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2D compact model to characterize phase separation in organic solar cell bulk heterojunctions (Conference Presentation) | 2017 | Organic, Hybrid, and Perovskite Photovoltaics XVIII 10363, 1036305, 2017 | 1 | aH52nxkAAAAJ:e_rmSamDkqQC | Farimani | Mechanical Engineering | [
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A physics-informed diffusion model for high-fidelity flow field reconstruction | 2023 | Journal of Computational Physics 478, 111972, 2023 | 108 | aH52nxkAAAAJ:7Hz3ACDFbsoC | Farimani | Mechanical Engineering | [
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A review on challenges and successes in atomic-scale design of catalysts for electrochemical synthesis of hydrogen peroxide | 2020 | Acs Catalysis 10 (14), 7495-7511, 2020 | 331 | aH52nxkAAAAJ:XvxMoLDsR5gC | Farimani | Mechanical Engineering | [
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A study on the measurement of mean velocity and its convergence in molecular dynamics simulations | 2011 | International Journal for Numerical Methods in Fluids 67 (12), 2130-2140, 2011 | 18 | aH52nxkAAAAJ:UeHWp8X0CEIC | Farimani | Mechanical Engineering | [
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AMGPT: a Large Language Model for Contextual Querying in Additive Manufacturing | 2024 | Additive Manufacturing Letters 11, 100232, 2024 | 2 | aH52nxkAAAAJ:silx2ntsSuwC | Farimani | Mechanical Engineering | [
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AbGPT: De Novo Antibody Design via Generative Language Modeling | 2024 | arXiv preprint arXiv:2409.06090, 2024 | 0 | aH52nxkAAAAJ:-DxkuPiZhfEC | Farimani | Mechanical Engineering | [
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Accelerating Lagrangian fluid simulation with graph neural networks | 2020 | ICLR 2021 SimDL Workshop 20, 2020 | 3 | aH52nxkAAAAJ:OP4eGU-M3BUC | Farimani | Mechanical Engineering | [
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Accelerating Optimization Using Vectorized Moth-Flame Optimizer (vMFO) | 2022 | Handbook of Moth-Flame Optimization Algorithm: Variants, Hybrids …, 2022 | 7 | aH52nxkAAAAJ:CaZNVDsoPx4C | Farimani | Mechanical Engineering | [
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Activation pathways of neurotensin receptor 1 elucidated using statistical machine learning | 2022 | ACS Chemical Neuroscience 13 (8), 1333-1341, 2022 | 5 | aH52nxkAAAAJ:vDZJ-YLwNdEC | Farimani | Mechanical Engineering | [
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Activity Map and Transition Pathways of G Protein-Coupled Receptor Revealed by Machine Learning | 2023 | Journal of Chemical Information and Modelling 63 (8), 2296–2304, 2023 | 10 | aH52nxkAAAAJ:CdxZDUztZiMC | Farimani | Mechanical Engineering | [
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Adaptive grey wolf optimizer | 2022 | Neural Computing and Applications 34 (10), 7711-7731, 2022 | 87 | aH52nxkAAAAJ:_OXeSy2IsFwC | Farimani | Mechanical Engineering | [
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Adsorb-Agent: Autonomous Identification of Stable Adsorption Configurations via Large Language Model Agent | 2024 | arXiv preprint arXiv:2410.16658, 2024 | 0 | aH52nxkAAAAJ:0aBXIfxlw9sC | Farimani | Mechanical Engineering | [
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Adsorption kinetics dictate monolayer self-assembly for both lipid-in and lipid-out approaches to droplet interface bilayer formation | 2015 | Langmuir 31 (47), 12883-12893, 2015 | 83 | aH52nxkAAAAJ:hMsQuOkrut0C | Farimani | Mechanical Engineering | [
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Airfoil Diffusion: Denoising Diffusion Model For Conditional Airfoil Generation | 2024 | arXiv preprint arXiv:2408.15898, 2024 | 0 | aH52nxkAAAAJ:v6i8RKmR8ToC | Farimani | Mechanical Engineering | [
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Airfoil GAN: encoding and synthesizing airfoils for aerodynamic shape optimization | 2023 | Journal of Computational Design and Engineering 10 (4), 1350-1362, 2023 | 52 | aH52nxkAAAAJ:SjuI4pbJlxcC | Farimani | Mechanical Engineering | [
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AlloyBERT: Alloy Property Prediction with Large Language Models | 2024 | Computational Material Science 244 (113256), 2024 | 1 | aH52nxkAAAAJ:HJSXoJQnj-YC | Farimani | Mechanical Engineering | [
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An Energy-Saving Snake Locomotion Pattern Learned in a Physically Constrained Environment With Online Model-Based Policy Gradient Method | 2023 | Journal of Mechanisms and Robotics 15 (4), 041007, 2023 | 8 | aH52nxkAAAAJ:lgwcVrK6X84C | Farimani | Mechanical Engineering | [
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Antibody subclass detection using graphene nanopores | 2017 | The journal of physical chemistry letters 8 (7), 1670-1676, 2017 | 29 | aH52nxkAAAAJ:VaXvl8Fpj5cC | Farimani | Mechanical Engineering | [
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AugLiChem: data augmentation library of chemical structures for machine learning | 2022 | Machine Learning: Science and Technology 3 (4), 045015, 2022 | 35 | aH52nxkAAAAJ:8xutWZnSdmoC | Farimani | Mechanical Engineering | [
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Autonomous Clay Sculpting from Human Demonstrations with Point Cloud Goal Conditioned Diffusion Policy | null | ICRA 2024 Workshop on 3D Visual Representations for Robot Manipulation, 0 | 0 | aH52nxkAAAAJ:PkcyUWeTMh0C | Farimani | Mechanical Engineering | [
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BAPULM: Binding Affinity Prediction using Language Models | 2024 | arXiv preprint arXiv:2411.04150, 2024 | 0 | aH52nxkAAAAJ:eAlLMO4JVmQC | Farimani | Mechanical Engineering | [
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BeadSight: An Inexpensive Tactile Sensor Using Hydro-Gel Beads | 2024 | IEEE Sensors Journal, 2024 | 1 | aH52nxkAAAAJ:Xl6nMSl579sC | Farimani | Mechanical Engineering | [
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Binding pathway of opiates to μ-opioid receptors revealed by machine learning | 2018 | Biophysical Journal 114 (3), 62a-63a, 2018 | 16 | aH52nxkAAAAJ:HbR8gkJAVGIC | Farimani | Mechanical Engineering | [
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Bio-informed protein sequence generation for multi-class virus mutation prediction | 2020 | bioRxiv, 2020.06. 11.146167, 2020 | 53 | aH52nxkAAAAJ:HIFyuExEbWQC | Farimani | Mechanical Engineering | [
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CaFA: Global Weather Forecasting with Factorized Attention on Sphere | 2024 | arXiv preprint arXiv:2405.07395, 2024 | 2 | aH52nxkAAAAJ:k_7cPK9k7w8C | Farimani | Mechanical Engineering | [
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Catalyst energy prediction with catberta: Unveiling feature exploration strategies through large language models | 2023 | ACS Catalysis 13 (24), 16032-16044, 2023 | 21 | aH52nxkAAAAJ:ziOE8S1-AIUC | Farimani | Mechanical Engineering | [
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Combining physics-based and evolution-based methods to design antibodies against an evolving virus | 2020 | Biophysical Journal 118 (3), 482a, 2020 | 3 | aH52nxkAAAAJ:8d8msizDQcsC | Farimani | Mechanical Engineering | [
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Computational approach to designing antibody for ebola virus | 2016 | Biophysical Journal 110 (3), 537a, 2016 | 2 | aH52nxkAAAAJ:tYavs44e6CUC | Farimani | Mechanical Engineering | [
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Computational modeling of the wind erosion on a sinusoidal pile using a moving boundary method | 2011 | Geomorphology 130 (3-4), 299-311, 2011 | 20 | aH52nxkAAAAJ:2osOgNQ5qMEC | Farimani | Mechanical Engineering | [
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Convolutional neural networks for melt depth prediction and visualization in laser powder bed fusion | 2023 | The International Journal of Advanced Manufacturing Technology 129 (7), 3047 …, 2023 | 8 | aH52nxkAAAAJ:pAkWuXOU-OoC | Farimani | Mechanical Engineering | [
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Creativity in robot manipulation with deep reinforcement learning | 2019 | arXiv preprint arXiv:1910.07459, 2019 | 3 | aH52nxkAAAAJ:9pM33mqn1YgC | Farimani | Mechanical Engineering | [
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Crystal twins: self-supervised learning for crystalline material property prediction | 2022 | npj Computational Materials 8 (1), 231, 2022 | 43 | aH52nxkAAAAJ:6yz0xqPARnAC | Farimani | Mechanical Engineering | [
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DNA Base Detection Using a Single-Layer MoS2 | 2014 | ACS nano 8 (8), 7914-7922, 2014 | 395 | aH52nxkAAAAJ:vDijr-p_gm4C | Farimani | Mechanical Engineering | [
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DNA Detection Using a Single-Layer Phosphorene Nanopore | 2023 | ACS Applied Nano Materials 6 (9), 7814-7820, 2023 | 3 | aH52nxkAAAAJ:prdVHNxh-e8C | Farimani | Mechanical Engineering | [
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DNA Detection with Single-Layer Ti3C2 MXene Nanopore | 2021 | ACS nano 15 (3), 4861-4869, 2021 | 53 | aH52nxkAAAAJ:OcBU2YAGkTUC | Farimani | Mechanical Engineering | [
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DNA origami–graphene hybrid nanopore for DNA detection | 2017 | ACS applied materials & interfaces 9 (1), 92-100, 2017 | 99 | aH52nxkAAAAJ:tuHXwOkdijsC | Farimani | Mechanical Engineering | [
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Data-driven identification of 2D partial differential equations using extracted physical features | 2021 | Computer Methods in Applied Mechanics and Engineering 381, 113831, 2021 | 20 | aH52nxkAAAAJ:dBIO0h50nwkC | Farimani | Mechanical Engineering | [
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Deep Learning Fluid Mechanics | 2017 | APS Division of Fluid Dynamics Meeting Abstracts, E31. 004, 2017 | 3 | aH52nxkAAAAJ:-FonjvnnhkoC | Farimani | Mechanical Engineering | [
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Deep Learning Phase Segregation | 2018 | arXiv e-prints, arXiv: 1803.08993, 2018 | 2 | aH52nxkAAAAJ:86PQX7AUzd4C | Farimani | Mechanical Engineering | [
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Deep Learning based Optical Image Super-Resolution via Generative Diffusion Models for Layerwise in-situ LPBF Monitoring | 2024 | arXiv preprint arXiv:2409.13171, 2024 | 0 | aH52nxkAAAAJ:YlPif8NxrbYC | Farimani | Mechanical Engineering | [
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] |
Deep learning convective flow using conditional generative adversarial networks | 2020 | arXiv preprint arXiv:2005.06422, 2020 | 10 | aH52nxkAAAAJ:HtEfBTGE9r8C | Farimani | Mechanical Engineering | [
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Deep learning for efficient reconstruction of high-resolution turbulent dns data | 2020 | arXiv preprint arXiv:2010.11348, 2020 | 13 | aH52nxkAAAAJ:QYdC8u9Cj1oC | Farimani | Mechanical Engineering | [
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Deep learning for melt pool depth contour prediction from surface thermal images via vision transformers | 2024 | Additive Manufacturing Letters 11, 100243, 2024 | 3 | aH52nxkAAAAJ:65Yg0jNCQDAC | Farimani | Mechanical Engineering | [
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Deep learning for reduced order modelling and efficient temporal evolution of fluid simulations | 2021 | Physics of Fluids 33 (10), 2021 | 91 | aH52nxkAAAAJ:r_AWSJRzSzQC | Farimani | Mechanical Engineering | [
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Deep learning of material transport in complex neurite networks | 2021 | Scientific reports 11 (1), 11280, 2021 | 26 | aH52nxkAAAAJ:3htObqc8RwsC | Farimani | Mechanical Engineering | [
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] |
Deep learning phase segregation | 2018 | arXiv preprint arXiv:1803.08993, 2018 | 6 | aH52nxkAAAAJ:q3CdL3IzO_QC | Farimani | Mechanical Engineering | [
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Deep learning the physics of transport phenomena | 2017 | arXiv preprint arXiv:1709.02432, 2017 | 142 | aH52nxkAAAAJ:WZBGuue-350C | Farimani | Mechanical Engineering | [
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Deep learning the physics of transport phenomena | 2017 | arXiv e-prints, arXiv: 1709.02432, 2017 | 4 | aH52nxkAAAAJ:DBa1UEJaJKAC | Farimani | Mechanical Engineering | [
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Deep reinforcement learning for predicting kinetic pathways to surface reconstruction in a ternary alloy | 2021 | Machine Learning: Science and Technology 2 (4), 045018, 2021 | 26 | aH52nxkAAAAJ:LhH-TYMQEocC | Farimani | Mechanical Engineering | [
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Deep-learned generators of porosity distributions produced during metal Additive Manufacturing | 2022 | Additive Manufacturing 60, 103250, 2022 | 19 | aH52nxkAAAAJ:NyGDZy8z5eUC | Farimani | Mechanical Engineering | [
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Denoise Pre-training on Non-equilibrium Molecules for Accurate and Transferable Neural Potentials | 2023 | Journal of Chemical Theory and Computation 19 (15), 5077–5087, 2023 | 24 | aH52nxkAAAAJ:OBSaB-F7qqsC | Farimani | Mechanical Engineering | [
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Discovery of novel brain permeable and G protein-biased beta-1 adrenergic receptor partial agonists for the treatment of neurocognitive disorders | 2017 | PloS one 12 (7), e0180319, 2017 | 26 | aH52nxkAAAAJ:oNZyr7d5Mn4C | Farimani | Mechanical Engineering | [
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Dissolution of monocrystalline silicon nanomembranes and their use as encapsulation layers and electrical interfaces in water-soluble electronics | 2017 | ACS nano 11 (12), 12562-12572, 2017 | 110 | aH52nxkAAAAJ:Dip1O2bNi0gC | Farimani | Mechanical Engineering | [
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Dominant motion identification of multi-particle system using deep learning from video | 2022 | Neural Computing and Applications 34 (20), 18183-18193, 2022 | 8 | aH52nxkAAAAJ:KbBQZpvPDL4C | Farimani | Mechanical Engineering | [
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] |
Effects of sparse rewards of different magnitudes in the speed of learning of model-based actor critic methods | 2020 | arXiv preprint arXiv:2001.06725, 2020 | 2 | aH52nxkAAAAJ:FAceZFleit8C | Farimani | Mechanical Engineering | [
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Efficient water desalination with graphene nanopores obtained using artificial intelligence | 2021 | npj 2D Materials and Applications 5 (1), 66, 2021 | 66 | aH52nxkAAAAJ:yqoGN6RLRZoC | Farimani | Mechanical Engineering | [
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Electromechanical signatures for DNA sequencing through a mechanosensitive nanopore | 2015 | The journal of physical chemistry letters 6 (4), 650-657, 2015 | 25 | aH52nxkAAAAJ:BUYA1_V_uYcC | Farimani | Mechanical Engineering | [
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] |
Energy Dissipation in Monolayer MoS2 Electronics | 2017 | Nano letters 17 (6), 3429-3433, 2017 | 236 | aH52nxkAAAAJ:k8Z6L05lTy4C | Farimani | Mechanical Engineering | [
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Engineering rotating apical-out airway organoid for assessing respiratory cilia motility | 2022 | Iscience 25 (8), 2022 | 19 | aH52nxkAAAAJ:wMgC3FpKEyYC | Farimani | Mechanical Engineering | [
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Existence of multiple phases of water at nanotube interfaces | 2016 | The Journal of Physical Chemistry C 120 (41), 23763-23771, 2016 | 75 | aH52nxkAAAAJ:gsN89kCJA0AC | Farimani | Mechanical Engineering | [
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Explain Like I'm Five: Using LLMs to Improve PDE Surrogate Models with Text | 2024 | arXiv preprint arXiv:2410.01137, 2024 | 0 | aH52nxkAAAAJ:kJDgFkosVoMC | Farimani | Mechanical Engineering | [
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Explainable Data-driven Modeling of Adsorption Energy in Heterogeneous Catalysis | 2024 | arXiv preprint arXiv:2405.20397, 2024 | 0 | aH52nxkAAAAJ:SIv7DqKytYAC | Farimani | Mechanical Engineering | [
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Fast Water Desalination with a Graphene–MoS2 Nanoporous Heterostructure | 2024 | ACS Applied Materials & Interfaces 16 (22), 29355, 2024 | 1 | aH52nxkAAAAJ:QsaTk4IG4EwC | Farimani | Mechanical Engineering | [
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FaultFormer: Pretraining Transformers for Adaptable Bearing Fault Classification | 2024 | IEEE Access, 2024 | 4 | aH52nxkAAAAJ:aIdbFUkbNIkC | Farimani | Mechanical Engineering | [
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FaultNet: A deep convolutional neural network for bearing fault classification | 2021 | IEEE access 9, 25189-25199, 2021 | 79 | aH52nxkAAAAJ:2VqYfGB8ITEC | Farimani | Mechanical Engineering | [
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Fluid viscosity prediction leveraging computer vision and robot interaction | 2024 | Engineering Applications of Artificial Intelligence 135, 108603, 2024 | 1 | aH52nxkAAAAJ:qe6vwMD2xtsC | Farimani | Mechanical Engineering | [
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Forecasting COVID-19 new cases using deep learning methods | 2022 | Computers in biology and medicine 144, 105342, 2022 | 85 | aH52nxkAAAAJ:artPoR2Yc-kC | Farimani | Mechanical Engineering | [
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GPCR-BERT: Interpreting Sequential Design of G Protein-Coupled Receptors Using Protein Language Models | 2024 | Journal of Chemical Information and Modeling 64 (4), 1134-1144, 2024 | 6 | aH52nxkAAAAJ:HhcuHIWmDEUC | Farimani | Mechanical Engineering | [
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GPT-MolBERTa: GPT Molecular Features Language Model for molecular property prediction | 2023 | arXiv:2310.03030, 2023 | 15 | aH52nxkAAAAJ:SGW5VrABaM0C | Farimani | Mechanical Engineering | [
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Generative Lattice Units with 3D Diffusion for Inverse Design: GLU3D | 2024 | Advanced Functional Materials, 2404165, 2024 | 6 | aH52nxkAAAAJ:UmS_249rOGwC | Farimani | Mechanical Engineering | [
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GradNav: Accelerated Exploration of Potential Energy Surfaces with Gradient-Based Navigation | 2024 | Journal of Chemical Theory and Computation, 2024 | 1 | aH52nxkAAAAJ:SnGPuo6Feq8C | Farimani | Mechanical Engineering | [
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Graph convolutional networks applied to unstructured flow field data | 2021 | Machine Learning: Science and Technology 2 (4), 045020, 2021 | 60 | aH52nxkAAAAJ:a3BOlSfXSfwC | Farimani | Mechanical Engineering | [
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Graph neural network-accelerated Lagrangian fluid simulation | 2022 | Computers & Graphics 103, 201-211, 2022 | 93 | aH52nxkAAAAJ:gKiMpY-AVTkC | Farimani | Mechanical Engineering | [
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Graph neural networks accelerated molecular dynamics | 2022 | The Journal of Chemical Physics 156 (14), 2022 | 61 | aH52nxkAAAAJ:GFxP56DSvIMC | Farimani | Mechanical Engineering | [
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Graph neural networks for molecules | 2023 | Machine Learning in Molecular Sciences, 21-66, 2023 | 30 | aH52nxkAAAAJ:QyXJ3EUuO1IC | Farimani | Mechanical Engineering | [
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Heteroatom-doped transition metal nitrides for CO electrochemical reduction: A density functional theory screening study | 2020 | The Journal of Physical Chemistry C 124 (48), 26344-26351, 2020 | 9 | aH52nxkAAAAJ:4vMrXwiscB8C | Farimani | Mechanical Engineering | [
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High-resolution melt pool thermal imaging for metals additive manufacturing using the two-color method with a color camera | 2023 | Additive Manufacturing 73, 103663, 2023 | 24 | aH52nxkAAAAJ:AXkvAH5U_nMC | Farimani | Mechanical Engineering | [
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IDP-Bert: Predicting Properties of Intrinsically Disordered Proteins Using Large Language Models | 2024 | The Journal of Physical Chemistry B, 2024 | 1 | aH52nxkAAAAJ:DrR-2ekChdkC | Farimani | Mechanical Engineering | [
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Identification of amino acids with sensitive nanoporous MoS2: towards machine learning-based prediction | 2018 | npj 2D Materials and Applications 2 (1), 14, 2018 | 71 | aH52nxkAAAAJ:Ug5p-4gJ2f0C | Farimani | Mechanical Engineering | [
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Identification of parametric dynamical systems using integer programming | 2023 | Expert Systems with Applications 219, 119622, 2023 | 11 | aH52nxkAAAAJ:WHdLCjDvYFkC | Farimani | Mechanical Engineering | [
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Improving molecular contrastive learning via faulty negative mitigation and decomposed fragment contrast | 2022 | Journal of Chemical Information and Modeling 62 (11), 2713-2725, 2022 | 72 | aH52nxkAAAAJ:tH6gc1N1XXoC | Farimani | Mechanical Engineering | [
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Inexpensive high fidelity melt pool models in additive manufacturing using generative deep diffusion | 2024 | Materials & Design 245, 113181, 2024 | 11 | aH52nxkAAAAJ:mUJArPsKIAAC | Farimani | Mechanical Engineering | [
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Inpainting Computational Fluid Dynamics with Deep Learning | 2024 | arXiv preprint arXiv:2402.17185, 2024 | 0 | aH52nxkAAAAJ:eO3_k5sD8BwC | Farimani | Mechanical Engineering | [
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Integrating Multi-Physics Simulations and Machine Learning to Define the Spatter Mechanism and Process Window in Laser Powder Bed Fusion | 2024 | arXiv preprint arXiv:2405.07823, 2024 | 1 | aH52nxkAAAAJ:Hck25ST_3aIC | Farimani | Mechanical Engineering | [
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Isolating Specific vs. Non-Specific Binding Responses in Conducting Polymer Biosensors for Bio-Fingerprinting | 2021 | Sensors 21 (19), 6335, 2021 | 2 | aH52nxkAAAAJ:oi2SiIJ9l4AC | Farimani | Mechanical Engineering | [
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Kinetic machine learning unravels ligand-directed conformational change of µ opioid receptor. bioRxiv | 2017 | 2 | aH52nxkAAAAJ:Aul-kAQHnToC | Farimani | Mechanical Engineering | [
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|
Kinetic machine learning unravels ligand-directed conformational change of μ opioid receptor | 2018 | Biophysical Journal 114 (3), 56a, 2018 | 10 | aH52nxkAAAAJ:kz9GbA2Ns4gC | Farimani | Mechanical Engineering | [
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LLM-Craft: Robotic Crafting of Elasto-Plastic Objects with Large Language Models | 2024 | arXiv preprint arXiv:2406.08648, 2024 | 2 | aH52nxkAAAAJ:CYCckWUYoCcC | Farimani | Mechanical Engineering | [
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Large Language Models Monitor and Control Additive Manufacturing | 2024 | 2024 AIChE Annual Meeting, 2024 | 0 | aH52nxkAAAAJ:2ywjKiB__4kC | Farimani | Mechanical Engineering | [
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Large language model agent as a mechanical designer | 2024 | arXiv preprint arXiv:2404.17525, 2024 | 8 | aH52nxkAAAAJ:IsPWOBWtZBwC | Farimani | Mechanical Engineering | [
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Latent Neural PDE Solver: a reduced-order modelling framework for partial differential equations | 2024 | arXiv preprint arXiv:2402.17853, 2024 | 8 | aH52nxkAAAAJ:pS0ncopqnHgC | Farimani | Mechanical Engineering | [
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Learning Lagrangian fluid dynamics with graph neural networks | 2021 | 3 | aH52nxkAAAAJ:LdasjJ6CEcoC | Farimani | Mechanical Engineering | [
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|
Learning from mistakes: Sampling strategies to efficiently train machine learning models for material property prediction | 2023 | Computational Materials Science 224, 112167, 2023 | 17 | aH52nxkAAAAJ:jU7OWUQzBzMC | Farimani | Mechanical Engineering | [
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Llm-3D print: large language models to monitor and control 3D printing | 2024 | arXiv preprint arXiv:2408.14307, 2024 | 2 | aH52nxkAAAAJ:hSRAE-fF4OAC | Farimani | Mechanical Engineering | [
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Llm-sr: Scientific equation discovery via programming with large language models | 2024 | arXiv preprint arXiv:2404.18400, 2024 | 15 | aH52nxkAAAAJ:jSAVyFp_754C | Farimani | Mechanical Engineering | [
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Low Fidelity Visuo-Tactile Pretraining Improves Vision-Only Manipulation Performance | 2024 | arXiv preprint arXiv:2406.15639, 2024 | 0 | aH52nxkAAAAJ:isU91gLudPYC | Farimani | Mechanical Engineering | [
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MAB-OS: multi-armed bandits metaheuristic optimizer selection | 2022 | Applied Soft Computing 128, 109452, 2022 | 11 | aH52nxkAAAAJ:48xauSegjOkC | Farimani | Mechanical Engineering | [
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MOFormer: Self-Supervised Transformer Model for Metal–Organic Framework Property Prediction | 2023 | Journal of the American Chemical Society 145 (5), 2958–2967, 2023 | 86 | aH52nxkAAAAJ:wKETBy42zhYC | Farimani | Mechanical Engineering | [
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Machine Learning Harnesses Molecular Dynamics to Discover New Opioid Chemotypes | 2018 | arXiv preprint arXiv:1803.04479, 2018 | 12 | aH52nxkAAAAJ:foquWX3nUaYC | Farimani | Mechanical Engineering | [
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