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Found 35 papers, 19 papers with code
Optimizing Multiagent Cooperation via Policy Evolution and Shared Experiences
Training policies solely on the team-based reward is often difficult due to its sparsity.
Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials
Developing computationally efficient MLIPs to execute MD simulations that accurately model a broad set of materials properties.
SymmCD: Symmetry-Preserving Crystal Generation with Diffusion Models
Generating novel crystalline materials has the potential to lead to advancements in fields such as electronics, energy storage, and catalysis.
Stiefel Flow Matching for Moment-Constrained Structure Elucidation
We consider the task of predicting a molecule's all-atom 3D structure given only its molecular formula and moments of inertia, motivated by the ability of rotational spectroscopy to measure these moments.
Foundational Large Language Models for Materials Research
Here, we present LLaMat, a family of foundational models for materials science developed through continued pretraining of LLaMA models on an extensive corpus of materials literature and crystallographic data.
MatExpert: Decomposing Materials Discovery by Mimicking Human Experts
Material discovery is a critical research area with profound implications for various industries.
Deconstructing equivariant representations in molecular systems
In this work, we report on a set of experiments using a simple equivariant graph convolution model on the QM9 dataset, focusing on correlating quantitative performance with the resulting molecular graph embeddings.
HoneyComb: A Flexible LLM-Based Agent System for Materials Science
The emergence of specialized large language models (LLMs) has shown promise in addressing complex tasks for materials science.
From Text to Insight: Large Language Models for Materials Science Data Extraction
The vast majority of materials science knowledge exists in unstructured natural language, yet structured data is crucial for innovative and systematic materials design.
MatText: Do Language Models Need More than Text & Scale for Materials Modeling?
This challenge is further compounded by the absence of a comprehensive benchmark to rigorously evaluate the capabilities and limitations of these text representations in capturing the complexity of material systems.
Are large language models superhuman chemists?
Large language models (LLMs) have gained widespread interest due to their ability to process human language and perform tasks on which they have not been explicitly trained.
Are LLMs Ready for Real-World Materials Discovery?
Given those shortcomings, we outline a framework for developing Materials Science LLMs (MatSci-LLMs) that are grounded in materials science knowledge and hypothesis generation followed by hypothesis testing.
A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems
In these graphs, the geometric attributes transform according to the inherent physical symmetries of 3D atomic systems, including rotations and translations in Euclidean space, as well as node permutations.
Towards equilibrium molecular conformation generation with GFlowNets
Sampling diverse, thermodynamically feasible molecular conformations plays a crucial role in predicting properties of a molecule.
Reflection-Equivariant Diffusion for 3D Structure Determination from Isotopologue Rotational Spectra in Natural Abundance
KREED's top-1 predictions identify the correct 3D structure with >98% accuracy on the QM9 and GEOM datasets when provided with substitution coordinates of all heavy atoms with natural isotopic abundance.
HoneyBee: Progressive Instruction Finetuning of Large Language Models for Materials Science
We propose an instruction-based process for trustworthy data curation in materials science (MatSci-Instruct), which we then apply to finetune a LLaMa-based language model targeted for materials science (HoneyBee).
On the importance of catalyst-adsorbate 3D interactions for relaxed energy predictions
The use of machine learning for material property prediction and discovery has traditionally centered on graph neural networks that incorporate the geometric configuration of all atoms.
Searching for High-Value Molecules Using Reinforcement Learning and Transformers
Reinforcement learning (RL) over text representations can be effective for finding high-value policies that can search over graphs.
EGraFFBench: Evaluation of Equivariant Graph Neural Network Force Fields for Atomistic Simulations
In addition to our thorough evaluation and analysis on eight existing datasets based on the benchmarking literature, we release two new benchmark datasets, propose four new metrics, and three challenging tasks.
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MatSciML: A Broad, Multi-Task Benchmark for Solid-State Materials Modeling
We propose MatSci ML, a novel benchmark for modeling MATerials SCIence using Machine Learning (MatSci ML) methods focused on solid-state materials with periodic crystal structures.
Using Multiple Vector Channels Improves E(n)-Equivariant Graph Neural Networks
We present a natural extension to E(n)-equivariant graph neural networks that uses multiple equivariant vectors per node.
MatSci-NLP: Evaluating Scientific Language Models on Materials Science Language Tasks Using Text-to-Schema Modeling
Our experiments in this low-resource training setting show that language models pretrained on scientific text outperform BERT trained on general text.
FAENet: Frame Averaging Equivariant GNN for Materials Modeling
Applications of machine learning techniques for materials modeling typically involve functions known to be equivariant or invariant to specific symmetries.
ProtST: Multi-Modality Learning of Protein Sequences and Biomedical Texts
On downstream tasks, ProtST enables both supervised learning and zero-shot prediction.
Can Retriever-Augmented Language Models Reason? The Blame Game Between the Retriever and the Language Model
Our findings indicate that the simple similarity metric employed by retrievers is insufficient for retrieving all the necessary statements for reasoning.
Group SELFIES: A Robust Fragment-Based Molecular String Representation
We introduce Group SELFIES, a molecular string representation that leverages group tokens to represent functional groups or entire substructures while maintaining chemical robustness guarantees.
PhAST: Physics-Aware, Scalable, and Task-specific GNNs for Accelerated Catalyst Design
Catalyst materials play a crucial role in the electrochemical reactions involved in numerous industrial processes key to this transition, such as renewable energy storage and electrofuel synthesis.
The Open MatSci ML Toolkit: A Flexible Framework for Machine Learning in Materials Science
We present the Open MatSci ML Toolkit: a flexible, self-contained, and scalable Python-based framework to apply deep learning models and methods on scientific data with a specific focus on materials science and the OpenCatalyst Dataset.
Multi-Objective GFlowNets
We study the problem of generating diverse candidates in the context of Multi-Objective Optimization.
Neuroevolution-Enhanced Multi-Objective Optimization for Mixed-Precision Quantization
In this work, we present a flexible and scalable framework for automated mixed-precision quantization that concurrently optimizes task performance, memory compression, and compute savings through multi-objective evolutionary computing.
Learning Intrinsic Symbolic Rewards in Reinforcement Learning
We show that the discovered dense rewards are an effective signal for an RL policy to solve the benchmark tasks.
Safety Aware Reinforcement Learning (SARL)
Since the safe agent effectively abstracts a task-independent notion of safety via its action probabilities, it can be ported to modulate multiple policies solving different tasks within the given environment without further training.
Optimizing Memory Placement using Evolutionary Graph Reinforcement Learning
For deep neural network accelerators, memory movement is both energetically expensive and can bound computation.
Evolutionary Reinforcement Learning for Sample-Efficient Multiagent Coordination
Training policies solely on the team-based reward is often difficult due to its sparsity.
Collaborative Evolutionary Reinforcement Learning
Deep reinforcement learning algorithms have been successfully applied to a range of challenging control tasks.
