Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids | Paper Citation Record

Observation 9137357f-8075-4979-b90c-6124c7c7322d · outbound

This paper cites Elena, Dávid P.

Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids Elena, Dávid P

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Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids Density-functional exchange-energy approximation with correct asymptotic behavior.Physical review A, 38(6):3098, 1988

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Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids Density-functional thermochemistry

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This paper cites Melting points of water models: Current situation.The Journal of Chemical Physics, 156(21), 2022.

Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids Melting points of water models: Current situation.The Journal of Chemical Physics, 156(21), 2022

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This paper cites Shapeev, and Zhuang Xiaoying.

Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids Shapeev, and Zhuang Xiaoying

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This paper cites Podryabinkin Evgeny, S.

Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids Podryabinkin Evgeny, S

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This paper cites A methodical selection process for the development of ketones and esters as bio-based replacements for traditional hydrocarbon solvents.

Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids A methodical selection process for the development of ketones and esters as bio-based replacements for traditional hydrocarbon solvents

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This paper cites URLhttps://cameochemicals.noaa.gov/chris/EFM.pdf.

Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids URLhttps://cameochemicals.noaa.gov/chris/EFM.pdf

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Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids Finite size effects in determination of thermal conductivities: comparing molecular dynamics results with simple models.J

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This paper cites A universal graph deep learning interatomic potential for the periodic table.Nat.

Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids A universal graph deep learning interatomic potential for the periodic table.Nat

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This paper cites Density and refractive index at 298.15 k and vapor- liquid equilibria at 101.3 kpa for four binary systems of methanol, n-propanol, n-butanol, or isobutanol with n-methylpiperazine.

Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids Density and refractive index at 298.15 k and vapor- liquid equilibria at 101.3 kpa for four binary systems of methanol, n-propanol, n-butanol, or isobutanol with n-methylpiperazine

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This paper cites Computing the heat conductivity of fluids from density fluctuations.Physical Review Letters, 125(13):130602, 2020.

Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids Computing the heat conductivity of fluids from density fluctuations.Physical Review Letters, 125(13):130602, 2020

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This paper cites Bartel, and Gerbrand Ceder.

Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids Bartel, and Gerbrand Ceder

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Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids Sigmoid-weighted linear units for neural network function approxi- mation in reinforcement learning.Neural networks, 107:3–11, 2018

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This paper cites Understanding surface interactions in aqueous miscible organic solvent treated layered double hydroxides.RSC advances, 7(9):5076–5083, 2017.

Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids Understanding surface interactions in aqueous miscible organic solvent treated layered double hydroxides.RSC advances, 7(9):5076–5083, 2017

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Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids Measurement of the thermal conductivity of five aliphatic esters in the liquid phase.The Journal of Chemical Thermodynamics, 138:140–146, 2019

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This paper cites Jecfa flavonoids - details.

Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids Jecfa flavonoids - details

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Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids Thermal conductivity of ionic liquids and ionanofluids and their feasibility as heat transfer fluids.Industrial & Engineering Chemistry Research, 57(18):6516–6529, 2018

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Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids Elsevier, 2023

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Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids Pvt property measurements for some aliphatic esters from (298 to 393) k and up to 35 mpa.Journal of Chemical & Engineering Data, 52(3):737–751, 2007

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Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids A predictive machine learning force-field framework for liquid electrolyte development

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Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids Double-hybrid density functional theory for excited electronic states of molecules

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Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids Refining potential energy surface through dynamical properties via differentiable molecular simulation

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Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids Haynes.CRC Handbook of Chemistry and Physics

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Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids Jorgensen, David S

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Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids MACE-OFF: Transferable Short Range Machine Learning Force Fields for Organic Molecules

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Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids Langer, J

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Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids CRC Press, 2018

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Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids Capturing the nuclear quantum effects in molecular dynamics for lattice thermal conductivity calculations: Using ice as example.The Journal of Chemical Physics, 153(19), 2020

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Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids URL https://www.matweb.com/search/datasheet.aspx?matguid= f2c9a5d8608e4f5aac1d570d37a54ffc&ckck=1

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Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids Scaling deep learning for materials discovery.Nature, 624:1–6, 11 2023

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Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids Advances in the improvement of thermal- conductivity of phase change material-based lithium-ion battery thermal management systems: An updated review

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Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids Advances in liquid coolant technologies for electronics cooling

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Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids A simple nonequilibrium molecular dynamics method for calculating the thermal conduc- tivity

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Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids Thermodynamic properties of four ester-hydrocarbon mixtures.Journal of Chemical and Engineering Data, 25(3):283–286, 1980

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Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids Densities and refractive indices of pure alcohols as a function of temperature.Journal of Chemical and Engineering Data, 27(3):312–317, 1982

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Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids Pytorch: An imperative style, high-performance deep learning library

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Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids Generalized gradient approximation made simple

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Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids Atomistic simulations of the thermal conductivity of liquids.Physical Review Materials, 4(5):053801, 2020

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Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids A fifth-order perturbation comparison of electron correlation theories.Chemical Physics Letters, 157(6):479–483, 1989

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Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids Reformulation of the d3(becke–johnson) dispersion correction without resorting to higher than c6 dispersion coefficients.J

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Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids Size effects in molecular dynamics thermal conductivity predictions.Physical Review B—Condensed Matter and Materials Physics, 81 (21):214305, 2010

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Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids Serijan and Paul H

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Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids Approaching coupled cluster accuracy with a general- purpose neural network potential through transfer learning.Nature communications, 10(1):2903, 2019

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Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids Strongly constrained and appropriately normed semilocal density functional

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Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids Reference correlation of the thermal conductivity of methanol from the triple point to 660 k and up to 245 mpa.Journal of Physical and Chemical Reference Data, 42(4), 2013

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Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids New thin lithium-ion batteries using a liquid electrolyte with thermal stability.Journal of power sources, 97:677–680, 2001

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Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids URLhttps://thermtestasia.cn/material-database

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This paper cites Lammps-a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales.Computer Physics Communications, 271:108171, 2022.

Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids Lammps-a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales.Computer Physics Communications, 271:108171, 2022

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Observation 59461f07-0ee2-49cd-a694-cc92000bfc4d · outbound

This paper cites Triton: an intermediate language and compiler for tiled neural network computations.

Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids Triton: an intermediate language and compiler for tiled neural network computations

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Observation cc55bb67-b17b-4b9f-b630-7252f8419dcc · outbound

This paper cites Heat transport in liquid water from first-principles and deep neural network simulations.Physical Review B, 104(22):224202, 2021.

Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids Heat transport in liquid water from first-principles and deep neural network simulations.Physical Review B, 104(22):224202, 2021

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Observation dd0f2d9a-56f1-44cb-9c1d-b65ab8f64824 · outbound

This paper cites Vanegas, and Marino Arroyo.

Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids Vanegas, and Marino Arroyo

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Observation 1e2837f6-4014-41b5-96e5-06480f09db77 · outbound

This paper cites Enhancing thermal conductivity computation of polymers via machine learning techniques.The Journal of Physical Chemistry B, 129(33):8593–8602, 2025.

Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids Enhancing thermal conductivity computation of polymers via machine learning techniques.The Journal of Physical Chemistry B, 129(33):8593–8602, 2025

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Observation 765870a8-e382-404b-a65c-9f1576e56163 · outbound

This paper cites Physnet: A neural network for predicting energies, forces, dipole moments and partial charges.

Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids Physnet: A neural network for predicting energies, forces, dipole moments and partial charges

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Observation c5fb3f82-74fa-4fb5-9fd2-739a3e4ea3a3 · outbound

This paper cites Machine learning force fields.Chem.

Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids Machine learning force fields.Chem

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Observation d4ebec2f-cf10-4cc7-b7cd-b5b1c30d8c85 · outbound

This paper cites Gromacs: fast, flexible, and free.Journal of computational chemistry, 26(16):1701–1718, 2005.

Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids Gromacs: fast, flexible, and free.Journal of computational chemistry, 26(16):1701–1718, 2005

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Observation 8c8ec6f1-bfee-4aa8-9988-5e43fbec28a4 · outbound

This paper cites Thermochemistry of ionic liquid heat-transfer fluids.Thermochimica Acta, 425(1-2):181–188, 2005.

Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids Thermochemistry of ionic liquid heat-transfer fluids.Thermochimica Acta, 425(1-2):181–188, 2005

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Observation d9409bed-14bf-4694-a8d0-9aca4fbb6b7f · outbound

This paper cites Attention is all you need.Advances in neural information processing systems, 30, 2017.

Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids Attention is all you need.Advances in neural information processing systems, 30, 2017

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Observation 9c52500e-8c7d-4ef4-adbe-749053526b8c · outbound

This paper cites Modeling thermal conductivity of concentrated and mixed-solvent electrolyte systems.

Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids Modeling thermal conductivity of concentrated and mixed-solvent electrolyte systems

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Observation 93d042ac-8ad9-414d-a779-1d9f80299059 · outbound

This paper cites Material properties of porous asphalt pavement cold patch mixtures with different solvents.Journal of Materials in Civil Engineering, 32(10):06020015, 2020.

Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids Material properties of porous asphalt pavement cold patch mixtures with different solvents.Journal of Materials in Civil Engineering, 32(10):06020015, 2020

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Observation 9a415713-9b6a-436c-b578-81236b80945d · outbound

This paper cites Dmff: An open-source automatic differentiable platform for molecular force field development and molecular dynamics simulation.J.

Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids Dmff: An open-source automatic differentiable platform for molecular force field development and molecular dynamics simulation.J

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Observation e52131d7-622e-4e7b-b8f0-30912fa3013a · outbound

This paper cites Enhancing gpu-acceleration in the python-based simulations of chemistry frameworks.WIREs Computational Molecular Science, 15(2):e70008, 2025.

Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids Enhancing gpu-acceleration in the python-based simulations of chemistry frameworks.WIREs Computational Molecular Science, 15(2):e70008, 2025

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Observation 76aff5ef-5a1b-4d81-8e64-590baee22dc5 · outbound

This paper cites Accurate prediction of heat conductivity of water by a neuroevolution potential.The Journal of Chemical Physics, 158(20), 2023.

Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids Accurate prediction of heat conductivity of water by a neuroevolution potential.The Journal of Chemical Physics, 158(20), 2023

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Observation 5ae92a42-e54c-4205-aa7a-2d9787494599 · outbound

This paper cites NEP-MB-pol: A unified machine-learned framework for fast and accurate prediction of water's thermodynamic and transport properties.

Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids NEP-MB-pol: A unified machine-learned framework for fast and accurate prediction of water's thermodynamic and transport properties

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Observation ed1f1475-9d1a-4c8e-be25-bbe31bf8ad29 · outbound

This paper cites Elsevier, 1995.

Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids Elsevier, 1995

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Observation eba4fbbd-b8a7-41a5-92ff-d47701766819 · outbound

This paper cites Elsevier, 2009.

Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids Elsevier, 2009

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Observation e3ebf2f4-73c0-4fe3-a95f-45b38bdfa9cd · outbound

This paper cites Elsevier, 1997.

Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids Elsevier, 1997

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Observation ee80bc49-4176-4b2b-b189-d715ec939f04 · outbound

This paper cites Development of the electrolyte in lithium-ion battery: a concise review on its thermal hazards.Journal of Thermal Analysis and Calorimetry, pages 1–20, 2024.

Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids Development of the electrolyte in lithium-ion battery: a concise review on its thermal hazards.Journal of Thermal Analysis and Calorimetry, pages 1–20, 2024

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Observation d7b71577-2cdf-4365-a80e-3a0d4afa4bb0 · outbound

This paper cites Differential Transformer.

Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids Differential Transformer

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Observation b27e5950-dc67-4510-9df4-cb9eafe33cee · outbound

This paper cites Study of thermal conductivity of nanofluids for the application of heat transfer fluids.Thermochimica Acta, 455(1-2):66–69, 2007.

Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids Study of thermal conductivity of nanofluids for the application of heat transfer fluids.Thermochimica Acta, 455(1-2):66–69, 2007

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Observation c38e42d5-6930-4802-9e9e-7369de227cc9 · outbound

This paper cites Thermal conductivity of water at extreme conditions.The Journal of Physical Chemistry B, 127(31):7011–7017, 2023.

Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids Thermal conductivity of water at extreme conditions.The Journal of Physical Chemistry B, 127(31):7011–7017, 2023

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Observation b21a95a9-17d9-4739-9069-da992d6843c0 · outbound

This paper cites York, Shi Liu, Tong Zhu, Zhicheng Zhong, Jian Lv, Jun Cheng, Weile Jia, Mohan Chen, Guolin Ke, Weinan E, Linfeng Zhang, and Han Wang.

Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids York, Shi Liu, Tong Zhu, Zhicheng Zhong, Jian Lv, Jun Cheng, Weile Jia, Mohan Chen, Guolin Ke, Weinan E, Linfeng Zhang, and Han Wang

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Observation 04631294-f432-4bc0-b262-78d691e72591 · outbound

This paper cites embeddingMLP.

Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids embeddingMLP

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Resolution: unresolved; no resolver link, observed 2026-08-03T19:14:15.545945Z

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