今天，笔者的第二篇第一作者论文“Complex reaction processes in combustion unraveled by neural network-based molecular dynamics simulation”在Nature Communications正式发表。华东师范大学为论文的第一单位。

在这篇论文中，笔者生成了甲烷燃烧的数据集，并使用DeePMD-kit软件构建了基于深度学习方法的反应势能面，进行了长达1 ns的甲烷燃烧反应的分子动力学模拟。借助笔者开发的ReacNetGenerator软件，笔者探明了甲烷燃烧的微观反应机理，对每个基元反应提供了原子尺度的理解。

在此，本文以个人视角，回顾这篇论文产生和发表的历程。

组里拨科研经费新入了16块3090，正好用3090测试一下深度学习势能+分子动力学模拟（MD）的性能。

程序版本：DeePMD-kit 2.0.0.b0 双精度^[1][2]，TensorFlow 2.4.1，CUDA Toolkit 11.1，LAMMPS 29Oct2020

测试结果

测试样本为examples中的水，模型为se_e2_a。

可以看出，在上述测试案例中，3090的双精度性能较2080Ti有较大提升，但仍落后于V100和P100等专业计算显卡。当然，考虑到计算卡的价格也是3090的数倍，3090仍是不错的选择。

惊闻微软终——于——放出了WSL GUI（WSLg），赶紧体验一把！

三年前的一个平常的清晨，天尚未亮，笔者前往秋实阁校车站，乘坐6:30的校车。

2018年1月8日晨

一校两区，理科大楼到宿舍的距离长达25公里。交通不便，除了100元一次的出租车外，5元一次的校车则是唯一的选择。40分钟的车程后，校车准时抵达河东食堂门口，笔者愉快地开始用餐，同时在知乎上浏览最新的劝退信息。30分钟后，笔者掏出钥匙，打开办公室的大门。此时的办公室空无一人。打开显示器，一天的科研生活正式开始。

9:30，每周的组会开始，大家聚集在一起，交流领域内的文章和工作进展。两校区之间路途遥远，因此每周笔者只去办公室1-2次，剩下的日子则在宿舍工作，每周的例会时间便由此固定下来。

中餐和晚餐，笔者往往在河西食堂用餐，早已对每个窗口卖什么菜烂熟于胸。20:15，笔者结束手头的科研工作，前往停车场乘坐20:50的返程校车。又是40分钟的车程后，笔者抵达闵行校区校医院，一天的科研时光到此结束。

今天，上海轨道交通15号线正式开通，笔者却回想起了当年的“科研路”。正是坚持在往返两校区的路途中，笔者才产出了人生中最初的科研成果，开启了人生的科研路。

后记：这个系列目前的更新速度是每年一篇，那么我们明年再见吧。

Install TensorFlow’s C++ interface

The tensorflow’s C++ interface will be compiled from the source code. Firstly one installs bazel. The bazel version 3.1.0 should be used. A full instruction of bazel installation can be found here.

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cd /some/workspace
wget https://github.com/bazelbuild/bazel/releases/download/3.1.0/bazel-3.1.0-installer-linux-x86_64.sh
chmod +x bazel-3.1.0-installer-linux-x86_64.sh
./bazel-3.1.0-installer-linux-x86_64.sh --prefix /some/workspace/bazel
export PATH=/some/workspace/bazel/bin:$PATH

Firstly get the source code of the tensorflow

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git clone https://github.com/tensorflow/tensorflow tensorflow -b v2.3.0 --depth=1
cd tensorflow
./configure

You will answer a list of questions that help configure the building of tensorflow. You may want to answer the question like the following. If you do not want to add CUDA support, please answer no.

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Please specify the location of python. [Default is xxx]:

Found possible Python library paths:
  xxx
Please input the desired Python library path to use.  Default is [xxx]

Do you wish to build TensorFlow with OpenCL SYCL support? [y/N]:
No OpenCL SYCL support will be enabled for TensorFlow.

Do you wish to build TensorFlow with ROCm support? [y/N]:
No ROCm support will be enabled for TensorFlow.

Do you wish to build TensorFlow with CUDA support? [y/N]: y
CUDA support will be enabled for TensorFlow.

Do you wish to build TensorFlow with TensorRT support? [y/N]:
No TensorRT support will be enabled for TensorFlow.

Found CUDA 10.2 in:
    /usr/local/cuda/lib64
    /usr/local/cuda/include
Found cuDNN 7 in:
    /usr/local/cuda/lib64
    /usr/local/cuda/include

Please specify a list of comma-separated CUDA compute capabilities you want to build with.
You can find the compute capability of your device at: https://developer.nvidia.com/cuda-gpus.
Please note that each additional compute capability significantly increases your build time and binary size, and that TensorFlow only supports compute capabilities >= 3.5 [Default is: 7.5,7.5]:

Do you want to use clang as CUDA compiler? [y/N]:
nvcc will be used as CUDA compiler.

Please specify which gcc should be used by nvcc as the host compiler. [Default is /usr/bin/gcc]:

Please specify optimization flags to use during compilation when bazel option "--config=opt" is specified [Default is -march=native -Wno-sign-compare]:

Would you like to interactively configure ./WORKSPACE for Android builds? [y/N]:
Not configuring the WORKSPACE for Android builds.

Preconfigured Bazel build configs. You can use any of the below by adding "--config=<>" to your build command. See .bazelrc for more details.
	--config=mkl         	# Build with MKL support.
	--config=monolithic  	# Config for mostly static monolithic build.
	--config=ngraph      	# Build with Intel nGraph support.
	--config=numa        	# Build with NUMA support.
	--config=dynamic_kernels	# (Experimental) Build kernels into separate shared objects.
	--config=v2          	# Build TensorFlow 2.x instead of 1.x.
Preconfigured Bazel build configs to DISABLE default on features:
	--config=noaws       	# Disable AWS S3 filesystem support.
	--config=nogcp       	# Disable GCP support.
	--config=nohdfs      	# Disable HDFS support.
	--config=nonccl      	# Disable NVIDIA NCCL support.
Configuration finished

The library path for Python should be set accordingly.

Now build the shared library of tensorflow:

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bazel build -c opt --verbose_failures //tensorflow:libtensorflow_cc.so

You may want to add options --copt=-msse4.2, --copt=-mavx, --copt=-mavx2 and --copt=-mfma to enable SSE4.2, AVX, AVX2 and FMA SIMD accelerations, respectively. It is noted that these options should be chosen according to the CPU architecture. If the RAM becomes an issue of your machine, you may limit the RAM usage by using --local_resources 2048,.5,1.0.

Now I assume you want to install tensorflow in directory $tensorflow_root. Create the directory if it does not exists

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mkdir -p $tensorflow_root

Now, copy the libraries to the tensorflow’s installation directory:

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mkdir -p $tensorflow_root/lib
cp -d bazel-bin/tensorflow/libtensorflow_cc.so* $tensorflow_root/lib/
cp -d bazel-bin/tensorflow/libtensorflow_framework.so* $tensorflow_root/lib/
cp -d $tensorflow_root/lib/libtensorflow_framework.so.2 $tensorflow_root/lib/libtensorflow_framework.so

Then copy the headers

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mkdir -p $tensorflow_root/include/tensorflow
rsync -avzh --exclude '_virtual_includes/' --include '*/' --include '*.h' --include '*.inc' --exclude '*' bazel-bin/ $tensorflow_root/include/
rsync -avzh --include '*/' --include '*.h' --include '*.inc' --exclude '*' tensorflow/cc $tensorflow_root/include/tensorflow/
rsync -avzh --include '*/' --include '*.h' --include '*.inc' --exclude '*' tensorflow/core $tensorflow_root/include/tensorflow/
rsync -avzh --include '*/' --include '*' --exclude '*.cc' third_party/ $tensorflow_root/include/third_party/
rsync -avzh --include '*/' --include '*' --exclude '*.txt' bazel-tensorflow/external/eigen_archive/Eigen/ $tensorflow_root/include/Eigen/
rsync -avzh --include '*/' --include '*' --exclude '*.txt' bazel-tensorflow/external/eigen_archive/unsupported/ $tensorflow_root/include/unsupported/
rsync -avzh --include '*/' --include '*.h' --include '*.inc' --exclude '*' bazel-tensorflow/external/com_google_protobuf/src/google/ $tensorflow_root/include/google/
rsync -avzh --include '*/' --include '*.h' --include '*.inc' --exclude '*' bazel-tensorflow/external/com_google_absl/absl/ $tensorflow_root/include/absl/

Troubleshooting

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git: unknown command -C ...

This may be your git version issue, because low version of git does not support this command. Upgrading your git maybe helpful.

每个知乎答主都有退乎的梦想，但退乎前如果删光回答，则十分可惜。因此，我用Python写了60行的脚本，可以在退乎前备份自己的所有回答和文章，以免事后后悔。

GitHub - njzjz/zhihubackup

安装

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pip install git+https://github.com/njzjz/zhihubackup

使用

假如你是@贱贱，你的id是splitter，那么可以编写Python脚本：

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from zhihubackup import backup_zhihu
backup_zhihu("splitter")

静等一段时间。运行结束后，可以看到产生了名为splitter的文件夹：

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- splitter
|- answer   (842 files)
|- article  (101 files)
|- pin      (3214 files)
|- question (57 files)

备份已经成功，现在可以删光回答和文章了。

2019年5月，林峰邀请我暑假到北大实习，我欣然答应。拿到学士学位证书后，我在家里休息了一周，顺便处理了升学的各项事宜。这些全部搞定后，6月下旬，我启程前往北京。

统计了论文宣传稿在微信上的传播路径（图），产生几个有意思的结论。一是学校媒体和传统媒体的影响力仍然是最大的，应当作为宣传时优先考虑的对象；二是自媒体喜欢抄来抄去，扩大了宣传稿的阅读量；三是一些媒体乱改标题，总喜欢搞个大新闻。

华东师范大学化学与分子工程学院朱通副研究员指导学生结合人工智能算法、量子化学理论以及分子动力学方法，实现了燃料燃烧的高精度计算机模拟，在原子尺度和亚飞秒时间分辨率下获得了甲烷燃烧的化学反应网络。该成果以“Complex reaction processes in combustion unraveled by neural network-based molecular dynamics simulation”为题近日发表在Nature Communications [Nat. Commun. 2020, 11, 5713.]。华东师范大学为论文第一单位，2019届本科毕业生曾晋哲为论文的第一作者，朱通副研究员和张增辉教授为论文的共同通讯作者。

在搞定libdeepmd的构建后，我们终于来到了最后一关：LAMMPS。

这里以今年3月发布的LAMMPS stable_3Mar2020为例。