., et al. (2018). "A Per-Cent-Level Determination of the Nucleon Axial Coupling from Quantum Chromodynamics." Nature, 558(7708), 91 - 94. doi: 10.1038/s41586-018-0161-8

Vranas, P., et at.(2018). “Simulating the Weak Death of the Neutron in a Femtoscale Universe with Near-Exascale Computing." Proceedings of The International Conference for High Performance Computing, SC18.

Joint Design of Advanced Computing Solutions for Cancer

Carpenter, T.S., Lopez, C.A., Neale, C., et al. (2018). “Capturing Phase Behavior of Ternary Lipid Mixtures with a Refined Martini Coarse-Grained Force Field.” Journal of Chemical Theory and Computation, 14(11), 6050 – 6062. doi: 10.1021/acs.jctc.8b00496

Travers, T., Lopez, C.A., Van, Q.N., et al. (2018). “Molecular Recognition of RAS/RAF Complex at the Membrane: Role of RAF Cysteine-Rich Domain.” Scientific Reports, 8, 8461. doi: 10.1038/s41598-018-26832-4

Neale, C., Garcia, A.E. (2018). “Methionine 170 is an Environmentally Sensitive Membrane Anchor in the Disordered HVR of K-Ras4B.” Journal of Physical Chemistry B, 122(44). doi: 10.1021/acs.jpcb.8b07919

Bulatov, V., et al. (2017). "Dynamic Disclocations: Atomic- Scale Simulations Reveal How Crystals Flow Under Stress." Cover of Nature, 550(7677).

Ingólfsson, H.I., Carpenter, T.S., Bhatia, H., et al. (2017). “Computational Lipidomics of the Neuronal Plasma Membrane.” Biophysical Journal, 113(10), 2271 – 2280. doi: 10.1016/j.bpj.2017.10.017