Computational prediction of stable crystal structures has a profound impact
on the large-scale discovery of novel functional materials. However, predicting
the crystal structure solely from a material's composition or formula is a
promising yet challenging task, as traditional ab initio crystal structure
prediction (CSP) methods rely on time-consuming global searches and
first-principles free energy calculations. Inspired by the recent success of
deep learning approaches in protein structure prediction, which utilize
pairwise amino acid interactions to describe 3D structures, we present
AlphaCrystal-II, a novel knowledge-based solution that exploits the abundant
inter-atomic interaction patterns found in existing known crystal structures.
AlphaCrystal-II predicts the atomic distance matrix of a target crystal
material and employs this matrix to reconstruct its 3D crystal structure. By
leveraging the wealth of inter-atomic relationships of known crystal
structures, our approach demonstrates remarkable effectiveness and reliability
in structure prediction through comprehensive experiments. This work highlights
the potential of data-driven methods in accelerating the discovery and design
of new materials with tailored properties.

AlphaCrystal-II 利用已知的晶体结构中丰富的原子间相互作用模式，预测目标晶体材料的原子距离矩阵，并利用该矩阵重构其三维晶体结构，通过全面的实验验证，展示了数据驱动方法在加速新材料的发现和设计中的潜力。

AlphaCrystal-II：基于距离矩阵的晶体结构预测与深度学习

AlphaCrystal-II: Distance matrix based crystal structure prediction  using deep learning

Due to the vast chemical space, discovering materials with a specific
function is challenging. Chemical formulas are obligated to conform to a set of
exacting criteria such as charge neutrality, balanced electronegativity,
synthesizability, and mechanical stability. In response to this formidable
task, we introduce a deep learning-based generative model for material
composition and structure design by learning and exploiting explicit and
implicit chemical knowledge. Our pipeline first uses deep diffusion language
models as the generator of compositions and then applies a template-based
crystal structure prediction algorithm to predict their corresponding
structures, which is then followed by structure relaxation using a universal
graph neural network-based potential. The density functional theory (DFT)
calculations of the formation energies and energy-above-the-hull analysis are
used to validate new structures generated through our pipeline. Based on the
DFT calculation results, six new materials, including Ti2HfO5, TaNbP, YMoN2,
TaReO4, HfTiO2, and HfMnO2, with formation energy less than zero have been
found. Remarkably, among these, four materials, namely Ti2$HfO5, TaNbP, YMoN2,
and TaReO4, exhibit an e-above-hull energy of less than 0.3 eV. These findings
have proved the effectiveness of our approach.

基于深度学习的生成模型和密度泛函理论，我们通过学习和利用显式和隐式化学知识，提出了一种材料组成和结构设计的方法。通过深度扩散语言模型生成组合，并应用基于模板的晶体结构预测算法预测相应的结构，然后使用基于通用图神经网络的势函数进行结构弛豫。通过密度泛函理论的计算和能量分析验证了我们的方法，并发现了六种新的具有负能量生成焓的材料，证明了我们方法的有效性。

基于深度扩散语言模型的无机化合物生成设计

Generative Design of inorganic compounds using deep diffusion language  models

Recovering the radiometric properties of a scene (i.e., the reflectance,
illumination, and geometry) is a long-sought ability of computer vision that
can provide invaluable information for a wide range of applications.
Deciphering the radiometric ingredients from the appearance of a real-world
scene, as opposed to a single isolated object, is particularly challenging as
it generally consists of various objects with different material compositions
exhibiting complex reflectance and light interactions that are also part of the
illumination. We introduce the first method for radiometric scene decomposition
that handles those intricacies. We use RGB-D images to bootstrap geometry
recovery and simultaneously recover the complex reflectance and natural
illumination while refining the noisy initial geometry and segmenting the scene
into different material regions. Most important, we handle real-world scenes
consisting of multiple objects of unknown materials, which necessitates the
modeling of spatially-varying complex reflectance, natural illumination,
texture, interreflection and shadows. We systematically evaluate the
effectiveness of our method on synthetic scenes and demonstrate its application
to real-world scenes. The results show that rich radiometric information can be
recovered from RGB-D images and demonstrate a new role RGB-D sensors can play
for general scene understanding tasks.

通过利用 RGB-D 图像重建场景的几何和同时恢复复杂反射和自然照明的 radiometric 成分，从而对多个未知材料对象进行空间变化的建模，可以实现从 RGB-D 图像中恢复丰富的 radiometric 信息，并展示 RGB-D 传感器在场景理解任务中的新角色。