Machine learning has recently emerged as a powerful tool for generating new
molecular and material structures. The success of state-of-the-art models stems
from their ability to incorporate physical symmetries, such as translation,
rotation, and periodicity. Here, we present a novel generative method called
Response Matching (RM), which leverages the fact that each stable material or
molecule exists at the minimum of its potential energy surface. Consequently,
any perturbation induces a response in energy and stress, driving the structure
back to equilibrium. Matching to such response is closely related to score
matching in diffusion models. By employing the combination of a machine
learning interatomic potential and random structure search as the denoising
model, RM exploits the locality of atomic interactions, and inherently respects
permutation, translation, rotation, and periodic invariances. RM is the first
model to handle both molecules and bulk materials under the same framework. We
demonstrate the efficiency and generalization of RM across three systems: a
small organic molecular dataset, stable crystals from the Materials Project,
and one-shot learning on a single diamond configuration.

最近，机器学习已经成为生成新的分子和材料结构的强大工具。我们提出了一种新颖的生成方法，称为响应匹配（RM），它利用了每个稳定材料或分子存在于其势能面最低点的事实。通过结合机器学习的原子间相互作用势能和随机结构搜索作为去噪模型，RM 利用了原子间相互作用的局部性，并固有地保持了置换、平移、旋转和周期性不变性。RM 是第一个在相同框架下处理分子和块材料的模型。我们在三个系统上展示了 RM 的效率和泛化能力：一个小有机分子数据集，来自材料项目的稳定晶体，以及对单个金刚石构型的一次性学习。

生成材料和分子的响应匹配

Response Matching for generating materials and molecules

Scientific advancement is universally based on the dynamic interplay between
theoretical insights, modelling, and experimental discoveries. However, this
feedback loop is often slow, including delayed community interactions and the
gradual integration of experimental data into theoretical frameworks. This
challenge is particularly exacerbated in domains dealing with high-dimensional
object spaces, such as molecules and complex microstructures. Hence, the
integration of theory within automated and autonomous experimental setups, or
theory in the loop automated experiment, is emerging as a crucial objective for
accelerating scientific research. The critical aspect is not only to use theory
but also on-the-fly theory updates during the experiment. Here, we introduce a
method for integrating theory into the loop through Bayesian co-navigation of
theoretical model space and experimentation. Our approach leverages the
concurrent development of surrogate models for both simulation and experimental
domains at the rates determined by latencies and costs of experiments and
computation, alongside the adjustment of control parameters within theoretical
models to minimize epistemic uncertainty over the experimental object spaces.
This methodology facilitates the creation of digital twins of material
structures, encompassing both the surrogate model of behavior that includes the
correlative part and the theoretical model itself. While demonstrated here
within the context of functional responses in ferroelectric materials, our
approach holds promise for broader applications, the exploration of optical
properties in nanoclusters, microstructure-dependent properties in complex
materials, and properties of molecular systems. The analysis code that supports
the funding is publicly available at
this https URL

通过贝叶斯共同导航理论模型空间和实验来实现理论在循环中的集成，以减小实验对象空间的认识不确定性，进而创建包括关联部分和理论模型本身的材料结构数字孪生体，具有广泛的应用前景。

贝叶斯协同导航：通过主动学习实现材料数字孪生动态设计

Bayesian Co-navigation: Dynamic Designing of the Materials Digital Twins  via Active Learning

Generative machine learning models can use data generated by scientific
modeling to create large quantities of novel material structures. Here, we
assess how one state-of-the-art generative model, the physics-guided crystal
generation model (PGCGM), can be used as part of the inverse design process. We
show that the default PGCGM's input space is not smooth with respect to
parameter variation, making material optimization difficult and limited. We
also demonstrate that most generated structures are predicted to be
thermodynamically unstable by a separate property-prediction model, partially
due to out-of-domain data challenges. Our findings suggest how generative
models might be improved to enable better inverse design.

使用物理引导的晶体生成模型（PGCGM）来作为逆向设计过程的一部分，评估生成式机器学习模型如何利用科学建模生成的数据来创造大量的新材料结构。研究发现，PGCGM 的输入空间对参数变化并不平滑，导致了材料优化的困难和局限性；同时，通过一个独立的属性预测模型表明，大部分生成的结构在热力学上是不稳定的，部分原因是由于领域外数据挑战。研究结果为改进生成模型以实现更好的逆向设计提供了建议。