2 research outputs found
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Όλ¬Έ(λ°μ¬)--μμΈλνκ΅ λνμ :μμ°κ³Όνλν ννλΆ,2020. 2. μ μ°μ€.μ΅κ·Ό κΈ°κ³νμ΅ κΈ°μ μ κΈκ²©ν λ°μ κ³Ό μ΄μ νν λΆμΌμ λν μ μ©μ λ€μν ννμ μ±μ§μ λν ꡬ쑰-μ±μ§ μ λ κ΄κ³λ₯Ό κΈ°λ°μΌλ‘ ν μμΈ‘ λͺ¨νμ κ°λ°μ κ°μνκ³ μλ€. μ©λ§€ν μμ μλμ§λ κ·Έλ¬ν κΈ°κ³νμ΅μ μ μ© μμ€ νλμ΄λ©° λ€μν μ©λ§€ λ΄μ ννλ°μμμ μ€μν μν μ νλ κ·Όλ³Έμ μ±μ§ μ€ νλμ΄λ€. λ³Έ μ°κ΅¬μμ μ°λ¦¬λ λͺ©νλ‘ νλ μ©λ§€ν μμ μλμ§λ₯Ό μμκ°μ μνΈμμ©μΌλ‘λΆν° ꡬν μ μλ μλ‘μ΄ μ¬μΈ΅νμ΅ κΈ°λ° μ©λ§€ν λͺ¨νμ μκ°νλ€. μ μλ μ¬μΈ΅νμ΅ λͺ¨νμ κ³μ° κ³Όμ μ μ©λ§€μ μ©μ§ λΆμμ λν λΆνΈν ν¨μκ° κ° μμμ λΆμλ€μ ꡬ쑰μ μ±μ§μ λν λ²‘ν° ννμ μΆμΆνλ©°, μ΄λ₯Ό ν λλ‘ μμκ° μνΈμμ©μ 볡μ‘ν νΌμ
νΈλ‘ μ κ²½λ§ λμ 벑ν°κ°μ κ°λ¨ν λ΄μ μΌλ‘ ꡬν μ μλ€. 952κ°μ§μ μ κΈ°μ©μ§κ³Ό 147κ°μ§μ μ κΈ°μ©λ§€λ₯Ό ν¬ν¨νλ 6,493κ°μ§μ μ€νμΉλ₯Ό ν λλ‘ κΈ°κ³νμ΅ λͺ¨νμ κ΅μ°¨ κ²μ¦ μνμ μ€μν κ²°κ³Ό, νκ· μ λ μ€μ°¨ κΈ°μ€ 0.2 kcal/mol μμ€μΌλ‘ λ§€μ° λμ μ νλλ₯Ό κ°μ§λ€. μ€μΊν΄λ-κΈ°λ° κ΅μ°¨ κ²μ¦μ κ²°κ³Ό μμ 0.6 kcal/mol μμ€μΌλ‘, μΈμ½μΌλ‘ λΆλ₯ν μ μλ λΉκ΅μ μλ‘μ΄ λΆμ ꡬ쑰μ λν μμΈ‘μ λν΄μλ μ°μν μ νλλ₯Ό 보μΈλ€. λν, μ μλ νΉμ κΈ°κ³νμ΅ λͺ¨νμ κ·Έ ꡬ쑰 μ νΉμ μ©λ§€μ νΉνλμ§ μμκΈ° λλ¬Έμ λμ μλμ±μ κ°μ§λ©° νμ΅μ μ΄μ©ν λ°μ΄ν°μ μλ₯Ό λμ΄λ λ° μ©μ΄νλ€. μμκ° μνΈμμ©μ λν λΆμμ ν΅ν΄ μ μλ μ¬μΈ΅νμ΅ λͺ¨ν μ©λ§€ν μμ μλμ§μ λν κ·Έλ£Ή-κΈ°μ¬λλ₯Ό μ μ¬νν μ μμμ μ μ μμΌλ©°, κΈ°κ³νμ΅μ ν΅ν΄ λ¨μν λͺ©νλ‘ νλ μ±μ§λ§μ μμΈ‘νλ κ²μ λμ΄ λμ± μμΈν 물리ννμ μ΄ν΄λ₯Ό νλ κ²μ΄ κ°λ₯ν κ²μ΄λΌ κΈ°λν μ μλ€.Recent advances in machine learning technologies and their chemical applications lead to the developments of diverse structure-property relationship based prediction models for various chemical properties; the free energy of solvation is one of them and plays a dominant role as a fundamental measure of solvation chemistry. Here, we introduce a novel machine learning-based solvation model, which calculates the target solvation free energy from pairwise atomistic interactions. The novelty of our proposed solvation model involves rather simple architecture: two encoding function extracts vector representations of the atomic and the molecular features from the given chemical structure, while the inner product between two atomistic features calculates their interactions, instead of black-boxed perceptron networks. The cross-validation result on 6,493 experimental measurements for 952 organic solutes and 147 organic solvents achieves an outstanding performance, which is 0.2 kcal/mol in MUE. The scaffold-based split method exhibits 0.6 kcal/mol, which shows that the proposed model guarantees reasonable accuracy even for extrapolated cases. Moreover, the proposed model shows an excellent transferability for enlarging training data due to its solvent-non-specific nature. Analysis of the atomistic interaction map shows there is a great potential that our proposed model reproduces group contributions on the solvation energy, which makes us believe that the proposed model not only provides the predicted target property, but also gives us more detailed physicochemical insights.1. Introduction 1
2. Delfos: Deep Learning Model for Prediction of Solvation Free Energies in Generic Organic Solvents 7
2.1. Methods 7
2.1.1. Embedding of Chemical Contexts 7
2.1.2. Encoder-Predictor Network 9
2.2. Results and Discussions 13
2.2.1. Computational Setup and Results 13
2.2.2. Transferability of the Model for New Compounds 17
2.2.3. Visualization of Attention Mechanism 26
3. Group Contribution Method for the Solvation Energy Estimation with Vector Representations of Atom 29
3.1. Model Description 29
3.1.1. Word Embedding 29
3.1.2. Network Architecture 33
3.2. Results and Discussions 39
3.2.1. Computational Details 39
3.2.2. Prediction Accuracy 42
3.2.3. Model Transferability 44
3.2.4. Group Contributions of Solvation Energy 49
4. Empirical Structure-Property Relationship Model for Liquid Transport Properties 55
5. Concluding Remarks 61
A. Analyzing Kinetic Trapping as a First-Order Dynamical Phase Transition in the Ensemble of Stochastic Trajectories 65
A1. Introduction 65
A2. Theory 68
A3. Lattice Gas Model 70
A4. Mathematical Model 73
A5. Dynamical Phase Transitions 75
A6. Conclusion 82
B. Reaction-Path Thermodynamics of the Michaelis-Menten Kinetics 85
B1. Introduction 85
B2. Reaction Path Thermodynamics 88
B3. Fixed Observation Time 94
B4. Conclusions 101Docto