分子模拟领域面临着速度与精度难以兼顾的挑战。传统方法要么速度快但结果不准确，要么准确但速度过慢，难以用于实际应用。本期播客探讨了一种新的解决方案——神经网络势能函数（NNP）。两位科学家Corin和Ari分享了他们关于NNP的研究，包括其学习机制、潜在的失败案例、以及在药物设计和材料科学等领域的应用前景。此外，他们还探讨了分子动力学模拟的价值、溶剂效应和量子效应等问题，并展望了NNP在未来分子模拟领域的发展方向。

🤔**分子模拟面临挑战：**传统方法在速度和精度之间难以平衡，快速模拟结果不准确，而准确模拟速度过慢，限制了实际应用。

💡**神经网络势能函数（NNP）的出现：**通过使用黑盒模型近似物理方程，NNP能够在一定程度上解决速度和精度之间的矛盾，为分子模拟提供新的思路。

🧬**NNP在药物设计和材料科学中的应用：**NNP在分子动力学模拟中展现出潜力，可以用于药物设计、材料科学等领域，加速相关研究和应用。

🧪**NNP的局限性和未来发展：**NNP也存在局限性，例如在某些情况下可能出现预测错误。未来，NNP的发展方向包括构建更完善的模拟流程、提升模型的准确性和适用范围等。

💰**分子模拟的价值和投资方向：**播客中探讨了分子模拟数据对科学研究的独特价值，并引发了对未来分子模拟领域投资方向的思考。

Published on December 6, 2024 8:22 PM GMT

Hey LW! I recently filmed a two-hour long scientific podcast. It's niche, but may be of interest to some people here.

Here's a quick summary: Molecular simulation is in a tough situation. Fast simulations give the wrong answers, but accurate simulations are too slow for anything useful. But, instead of relying on physical equations for our simulation, perhaps we can approximate them using black-box models? As it turns out, there's an entire research field devoted to this question, and these models are often referred to as neural network potentials, or NNP's. Here, I interview two scientists (Corin and Ari) building neural network potentials (NNP’s). We talk about whether molecular dynamics are useful at all, the role of computational chemistry in drug design, what the future of the field looks like for molecular simulation, and a lot more.

If you're confused by this episode, I have a 'Jargon Explanation' section.

Here is a transcript of this episode (contains links to all referenced organizations and papers).

And a Youtube link, in case that's easier.

And timestamps, just so you know whats in the podcast: 
00:00 Introduction
01:19 Divide between classical and quantum simulation
03:48 What are NNP's actually learning?
06:02 What will NNP's fail on?
08:08 Short range and long range interactions in NNP's
10:23 Emergent behavior in NNP's
16:58 Enhanced sampling
18:16 Cultural distinctions in NNP's for life-sciences and material sciences
21:13 Gap between simulation and real-life
36:18 Benchmarking in NNP's
41:49 Is molecular dynamics actually useful?
53:14 Solvent effects
55:17 Quantum effects in large biomolecules
57:03 The legacy of DESRES and Anton
01:02:27 Unique value add of simulation data
01:06:34 NNP's in material science
01:13:57 The road to building NNP's
01:21:13 Building the SolidWorks of molecular simulation
01:30:05 Simulation workflows
01:41:06 The role of computational chemistry
01:44:06 The future of NNP's
01:51:23 Selling to scientists
02:01:41 What would you spend 200 million on?

Discuss