Computational catalysis is playing an increasingly significant role in the design of catalysts across a wide range of applications. A common task for many computational methods is the need to accurately compute the minimum binding energy - the adsorption energy - for an adsorbate and a catalyst surface of interest. Traditionally, the identification of low energy adsorbate-surface configurations relies on heuristic methods and researcher intuition. As the desire to perform high-throughput screening increases, it becomes challenging to use heuristics and intuition alone. In this paper, we demonstrate machine learning potentials can be leveraged to identify low energy adsorbate-surface configurations more accurately and efficiently. Our algorithm provides a spectrum of trade-offs between accuracy and efficiency, with one balanced option finding the lowest energy configuration, within a 0.1 eV threshold, 86.63% of the time, while achieving a 1387x speedup in computation. To standardize benchmarking, we introduce the Open Catalyst Dense dataset containing nearly 1,000 diverse surfaces and 87,045 unique configurations.

建模原子系统的能量和力是计算化学中的一个基本问题，有可能帮助解决世界上许多最紧迫的问题，包括与能源稀缺和气候变化有关的问题。这些计算传统上是使用密度函数理论进行的，这在计算上非常昂贵。机器学习有可能从天数或小时到秒从天数大幅提高这些计算的效率。我们建议球形通道网络（SCN）对原子能量和力进行建模。 SCN是一个图神经网络，节点代表原子并边缘其相邻原子。原子嵌入是使用球形谐波表示的一组球形函数，称为球形通道。我们证明，通过基于3D边缘方向旋转嵌入式，可以在保持消息的旋转模糊性的同时使用更多信息。虽然均衡性是理想的属性，但我们发现，通过在消息传递和聚合中放松这种约束，可以提高准确性。我们在大规模开放催化剂2020数据集中展示了最新的结果，这些数据集在能源和力量预测中，用于许多任务和指标。

计算催化和机器学习社区在开发用于催化剂发现和设计的机器学习模型方面取得了长足的进步。然而，跨越催化的化学空间的一般机器学习潜力仍然无法触及。一个重大障碍是在广泛的材料中获得访问培训数据的访问。缺乏数据的一类重要材料是氧化物，它抑制模型无法更广泛地研究氧气进化反应和氧化物电催化。为了解决这个问题，我们开发了开放的催化剂2022（OC22）数据集，包括62,521个密度功能理论（DFT）放松（〜9,884,504个单点计算），遍及一系列氧化物材料，覆盖范围，覆盖率和吸附物（ *H， *o， *o， *o， *o， *o， * n， *c， *ooh， *oh， *oh2， *o2， *co）。我们定义广义任务，以预测催化过程中适用的总系统能量，发展几个图神经网络的基线性能（Schnet，Dimenet ++，Forcenet，Spinconv，Painn，Painn，Gemnet-DT，Gemnet-DT，Gemnet-OC），并提供预先定义的数据集分割以建立明确的基准，以实现未来的努力。对于所有任务，我们研究组合数据集是否会带来更好的结果，即使它们包含不同的材料或吸附物。具体而言，我们在Open Catalyst 2020（OC20）数据集和OC22上共同训练模型，或OC22上的微调OC20型号。在最一般的任务中，Gemnet-OC看到通过微调来提高了约32％的能量预测，通过联合训练的力预测提高了约9％。令人惊讶的是，OC20和较小的OC22数据集的联合培训也将OC20的总能量预测提高了约19％。数据集和基线模型是开源的，公众排行榜将遵循，以鼓励社区的持续发展，以了解总能源任务和数据。

机器学习方法有可能以计算有效的方式近似于原子模拟的密度功能理论（DFT），这可能会大大增加计算模拟对现实世界问题的影响。但是，它们受到其准确性和生成标记数据的成本的限制。在这里，我们提出了一个在线主动学习框架，该框架通过合并了开放催化剂项目的大规模预训练的图形神经网络模型，通过合并了先前的物理信息，从而有效，准确地加速了原子系统的模拟。加速这些模拟使有用的数据更便宜地生成，从而可以训练更好的模型，并可以筛选更多的原子系统。我们还提出了一种基于其速度和准确性比较局部优化技术的方法。 30基准测试吸附剂催化剂系统的实验表明，我们的转移学习方法以预先训练模型合并先前的信息通过将DFT计算的数量减少91％，从而加速模拟，同时达到0.02 EV的准确性阈值93％。 。最后，我们展示了一种技术，用于利用VAS中内置的交互式功能，以在我们的在线活动框架内有效地计算单点计算，而无需大量启动成本。这使VASP与我们的框架同时起作用，同时需要比常规的单点计算要少75％。在GitHub的开源Finetuna软件包中可用在线主动学习实现以及使用VASP交互式代码的示例。

近年来，分子模拟数据集的出现是大数量级，更多样化的阶。这些新数据集在复杂性的四个方面有很大差异：1。化学多样性（不同元素的数量），2。系统大小（每个样品原子数），3。数据集大小（数据样本数）和4.域移动（培训和测试集的相似性）。尽管存在这些较大的差异，但在狭窄和狭窄的数据集上的基准仍然是证明分子模拟的图形神经网络（GNN）进展的主要方法，这可能是由于较便宜的训练计算要求所致。这就提出了一个问题 - GNN在小和狭窄的数据集上的进展是否转化为这些更复杂的数据集？这项工作通过首先根据大型开放催化剂2020（OC20）数据集开发Gemnet-OC模型来研究这个问题。 Gemnet-OC的表现优于OC20上的先前最新ART，同时将训练时间减少10倍。然后，我们比较了18个模型组件和超参数选择对多个数据集的性能的影响。我们发现，根据用于做出模型选择的数据集，所得模型将大不相同。为了隔离这种差异的来源，我们研究了OC20数据集的六个子集，这些子集分别测试了上述四个数据集方面的每个数据集。我们发现，OC-2M子集的结果与完整的OC20数据集良好相关，同时训练得更便宜。我们的发现挑战了仅在小型数据集上开发GNN的常见做法，但突出了通过中等尺寸的代表性数据集（例如OC-2M）以及Gemnet-oc等高效模型来实现快速开发周期和可推广结果的方法。我们的代码和预估计的模型权重是开源的。

Course load analytics (CLA) inferred from LMS and enrollment features can offer a more accurate representation of course workload to students than credit hours and potentially aid in their course selection decisions. In this study, we produce and evaluate the first machine-learned predictions of student course load ratings and generalize our model to the full 10,000 course catalog of a large public university. We then retrospectively analyze longitudinal differences in the semester load of student course selections throughout their degree. CLA by semester shows that a student's first semester at the university is among their highest load semesters, as opposed to a credit hour-based analysis, which would indicate it is among their lowest. Investigating what role predicted course load may play in program retention, we find that students who maintain a semester load that is low as measured by credit hours but high as measured by CLA are more likely to leave their program of study. This discrepancy in course load is particularly pertinent in STEM and associated with high prerequisite courses. Our findings have implications for academic advising, institutional handling of the freshman experience, and student-facing analytics to help students better plan, anticipate, and prepare for their selected courses.

We present a differentiable formulation of rigid-body contact dynamics for objects and robots represented as compositions of convex primitives. Existing optimization-based approaches simulating contact between convex primitives rely on a bilevel formulation that separates collision detection and contact simulation. These approaches are unreliable in realistic contact simulation scenarios because isolating the collision detection problem introduces contact location non-uniqueness. Our approach combines contact simulation and collision detection into a unified single-level optimization problem. This disambiguates the collision detection problem in a physics-informed manner. Compared to previous differentiable simulation approaches, our formulation features improved simulation robustness and a reduction in computational complexity by more than an order of magnitude. We illustrate the contact and collision differentiability on a robotic manipulation task requiring optimization-through-contact. We provide a numerically efficient implementation of our formulation in the Julia language called Silico.jl.

As Artificial and Robotic Systems are increasingly deployed and relied upon for real-world applications, it is important that they exhibit the ability to continually learn and adapt in dynamically-changing environments, becoming Lifelong Learning Machines. Continual/lifelong learning (LL) involves minimizing catastrophic forgetting of old tasks while maximizing a model's capability to learn new tasks. This paper addresses the challenging lifelong reinforcement learning (L2RL) setting. Pushing the state-of-the-art forward in L2RL and making L2RL useful for practical applications requires more than developing individual L2RL algorithms; it requires making progress at the systems-level, especially research into the non-trivial problem of how to integrate multiple L2RL algorithms into a common framework. In this paper, we introduce the Lifelong Reinforcement Learning Components Framework (L2RLCF), which standardizes L2RL systems and assimilates different continual learning components (each addressing different aspects of the lifelong learning problem) into a unified system. As an instantiation of L2RLCF, we develop a standard API allowing easy integration of novel lifelong learning components. We describe a case study that demonstrates how multiple independently-developed LL components can be integrated into a single realized system. We also introduce an evaluation environment in order to measure the effect of combining various system components. Our evaluation environment employs different LL scenarios (sequences of tasks) consisting of Starcraft-2 minigames and allows for the fair, comprehensive, and quantitative comparison of different combinations of components within a challenging common evaluation environment.

Rearrangement puzzles are variations of rearrangement problems in which the elements of a problem are potentially logically linked together. To efficiently solve such puzzles, we develop a motion planning approach based on a new state space that is logically factored, integrating the capabilities of the robot through factors of simultaneously manipulatable joints of an object. Based on this factored state space, we propose less-actions RRT (LA-RRT), a planner which optimizes for a low number of actions to solve a puzzle. At the core of our approach lies a new path defragmentation method, which rearranges and optimizes consecutive edges to minimize action cost. We solve six rearrangement scenarios with a Fetch robot, involving planar table puzzles and an escape room scenario. LA-RRT significantly outperforms the next best asymptotically-optimal planner by 4.01 to 6.58 times improvement in final action cost.

Measuring growth rates of apple fruitlets is important because it allows apple growers to determine when to apply chemical thinners to their crops to optimize yield. The current practice of obtaining growth rates involves using calipers to record sizes of fruitlets across multiple days. Due to the number of fruitlets needed to be sized, this method is laborious, time-consuming, and prone to human error. In this paper, we present a computer vision approach to measure the sizes and growth rates of apple fruitlets. With images collected by a hand-held stereo camera, our system detects, segments, and fits ellipses to fruitlets to measure their diameters. To measure growth rates, we utilize an Attentional Graph Neural Network to associate fruitlets across different days. We provide quantitative results on data collected in an apple orchard, and demonstrate that our system is able to predict abscise rates within 3% of the current method with a 7 times improvement in speed, while requiring significantly less manual effort. Moreover, we provide results on images captured by a robotic system in the field, and discuss the next steps to make the process fully autonomous.