John L. Weber

43‐1: Accelerating OLED Design: Integrating Machine Learning and Physics‐based Simulation

Hadi Abroshan, Paul Winget, David J. Giesen, H. Shaun Kwak et autres

The experimental development of innovative OLED device architectures and material compositions is time‐consuming, labor‐intensive, and resource‐heavy due to the complexity and cost associated with fabrication, characterization, and analysis. Predictive modeling offers a powerful alternative, enabling efficient and targeted evaluation of devices across …

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Accurate Hydration Free Energy Calculations for Diverse Organic Molecules With a Machine Learning Force Field

Xiaowei Xie, John L. Weber, Mats Svensson, Ryne C. Johnston et autres

Free energy perturbation (FEP) calculations using classical force fields remain the dominant approach for large-scale, computational drug discovery efforts, but the accuracy is fundamentally limited by simplified forms that cannot quantitatively reproduce ab initio methods without significant fine-tuning. Machine Learning force fields …

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Evaluating Multiconfigurational Trials for Accurate Phaseless Auxiliary-Field Quantum Monte Carlo on 3d Transition Metal Complexes

Hung Vuong, Ankit Mahajan, John L. Weber, James Shee et autres

In this study, we evaluate multiconfigurational trial wave function protocols for phaseless auxiliary field quantum Monte Carlo (ph-AFQMC) on transition metal containing systems. First, we benchmark vertical ionization potentials for 22 3 d transition metal complexes against published high-accuracy ph-AFQMC values in …

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An Accurate Charge-Aware Machine-Learning Interatomic Potential for the Reduction of Li-Ion Battery Electrolytes in Solution

Yujing Wei, John L. Weber, James M. Stevenson, Zachary K. Goldsmith et autres

Machine learning interatomic potentials (MLIPs), also known as machine learning force fields (MLFFs), offer scalable means of simulating complex systems and processes at ab initio level accuracy. One such process is the critical yet still poorly understood formation of the solid electrolyte …

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An Accurate Charge-Aware Machine-Learning Interatomic Potential for the Reduction of Li-ion Battery Electrolytes in Solution

Yujing Wei, John L. Weber, James M. Stevenson, Zachary K. Goldsmith et autres

Machine learning interatomic potentials (MLIPs), also known as machine learning force fields (MLFFs), offer scalable means of simulating complex systems and processes at \textit{ab initio} level accuracy. One such process is the critical yet still poorly understood formation of the solid electrolyte …

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Digital discovery of large-scale optoelectronic materials via MPNICE machine-learning force fields

Hadi Abroshan, Hyunwook Shaun Kwak, David J. Giesen, John L. Weber et autres

for predictive modeling of OLED materials. Trained on high-level electronic-structure data and incorporating iterative charge equilibration and long-range electrostatics, the MLFF framework enables rapid geometry optimization and molecular dynamics simulations with near quantum-mechanical accuracy. We show that MPNICE-optimized geometries closely reproduce density …

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Accurate Hydration Free Energy Calculations for Diverse Organic Molecules With a Machine Learning Force Field

Xiaowei Xie, John L. Weber, Mats Svensson, Ryne C. Johnston et autres

Free energy perturbation (FEP) calculations using classical force fields remain the dominant approach for large-scale, computational drug discovery efforts but the accuracy is fundamentally limited by simplified forms that cannot quantitatively reproduce ab initio methods without significant fine tuning. Machine Learning force …

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(Invited) Scalable and Generalizable Machine Learning Force Fields for Modeling Complex Battery Materials

Garvit Agarwal, Rishabh D. Guha, John L. Weber, M. Mondal et autres

The rapid advancements in rechargeable Li-ion battery (LIB) technology has revolutionized several key industries such as automotive and consumer electronics . However, new battery chemistries are needed to improve the power density, safety, reliability, and lifetime of LIBs. Existing classical force fields …

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An Accurate Charge-Aware Machine-Learning Interatomic Potential for the Reduction of Li-ion Battery Electrolytes in Solution

Yujing Wei, John L. Weber, James Stevenson, Zachary K. Goldsmith et autres

Machine learning interatomic potentials (MLIPs), also known as machine learning force fields (MLFFs), offer scalable means of simulating complex systems and processes at \textit{ab initio} level accuracy. One such process is the critical yet still poorly understood formation of the solid electrolyte …

us (code pays fourni par la source)

Evaluating Multiconfigurational Trials for Accurate Phaseless Auxiliary-Field Quantum Monte Carlo on 3d Transition Metal Complexes

Hung Vuong, Ankit Mahajan, John L. Weber, James Shee et autres

In this study, we evaluate multi-configurational trial wave function protocols for phaseless auxiliary field quantum Monte Carlo (ph-AFQMC) on transition metal containing systems. First, we benchmark vertical ionization potentials for 22 3d transition metal complexes against published high-accuracy ph-AFQMC values in a …

Accurate Hydration Free Energy Calculations for Diverse Organic Molecules With a Machine Learning Force Field

Xiaowei Xie, John L. Weber, Mats Svensson, Ryne C. Johnston et autres

Free energy perturbation (FEP) calculations using classical force fields remain the dominant approach for large-scale, computational drug discovery efforts but the accuracy is fundamentally limited by simplified forms that cannot quantitatively reproduce ab initio methods without significant fine tuning. Machine Learning force …

us (code pays fourni par la source)

Efficient Long-Range Machine Learning Force Fields for Liquid and Materials Properties

John L. Weber, Rishabh D. Guha, Garvit Agarwal, Aidan A. Fike et autres

Machine learning force fields (MLFFs) have emerged as a sophisticated tool for cost-efficient atomistic simulations approaching DFT accuracy, with recent message passing MLFFs able to cover the entire periodic table. We present an invariant message passing MLFF architecture (MPNICE) which iteratively predicts …