{"id":2720,"date":"2025-07-10T11:14:21","date_gmt":"2025-07-10T02:14:21","guid":{"rendered":"https:\/\/www.insilico.jp\/blog\/?p=2720"},"modified":"2025-09-11T12:54:19","modified_gmt":"2025-09-11T03:54:19","slug":"direct-maxflux","status":"publish","type":"post","link":"https:\/\/www.insilico.jp\/blog\/2025\/07\/10\/direct-maxflux\/","title":{"rendered":"Direct MaxFlux\u6cd5\u306b\u3088\u308b\u9077\u79fb\u72b6\u614b\u4e88\u6e2c"},"content":{"rendered":"\n

\u7406\u8ad6\u5275\u85ac\u7814\u7a76\u6240\u306e\u91d1\u5b50\u4fe1\u4eba\u3067\u3059\u3002
\u4eca\u56de\u306f\u5316\u5b66\u53cd\u5fdc\u306e\u9077\u79fb\u72b6\u614b\u3092\u4e88\u6e2c\u3059\u308b\u30aa\u30fc\u30d7\u30f3\u30bd\u30fc\u30b9\u30bd\u30d5\u30c8\u30a6\u30a7\u30a2\u3092\u7d39\u4ecb\u3057\u307e\u3059\u3002<\/p>\n\n\n\n

\u53c2\u8003\u6587\u732e
Locating Transition States by Variational Reaction Path Optimization with an Energy-Derivative-Free Objective Function
(Shin-ichi Koda and Shinji Saito, Journal of Chemical Theory and Computation<\/em>, 2024<\/strong>, 20<\/em>, 7, 2798\u20132811.)
https:\/\/doi.org\/10.1021\/acs.jctc.3c01246<\/a><\/p>\n\n\n\n

\u5316\u5b66\u53cd\u5fdc\u306b\u304a\u3044\u3066\u53cd\u5fdc\u7269\u304c\u751f\u6210\u7269\u3078\u3068\u5909\u5316\u3059\u308b\u969b\u306b\u306f\u30a8\u30cd\u30eb\u30ae\u30fc\u306e\u9ad8\u3044\u72b6\u614b\u3092\u7d4c\u7531\u3057\u307e\u3059\u3002\u3053\u306e\u6975\u5927\u5024\u3092\u9077\u79fb\u72b6\u614b\u3068\u3044\u3044\u3001\u53cd\u5fdc\u7269\u3068\u306e\u30a8\u30cd\u30eb\u30ae\u30fc\u5dee\u0394G\u2021\u3092\u6d3b\u6027\u5316\u30a8\u30cd\u30eb\u30ae\u30fc\u3068\u3044\u3044\u307e\u3059\u3002
\u9077\u79fb\u72b6\u614b\u306f\u305d\u306e\u53cd\u5fdc\u304c\u9032\u884c\u3059\u308b\u304b\u3069\u3046\u304b\u3001\u3042\u308b\u3044\u306f\u8907\u6570\u306e\u53cd\u5fdc\u7d4c\u8def\u304c\u8003\u3048\u3089\u308c\u308b\u5834\u5408\u306b\u3069\u308c\u304c\u78ba\u304b\u3089\u3057\u3044\u304b\u3092\u8a55\u4fa1\u3059\u308b\u4e0a\u3067\u975e\u5e38\u306b\u91cd\u8981\u3068\u306a\u3063\u3066\u304d\u307e\u3059\u3002\u4e00\u65b9\u3067\u3053\u306e\u9077\u79fb\u72b6\u614b\u306f\u30a8\u30cd\u30eb\u30ae\u30fc\u7684\u306b\u306f\u4e0d\u5b89\u5b9a\u306a\u72b6\u614b\u3067\u3042\u308b\u305f\u3081\u5b9f\u9a13\u7684\u306b\u691c\u51fa\u3059\u308b\u3053\u3068\u306f\u56f0\u96e3\u3067\u3042\u308a\u3001\u305d\u306e\u305f\u3081\u4eca\u65e5\u3067\u306f\u8a08\u7b97\u79d1\u5b66\u7684\u624b\u6cd5\u304c\u5e83\u304f\u7528\u3044\u3089\u308c\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

\u4e09\u6b21\u5143\u306e\u30a8\u30cd\u30eb\u30ae\u30fc\u30c0\u30a4\u30a2\u30b0\u30e9\u30e0\u4e0a\u3067\u9077\u79fb\u72b6\u614b\u306f\u4e0b\u56f3\u306b\u793a\u3059\u978d\u70b9\uff08suddle point\uff09\u306b\u8a72\u5f53\u3057\u3001\u3042\u308b\u65b9\u5411\uff08\u9752\u7dda\uff09\u304b\u3089\u306f\u6975\u5c0f\u5024\u3001\u5225\u65b9\u5411\uff08\u8d64\u7dda\uff09\u304b\u3089\u306f\u6975\u5927\u5024\u3092\u53d6\u308b\u3088\u3046\u306a\u70b9\u3068\u306a\u3063\u3066\u304a\u308a\u3001\u6700\u5c0f\u30a8\u30cd\u30eb\u30ae\u30fc\u306e\u7b97\u51fa\u3068\u306f\u7570\u306a\u308b\u8a08\u7b97\u65b9\u6cd5\u304c\u6c42\u3081\u3089\u308c\u307e\u3059\u3002<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

\u9077\u79fb\u72b6\u614b\u306e\u4e88\u6e2c\u306b\u306f\u3044\u304f\u3064\u304b\u306e\u65b9\u6cd5\u304c\u958b\u767a\u3055\u308c\u3066\u304a\u308a\u3001MEP(Minimum Energy Path)\u6cd5\u306f\u521d\u671f\u69cb\u9020\u3092\u53cd\u5fdc\u304c\u9032\u884c\u3059\u308b\u65b9\u5411\u3078\u3068\u5909\u5316\u3055\u305b\u305f\u3068\u304d\u306e\u8fd1\u508d\u306e\u6700\u5c0f\u30a8\u30cd\u30eb\u30ae\u30fc\u7d4c\u8def\u3092\u6c42\u3081\u308b\u8a08\u7b97\u65b9\u6cd5\u3067\u3001NEB(Nudged Elastic Band)\u6cd5\u306f\u5404\u539f\u5b50\u3092\u30d0\u30cd\u3067\u7d50\u3093\u3060\u3068\u304d\u306e\u4f38\u3073\u7e2e\u307f\u304b\u3089\u30a8\u30cd\u30eb\u30ae\u30fc\u3092\u8a08\u7b97\u3059\u308b\u65b9\u6cd5\u3067\u3059\u3002\u3057\u304b\u3057\u306a\u304c\u3089\u524d\u8005\u306f\u9077\u79fb\u72b6\u614b\u306b\u8fd1\u3044\u521d\u671f\u69cb\u9020\u3092\u7528\u610f\u3057\u306a\u3051\u308c\u3070\u306a\u3089\u306a\u3044\u70b9\u3001\u5f8c\u8005\u306f\u8a08\u7b97\u30b3\u30b9\u30c8\u304c\u975e\u5e38\u306b\u9ad8\u3044\u70b9\u304c\u554f\u984c\u3068\u306a\u3063\u3066\u3044\u307e\u3057\u305f\u3002
\u4eca\u56de\u7d39\u4ecb\u3059\u308bDirect MaxFlux\u6cd5\u3067\u306f\u5c11\u6570\u306e\u521d\u671f\u5ea7\u6a19\u304b\u3089\u52b9\u7387\u7684\u306b\u8a08\u7b97\u3059\u308b\u3053\u3068\u3067\u3001\u8a08\u7b97\u30b3\u30b9\u30c8\u3092\u4f4e\u6e1b\u3057\u3064\u3064\u9ad8\u7cbe\u5ea6\u306a\u9077\u79fb\u72b6\u614b\u4e88\u6e2c\u3092\u53ef\u80fd\u306b\u3057\u305f\u3082\u306e\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n\n\n\n

\u52d5\u4f5c\u74b0\u5883
Intel Core i7-1265U
Windows 11 Pro
conda 25.3.1<\/p>\n\n\n\n

Direct MaxFlux method\u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb<\/h2>\n\n\n\n

Python\u306e\u4eee\u60f3\u74b0\u5883\u3092\u4f5c\u6210\u3057\u3001\u5fc5\u8981\u306a\u30e9\u30a4\u30d6\u30e9\u30ea\u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3092\u884c\u3044\u3001\u7d9a\u3051\u3066Git\u304b\u3089Direct MaxFlux method (dmf)\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u307e\u3059\u3002<\/p>\n\n\n\n

$ conda create -n dmf python=3.12 -y\n$ conda activate dmf\n$ conda install numpy==1.26 scipy ase cyipopt jupyter -y\n$ pip install git+https:\/\/github.com\/shin1koda\/dmf.git<\/code><\/pre>\n\n\n\n
\u672c\u74b0\u5883\u3067\u306fnumpy-1.26.4 scipy-1.15.2 ase-3.25.0 cyipopt-1.5.0 direct_maxflux-0.1.0\u304c\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3055\u308c\u307e\u3057\u305f\u3002<\/p>\n\n\n\n
Direct MaxFlux method\u306b\u3088\u308b\u9077\u79fb\u72b6\u614b\u306e\u4e88\u6e2c<\/h2>\n\n\n\n
Github\u3067\u914d\u5e03\u3055\u308c\u3066\u3044\u308b\u30b5\u30f3\u30d7\u30eb\u30b9\u30af\u30ea\u30d7\u30c8\u3092\u5b9f\u884c\u3057\u3066\u307f\u307e\u3057\u3087\u3046\u3002
\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u3057\u305f\u30d5\u30a1\u30a4\u30eb\u306e\u3046\u3061\u3001react.xyz\u304c\u53cd\u5fdc\u539f\u6599\u3001prod.xyz\u304c\u751f\u6210\u7269\u306e\u69cb\u9020\u30d5\u30a1\u30a4\u30eb\u3068\u306a\u3063\u3066\u304a\u308a\u3001\u30a2\u30bb\u30c8\u30a2\u30eb\u30c7\u30d2\u30c9\u306e\u30a8\u30ce\u30fc\u30eb\u3078\u306e\u4e92\u5909\u7570\u6027\u53cd\u5fdc\u3068\u306a\u3063\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n
\"\"<\/figure>\n\n\n\n
$ git clone https:\/\/github.com\/shin1koda\/dmf.git\n$ cd dmf\n$ python sample.py<\/code><\/pre>\n\n\n\n
\u672c\u74b0\u5883\u3067\u306f\u6570\u79d2\u3067\u8a08\u7b97\u304c\u5b9f\u884c\u3055\u308c\u3001\u6700\u7d42\u884c\u306bOptimal Solution Found\u3068\u8868\u793a\u3055\u308c\u308c\u3070\u9077\u79fb\u72b6\u614b\u304c\u8a08\u7b97\u3055\u308c\u3066\u3044\u307e\u3059\u3002\u3044\u304f\u3064\u304b\u306e\u30d5\u30a1\u30a4\u30eb\u304c\u51fa\u529b\u3055\u308c\u307e\u3059\u304c\u3001\u305d\u306e\u3046\u3061sample_fin.traj\u304c\u6700\u7d42\u7684\u306a\u9077\u79fb\u72b6\u614b\u69cb\u9020\u306e\u542b\u307e\u308c\u308b\u3082\u306e\u306b\u306a\u308a\u307e\u3059\u3002
windows\u3067\u306f\u30c8\u30e9\u30b8\u30a7\u30af\u30c8\u30ea\u30d5\u30a1\u30a4\u30eb\u304c\u6b63\u3057\u304f\u958b\u3051\u306a\u3044\u3053\u3068\u304c\u3042\u308b\u306e\u3067jupyter notebook\u3067\u8868\u793a\u3057\u3066\u307f\u307e\u3057\u3087\u3046\u3002jupyter notebook\u3092\u8d77\u52d5\u3057\u3001\u4ee5\u4e0b\u306e\u30b9\u30af\u30ea\u30d7\u30c8\u3092\u5b9f\u884c\u3057\u307e\u3059\u3002<\/p>\n\n\n\n
from ase.io import read\nimport nglview as nv\nfrom IPython.display import display\nfrom ase.io import write\natoms_list = read(\"sample_fin.traj\", index=\":\")\nfor i, atoms in enumerate(atoms_list):\n    print(f\"structure {i}\")\n    view = nv.show_ase(atoms)\n    view.add_representation(\"ball+stick\")\n    display(view)<\/code><\/pre>\n\n\n\n
5\u3064\u306e\u69cb\u9020\u304c\u63cf\u753b\u3055\u308c\u3001\u305d\u306e\u3046\u3061structure0\u306freact.xyz\u306e\u69cb\u9020\u3001structure4\u306fprod.xyz\u306e\u69cb\u9020\u3067\u3042\u308a\u3001structure1-3\u306e\u3044\u305a\u308c\u304b\u304c\u9077\u79fb\u72b6\u614b\u3068\u306a\u308a\u307e\u3059\u3002\u4eca\u56de\u306fstructure1\u304c\u6700\u3082\u9077\u79fb\u72b6\u614b\u306b\u8fd1\u3044\u69cb\u9020\u3068\u306a\u3063\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n
\"\"<\/figure>\n\n\n\n
\u3088\u308a\u5b9f\u7528\u7684\u306a\u4f8b\u3068\u3057\u3066\u30d6\u30bf\u30b8\u30a8\u30f3\u3068\u7121\u6c34\u30de\u30ec\u30a4\u30f3\u9178\u306e\u30c7\u30a3\u30fc\u30eb\u30ba\u30a2\u30eb\u30c0\u30fc\u53cd\u5fdc\u306e\u9077\u79fb\u72b6\u614b\u3092\u6c42\u3081\u3066\u307f\u307e\u3057\u3087\u3046\u3002<\/p>\n\n\n\n
\"\"<\/figure>\n\n\n\n
\u53cd\u5fdc\u7269\u30fb\u751f\u6210\u7269\u305d\u308c\u305e\u308c\u306e\u69cb\u9020\u30d5\u30a1\u30a4\u30eb\u3092chemdraw\u7b49\u3092\u4f7f\u3063\u3066\u4f5c\u6210\u3057\u307e\u3059\u3002
\u3053\u306e\u3068\u304d\u53cd\u5fdc\u7269\u30fb\u751f\u6210\u7269\u305d\u308c\u305e\u308c\u306e\u539f\u5b50\u306e\u30e9\u30d9\u30eb\u756a\u53f7\u304c\u5bfe\u5fdc\u3059\u308b\u3088\u3046\u306b\u6ce8\u610f\u3057\u307e\u3059\u3002<\/p>\n\n\n\n
19\nreact\nC          0.41460        0.70090        1.79300\nC          0.51180       -0.67850        2.20430\nC         -0.55420        1.54310        2.17170\nC         -0.50720       -1.54590        2.20840\nH          1.23020        1.06960        1.17300\nH          1.50700       -1.02370        2.47880\nH         -0.54110        2.57740        1.84350\nH         -1.36190        1.23500        2.82700\nH         -0.34980       -2.57740        2.50780\nH         -1.50700       -1.25880        1.90520\nC         -2.35400        0.69050       -0.02110\nC         -2.33320       -0.63580       -0.00490\nC         -3.74810        1.10390       -0.00020\nO         -4.51230       -0.00630        0.02710\nC         -3.71380       -1.09240        0.02560\nO         -4.06290       -2.25680        0.04600\nO         -4.13400        2.25680       -0.00610\nH         -1.52790        1.37880       -0.04600\nH         -1.48770       -1.29930       -0.01280<\/code><\/pre>\n\n\n\n
19\nprod\nC          1.57650        0.36120        0.63560\nC          1.52190       -0.83050        0.03150\nC          0.43280        1.32030        0.68720\nC          0.30920       -1.35890       -0.66600\nH          2.49810        0.66410        1.12990\nH          2.40420       -1.46870        0.03310\nH          0.79580        2.31390        0.39530\nH          0.12260        1.41460        1.73610\nH          0.54890       -1.40460       -1.73610\nH          0.13930       -2.39260       -0.34300\nC         -0.74710        0.95280       -0.21770\nC         -0.95310       -0.52510       -0.48700\nC         -2.01000        1.27970        0.51550\nO         -2.49250        0.16320        1.11560\nC         -1.80350       -0.92420        0.68210\nO         -1.94090       -2.04950        1.13030\nO         -2.49810        2.39260        0.62320\nH         -0.70570        1.50160       -1.16490\nH         -1.59560       -0.66160       -1.36860<\/code><\/pre>\n\n\n\n
\u53cd\u5fdc\u7269\u3092react.xyz\u3001\u751f\u6210\u7269\u3092prod.xyz\u3068\u3057\u3066\u3001sample.py\u3068\u540c\u3058\u30d5\u30a9\u30eb\u30c0\u306b\u4fdd\u5b58\u3057\u3001sample.py\u3092\u5b9f\u884c\u3057\u307e\u3059\u3002
\u5148\u307b\u3069\u3068\u540c\u69d8\u306bjupyter notebook\u3067\u751f\u6210\u3055\u308c\u305f\u69cb\u9020\u3092\u8868\u793a\u3057\u3066\u307f\u307e\u3057\u3087\u3046\u3002<\/p>\n\n\n\n
\"\"<\/figure>\n\n\n\n
\u4eca\u56de\u306fstructure2\u304c\u6700\u3082\u9077\u79fb\u72b6\u614b\u306b\u8fd1\u3044\u69cb\u9020\u3068\u3057\u3066\u8868\u793a\u3055\u308c\u307e\u3057\u305f\u3002
\u6b21\u306b\u5f97\u3089\u308c\u305f\u69cb\u9020\u304b\u3089\u30a8\u30cd\u30eb\u30ae\u30fc\u3092\u8a08\u7b97\u3057\u3066\u3001\u30c0\u30a4\u30a2\u30b0\u30e9\u30e0\u3092\u4f5c\u6210\u3057\u3066\u307f\u307e\u3057\u3087\u3046\u3002
jupyter notebook\u304b\u3089\u305d\u308c\u305e\u308c\u306e\u69cb\u9020\u3092\u66f8\u304d\u51fa\u3057\u307e\u3059\u3002<\/p>\n\n\n\n
write('molecule0.xyz', atoms_list[0])\nwrite('molecule1.xyz', atoms_list[1])\nwrite('molecule2.xyz', atoms_list[2])\nwrite('molecule3.xyz', atoms_list[3])\nwrite('molecule4.xyz', atoms_list[4])<\/code><\/pre>\n\n\n\n
\u30a8\u30cd\u30eb\u30ae\u30fc\u306e\u8a08\u7b97\u306b\u306fASE\u30c7\u30d5\u30a9\u30eb\u30c8\u306eEMP\uff08effective-medium theory\uff09\u3067\u306f\u7cbe\u5ea6\u304c\u4f4e\u3044\u306e\u3067\u534a\u7d4c\u9a13\u7684\u8a08\u7b97\u6cd5\u306exTB\uff08Extended Tight Binding\uff09\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002<\/p>\n\n\n\n
$ conda install xtb xtb-python<\/code><\/pre>\n\n\n\n
\u4ee5\u4e0b\u306e\u30d7\u30ed\u30b0\u30e9\u30e0\u3067\u30a8\u30cd\u30eb\u30ae\u30fc\u8a08\u7b97\u3068\u30d7\u30ed\u30c3\u30c8\u3092\u3057\u3066\u307f\u307e\u3057\u3087\u3046\u3002<\/p>\n\n\n\n
import matplotlib.pyplot as plt\nfrom ase.io import read\nimport numpy as np\nfrom xtb.ase.calculator import XTB\nimport sys\nimport os\n\nNUM_INTERMEDIATES = 5\nEV_TO_KCAL_PER_MOL = 23.0605\n\ndef plot_from_xyz_sequence():\n    filenames = [f'molecule{i}.xyz' for i in range(NUM_INTERMEDIATES)]\n    calculator = XTB() \n\n    # --- 1. \u5404\u30d5\u30a1\u30a4\u30eb\u306e\u30a8\u30cd\u30eb\u30ae\u30fc\u3092\u8a08\u7b97 ---\n    energies_ev = []\n    labels = []\n    for filename in filenames:\n        atoms = read(filename)\n        label = os.path.splitext(os.path.basename(filename))[0]\n        labels.append(label)\n\n        atoms.set_calculator(calculator)\n        energy = atoms.get_potential_energy()\n        energies_ev.append(energy)\n\n        print(f\"{filename}: {energy:.6f} eV\")\n\n    print(\"--- \u8a08\u7b97\u5b8c\u4e86 ---\\n\")\n\n    # --- 2. \u76f8\u5bfe\u30a8\u30cd\u30eb\u30ae\u30fc\u306e\u8a08\u7b97 (\u5168\u4f53\u306e\u6700\u5c0f\u30a8\u30cd\u30eb\u30ae\u30fc\u30920\u3068\u3059\u308b) ---\n    energies_ev = np.array(energies_ev)\n    min_energy_ev = energies_ev.min()\n    relative_energies_ev = energies_ev - min_energy_ev\n\n    # eV\u304b\u3089kcal\/mol\u3078\u5358\u4f4d\u5909\u63db\n    relative_energies_kcal = relative_energies_ev * EV_TO_KCAL_PER_MOL\n\n    print(\"--- \u76f8\u5bfe\u30a8\u30cd\u30eb\u30ae\u30fc (\u5358\u4f4d: kcal\/mol) ---\")\n    for i, rel_e in enumerate(relative_energies_kcal):\n        print(f\"  {labels[i]:<15s}: {rel_e:+.4f} kcal\/mol\")\n    print(\"---------------------------------------\")\n\n    # --- 3. \u30a8\u30cd\u30eb\u30ae\u30fc\u30c0\u30a4\u30a2\u30b0\u30e9\u30e0\u306e\u30d7\u30ed\u30c3\u30c8 ---\n    plt.style.use('seaborn-v0_8-whitegrid')\n    fig, ax = plt.subplots(figsize=(12, 7))\n\n    x_values = range(len(relative_energies_kcal))\n    ax.plot(x_values, relative_energies_kcal, marker='o', linestyle='-', color='dodgerblue', zorder=2)\n\n    ax.set_title('Reaction Energy Profile', fontsize=16)\n    ax.set_xlabel('Structure', fontsize=12)\n    ax.set_ylabel('Relative Energy (kcal\/mol)', fontsize=12)\n\n    ax.set_xticks(x_values)\n    ax.set_xticklabels(labels, rotation=45, ha=\"right\")\n\n    ax.tick_params(axis='both', which='major', labelsize=10)\n    fig.tight_layout()\n    plt.show()\n\nif __name__ == '__main__':\n    plot_from_xyz_sequence()<\/code><\/pre>\n\n\n\n
\u8a08\u7b97\u304c\u5b9f\u884c\u3055\u308c\u308b\u3068\u30a8\u30cd\u30eb\u30ae\u30fc\u3068\u30c0\u30a4\u30a2\u30b0\u30e9\u30e0\u304c\u8868\u793a\u3055\u308c\u307e\u3059\u3002<\/p>\n\n\n\n
\"\"<\/figure>\n\n\n\n
\u9077\u79fb\u72b6\u614b\u306b\u8fd1\u3044structure2\u304c\u6700\u3082\u30a8\u30cd\u30eb\u30ae\u30fc\u304c\u9ad8\u3044\u72b6\u614b\u3068\u3057\u3066\u8a08\u7b97\u3055\u308c\u307e\u3057\u305f\u3002<\/p>\n\n\n\n
\u304a\u308f\u308a\u306b<\/h2>\n\n\n\n
\u5f93\u6765\u3001\u9077\u79fb\u72b6\u614b\u306e\u8a08\u7b97\u306b\u306f\u521d\u671f\u69cb\u9020\u306e\u4f5c\u308a\u65b9\u3084\u8a08\u7b97\u624b\u6cd5\u306e\u9078\u5b9a\u306a\u3069\u69d8\u3005\u306a\u30ce\u30a6\u30cf\u30a6\u304c\u5fc5\u8981\u3068\u3055\u308c\u3066\u304d\u307e\u3057\u305f\u3002\u3053\u306e\u3088\u3046\u306b\u5358\u7d14\u306a\u30a4\u30f3\u30d7\u30c3\u30c8\u306e\u307f\u3067\u8a08\u7b97\u304c\u3067\u304d\u308b\u3088\u3046\u306b\u306a\u308b\u3068\u5408\u6210\u5316\u5b66\u8005\u306b\u3068\u3063\u3066\u3082\u8a08\u7b97\u5316\u5b66\u304c\u3088\u308a\u8eab\u8fd1\u306a\u3082\u306e\u306b\u306a\u3063\u3066\u304f\u308b\u3068\u601d\u3044\u307e\u3059\u3002
\u307e\u305f\u3001\u4eca\u56de\u7d39\u4ecb\u3057\u305f\u30d7\u30ed\u30b0\u30e9\u30e0\u306fASE\uff08Atomic Simulation Environment\uff09\u3092\u7528\u3044\u3066\u8a08\u7b97\u3092\u884c\u3063\u3066\u3044\u307e\u3059\u3002\u305d\u306e\u305f\u3081\u5916\u90e8\u30bd\u30d5\u30c8\u30a6\u30a7\u30a2\u3092\u4f7f\u3063\u3066\u3088\u308a\u7cbe\u5ea6\u306e\u9ad8\u3044\u8a08\u7b97\u3092\u5b9f\u884c\u3059\u308b\u3053\u3068\u3082\u53ef\u80fd\u3067\u3059\u3002\u305d\u306e\u305f\u3081\u5fc5\u8981\u306b\u5fdc\u3058\u3066DFT\u8a08\u7b97\u3084\u865a\u632f\u52d5\u89e3\u6790\u3001IRC\u8a08\u7b97\u3078\u3068\u79fb\u884c\u3057\u3066\u3044\u304f\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n\n\n\n
\u524d\u56de\u8a18\u4e8b
GESim\u306b\u3088\u308b\u5206\u5b50\u985e\u4f3c\u6027\u8a55\u4fa1
https:\/\/www.insilico.jp\/blog\/2025\/06\/19\/gesim_similarity\/<\/a>
\u6b21\u56de\u8a18\u4e8b
Chemprop\u3092\u6d3b\u7528\u3057\u305f\u6a5f\u68b0\u5b66\u7fd2\u306b\u3088\u308b\u5316\u5b66\u7279\u6027\u4e88\u6e2c
https:\/\/www.insilico.jp\/blog\/2025\/09\/11\/chemprop\/<\/a><\/p>\n\n\n\n
<\/p>\n","protected":false},"excerpt":{"rendered":"
\u7406\u8ad6\u5275\u85ac\u7814\u7a76\u6240\u306e\u91d1\u5b50\u4fe1\u4eba\u3067\u3059\u3002\u4eca\u56de\u306f\u5316\u5b66\u53cd\u5fdc\u306e\u9077\u79fb\u72b6\u614b\u3092\u4e88\u6e2c\u3059\u308b\u30aa\u30fc\u30d7\u30f3\u30bd\u30fc\u30b9\u30bd\u30d5\u30c8\u30a6\u30a7\u30a2\u3092\u7d39\u4ecb\u3057\u307e\u3059\u3002 \u53c2\u8003\u6587\u732eLocating Transition States by Variational Reaction Pa…<\/p>\n","protected":false},"author":7,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[118],"tags":[97],"class_list":["post-2720","post","type-post","status-publish","format-standard","hentry","category-ai-drug-discovery","tag-python"],"yoast_head":"\nDirect MaxFlux\u6cd5\u306b\u3088\u308b\u9077\u79fb\u72b6\u614b\u4e88\u6e2c - In Silico \u5275\u85ac<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.insilico.jp\/blog\/2025\/07\/10\/direct-maxflux\/\" \/>\n<meta property=\"og:locale\" content=\"ja_JP\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Direct MaxFlux\u6cd5\u306b\u3088\u308b\u9077\u79fb\u72b6\u614b\u4e88\u6e2c - In Silico \u5275\u85ac\" \/>\n<meta property=\"og:description\" content=\"\u7406\u8ad6\u5275\u85ac\u7814\u7a76\u6240\u306e\u91d1\u5b50\u4fe1\u4eba\u3067\u3059\u3002\u4eca\u56de\u306f\u5316\u5b66\u53cd\u5fdc\u306e\u9077\u79fb\u72b6\u614b\u3092\u4e88\u6e2c\u3059\u308b\u30aa\u30fc\u30d7\u30f3\u30bd\u30fc\u30b9\u30bd\u30d5\u30c8\u30a6\u30a7\u30a2\u3092\u7d39\u4ecb\u3057\u307e\u3059\u3002 \u53c2\u8003\u6587\u732eLocating Transition States by Variational Reaction Pa...\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.insilico.jp\/blog\/2025\/07\/10\/direct-maxflux\/\" \/>\n<meta property=\"og:site_name\" content=\"In Silico \u5275\u85ac\" \/>\n<meta property=\"article:published_time\" content=\"2025-07-10T02:14:21+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2025-09-11T03:54:19+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.insilico.jp\/blog\/wp-content\/uploads\/2025\/07\/energy_diagram.png\" \/>\n\t<meta property=\"og:image:width\" content=\"800\" \/>\n\t<meta property=\"og:image:height\" content=\"600\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"kaneko\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"\u57f7\u7b46\u8005\" \/>\n\t<meta name=\"twitter:data1\" content=\"kaneko\" \/>\n\t<meta name=\"twitter:label2\" content=\"\u63a8\u5b9a\u8aad\u307f\u53d6\u308a\u6642\u9593\" \/>\n\t<meta name=\"twitter:data2\" content=\"16\u5206\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\\\/\\\/www.insilico.jp\\\/blog\\\/2025\\\/07\\\/10\\\/direct-maxflux\\\/#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/www.insilico.jp\\\/blog\\\/2025\\\/07\\\/10\\\/direct-maxflux\\\/\"},\"author\":{\"name\":\"kaneko\",\"@id\":\"https:\\\/\\\/www.insilico.jp\\\/blog\\\/#\\\/schema\\\/person\\\/b0ca2eef6dece0def1eda9368533cdac\"},\"headline\":\"Direct MaxFlux\u6cd5\u306b\u3088\u308b\u9077\u79fb\u72b6\u614b\u4e88\u6e2c\",\"datePublished\":\"2025-07-10T02:14:21+00:00\",\"dateModified\":\"2025-09-11T03:54:19+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/www.insilico.jp\\\/blog\\\/2025\\\/07\\\/10\\\/direct-maxflux\\\/\"},\"wordCount\":138,\"image\":{\"@id\":\"https:\\\/\\\/www.insilico.jp\\\/blog\\\/2025\\\/07\\\/10\\\/direct-maxflux\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/www.insilico.jp\\\/blog\\\/wp-content\\\/uploads\\\/2025\\\/07\\\/energy_diagram.png\",\"keywords\":[\"Python\"],\"articleSection\":[\"AI\u5275\u85ac\u95a2\u9023\"],\"inLanguage\":\"ja\"},{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/www.insilico.jp\\\/blog\\\/2025\\\/07\\\/10\\\/direct-maxflux\\\/\",\"url\":\"https:\\\/\\\/www.insilico.jp\\\/blog\\\/2025\\\/07\\\/10\\\/direct-maxflux\\\/\",\"name\":\"Direct MaxFlux\u6cd5\u306b\u3088\u308b\u9077\u79fb\u72b6\u614b\u4e88\u6e2c - 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