The availability of tera-byte scale experiment data calls for AI driven
approaches which automatically discover scientific models from data.
Nonetheless, significant challenges present in AI-driven scientific discovery:
(i) The annotation of large scale datasets requires fundamental re-thinking in
developing scalable crowdsourcing tools. (ii) The learning of scientific models
from data calls for innovations beyond black-box neural nets. (iii) Novel
visualization and diagnosis tools are needed for the collaboration of
experimental and theoretical physicists, and computer scientists. We present
Phase-Field-Lab platform for end-to-end phase field model discovery, which
automatically discovers phase field physics models from experiment data,
integrating experimentation, crowdsourcing, simulation and learning.
Phase-Field-Lab combines (i) a streamlined annotation tool which reduces the
annotation time (by ~50-75%), while increasing annotation accuracy compared to
baseline; (ii) an end-to-end neural model which automatically learns phase
field models from data by embedding phase field simulation and existing domain
knowledge into learning; and (iii) novel interfaces and visualizations to
integrate our platform into the scientific discovery cycle of domain
scientists. Our platform is deployed in the analysis of nano-structure
evolution in materials under extreme conditions (high temperature and
irradiation). Our approach reveals new properties of nano-void defects, which
otherwise cannot be detected via manual analysis.

通过 AI 驱动的科学发现，我们提供了一种自动从实验数据中发现相场物理模型的方案，结合了实验、众包、模拟和学习的全流程。我们的平台在分析极端条件下材料的纳米结构演变中被应用，揭示了手动分析无法检测到的纳米空缺缺陷的新特性。

基于实验、众包、模拟和学习的端到端相场模型发现

End-to-end Phase Field Model Discovery Combining Experimentation,  Crowdsourcing, Simulation and Learning

Learning symbolic expressions directly from experiment data is a vital step
in AI-driven scientific discovery. Nevertheless, state-of-the-art approaches
are limited to learning simple expressions. Regressing expressions involving
many independent variables still remain out of reach. Motivated by the control
variable experiments widely utilized in science, we propose Control Variable
Genetic Programming (CVGP) for symbolic regression over many independent
variables. CVGP expedites symbolic expression discovery via customized
experiment design, rather than learning from a fixed dataset collected a
priori. CVGP starts by fitting simple expressions involving a small set of
independent variables using genetic programming, under controlled experiments
where other variables are held as constants. It then extends expressions
learned in previous generations by adding new independent variables, using new
control variable experiments in which these variables are allowed to vary.
Theoretically, we show CVGP as an incremental building approach can yield an
exponential reduction in the search space when learning a class of expressions.
Experimentally, CVGP outperforms several baselines in learning symbolic
expressions involving multiple independent variables.

Control Variable Genetic Programming 利用控制变量实验快速进行符号回归，比起多个独立变量的简单表达，其优于其他基线算法。

控制变量遗传编程的符号回归

Symbolic Regression via Control Variable Genetic Programming

The ability of an agent to perform well in new and unseen environments is a
crucial aspect of intelligence. In machine learning, this ability is referred
to as strong or out-of-distribution generalization. However, simply considering
differences in data distributions is not sufficient to fully capture
differences in environments. In the present paper, we assay out-of-variable
generalization, which refers to an agent's ability to handle new situations
that involve variables never jointly observed before. We expect that such
ability is important also for AI-driven scientific discovery: humans, too,
explore 'Nature' by probing, observing and measuring subsets of variables at
one time. Mathematically, it requires efficient re-use of past marginal
knowledge, i.e., knowledge over subsets of variables. We study this problem,
focusing on prediction tasks that involve observing overlapping, yet distinct,
sets of causal parents. We show that the residual distribution of one
environment encodes the partial derivative of the true generating function with
respect to the unobserved causal parent. Hence, learning from the residual
allows zero-shot prediction even when we never observe the outcome variable in
the other environment.

本文旨在研究 AI 智能代理器对于陌生和新的因变量的预测和探索问题，提出了一种基于先前经验的较为有效的处理方法，并能够实现零样本预测。

非变量泛化

Out-of-Variable Generalization

Constructing a comprehensive, accurate, and useful scientific knowledge base
is crucial for human researchers synthesizing scientific knowledge and for
enabling Al-driven scientific discovery. However, the current process is
difficult, error-prone, and laborious due to (1) the enormous amount of
scientific literature available; (2) the highly-specialized scientific domains;
(3) the diverse modalities of information (text, figure, table); and, (4) the
silos of scientific knowledge in different publications with inconsistent
formats and structures. Informed by a formative study and iterated with
participatory design workshops, we designed and developed KnowledgeShovel, an
Al-in-the-Loop document annotation system for researchers to construct
scientific knowledge bases. The design of KnowledgeShovel introduces a
multi-step multi-modal human-AI collaboration pipeline that aligns with users'
existing workflows to improve data accuracy while reducing the human burden. A
follow-up user evaluation with 7 geoscience researchers shows that
KnowledgeShovel can enable efficient construction of scientific knowledge bases
with satisfactory accuracy.

通过多模式人工智能协作与地球科学研究人员的实践评估，本文设计了一种名为 KnowledgeShovel 的 Al-in-the-Loop 文档注释系统，旨在为研究人员构建科学知识库提供高效且准确的解决方案。