About

Jim Pfaendtner is a professor and associate faculty member of the Department of Chemistry at NC State University. His research focuses on areas including computational and theoretical chemistry, green chemistry, medicinal chemistry, inorganic, organic, physical chemistry, and energy-related chemical research. He is involved in various research centers and facilities such as the Molecular Education, Technology and Research Innovation Center (METRIC) and the ORaCEL Shared X-/Q-band Pulse EPR Spectrometer, supporting advanced research in chemical sciences. Pfaendtner's work emphasizes the development and application of computational methods to address complex chemical problems, contributing to the advancement of sustainable and innovative chemical solutions.

Research topics

• Materials science
• Chemistry
• Crystallography
• Organic chemistry
• Computational chemistry
• Chemical engineering
• Physics
• Nanotechnology
• Biochemistry
• Physical chemistry
• Chemical physics
• Stereochemistry
• Composite material
• Engineering

Selected publications

• Are Energy and Forces Really Enough? Using Structure to Evaluate the Accuracy and Transferability of Machine Learning Potentials of Biomolecules

  Research Square · 2026-01-14

  preprintOpen accessSenior author
  PublisherOA PDFDOI

• A Force-Kernel Reformulation of the Extended-System Adaptive Biasing Force for Free-Energy Calculations

  ArXiv.org · 2026-05-20

  articleOpen accessSenior author

  We introduce force-kernel extended-system adaptive biasing force (FK-eABF), a force-based kernel reformulation of eABF that replaces the histogram-based mean-force accumulator of conventional eABF with a sparse population of Gaussian kernels storing local running-mean forces. Biasing forces are recovered by Nadaraya-Watson regression, yielding smooth estimates from the earliest stages of a simulation without a minimum-count threshold, while the same kernel population also defines an auxiliary, self-attenuating exploration force that requires no prior knowledge of barrier heights. On N-acetyl-N'-methylalanylamide in explicit water, FK-eABF achieves full free-energy landscape coverage faster than well-tempered metadynamics (WT-MetaD), on-the-fly probability enhanced sampling (OPES), and WTM-eABF, while all four methods converge to comparable accuracy given sufficient time. FK-eABF also retains long-time accuracy: on the DFG-in/out transition of Abl1 kinase, multi-microsecond simulations recover the established near-isoenergetic balance between states. At the opposite extreme, applied to the electrocyclic ring closure of 1,3-butadiene at the ab initio molecular dynamics level, FK-eABF recovers the free-energy landscape within 30 ps. Together, these benchmarks, spanning more than four orders of magnitude in simulation time, establish FK-eABF as more than a kernelized implementation of eABF: A force-based kernel reformulation that delivers faster early-time convergence without sacrificing long-time quantitative accuracy.

  PublisherOA PDF

• A Force-Kernel Reformulation of the Extended-System Adaptive Biasing Force for Free-Energy Calculations

  arXiv (Cornell University) · 2026-05-20

  preprintOpen accessSenior author

  We introduce force-kernel extended-system adaptive biasing force (FK-eABF), a force-based kernel reformulation of eABF that replaces the histogram-based mean-force accumulator of conventional eABF with a sparse population of Gaussian kernels storing local running-mean forces. Biasing forces are recovered by Nadaraya-Watson regression, yielding smooth estimates from the earliest stages of a simulation without a minimum-count threshold, while the same kernel population also defines an auxiliary, self-attenuating exploration force that requires no prior knowledge of barrier heights. On N-acetyl-N'-methylalanylamide in explicit water, FK-eABF achieves full free-energy landscape coverage faster than well-tempered metadynamics (WT-MetaD), on-the-fly probability enhanced sampling (OPES), and WTM-eABF, while all four methods converge to comparable accuracy given sufficient time. FK-eABF also retains long-time accuracy: on the DFG-in/out transition of Abl1 kinase, multi-microsecond simulations recover the established near-isoenergetic balance between states. At the opposite extreme, applied to the electrocyclic ring closure of 1,3-butadiene at the ab initio molecular dynamics level, FK-eABF recovers the free-energy landscape within 30 ps. Together, these benchmarks, spanning more than four orders of magnitude in simulation time, establish FK-eABF as more than a kernelized implementation of eABF: A force-based kernel reformulation that delivers faster early-time convergence without sacrificing long-time quantitative accuracy.

  PublisherDOI

• SPARC: An Automated Workflow Toolkit for Accelerated Active Learning of Reactive Machine Learning Interatomic Potentials

  The Journal of Open Source Software · 2026-05-06

  articleOpen accessSenior author

  Verma et al., (2026). SPARC: An Automated Workflow Toolkit for Accelerated Active Learning of Reactive Machine Learning Interatomic Potentials. Journal of Open Source Software, 11(121), 9468, https://doi.org/10.21105/joss.09468

  PublisherOA PDFDOI

• Toward Computation-Guided Design of Tunable Organic–Inorganic CdS Quantum Dot Binary Superlattices

  Nano Letters · 2025-02-25 · 3 citations

  articleOpen access

  Combining the advantages of structural programmability in sequence-defined biomimetic molecules and the controllable packing geometry in nanoparticle superlattices, we demonstrate a self-assembled organic–inorganic superlattice whose structure can be altered with the slightest change in the sequence of the organic counterpart. Here, oleate-coated CdS quantum dots (QDs) form a square-packed superlattice with a 1:1 molar equivalence of a diblock amphiphilic peptoid (Nbrpe6Dig) in chloroform. In contrast, no apparent structure is observed in the organic solvent alone. Based on theoretical evidence, we show that the assembly is a binary superlattice where both the CdS QDs and the peptoids serve as building blocks and further predict a correlation between the superlattice structure and the peptoid sequence. The computationally guided prediction is validated by experiments where superlattice transformation is observed with modified peptoids. The mechanism identified in our work inspires new ways to control and tune organic–inorganic hybrid nanomaterial self-assembly.

  PublisherOA PDFDOI

• A Platform Approach for Designing Sustainable Indole Thiosemicarbazone Corrosion Inhibitors with Enhanced Adsorption Properties

  Langmuir · 2025-03-24 · 4 citations

  articleOpen access

  With an estimated global cost of $2.5 trillion per year, metal corrosion represents a major challenge across all industrial sectors. Numerous inorganic and organic corrosion inhibitors have been developed, but there are growing concerns about their toxicity and impact on the environment. Here, superior organic corrosion inhibitors based on indole-3-carboxaldehyde, a compound commonly found in the digestive system, and thiosemicarbazones, a safe class of ligands, were designed and studied for mild steel in pH 1 sulfuric acid solutions. Electroanalytical techniques and gravimetric tests revealed inhibition efficiencies as high as 98.9% at 30 °C. Models using Langmuir isotherms gave adsorption equilibrium constants Kads of 2 to 9 × 104 M–1 and corresponding Gibbs free energies of adsorption (ΔGads) as high as −41.44 kJ mol–1, indicating their chemisorption. SEM images confirmed the efficacy of these corrosion inhibitors, as surface features showed limited to no changes after tests. Surface analysis by XPS and LC-MS revealed inhibitor concentrations on the order of 0.7 to 1.8 μg cm–2 for the best compounds, further underlining their performance at low concentrations. Mapping of the surface by MALDI-MS further confirmed the homogeneous coating of the steel surface, with no visible fluctuations in concentrations. As all inhibitors shared the same indole thiosemicarbazone platform, unique structure–performance relationships were drawn from theoretical calculations. Notably, DFT and AIMD explained the differences in performance, highlighting the role of side groups in the distribution of the molecular orbitals and the role of water molecules in enhancing the electronic properties of the organic corrosion inhibitors and promoting their chemisorption.

  PublisherOA PDFDOI

• Genetic control of morphological transitions in a coacervating protein template

  Soft Matter · 2025-12-05

  articleOpen access

  Nature routinely exploits liquid-liquid phase separation (LLPS) of proteins to control the assembly and mineralization of hybrid materials. Here, we show that fusion of the Car9 silica-binding peptide to an elastin-like polypeptide (ELP) yields temperature- and sequence-programmable soft matter templates for the synthesis of silicified architectures ranging in size from nanometers to micrometers. Specifically, we demonstrate unprecedented control over the diameter of silica nanoparticles (SiNP) in the 30-60 nm range with 4 nm precision, show that a single arginine residue (R4) in the Car9 sequence underpins the transition from micelles to proteinosomes, and find that substitutions in other basic residues modulate electrostatic repulsion and solvation to enable access to kinetically trapped species. These structures, which include interconnected micelles, small (∼200 nm) and large (>5 µm) vesicles, are readily visualized by SEM imaging following silicification. Molecular dynamics (MD) simulations and AlphaFold predictions reveal that mutations in positively charged residues alter interfacial packing, hydration, and conformational freedom of the silica-binding segments. Overall, our results establish sequence and thermal energy as synergistic levers for morphological control across length scales using solid-binding ELPs and establish mineralization as a powerful tool to visualize the structure of dynamic soft matter assemblies.

  PublisherOA PDFDOI

• Estimation of vibrational spectra of Trp-cage protein from nonequilibrium metadynamics simulations

  Biophysical Journal · 2024-08-23 · 2 citations

  articleOpen accessSenior author

  The development of methods that allow a structural interpretation of linear and nonlinear vibrational spectra is of great importance, both for spectroscopy and for optimizing force field quality. The experimentally measured signals are ensemble averages over all accessible configurations, which complicates spectral calculations. To account for this, we present a recipe for calculating vibrational amide-I spectra of proteins based on metadynamics molecular dynamics simulations. For each frame, a one-exciton Hamiltonian is set up for the backbone amide groups, in which the couplings are estimated with the transition-charge coupling model for nonnearest neighbors, and with a parametrized map of ab initio calculations that give the coupling as a function of the dihedral angles for nearest neighbors. The local-mode frequency variations due to environmental factors such as hydrogen bonds are modeled by exploiting the linear relationship between the amide C-O bond length and the amide-I frequency. The spectra are subsequently calculated while taking into account the equilibrium statistical weights of the frames that are determined using a previously published reweighting procedure. By implementing all these steps in an efficient Fortran code, the spectra can be averaged over very large amounts of structures, thereby extensively covering the phase space of proteins. Using this recipe, the spectral responses of 2.5 million frames of a metadynamics simulation of the miniprotein Trp-cage are averaged to reproduce the experimental temperature-dependent IR spectra very well. The spectral calculations provide new insight into the origin of the various spectral signatures (which are typically challenging to disentangle in the congested amide-I region), and allow for a direct structural interpretation of the experimental spectra and for validation of the molecular dynamics simulations of ensembles.

  PublisherOA PDFDOI

• Virtual Special Issue on Machine Learning in Physical Chemistry Volume 2

  The Journal of Physical Chemistry C · 2024-07-11

  articleSenior authorCorresponding
  PublisherDOI

• Two-dimensional silk

  arXiv (Cornell University) · 2024-01-22

  preprintOpen access

  The ability to form silk films on semiconductors, metals, and oxides or as free-standing membranes has motivated research into silk-based electronic, optical, and biomedical devices. However, the inherent disorder of native silk limits device performance. Here we report the creation of highly ordered two-dimensional (2D) silk fibroin (SF) layers on van der Waals solids. Using in situ atomic force microscopy, synchrotron-based infrared spectroscopy, and molecular dynamics simulations, we develop a mechanistic understanding of the assembly process. We show that the films consist of lamellae having an epitaxial relationship with the underlying lattice and that the SF molecules exhibit the same Beta-sheet secondary structure seen in the crystallites of the native form. By increasing the SF concentration, multilayer films form via layer-by-layer growth, either along a classical pathway in which SF molecules assemble directly into the lamellae or, at sufficiently high concentrations, along a two-step pathway beginning with formation of a disordered monolayer that subsequently converts into the crystalline phase. Kelvin probe measurements show that these 2D SF layers substantially alter the surface potential. Moreover, the ability to assemble 2D silk on both graphite and MoS2 suggests that it may provide a general platform for silk-based electronics on vdW solids.

  PublisherOA PDFDOI

Recent grants

• BRIGE: Understanding Protein-Surface Interactions Through Multiscale Modeling: Application to Biofuel Cells

  NSF · $187k · 2010–2013

• International Research Fellowship Program: Biomass-Derived Fuels: Modeling and Simulation of Enzymatic Processes

  NSF · $156k · 2007–2009

• CAREER: Computational Enzymology of Non-Aqueous Biocatalysis: Application to Biomass Pretreatment

  NSF · $485k · 2012–2019

• NRT-DESE: Data Intensive Research Enabling Clean Technologies (DIRECT)

  NSF · $3.1M · 2016–2022

• Combined molecular simulation and experimental study to discover, predict and control enzyme immobilization in polymeric nanoparticles

  NSF · $396k · 2017–2022

Frequent coauthors

• Chun‐Long Chen

  University of Washington

  61 shared

• Tobias Weidner

  Aarhus University

  45 shared

• Christopher J. Mundy

  Physical Sciences (United States)

  40 shared

• Sarah Alamdari

  Microsoft (United States)

  40 shared

• Bin Cai

  City University of Hong Kong, Shenzhen Research Institute

  33 shared

• Shuai Zhang

  China Agricultural University

  29 shared

• James J. DeYoreo

  Pacific Northwest National Laboratory

  27 shared

• Peijun Zhang

  Diamond Light Source

  27 shared

Labs

• Jim Pfaendtner LabPI