FAIRChem v2 UMA 通用机器学习原子间势发布

在本教程中，我们探索 FAIRChem v2：https://github.com/facebookresearch/fairchem 以及 UMA 通用机器学习原子间势作为一个统一的原子模拟框架，涵盖分子化学、催化和无机材料。我们配置环境，使用 Hugging Face 进行身份验证以访问受限的 UMA 模型权重，并为 omol、oc20 和 omat 领域初始化特定任务计算器。然后，我们将相同的预训练势应用于广泛的计算化学工作流程，包括单点能量和力预测、分子几何优化、自旋态比较、反应能估算、振动分析、表面吸附、晶胞弛豫、状态方程拟合、分子动力学以及势能面扫描。在整个教程中，我们将 FAIRChem 与原子模拟环境（Atomic Simulation Environment, ASE）集成，用于管理原子结构、优化器、约束、热力学计算和轨迹分析，同时在可用时使用 GPU 加速。

我们安装所需的 FAIRChem、ASE、可视化和 Hugging Face 依赖项，同时确保在 Google Colab 中重复运行时环境安全。我们使用 Hugging Face 进行身份验证以访问受限的 UMA 模型权重，并自动检测 GPU 加速是否可用。然后加载 UMA 预测器，并为分子、催化和材料模拟创建独立的计算器。

我们使用分子 UMA 计算器评估水分子的能量、原子力和成键能。我们定义具有正确自旋重数的孤立氢和氧参考原子，以构建成键能计算。随后，我们扭曲水分子的几何结构，用 LBFGS 优化器进行弛豫，并分析收敛后的键长、键角和能量轨迹。

我们比较亚甲基的单重态和三重态电子状态，以计算其自旋态能量差。我们对甲烷、氧气、二氧化碳和水进行弛豫，然后将它们的预测能量组合以估算甲烷燃烧的电子反应能。同时，我们还对弛豫后的水进行有限差分振动分析，以获得其正常模式频率和零点能。

我们构建了一个周期性Cu(100)晶片，将一氧化碳分子放置在桥位吸附位点，并约束下层铜原子层。我们使用OC20计算器放松吸附物–表面系统，单独优化干净晶片和气态一氧化碳的参考体系。然后我们评估了一个教学性的吸附能量循环，同时注意到OC20使用了任务特定的能量参考约定。

我们使用材料领域的UMA计算器和Frechet单元滤波器放松BCC铁的原子位置和模拟单元。我们在不同压缩和膨胀体积范围内采样能量，拟合Birch–Murnaghan方程，估计平衡体积和体积模量。我们还对300 K下的水进行了Langevin分子动力学模拟，并随时间追踪其温度、势能和O–H键长。

我们通过系统地拉伸一个O–H键在选定的距离范围内扫描水的势能面。我们在每个几何结构下评估分子能量，相对于最小值对能量进行归一化，并可视化得到的解离曲线。我们通过确认更高精度的UMA模型、额外的化学领域以及更大规模的多GPU模拟，来结束本教程作为可能的扩展。

总之，我们围绕FAIRChem v2构建了完整的原子模拟工作流程，并展示了UMA如何在不同化学领域提供共享的学习势，而无需为每项任务单独建立模型。我们使用分子计算评估能量、力、原子化行为、自旋间隙、反应能学、振动模式和键拉伸曲线；使用催化领域放松Cu(100)表面上的CO并检查吸附能；使用材料领域放松BCC铁并通过方程拟合估计其体积模量。我们还进行了Langevin分子动力学模拟，以观察有限温度下的结构和能量波动。通过将UMA推理与ASE结构构建器、优化器、滤波器、振动工具和分子动力学工具结合，我们建立了一个可重复使用的基础，用于将工作流程扩展到更大分子、催化界面、晶体材料、金属有机框架、分子晶体以及更高精度的UMA模型变体。

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In this tutorial, we explore FAIRChem v2 ：https://github.com/facebookresearch/fairchem and the UMA universal machine-learning interatomic potential as a unified framework for atomistic simulation across molecular chemistry, catalysis, and inorganic materials. We configure an environment, authenticate with Hugging Face to access the gated UMA model weights, and initialize task-specific calculators for the omol, oc20, and omat domains. We then apply the same pretrained potential to a broad set of computational chemistry workflows, including single-point energy and force prediction, molecular geometry optimization, spin-state comparison, reaction-energy estimation, vibrational analysis, surface adsorption, crystal-cell relaxation, equation-of-state fitting, molecular dynamics, and potential-energy surface scanning. Throughout the tutorial, we integrate FAIRChem with the Atomic Simulation Environment to manage atomic structures, optimizers, constraints, thermodynamic calculations, and trajectory analysis while using GPU acceleration whenever it is available.

We install the required FAIRChem, ASE, visualization, and Hugging Face dependencies while ensuring the setup remains safe to rerun in Google Colab. We authenticate with Hugging Face to access the gated UMA model weights and automatically detect whether GPU acceleration is available. We then load the UMA predictor and create separate calculators for molecular, catalysis, and materials simulations.

We use the molecular UMA calculator to evaluate the energy, atomic forces, and atomization energy of a water molecule. We define isolated hydrogen and oxygen reference atoms with the correct spin multiplicities to construct the atomization-energy calculation. We then distort the water geometry, relax it with the LBFGS optimizer, and analyze the converged bond length, bond angle, and energy trajectory.

We compare the singlet and triplet electronic states of methylene to calculate its spin-state energy gap. We relax methane, oxygen, carbon dioxide, and water before combining their predicted energies to estimate the electronic reaction energy of methane combustion. We also perform a finite-difference vibrational analysis of relaxed water to obtain its normal-mode frequencies and zero-point energy.

We construct a periodic Cu(100) slab, place a carbon monoxide molecule at a bridge adsorption site, and constrain the lower copper layers. We relax the adsorbate–surface system with the OC20 calculator and separately optimize the clean slab and gas-phase carbon monoxide references. We then evaluate a pedagogical adsorption-energy cycle while recognizing that OC20 uses a task-specific energy reference convention.

We relax the atomic positions and simulation cell of BCC iron using the materials-domain UMA calculator and a Frechet cell filter. We sample energies across a range of compressed and expanded volumes, fit a Birch–Murnaghan equation of state, and estimate the equilibrium volume and bulk modulus. We also run Langevin molecular dynamics for water at 300 K and track its temperature, potential energy, and O–H bond length over time.

We scan the potential-energy surface of water by systematically stretching one O–H bond across a selected distance range. We evaluate the molecular energy at each geometry, normalize the energies relative to the minimum, and visualize the resulting dissociation profile. We conclude the tutorial by identifying higher-accuracy UMA models, additional chemical domains, and larger multi-GPU simulations as possible extensions.

In conclusion, we built a complete atomistic simulation workflow around FAIRChem v2 and demonstrated how UMA provides a shared learned potential across chemically distinct domains without requiring a separate model for every task. We used molecular calculations to evaluate energies, forces, atomization behavior, spin gaps, reaction energetics, vibrational modes, and bond-stretch profiles; we used the catalysis domain to relax CO on a Cu(100) surface and examine adsorption energetics; and we used the materials domain to relax BCC iron and estimate its bulk modulus from an equation-of-state fit. We also ran Langevin molecular dynamics to observe finite-temperature structural and energetic fluctuations over time. By combining UMA inference with ASE structure builders, optimizers, filters, vibrational tools, and molecular-dynamics utilities, we established a reusable foundation for extending the workflow to larger molecules, catalytic interfaces, crystalline materials, metal-organic frameworks, molecular crystals, and higher-accuracy UMA model variants.

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Sana Hassan, a consulting intern at Marktechpost and dual-degree student at IIT Madras, is passionate about applying technology and AI to address real-world challenges. With a keen interest in solving practical problems, he brings a fresh perspective to the intersection of AI and real-life solutions.

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