DistMLIP: A Distributed Inference Platform for Machine Learning Interatomic Potentials
Authors: Kevin Han
Bowen Deng
Amir Barati Farimani
Gerbrand Ceder
Venue: ICLR 2026 Poster (2026)
Keywords: machine learning interatomic potential
molecular dynamics
atomistic simulation
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
TL;DR: DistMLIP is a first-of-its-kind graph parallel inference platform for machine learning interatomic potentials.

Abstract: Large-scale atomistic simulations are essential to bridge computational materials and chemistry to realistic materials and drug discovery applications. In the past few years, rapid developments of machine learning interatomic potentials (MLIPs) have offered a solution to scale up quantum mechanical calculations. Parallelizing these interatomic potentials across multiple devices poses a challenging, but promising approach to further extending simulation scales to real-world applications. In this work, we present \textbf{DistMLIP}, an efficient distributed inference platform for MLIPs based on zero-redundancy, graph-level parallelization. In contrast to conventional spatial partitioning parallelization, DistMLIP enables efficient MLIP parallelization through graph partitioning, allowing multi-device inference on flexible MLIP model architectures like multi-layer graph neural networks. DistMLIP presents an easy-to-use, flexible, plug-in interface that enables distributed inference of pre-existing MLIPs. We demonstrate DistMLIP on four widely used and state-of-the-art MLIPs: CHGNet, MACE, TensorNet, and eSEN. We show that DistMLIP can simulate atomic systems 3.4x larger and up to 8x faster compared to previous multi-GPU methods. We show that existing foundation potentials can perform near-million-atom calculations at the scale of a few seconds on 8 GPUs with DistMLIP.

Published as a conference paper at ICLR 2026
DISTMLIP: A DISTRIBUTED INFERENCE PLATFORM FOR MACHINE LEARNING INTERATOMIC POTENTIALS
Kevin Han
Bowen Deng" UC Berkeley, LBNL
Carnegie Mellon University kevinhand cmu edu
bowendeng@barkellyyyoo edd:
Amir Barati Farimani Carnegie Mellon University barati @emeiieemmuemmmmmmmm
Gerbrand Ceder UC Berkeley, LBNL
goederr@errkeeey.com
ABSTRACT
Large-scale atomistic simulations are essential to bridge computational materials and chemistry realistic materials and drug discovery applications. In the past few years, rapid developments of machine learning interatomic potentials (MLIPs) have offered solution scale up quantum mechanical calculations. Parallelizing these interatomic potentials across multiple devices poses challenging, but promising approach to further extending simulation scales to real-world applications. In this work, we present DistMLIP, an efficient distributed inference platform for MLIPs based on redundadun,,,, graph-level parallelization. In contrast to conventional spatial partitioning parallelization, DistMLIP enables efficient MLIP parallelization through graph partitioning, allowing multi-device inference on flexible MLIP model architectures like multi-layer graph neural networks. DistMLIP presents easy- to-use, flexible, plug-in interface that enables distributed inference of pre-existing MLIPs. We demonstrate DistML. IIP four widely used and state-of-the-art MI.IPs: CHGNet, MACE, TensorNet, and eSEN. We show that DistMI.IP can simulate atomic systems 3.4x larger and up to 8x faster compared to previous multi-GPi methods. We show that existing foundation potentials can perform near-million- atom calculations the scale of few seconds on GPUs with DistMLIP.
INTRODUCTION
Atomistic simulation has been the workhorse in computational materials and drug discovery over the recent years (Merchant et al., 2023) Jain et al., (203) De Vivo al. (016). The chemical properties and behavior a material are essentially determined by the interactions in the given set of atomic arrangements. In the most simplified framework, one can formulate this problem as solving the function that determines the potential energy surface (PES) of a set of atoms given by E = (FF., C.). where the energy, F and C, are the positions and chemical identities of the atoms.
To study the material's properties, multiple fundamentally different methods have been developed to obtain or construct the function Φ. Classical force fields (FF) like embedded atom methods
(Daw & Baskes 1984), CHARMM (Vanommeslaeghe et al. (010). and Amber (Wang et al., 2004) qualitatively predict the PES and the bond energy between atoms. These classical HF's are cheap, intuitive, and explainable, but are often not accurate enough and have been constructed and applied to narrow chemical domains and a few elements. Fundamentally, the interactions in á are determined by the electronic structure of a material and can be solved in first principles by quantum mechanics. Quantum chemical simulation methods, such as Density Functional Theory (DFT) (Perdew et al. 1996) and coupled cluster (CC) methods (Raghavachari et al. 1989), enabled the ab initio calculations of atommic behavior that are much more accurate than empirical methods. However, their computational complexity limits the practical use of quantum chemical simulation methods for many realistic applications. DFT, the most widely used ab initio simulation method, scales cubically O(N2) with
"Correspondence Bowen Deng <bwwendeng@ bereddeng@berrkkeeeedu...

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the number of electrons and is therefore limited to simulating only few hundred atoms (Beck 2000). Prohibitively high computational cost makes DFT only useful in describing materials properties that can be learned from small simulation cell (Wang et al. 2024h.
Machine learning approaches such as machine learning interatomic potentials (MLIPs) open the possibility increase simulation scale while retaining quantum chemical accuracy by building
ML surrogate models trained on DFT and CC data (Bartók et al.] 2010: Zhang et al., 20188: Wang
al. 2024; Gasteiger al. 202 Deng et al., 2023 Batzner al., 2022] Musaelian et al. 2023
MLIPs enable improved learnailltyy to better model the PES data. Graph neural networks (GNNs), especially, have demonstrated extraordinary computational efficiency and accuracy by learning both long-range and high-order atomic interactions through message passing. By design, the computation time of MIIPPs scales linearly with the number of atoms (N), enabling simulations with tens of thousands of atoms at nano-second time scale.
Fu et al. 2025). Compared to feature-based classical FF's, dleep-learning-bsee
Many materials engineering problems involve finite-sized effects like protein folding (Jumper et al. 2021). interfacial reactions (Du et al. 2023), particle-size effects (Shi et al. (020), and formation of nano-domains (Holstun et al. 2005).. Such systems require meso-scale simulations with upwards of millions of atoms, necessitating the ability to further scale the capacity of MLIP simulations. One promising solution is to expand MLIP inference from single-device multi-device inference. Simu- lation packages such as LAMMPS provide ad hoc solutions for multi-GPU simulation (Thompson) et al. 2022). Multi-device simulations are realized by dividing the total simulation cell into multiple, mutually exclusive, small cells for each device. Each small cell is then padded with additional atoms beyond the cell boundary in order to properly calculate the energy and forces within the small cell. This method, known as spatial partitioning, is based on the fundamental assumption that the force field only contains short-range interactions (Plimpton 1995).
Since most MLIPs are designed to be relatively long-ranged, expanding the number of utilized GPUs during inference time a nontrivial task due to the necessity to distribute the large system cell across multiple devices. Currently, there exists no native multi-GPU support for GNN-based MLIPs as most MLIPs have been implemented for only single-device inference. In order to support fast, accurate, and parallelizable atomistic simulations, we hereby present a distributed MLIP inference platform, DistMLIP, that enables efficient multi-device inference without the need for a modified architecture or additional training. Our highlighted contributions are follows:
DistMLIP features a simple, efficient, general, and versatile parallel inference platform for MLIP inference. By design, most popular MLIPs can be supported with minimal amount of adaptation. this work, we include benchmarking results of 4 widely used MLIPs: MACE, TensorNet, CHGNet, and eSEN.
DisMMLIP leverages graph-level partitioning that allows node and edge information to transfer between GPUs at each layer of the forward pass while still maintaining the in- termediates required to perform backpropagation. This allows efficient parallelization of long-range GNN-based MLIPs, which is standard for most MLIPs today. Compared to spatial partitioning, graph partitioning has zero redundancy, meaning that no redundant computation is thrown away during parallel inference.
We implemented the distribution of both the atom graph and the augmented three-body line graph, a common graph structure used in MLIPs to encode three-body atomic interactions.
To allow flexible usage, DistMLIP does not depend on 3rd party distributed simulation library such as LAMMPS. As a result, DIs supports plug-in usage of any MLIP workflow.
We show that the
partitioning technique DistMLIP utilizes performs MD up to 8x faster compared with the more standard graph partitioning techniques.
RELATED WORK
2.1
MACHINE LEARNING INTERATOMIC POTENTIALS
The most common MLIP architecture today is GNN, where nodes in the atom graph represent atoms and edges in the atom graph represent the pair-wise distances between atoms that are within
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dre-defined cutoff distance (Batzner et al. 2022, Simeon & De Fabritiis
2023 Passaro & Zitnick
2023; Gasteiger et al. 2021 Schütt et al. 2021: 2018 Smith et al. (017). GNN computation scales linearly with the number of atoms, as the amount of computation associated with the neighbors within the receptive field of each atom. Some MLIPs also pass messages on top of higher-order graphs, such as threebody bond graphs, that encode angles as pairwise information between bonds [Choudhary & DeCost 2021] Deng et al., [023] Yang et al. 2024). MLIPs built on top of the transformer architecture have also been introduced, where "tokens" represent individual nodes and full self- eeff-attention is performed over all tokens (Liao et al.) 2024 Vaswani et al. (017). Recently,
class of foundation potentials (FPs) have been shown to generalize across diverse chemistries by
pretraining massive datasets (Chen & Ong 2022 Deng et al., 2023] Chanussot et al. [021]
BBarroso-Luque et al., 2024 Yang et al. 2024 Merchant et al. [023] Kaplan et al. 2025). These
pretrained FPs substantially reduce the need for farget-system training, and their open-sourced
pretrained checkpoints serve as ready-to-use universal MLIPs.
2.2 SPATIAL PARTITIONING
LAMMPS implements multi-GPU inference via spatial partitioning approach where the simulation space is split into mutually exclusive partitions. For each mutually exclusive partition, LAMMPS creates a second, larger partition that includes all atoms up to the interaction radius of the FF, commonly known as border or ghost nodes. This is required as the energy and force calculation of the atoms within each mutually exclusive partition requires the atomic information from all nodes within the model's 's interaction radius. This leads to highly redundant calculations the computation performed on the ghost nodes is thrown away after each time step. By estimate, a 64-molecule water system calculated with 6-layer GNN that has 6 angstrom cutoff distance would require the computation of 20,834 ghost atoms when using spatial partitioning (Musaelian et al., 2023). Furthermore, unlike classical FFs, most MLIPs do not have a mature interface with LAMMPS. making spatial partitioning practically infeasible for the majority of MLIPs that have been developed.
DeepMD is a short-range MLIP that has been applied to the simulation of 100 million atoms of water by 27, 37,360 NVIDIA V100 GPUs on the Summit supercomputer, utilizing the spatial partitioning features within LAMMPS (Jia et al., 2020). The atomic system size was further extended to 10 billion atoms after further optimization of model tabulation, kernel fusion, and redundancy removal of the Deep Potential architecture (Guo et al. 2022).
Allegro has been developed as strictly local, E(3--eruivariant interatomic potential that features efficient parallelization through spatial partitioning due to its short-range design (Musaelian et al.) 2023). Because of this strict locality, Allegro demonstrated good scaling on large atomic systems Kozinsky et al. (2023) used Allegro and LAMMPS to simulate a bulk Ag model with 100 million atoms, achieving 0.003 microseeonnds/too-tinnee -imoesttetttpppppeee using 128 NVIDIA-A NVIDIA-AI00-SOGBB.
However, strictly local models experience key limitations. The need for efficient parallelization restricts their interaction range to only a few angstroms, preventing their use systems that require the modeling of long-range interactions (Zhou et al. 2023; Song et al.) 2024 Gong et al. 2025 Cheng. 2025 Anstine & Isayev] (023). Furthermore, the short-range design prevents the ML.IP's application from simultancouslly learning diverse chemical environments. As the short-range MLIP's cutoff is often determined by the radial distribution function of one targeted material system, is infeasible to determine a universal cutoff that efficiently works for many materials, which is becoming a common scenario with the increased interest in FPs. These problems raise the need for a simple, unified, and versatile API to parallelize MLIPs.
SevenNet, derived from the Nequip architecture (Batzner et al., 2022). is one of the first MLIPs that support graph-parallel inference (Park et al., 2024). A simulation of 112 112,000 atoms Si was demonstrated by distributing the 0.84 million parameter SevenNet-O on A100-80GB GPUs. However, its graph parallel algorithm is not casily transferable to other MLIP architectures and relies on the combination of TorchScript and LAMMPS, making it unapplicable to simulation tasks and
workflows that are not built upon LAMMPS (Larsen et al. 2017 Ganose et al. 2025
Barroso-Luque
et al. 2022 Ko et al.) 2025).
In addition, paaph-aprallllization has been previously explored for training large GNN models Sriram
jtt al. (2022). In comparison, the parallelization in MLIP inference poses a fundamentally different challenge compared to training. During training, the samples are almost always restricted very
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small-scale chemical systems due the computational complexity of acquiring labels. In training, the goal of graph- paraallllalliinnoonnn is to increase MLIP model sizes and batch sizes. During inference, graph parallelization is applied for the simulation of single large chemical system. As result, the application of graph- graph-paralleizztion in large-scale MLLP simulations remains an open challenge.
METHODS
Figure aa) denotes an overview DistMLIP. Public MLIP models can be easily adapted to perform distributed large simulations with DistMLIP. The core infrastructure of DistMLIP is in the construction of graphs, subgraphs, and communication-relatee metadata.
(b)
FAIR ANAA Chemitty
1-hop
2-hop
DistMLIP
Node
Edge Table
Distributed Large Simulation
Edge IDs
Layer
Layer
(d)
Atom Graph
Bond Graph
GPU
GPU1
GPU1
CPUMEMM
GPUZ
GPUZ
GPUZ
GPU2
Figure 1: An overview of DistMLIP. (a) DistMLIP takes public MLIP models and performs large- scale, distributed simulations. (b) Partition the atom graph using vertical spatial partitioning scheme, and construct subgraphs containing the 1-hop neighbors and 2-hop neighbors of the original partition, which are later used to calculate the distributed bond graphs. (c) Take the 2-hop atom graph and create an edge table backbone mapping node IDs (black) to edge IDs (orange) that contain the node ID as a source node. (d) Recursively traverse the edge table to construct the atom graph and bond graph. (e) Data transfer in simple 2-layer graph neural network with both atom graph and bond graph.
3.1 PARALLEE MESSAGE PASSING
After the material system is converted into graph using neighbor list construction algorithm, the graph can be partitioned into subgraphs for each device, as illustrated in Fig. [b). At each graph convolution, each node's features updated according to the edge and node features of its incoming neighbors. We can partition the nodes of the graph G into disjoint sets, constructing graphs G ... Gn. where p the number of partitions. Each of the graph partitions are distributed to its own GPU, To accurately calculate the features after one graph convolution, we expand G; into G% where G"; consists of all nodes v E such that there exists an edge (u, u) e with G,. Formally,
fw E Eu E G., (v, u) E E}.
where V and E are the set of nodes and edges of the graph G, respectively. This ensures that all incoming information necessary for computing the convolution included within G¹ Let H2 denote the set of all I hhhop nodes that were added to G; to create G². Formally,
H {u E V G. Fu E G,, (v, u) E E).
This represents the set nodes in V that are not in G; but have an outgoing edge into G, We refer to these nodes border nodes. Let FV denote the of edges not in G² that point the border nodes.
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Algorithm 1 Atom Subgraph Creation
Input: Atomic system nodes and edges
Output: Partitioned subgraph with mappings
1. Create a partition rule based on the longest cell dimension (vertical walls)
2. Assign atoms to buckets (PURE/OO/ROM) using algorithm
3. Create node array and corresponding marker array for each partition:
for each starting partition (creating marker arrays) do initialize markers array
markers [0] = 0
markers[]] Lem((URI)
marker_inee ficker__ieee___e = 0
for each destination partition pj do
concatenate TO[p;] to p; node array maneer_indexx 1] + len(TO(p;))
markers (warkmmmindeeeee = markers markers[marker marker_index = marker_indes, +
end for
for each source partition ps: do
concatenate FROM[p:] to p; node array [marker marker index] markers mmarker index 1] (en(FROM(p:)) marker index = marker index +
end for end for
Formally,
E v) E v H, and (u, & G.)--
We refer to these edges as border edges, which we use extensively when distributing the bond graph.
After each graph convolution, transfer the border node and border edge features to and from each partition, as shown in Fig. dd. After this transfer process, each GPU has the most updated node and edge feature begin the next convolution. This implementation is completely model-agnostic and can be applied to both conservative and direct force prediction MLIPs.
3.2 DISTRIBUTING ATOM GRAPHS
In order distribute the atom graph, we first partition the graph spatially using vertical wall partitions, Once these partitions are created, we specify algorithm to identify the border nodes that each partition requires as well as the border nodes within each partition that other partitions require. For each partition, create TO, FROM, and PURE arrays of node ids. We denote TO, [i] as the bucket of node ids associated with G² required to be used in G Similarly, we denote FROM, [i] as the node ids associated with G/, required to be used in G As a result, [o] and FROM, i should be the same array. The PURE bucket specifies the nodes that are not required in the data transfer process. Furthermore, each edge drawn from a border node to a pure node is assigned to the partition responsible for the pure node.
M2
For each partition, we concatenate each of the arrays while maintaining a marker array containing the indices of the spans of each bucket. The marker array is used to efficiently index the spans of each of the features for data transfer between GPUs. The entire atom graph creation algorithm can be found in algorithm
3.3 DISTRIBUTING HIGHER- ORDER GRAPHS
Higher-order graphs, sometimes referred to as line graphs or bond graphs (for the three-body case), are frequently used in MLIPs to featurize higher-order interactions Choudhary & DeCost (202I): Deng et al. )2023); Zhang et al. (2025). Distributing the bond graph involves selecting all 1-hop and 2-hop neighbors the pure atom graph nodes assigned to a partition. We then create an edge table mapping from node ids to edges originating from the node id pointing
different node. By recursively traversing the table, we are able to create the bond graph for each partition in parallel. Border nodes within the bond graph associated with the 1-hop edge neighbors of border edges
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within the atom graph hence necessitating the inclusion of 2-hop neighbors. The parallel bond graphs thus contain the 1hhop neighbors of each pure bond graph node assigned to the partition. The complete procedure is found in algorithm of the appendix.
DistMLIP graph creation runs purely on CPU memory, written in high-performance standalone library that does not depend external libraries such LAMMPS, Pytorch, JAX, or Pytorch Geometric, and can be, in principle, applied to any MLIP that includes an atom graph and/or three-body graph.
a
3.4 CURRENTLY SUPPORTED MLIPS
Currently, we have implemented four widely used models in DistMLIP: 1) CHGNet (Deng et al. 2023), an invariant MI.IP that features both an atom graph as well as a bond graph, 2) TensorNet atom graph-only, computationally--ffici
(Simeon & De Fabritüis 2023 Ko et al. 2025), cquivariant MLIP with performance on par with Nequip. 3) MACE (Batatia et al. (2023). ) an atom graph-only equivariant MLIP that directly models many body interactions between atoms, and 4)
eSEN (Fu et al., 2025), an atom graph-only, smooth and equivariant MLIP. Specific architecture details and usage found in appendix Cland appendix
4 RESULTS
In this section, benchmark DistMLIP with the 4 FPs loaded with their public pretrained check- points: MACE-MP-0b-small-3..8 8M, CHGNet-2.7M TensorNet-O.8M, and eSEN-3.2M. The details of the models and checkpoints can be found in appendix C
For all scaling timing-related benchmarks, model inference is performed 20 times, with the average of the final 10 trials reported. This is allow GPUs to warm up before performing calculations. We use crystalline o-quartz SiO, supercells for each timing benchmark, unless stated otherwise. The benchmarks are performed with a GPU cluster with 8XNVIDIA-AION HIIDIAA-A10--OGAA 1A--A1000000-PPPP 80GB-PCIe.
(a)
[b)
(c)
Wall-Clock Clock Time (Normalized)
Max Capacity (Normalized]
MACE-3.8M
Wall-Clock Time (Normalizeed)
1.0
TensorNet-G..M
0.8
2M
Ideal scaling
0.6
0.4
0.2
0.0
Number of GPUs
Number GPUs
Number of GPUs
Figure 2: Performance scaling of DistMLIP inference with 4 pretrained MLIPs: MACE-3.8M. TensorNet-O.8MM. CHGNet-2.7M, and eSEN-3.2M All results are averaged over 10 inferences on SiO, supercell. (a) Maximum capacity (number of simulatable atoms) vs. the number of GPUs. Values are normalized by the I-GPU capacity.
(b) Strong scaling of MLIP inference on DistMLIP, where the total number of atoms in the supercell is held constant while the number of GPUs increases. (c) Weak scaling behavior of MLIP inference on DistMLIP, where the number of atoms on each GPU device held constant while the number of GPUs increases.
4.1 MAXIMUM CAPACITY
A key performance metric is the maximum number of atoms that can be simulated by extending to multi-GPU inference. The maximum capacity scaling tests, with respect to the number of GPUs, can be found in Figure a). All atom counts normalized to be represented multiples of the 1-GPU maximum capacity. As the number GPUs (and thus, total GPU memory) increases, the maximum simulatable capacity increases linearly. The scaling of eSEN and MACE is further away from ideal

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scaling due to the one-time equivariant feature calculations that are occurring single GPU due to numerical stability concerns. The single GPU poses as a memory bottleneck for the system.
We also benchmark the maximum capacity and corresponding inference time against the SevenNet model. The results can be found in Appendix GG] After matching the total number of parameters to SevenNet (800k), we find that MACE, TensorNet, and CHGNet can achieve up to 10x higher maximum capacity and 4x faster inference speed when incorporated with DistMLIP compared to the distributed inference of SevenNet.
4.2 STRONG AND WEAK SCALING
Strong scaling tests, where the total size of the system remains constant while the number of GPUs increases, can be found in Figure bb) All times are normalized to be represented as multiples of the GPU time. The system sizes for MACE-3.8M, TensorNet-O.8M, CHGNet-2.7M, and eSEN--.2MM 3.M were 33.5k, 22.0k, 9.8k, and 1.4k atoms respectively. We also plot the ideal scaling under the assumption that computation performed by each GPU is purely independent and perfectly parallelizable. particular, eSEN's high memory consumption results small atomic cells. However, small atomic cells with partition widths that aren't 't sufficiently large results in overlapping border nodes during each convolution leading to increased overhead.
Weak scaling tests, where the total size of the system increases proportionally with the number of GPUs (such that each GPU performs computation on the same number of atoms), are found in Figure 2(.). All times normalized to be represented multiples of the 1 GPU time. The per GPU atom count for MACE-3.8M,
PensorNer--.8MM. CHGNet-2.7M. and eSEN-3.22 3.2M are 34.6k, 19.9k, 9.9k, and 1.4k, respectively. In the case of ESEN-3.2M, weak scaling moves away from ideal scaling due to the high constant overhead associated with initial feature calculation. For CHGNet-2.77M the computation required for the construction of the three-body graph scales with ((N9) where N is the number of atoms within the three-body cutoff, leading to suboptimal weak scaling when the simulation cell size increases. In Table 33 of Appendix K.1 we show that the simple vertical partitioning rule used in DistMLIP and specified in Algorithm 3 is up to 8x faster compared to standard graph partitioning baselines.
4.3 INTERACTION RANGE
In this section, we benchmark how the parallelized simulation speed and capacity is affected by the MLIP's interaction range and number of parameters. In Fig. 3 (a). we fix each model to around 0.8M parameters and vary the number of message passing layers increase the interaction range of the model. The 8 GPU inference is performed on the a-quartz SiO, of 72k atoms. The measured inference times are then divided by the inference time of the baseline 10Â version cach MLIP. eSEN ran out of memory for the 45 and 50 angstrom tests.
The results in Fig. (a) show that DistMLIP only has a linear relation between parallelized inference time interaction range. This is due to the additional computation cost from each increased message passing layer. Conversely, in conventional spatial partitioning, the volume of the simulation cell, and therefore the number of ghost atoms, grows cubically with the interaction range. This highlights the parallelization efficiency and zero calculation redundancy in graph partitioning.
4.4 SCALING MODEL SIZE
In Fig. (b) and (c), we fix the number of message passing layers and vary the feature embedding sizes in the MLIP, therefore measuring the relation between parallelized inference speed/capacity and model parameter size. The result shows that by decreasing the model parameter size, a significant increase in simulation speed and maximum capacity can be achieved. The result suggests an estimated performance gain when distributed inference can be combined with smaller model sizes through MLIP model distillation (Amin et al. 2025).
4.5 REAL WORLD SIMULATIONS
We also show the performance of real distributed simulations on a variety of solid-state and biomolecular systems, utilizing 4, and 8 GPUs. The results are found in Table [] We report
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(a)
Wall-clook Time (Normalized)
2.25
MAGE Tamsprrket CHSNNt eSEN
(b)
200
(c)
100
Max capacity (atorno)
jus/atom--te 4im-stmmssppppp
10"
1.75
40-
1.50
10"
1.25
20
1.00
10"
15
20
35
40
90K
770K
1.4M
2.1M
2.8M
3.5M
90K
770K
1.4M 2.1M 2,8M 3.5M Number parameters
Interaction Range
Number of parameters
Figure 3: Effect of model configurations on praph-parallelized inference performance. (a) Inference time vs. MLIP interaction range while keeping model parameter size fixed. Values are represented as multiples of the 10Å interaction range. (b) Inference time and (c) maximum simulation capacity vs. number of parameters in the MIIP, while keeping interaction range fixed.
the microsceonds/atom teeeonttttmeteppppe each model-system pair as well as the number of simulated atoms in the system. The simulated systems be found in Figure In Table L-MACE-3.8M, refers to multi-GPU inference of MACE using LAMMPS spatial partitioning, while the other models are distributed with DistMLIP.
Table 1: MD step time (in uus / (atomxstep)) for the max capacity of 4 pretrained FPs on DistMLIP: CHGNet-MaIPES-2.
LSEN-3.2M, L-MACE- 3.8M refers to MACE running on LAMMPS spatial partitioning. L-MACE-3 8M is compiled model using custom equivariant CUDA kernels while MACE-3.8M uses the pure-Py Torch implementation of MACE.
MACE-MP-ON -b-smalll Sansorrer--MaPES-OO..
Model
GPUs
pa8 (atomxstep) atoms (in thousands)
LigPO,
H₂O
GaN
MOF
2w49
GPU
82.47 5.2
33.4 10.4
19.8 9.7
53.8 8.0
OOM
L-MACE-3.8M
4 GPUs
16.9 41.4
10.1 24.6
5.1
45.0
9.4
27.0
OOM
GPUs
12.3 65.9
8.5 82.9
2.7 77.8
6.2 64.0
OOM
GPU
44.8 21.9
45.9 20.7
39.5
43.9
41.0
16.0
OOM
MACE-3.8M
4 GPUs
15.3 110.6
18.2 96.0
14.6
128.0
14.7
128.0
20.1
69.3
GPUs
11.0 216.0
11.6 210.1
9.6]
250.0
10.9
216.0
14.0 69.3
GPU
81.7 | 21.9
92.1 |.1
6.1
79.1
16.0
79.1
16.0
OOM
TensorNet-O.8M 4 GPUs
24.3 64
26.91 49.1
22.9
65.5
23.2
54.0
OOM
GPUs
16.3 140.0
18.0 82.9
15.9
123.0
15.5
125.0
19.6 69.3
GPU
179.7 4.1
154.8 6.1
100.0
5.5
174.6
2.0
OOM
CMGNet-2.7M
4 GPUs
94.8 21.9
80.5 20.7
45.5
43.9
81.1
16.0
00M
GPUs
75.4 46.7
64.5 49.1
41.9
77.8
67.1
54.0
00M
GPU
727.3 0.9
|..
663.2 1.3
438.9
1.0
454.3
1.0
00M
eSEN-3.2M
4 GPUs
273.4|4.1
284.0
2.6
222.3
5.5
236.3
3.0
00M
8 GPUs
241.2 8.0
249.1 6.1
198.9
8.2
210.0
6.0
00M
Our result shows that DistMLIP provides tripled maximum simulation sizes compared to LAMMPS spatial partitioning within the MACE-3.8M model. Note that L-MACE-3.8M uses compiled model with custom equivariant CUDA kernels, while DistMLIP MACE-3.8M only runs the pure-Py Torch implementation. Custom equivariant CUDA kernels shown accelerate MACE inference time
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(a)
133A
158A
(b)
135A
155A
(c)
149A
140A
114A
1274 A
143A
Battery Electrolyte
Water
Semiconductor
118À
1664
(c)
(d)
511Å
127A
Metal Organic Framework
Biomolecute
Figure 4: Sample simulation cells from real-world systems that are benchmarked Table (a) Li,PO, supercell of 216.0k atoms. (b) H2O supercell of Ik atoms. (c) GaN supercell of 250.0k atoms. (d) C metal organic framework (MOF) system of 216.0k atoms. (e) 2w49, insect flight muscle protein of 69.3k atoms.
by up to 7.2x on large models (Geiger al. 2024). Nevertheless, observed similar simulation speed between the standard model on DistMLIP and the compiled model on LAMMPS, which supports the efficiency of DistMLIP graph-partitioning due to the lack of LAMMPS multi-GPU inference support. For other FPs, which typically have longer interaction ranges compared to 12Á in MACE-3.8M, the capacity increase and inference speed-up should be much more significant the efficiency of spatial partitioning degrades rapidly with increased cutoffs.
No other model beyond MACE is reported
In Table 1 we highlight that most FPs with few million parameters capable of simulating sear-millon--too scale systems when parallelized with only GPUs. Moreover, we noticed that the inference time, when normalized by the number of atoms, is significantly decreased when any MLIP is being parallelized. This observation suggests chemically rare events can be cheaply simulated using larger cell for shorter simulation time, rather than a smaller cell for a longer simulation time, which has been the standard simulation procedure due to the inability to efficiently perform large simulations. As estimated from the benchmark result in Table nanosecond caa--millin--tooe simulations can now be achieved at the order of 10 days with standard FPs and DistMLIP on few GPUs.
5 CONCLUSION
Scaling quantum- cheettccmcceecclll simulations to the size of realistic applications remains a critical challenge, even with recent developments MLIPs and FPs. To address this challenge. we present DistMLIP, distributed MILP inference platform based on efficient graph-level partitioning. Compared to the conventional spatial partitioning through LAMMPS, DistMLII serves as an easy and versatile distributed inference platform that supports long range MLIPs. DistMLIP provides infrastructures for constructing and distributing atom and bond graphs, allowing the distribution of GNN-based MLIPs that are otherwise infeasible to parallelize.
We benchmarked the parallelized inference of popular MLIPs: MACE, TensorNet, CHGNet and eSEN. Our result shows that efficient and blug-and-play parallelization can be achieved when combining DistMLIP with existing interatomic potentials. By distributing the MLIP simulation on NVIDIA-A 100 GPUs, our result shows that nanosecond, near-million-atom scale simulations can

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be accomplished at the scale of 10 physical days with state-of-the-ar FPs. We believe this effort to enable large-scale simulation would accelerate chemical, materials, and biological discovery.
ACKNOWLEDGEMENTS AND DISCLOSURE OF FUNDING
This work was funded by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Materials Sciences and Engineering Division under Contract No. DE-AC0205CHI 1231 (Materials Project program KC23MP). The authors would also like thank Luis Barroso-Luque and Zijie Li for helpful discussions. The authors declare no competing interests.
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A DISTRIBUTING BOND GRAPHS
Algorithm deppits the method to distribute three-body graphs (bond graphs), as well as calculating the necessary information perform data transfer between various partitions at each convolution of the three-body graph.
Algorithm Distributed Bond Graph Construction
Input: Global edges E, partitions {P.). bond cutoff r, tolerance
Output: Line graphs {Li} for partitions (P:}
for each partition P: do
Initialize TO/FROM/PURE arrays for bond graph nodes (edges within atom graph)
Initialize edge tables T; for each partition
Build Edge Table T;:
for each edge E E with dist(e) S do if dst(e) P: then
append to Tife.src] border edge for P: then add FROMp, |which p else if is border edge for another partition Pj then
add TO which /atichh_ariiioonesss)]. [P.]
end if end if end for
for each edge e E with dist(e) Sr+ do
if e is pure edge assigned to P: then
Append e to [e.ssrc]
add to PURE [UURR[/wich_pprtttioneeddd
end if end for
Localize Edges
for each E T; do
Create mappings between global and local bond graph node indices Assign local node indices to each in T, Vi
end for
Build Line Graph Li
for each partition P; do do
for each for each E Ti[ do for each e' [e do if needs_in lin((ieeee) then
Draw an edge in bond graph from e to e' using local node indices end if end for end for
end for
end for
end for
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B ASSIGN TO PARTITIONS
Algorithm is the method used to determine assign individual nodes to the PURE/TO/ROM buckets of each partition. It is used extensively in both atom graph creation (algorithm I and three-body graph creation (algorithm
Algorithm 3 assign_to to_pattttions Subroutine
Input:
Nodes, edges, partitions
Output:
PURE, TO, FROM arrays for each partition
1. Initialize node tracking:
Create table node to leoppratttionnnnn [node_ id] <- nodes
2. Populating node node_to ttt_tt_ppatt to_parrition
for each edge e do
tete_tt__prritaee whtich_pattition(e.dde.
whiich_parttionn(sssrr]]]
end for
3. Assigning nodes to partition buckets for each node n do
if node__t tt partition[n] = -1 then
add to PURE array of which_partition()) else
add to Thwhich W partition()) (ooee))nnoonenoooo toadd__to_oo httpt//_ooeeeeeotttttiiolo/ot// add to FROM wttp:rrrtttttotlowicteeeeeereiee
end if end for
C MLIP VERSIONS IN BENCHMARK
The table below shows the checkpoint versions of the MLIPs tested. The CHGNet model taken from recent release of MatGL library Ko al. (2025). The eSEN model in benchmark is not taken from the public pretrained checkpoints of 30.2M parameters, which too big for efficient parallelized simulation. Instead, initialized 3.2M eSEN in accordance with the eSEN-MPTri-
3.2M configuration found in Fu et al. 2025)
Table 2: Pretrained MLIPs Model Specifications
Model
Version
ModelSize InteractionRange
Reference
CHGNet MACE
matgl-MatPES. PEEPPES-PEEEBEE 20E-5222.2210
2.7M
A
Deng et al. 2023
MACE-MP-M -0----0b-mmmaa
3.8M
12Á
Batatia et al. 2023
TensorNet
Manell-MaPES--BE-220
0.8M
10Å
Ko et al 2025
eSEN
cSEN-MPTT-3.2M
3.2M
12Á
Fu et al.
2025
D SINGLE GPU BENCHMARKING DETAILS
Because DistMLIP parallelizes neighbor list construction as well as underlying threebody graph creation, utilizing only 2 DistMLIP partitions can already lead to faster total inference time and less total memory consumption compared to a baseline implementation without DistMLIP (this is especially the case with CHGNet), Therefore, to maintain fair comparison, all single-GPU results reported in any benchmark utilize 2 DistMLIP partitions performing operations on the same GPU. Therefore, only 1 GPU is utilized, but the same fast graph creation algorithms and implementation are shared. For all benchmarking tasks, 128 threads were used for neighbor list construction and graph creation.
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E INFERENCE TIME BREAKDOWN
Neighbor list construction could take substantial amount of inference time when the simulated system is large. In order to address this issue, we parallelized neighbor list construction DistMLIP through multi-threading, single-thread neighbor list construction in Pymatgen (Ong et al. 2013). Furthermore, we show the breakdown of overall inference time using DistMLIP when fixing the total atomic system size in Figure 5 We also include the breakdown of overall inference time when fixing the total number of atoms per GPU while scaling the total number GPUs in Figure The atomic system used was crystalline SiO2 supercell expanded in cubic fashion.
that graph creation time substantially decreased compared to the
GPUS
Tensorhett (22032 atoms)
CSEN 11275 atomss)
38
300
Construction
Figure 5: Timing breakdown, by percentage, for CHGNet-2.7M, MACE-3.8M TensorNet-O.8M and cSEN-3.2M models across data transfer, backward pass (for force calculation), forward pass, and graph construction. The total number of atoms held fixed across all GPUs runs.
F SCALING SYSTEM DENSITY
Fig. we plot the memory consumption and inference time of scaling system density (atoms/A3) of an SiO, system with 3456 atoms. DistMLIP inference with 4 A100-80GB GPUs were used. Denser atomic systems lead to a linear increase total neighbor list size, driving up memory usage as well inference time due to the decreased sparsity within the underlying atom graph's 's adjacency matrix. DistMLIP and its zero-redundancy inference algorithm scales memory consumption according to the increase in edge count.
17

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Published as a conference paper at ICLR 2026
CHCcet
MACE
300
GPUS
GPUS
33775 aborss
15.500 deee
73.022 stoms
3S0334 sloms
3,275 Jten
2553 sooms
5,390 stoen
10,00 dlers
Transfer
Construction
Figure 6: Timing breakdown, by percentage. for CHGNet-2.7M MACE-3.88 TensorNet-O.8M and cSEN-3.2M models across data transfer, backward pass (for force calculation), forward pass, and graph construction. The total number of atoms increase proportionally to the number of GPUs such that the number of atoms GPUs is held fixed as the number of GPUs increases.
(a)
(b)
(GIB)
200
TensorNot-0.92 BM
CHGNab-2.7M
Average time (9)
<SEN-3.2M
150
Density (atoms/A?)
Density (atoms/A²)
Figure 7: The effects of scaling density on (a) memory consumption, and (b) inference time. Both plots are the result of scaling atomic density (atoms/A²) an arbitrary system with fixed atom count using DistMLIP and A100-80GB GPUs. eSEN missing datapoint due to out-of-memory issues.
G BENCHMARKING AGAINST SEVENNET
In this section, we benchmark the inference time and max capacity of the 4 MLIPs in DistMLIP against the distributed inference of SevenNet (Park et al. 2024). All the MLIPs are constructed to have a similar number parameters as SevenÑet-O (0.8M parameters). the supercells of the a-quartz SiO, Inference times are averaged over 10 trials after 5 warmup trials.
tests are performed on
Fig. R shows the result for (a)SevenNet, (b)AACE (e),ensorNet, (d)CHGNet, and (e)+SEN. The number in each box in the heat map indicates the inference time of the given cell and the number of GPUs, and darker color represents faster inference. Grey boxes indicate the simulation failed due to the GPU a-quartz SiO, with SevenNet on 8 NVIDIA-A 80G0,, as indicated in the original manuscript. Our results indicated that MACE, TensorNet, and CHGNet generally simulate larger maximum
error. We reproduced similar maximum simulation size of 11k
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Published as a conference paper at ICLR 2026
[n]
SevenNet
NAGE
9.48
9.26
9.72
1:50
3:45
4.4
(4) MARK Day
0.49
0:71
1.24
Time
Nomber
8:14
122
9:1
975
1.16
1.43
2:31
srepp
122000
Number
Number Atoms
(6)
TermorNel
CHIclet
8.70
Ang Time 1o
1.7
Number
9:34
9:78
1.44
(180000
Number Atoma ESEN
Number of Atoms
(6)
Ave Tune 54
1200
elwayy
creas
Number
Figure 8: Inference speed and max capacity on a-quartz SiO,. Except for (a), all other models are distributed through DistMLIP. (a) SevenNet plus LAMMPS support. (b) MACE, (c) TensorNet, (d) CHGNet, and (e) eSEN. Because SevenNet is a 0.&M parameter model, all other DistMLIP models initialized at 0.8M parameters for comparison purposes. Grey boxes denote the inability to simulate the system either due to mettof- issues or system-size issues.
capacity at faster speed in DistMLIP. For eSEN, all experiments failed due the extensive memory consumption.
H ALIGNMENT OF SINGLE-DEVICE AND MULTI-DEVICE PREDICTIONS
DistMLIP's atom graph and bond graph distribution algorithms are exact in principle. However, numerical differences arise when performing computation a distributed manner compared to on single GPU. This is result of non-determinism occurring during matrix multiplications and other operations on different GPUs (Fatahalian et al. 2004). Therefore, the exact same model and weights rumning single GPU inference on different GPUs within the same node will also yield slightly different results. In Fig. we plot the energy/atom error in me V/atom units for MACE-3.8M. TensorNet-O. 8M, and 2. The result shows that the numerical error from different GPUs far below chemical accuracy.
19

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Published as a conference paper at ICLR 2026
(8)
(b)
(c)
1em4
16-5
1.8
(maklatom)
25
0.8
2.
100
L33044
1.0
0.2
Figure 9: Energy (aatm)) discrepancy between DistMLIP's multi-GPU inference and baseline single-GPU inference for (a) MACE-3.8M, (b) TensorNet-O.8M. and (c) CHGNet-2.7M 7M multiple chemical systems, Note that DistMLIP's graph partitioning and distribution algorithms are exact, and these non-perte non-perfect discrepancies are a result of non-deterministic matrix multiplication operations on different devices.
I USAGE
Load the MLIP as usual
chgnet
from DistMLIP. wmplementations... matgl import CHGNet_Dist
chgnet_dist = CHGNet_Distd fromeexistooeennnneoeeeoooo (chgnet) (chegneeeetgggheett)
Shgnet_cist.enue seanaaleddisrrbtteeddddeeedeeedd seceeddddddeeeeeeeeeeeeeeeeeeeee ( 1, 2, ...I) # Specify GPU ids Run inference/simmlaleeee
as usual
Code 1: Example code for using DistMLIP along with CHGNet. DistMLIP is designed to be plug- and-play platform for distributed inference. The current implementation only supports single-node multi-GPU inference.
J PARALLELIZING A MODEL IN DISTMLIP
DistMLIP is designed to be both high-performant as well as easily usable. Parallelizing new MLIPs using DistMLIP straightforward process that can be done purely Python. A few key points of DistMLIP are outlined in J.1. The primary data structure, the Distributed object, inputs atom positions, periodic boundary conditions, and (optionally) cell lattice, and constructs the graph partitions and associated metadata. The data structure can partition, aggregate, and perform data transfer for node features, edge features, and node features within the threebody graph. These simple distributed primitives are implemented in high performance C and makes distributing a new MI.IP very straightforward to implement but still high performing.
J.1 MODEL PARALLELIZATION EXAMPLE
Creating DistMLIP distributed object dist = Distributed, create distributed(... Distributed edge information
dist.dst_nodes nodes # List of sre and dist node pairs for each partition
Distributing node/edge features bodde_eeatures
dist.ddistribute_nddeeee features (node_ dist Exchanging node information
(tode_featured features) (edge_ features)
edge_features_dee
iist.atoo_trane (none__ (node_featuree_ddis
Aggregating node features
node features aist.aggregate (node features dist)
Code 2: A subset of the available features implemented into DistMLIP. These features, implemented as Python wrapper over efficient C and Py Torch code, allow for the straightforward distribution of any arbitrary MLIP
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Published as a conference paper at ICLR 2026
K GRAPH PARTITIONING
Graph partitioning algorithms find applications in solving PDEs via domain decomposition, solving sparse linear systems of equations, circuit partitioning and layout, VLSI design, social network analysis, clustering algorithms, and image segmentation (Pothen 1997] Stanton & Kliot [012] Bader et al. 2013 Tolliver & Miller [006 Peng et al. 2013). One common use case in graph partitioning is to create mutually exclusive spanning sets of nodes that contain the minimum number of edges that cross from one partition to another. This use case can be tackled using sparse, symmetric matrix reordering methods such as the Reverse Cuthell-Mckee (RCM) algorithm, which permutes a sparse matrix to minimize its bandwidth (i.e. reordering rows and columns such that non-zero values are closer to the diagonal) (Cuthill & McKee, 1969. Azad et al. 2017). The reordered matrix can then be partitioned along the columns in order to calculate graph node partitions. METIS is graph partitioning algorithm that utilizes a graph coarsening phase, an initial partitioning sequence over the coarsened graph, and an uncoarsening and partition refinement stage (Karypis & Kumar 1998). However, applications depending on algorithms such as RCM or METIS typically don't have latency requirements during the graph partitioning stage. In atomistic simulation, the underlying graphs are recalculated and repartitioned at each time step. Therefore, even small latency increases during the graph creation and partitioning stage get compounded into significant increases in overall simulation time. In our own experiments, we find that RCM and METIS could increase inference time for million-atom graphs by several seconds per timestep.
K.1 BENCHMARKING PARTITION STRATEGIES
We compare DistMLIP's vertical wall partitioning strategy with other common graph partitioning algorithms. In Table 3 replace DistMLIP's current vertical wall partitioning strategy with the Reverse Cuthill-Mckee (RCMK) and METIS algorithms while holding the other components of Algorithm and Algorithm 3 Neither RCMK nor METIS supports threebody bond graph creation. RCMK and METIS both utilize the graph's 's topology in order to partition the graph such that the number of crossing edges between partitions is minimized. DistMLIP's current partitioning strategy, on the other hand, doesn't I perform graph traversals but rather uses atomic positions as heuristic in order to partition the graph. In Table we also include the LAMMPS spatial partitioning results for comparison. As a result, we perform all benchmarks with the MACE-3.8M. model.
Table 3: MD step time (in μss / (atomxstep)) for various graph and spatial partitioning strategies. RCMK refers to the Reverse Cuthill- algorithm used for graph partitioning. Both RCMK and METIS still utilize the DistMLIP platform, only the partitioning strategy is replaced. The model used was MACE-3.8M and LAMMPS spatial partitioning values are included for comparison.
Method # GPUs
ju8 / (atomxstep)
# of atoms (in thousands)
LigPO,
H₂O
GaN
MOF
2w49
4 GPUs
81.19
108.0
101.67
96.0
68.98
77.0
83.60
125.0
78.93
69.0
METIS
8 GPUs
62.18
216.0
56.76
216.0
77.15
207.0
69.63
216.0
67.10
69.0
4 GPUs
77.49
110.0
98.02
96.0
66.92
77.0
82.24
125.0
79.94
69.0
RCMK
8 GPUs
57.22
216.0
51.67
216.0
74.01
207.0
65.74
216.0
65.51
69.0
4 GPUs
15.30
110.6
18.20
96.0
14.60
128.0
14.70
128.0
20.10
69.3
Vert. wall
8 GPUs
11.00
216.0
11.60
210.1
9.60
250.0
10.90
216.0
14.00
69.3
L MOLECULAR DYNAMICS SIMULATION STABILITY
To validate the numerical robustness and long-term stability of DistMLIP simulations, we performed a nanosecond TensorNet MD simulation of Li-ion cathode material containing 0.1 million atoms on 8 A100 GPUs Fig. 10 shows the energy of the material as function of time. Throughout the simulation, the system maintained structural stability. Except for the expected thermal fluctuation, no severe energy oscillation is observed during the entire simulation. The initial decrease in energy is not a numerical artifact; rather, it is attributed to the energy equilibration of the simulated material, representing a crucial physical process successfully captured by the DistMLII IP simulation.
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Published as a conference paper at ICLR 2026
-136000
-137000
138000
Energy (eV)
139000
potential energy
-140000
total energy
114000
142000
-143000
114000
0.0
0.5
1.0
1.5
2.0
Time (ns)
Figure 10: Evolution of potential energy and total energy 2 nanosecond long DistMLIP MD simulation of Li-ion cathode material using 8 GPUs and the TensorNet model. The oongtimme numerical stability of DistMLIP is indicated in the smooth profile energies with only expected thermal fluctuations.
M LARGE SYSTEM TRAINING USING DISTMLIP
Machine learning interatomic potentials are trained on quantum mechanical calculations such as density functional theory coupled cluster techniques. Duc to the computational complexity of these techniques, however, calculating the energy and forces of atomic systems with greater than several hundred atoms is computationally intractable. As result, other than instances in which large batch sizes are used in training, DistMLIP's primary use case would be for large scale inference. However, for technical completeness, data bank (Berman et al., 2000) consisting of 8. 1k atoms. Energy and forces were calculated using Lennard-Joner potential. With batch size of 16, we achieve 2.1 seconds per training step on A A100-80 GB GPUs using a 0.4M parameter CHGNet model demonstrating DistMLII's ability in large-scale, large-batch training.
perform training on a GPCR protein, 6P9X from the protein
22


Reviews:
[{"review_id": "Os9Xvt1lnU", "reviewer": "Reviewer_dy5L", "summary": "This paper introduces DistMLIP, a distributed inference platform designed to scale Machine Learning Interatomic Potentials (MLIPs) to large-scale atomistic simulations. The core problem it addresses is the computational bottleneck of running modern, high-accuracy MLIPs—many of which are based on Graph Neural Networks (GNNs) with long-range interactions—on systems with millions of atoms.\n\nThe key contribution is a \"zero-redundancy, graph-level parallelization\" strategy. This method contrasts with conventional spatial partitioning (e.g., in LAMMPS), which suffers from high computational redundancy due to the need to calculate \"ghost atoms\" at partition boundaries. DistMLIP partitions the atomic graph itself and distributes subgraphs to different GPUs, enabling efficient parallel inference. The platform is presented as a flexible, \"plug-in\" library that does not depend on third-party simulation packages like LAMMPS.\n\nThe authors demonstrate DistMLIP's effectiveness by benchmarking four popular MLIPs: CHGNet, MACE, TensorNet, and eSEN. The results show that DistMLIP can simulate systems 3.4x larger and achieve up to 8x faster performance compared to previous multi-GPU methods , enabling near-million-atom simulations on a single 8-GPU node.", "questions": "## 1. Multi-Node Scalability: \n\nThe current work is an excellent demonstration of single-node, multi-GPU scaling. Could you elaborate on the primary challenges for extending DistMLIP to a multi-node environment? Specifically, how do you envision managing the atom_transfer step across a network interconnect, and what do you anticipate will be the new performance bottleneck (e.g., network latency vs. bandwidth)?\n\n## 2. High-Order Graph Construction Bottleneck: \n\nThe suboptimal weak scaling of CHGNet is due to the three-body graph construction. Is this construction step (described in Algorithm 2) fully parallelized within DistMLIP, or is it a separate, serial (or partially parallel) step that acts as a bottleneck before the GNN forward pass? Does this imply a fundamental limitation for DistMLIP's performance on future models that might incorporate even higher-order (e.g., four-body) interactions?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 4, "strengths": "### High-Impact Problem: \n\nThe paper tackles a critical and timely bottleneck in computational science. Scaling MLIPs to the meso-scale (millions of atoms) is essential for bridging quantum-accurate simulations with real-world applications in materials science, chemistry, and biology.\n\n### Sound and Novel Method: \n\nThe graph-level parallelization approach is fundamentally better suited for GNN-based MLIPs than traditional spatial partitioning. The paper clearly articulates the \"zero-redundancy\" advantage , which correctly avoids the cubic scaling of redundant computations (ghost nodes) that spatial partitioning faces as the MLIP interaction range increases. The method's native support for both atom graphs and higher-order bond graphs (used in models like CHGNet) is a significant advantage.\n\n- Comprehensive and Rigorous Empirical Validation: This is the paper's strongest aspect.\n\n- Diverse Models: The method is validated on four distinct and widely-used MLIPs, demonstrating its generality.\n\n- Strong Baselines: The authors provide direct comparisons against two crucial baselines: (1) The industry-standard spatial partitioning (LAMMPS-MACE) and (2) Another graph-parallel method (SevenNet). DistMLIP shows superior performance in maximum capacity and speed against both.\n\n- Excellent Scaling Analysis: The paper provides clear strong and weak scaling plots (Fig. 2) , as well as detailed analyses of how performance scales with model parameters and, most importantly, interaction range (Fig. 3). The linear scaling with interaction range (vs. cubic for spatial partitioning) is a key result.\n\n### Pragmatic Design Insight:\n\nA standout finding is the justification for using a simple \"vertical wall\" partitioning scheme. The authors correctly identify that for MD, the partitioning latency (which must be paid at every time step) is a critical bottleneck . Their simple heuristic is shown to be up to 8x faster than more complex graph partitioning algorithms like METIS or RCMK (Table 4), demonstrating a deep, practical understanding of the problem domain.\n\n### Practicality and Usability: \n\nBy designing DistMLIP as a standalone, \"plug-and-play\" Python-based library , the authors have significantly lowered the barrier to adoption for the broad community of researchers who use these models but are not experts in distributed computing.", "weaknesses": "### Single-Node Limitation:\n\n The paper states the current implementation only supports \"single-node multi-GPU inference\". This is a significant limitation for scaling to truly massive systems (tens of millions+ atoms), which would require a multi-node, multi-GPU setup. The paper would be stronger if it discussed the roadmap and key challenges (e.g., managing communication overhead of border node features across a network interconnect) for a multi-node implementation.\n\n### Clarification of \"8x Faster\" Claim: \n\nThe abstract and introduction claim \"up to 8x faster\". However, the direct end-to-end inference comparison with SevenNet shows a ~4x speedup , and the LAMMPS-MACE comparison shows similar (not 8x faster) speeds, albeit with a non-compiled model. The 8x speedup figure appears to be sourced from the partitioning algorithm comparison in Table 4. The authors should clarify this in the main text to avoid overstating the end-to-end simulation speedup.\n\n### Scaling of High-Order Graphs: \n\nThe paper honestly reports \"suboptimal\" weak scaling for CHGNet, attributing it to the three-body graph construction cost. This is an important detail, as it suggests that the performance benefits of DistMLIP may be partially bottlenecked by models with complex, high-order interactions. A brief discussion of whether this construction is (or can be) parallelized within DistMLIP would be beneficial.\n\n### eSEN Performance: \n\nThe eSEN model consistently performs poorly, with high memory usage and frequent OOM errors. While this is likely due to the model's architecture rather than DistMLIP, its poor performance slightly detracts from the platform's \"general and versatile\" claim.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "85MQiSIMmI", "reviewer": "Reviewer_9NL9", "summary": "This paper introduces DistMLIP, a platform designed for efficient distributed inference of Machine Learning Interatomic Potentials (MLIPs), particularly targeting large-scale atomistic simulations (up to millions of atoms). The authors argue that while MLIPs offer quantum accuracy at lower cost, scaling them further requires multi-device parallelization . Existing methods, primarily based on spatial partitioning (like in LAMMPS), suffer from computational redundancy (ghost atoms) and are often ill-suited for modern, long-range GNN-based MLIPs . DistMLIP proposes a graph-level parallelization strategy based on graph partitioning, aiming for zero redundancy. It partitions the atom graph (and optionally higher-order graphs like bond graphs) across multiple GPUs and manages the communication of necessary node/edge features between partitions at each GNN layer . A key feature is its design as a flexible, easy-to-use, standalone platform that can wrap existing MLIPs without requiring model modification or reliance on specific simulation packages . The effectiveness is demonstrated on four diverse MLIPs (CHGNet, MACE, TensorNet, eSEN), showing significant improvements in maximum simulatable system size (up to 3.4x larger) and speed (up to 8x faster) compared to previous methods, enabling near-million-atom calculations in seconds on 8 GPUs.", "questions": "- Can the authors provide a breakdown of inference time into computation, communication, and graph construction/partitioning for different scenarios (varying GPU counts, system sizes)?\n- What are the main challenges and potential strategies for extending DistMLIP to multi-node environments?\n- How much effort is typically required to integrate a new MLIP model into the DistMLIP framework? Does it require modifications to the model's forward pass implementation? (Code 2 gives hints, but more context would be useful).", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "- Scaling MLIP simulations to biologically and materially relevant sizes (millions of atoms) is a major challenge. DistMLIP provides a much-needed solution specifically tailored for efficient distributed inference of modern GNN-based MLIPs.\n- DistMLIP is designed as a model-agnostic, plug-in platform . This allows researchers to apply it to their existing, pre-trained MLIPs with minimal adaptation (as demonstrated with four different models), significantly lowering the barrier to large-scale simulations. Its independence from specific simulation software like LAMMPS increases flexibility.\n- The reported results are substantial: linear scaling of capacity, significant speedups in strong scaling tests (up to 8x faster), and the ability to perform near-million atom calculations in seconds on modest hardware (8 GPUs). Comparisons vs LAMMPS MACE and SevenNet further highlight the advantages.", "weaknesses": "- Graph partitioning inherently requires communication between GPUs after each message-passing layer to exchange border node information. The paper acknowledges scaling isn't always ideal (Fig 2b, 2c) partly due to overheads, but a more detailed analysis of communication cost vs. computation cost, and how it scales with the number of GPUs, graph density, and partition quality, would be valuable.\n- The paper appears to be lacking comparisons against several relevant baselines. For instance, a critical benchmark is missing: how does the speed (e.g., throughput or latency) of the proposed system compare to other established distributed systems, such as Allegro?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "quSF9gOpQ9", "reviewer": "Reviewer_SsYg", "summary": "DistMLIP is a distributed inference platform for ML interatomic potentials (MLIPs) that parallelizes graph-based models across GPUs with zero redundant compute. It partitions the atom graph (and the three-body “bond” line graph) and exchanges border features every GNN layer, so existing models (MACE, CHGNet, TensorNet, eSEN) can run multi-GPU without architectural changes. It can use up to 3.4× larger systems and is up to 8× faster than prior multi-GPU approaches.", "questions": "See weaknesses.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. Works with popular MLIPs (MACE, TensorNet, CHGNet, eSEN) with minimal adaptation, so we don’t need model-specific rewrites.\n2. The “vertical” partition rule is reported up to 8x faster than standard graph partitioners (e.g., METIS/RCMK). And against SevenNet’s distributed inference, DistMLIP has up to 10x higher max capacity and is 4x faster.", "weaknesses": "Majors:\n1. The authors say the design keeps backprop intermediates in their contribution claims, but they only benchmark inference; there’s no distributed-training result or accuracy/stability study over long MD runs.\n2. All inference timing is on one cluster of 8x A100-80GB; there’s no multi-node or NVLink study to justify capability of large scale simulation.\n\nMinors:\n1. Line 39: \"CHARM\" -> \"CHARMM\"\n2. Line 44: \"coupled clustering\" -> \"coupled cluster\"\n3. Line 132: Citation format\n4. Line 201: \"G_n\" -> \"G_p\"\n5. Line 271: \"Pytorch, Jax\" -> \"PyTorch, JAX\"\n6. Line 346: \"partitoining\" -> \"partitioning\"", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "LlpB24ukHP", "reviewer": "Reviewer_18ns", "summary": "The authors propose a novel distributed inference platform, DistMLIP, for machine learning force fields.  DistMLIP enables efficient MLIP parallelization through graph partitioning, overcoming the requirement for LAMMPS. The results show that this platform 1)  significantly outperforms previous SOTA regarding the inference efficiency, 2) supports large-molecule inference, and 3) supports multiple network architectures.", "questions": "Weakness 3 seems like an inherent limitation of DistMLIP by nature, but do you have any plan to address it, or to show that the method could also perform well for anisotropic molecules?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. DistMLIP significantly improves the inference speed for several models.\n2. DistMLIP enables large molecular inference.\n3. DistMLIP is model-agnostic.\n4. DistMLIP achieves above features via full-atom level graph partition instead of spatial partition, omitting ghost atoms utilized by SevenNet, which are methodologically novel.", "weaknesses": "1. This platform does not support training.\n2. This platform does not support multi-node inference.\n3. The vertical wall is ad hoc. Although it performs well for the selected molecular system, it could fail for anisotropic molecules.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)