MLCM-24-codes_links


1	Speaker	Codes \| Papers \| Intresting Links	Job Openings
2	David Balcells University of Oslo Generative Machine Learning for Transition Metal Chemistry	tmQM and tmQM-L dataset and PL-MOGA genetic algorithm
3	tmQM Dataset—Quantum Geometries and Properties of 86k Transition Metal Complexes \| Journal of Chemical Information and Modeling
4	Directional Multiobjective Optimization of Metal Complexes at the Billion-Scale with the tmQMg-L Dataset and PL-MOGA Algorithm \| Theoretical and Computational Chemistry \| ChemRxiv \| Cambridge Open Engage
5	Repository for the tmQMg-L dataset files.
6	GitHub - hkneiding/PL-MOGA: Pareto Lighthouse Multiobjective Genetic Algorithm for the de novo design of transition metal complexes.
7	Volker Deringer University of Oxford Data-driven interatomic potentials for inorganic materials chemistry	How to validate machine-learned interatomic potentials \| The Journal of Chemical Physics \| AIP Publishing
8	GitHub - MorrowChem/how-to-validate-potentials: Some tutorial-style examples for validating machine-learned interatomic potentials
9
10
11
12	Eva Zurek University at Buffalo, SUNY Machine Learned Interatomic Potentials for Binary Carbides Trained on the AFLOW Database	XtalOpt - Zurek Lab
13	RandSpg: Generate Random Crystals with Specific Spacegroups
14
15
16
17	Matthew Carbone Brookhaven National Laboratory Flexible formulation of value for experiment interpretation and design	GitHub - matthewcarbone/ScientificValueAgent: The Scientific Value Agent
18
19
20
21
22	Richard Hennig University of Florida Accelerating Materials Discovery Through Deep Learning and Ultra-Fast Potentials	Ultra-fast force field
23	UF3: a python library for generating ultra-fast interatomic potentials
24
25
26
27	Fang Liu Emory University Machine learning aided chemical discovery in the solution phase	GitHub - Liu-group/AutoSolvate: Automated workflow for generating quantum chemistry calculation of explicitly solvated molecules
28
29
30
31
32	Chenru Duan Microsoft Diffusion models on sampling rare events in chemistry	GitHub - chenruduan/OAReactDiff: An object-aware diffusion model for generating chemical reactions
33
34
35
36
37	Rose Cersonsky University of Wisconsin-Madison Data-driven approaches to chemical and materials science:the impact of data selection, representation, and interpretability	GitHub - scikit-learn-contrib/scikit-matter: A collection of scikit-learn compatible utilities that implement methods born out of the materials science and chemistry communities
38
39
40
41
42	Will Bricker University of New Mexico Machine-learned electron densities of nucleic acids	Predicting accurate ab initio DNA electron densities with equivariant neural networks - ScienceDirect
43
44
45
46
47	Kieron Burke University of California, Irvine Machine Learning Density Functionals	Machine learning and density functional theory \| Nature Reviews Physics
48	https://pubs.rsc.org/en/content/articlelanding/2024/cp/d4cp00878b
49
50
51
52	Gabor Csanyi University of Cambridge Foundation models for materials chemistry
53
54
55
56
57	Mario Barbatti Aix Marseille University, CNRS, ICR Machine Learning Nonadiabatic Dynamics	Newton-X package for non-adibatic dynamics + ML
58	The Newton-X platform for surface hopping and nuclear ensembles
59	Newton-X
60
61
62	Puck van Gerwen EPFL EquiReact: Equivariant Neural Networks for Chemical Reactions	EquiReact model and datasets
63	GitHub - lcmd-epfl/EquiReact
64	Comprehensive exploration of graphically defined reaction spaces \| Scientific Data
65	High accuracy barrier heights, enthalpies, and rate coefficients for chemical reactions \| Scientific Data
66	Reaction profiles for quantum chemistry-computed [3 + 2] cycloaddition reactions \| Scientific Data
67	Reaction-based machine learning representations for predicting the enantioselectivity of organocatalysts
68	Y Z University of Michigan Unusual Dynamics of Tetrahedral Liquids Caused by the Competition between Dynamic Heterogeneity and Structural Heterogeneity	LiquidLib by Z Lab (z-laboratory.github.io)
69
70
71
72
73	Julien Lam CNRS Exploiting constrained linear models for machine-learning interaction potentials	Physical LassoLars Interactions Potential
74	GitHub - LAM-GROUP/PLIP: Linear Machine learning Interatomic Potential for atomistic simulations
75
76
77
78	Dmitry Zubarev IBM Research Foundational Models for Chemical Discovery	MolFormer
79	Large-scale chemical language representations capture molecular structure and properties \| Nature Machine Intelligence
80	GitHub - IBM/molformer
81
82
83	Joshua Rackers Genentech / Prescient Design Building Physics into AI for Drug Discovery	Equivariant NNs
84	e3nn
85	19. Equivariant Neural Network for Predicting Trajectories
86	Structure-based drug design by denoising voxel grids
87
88	Sakib Matin LANL Machine Learning Potentials with long-range Coulomb interaction: Atomistic simulations of water	HIP-NN potential with tensor sensitivity
89	GitHub - lanl/hippynn: python library for atomistic machine learning
90	Lightweight and effective tensor sensitivity for atomistic neural networks \| The Journal of Chemical Physics \| AIP Publishing
91
92
93	Chiho Kim GATech / Matmerize, Inc. Machine Learning-Aided Design of Biodegradable Polymers	Bioplastic design using multitask deep neural networks \| Communications Materials
94	PolymRize TM
95
96
97
98	Daniel Schwalbe-Koda UCLA Unifying Views on the Extrapolation Power of Machine Learning Potentials and Materials Thermodynamics	dskoda/quests: Quick Uncertainty and Entropy via STructural Similarity (github.com)
99
100
101
102
103	Rebecca Lindsey University of Michigan, Ann Arbor Explaining Performance of Physics-Informed Machine-Learned Interatomic Models	ChIMES ML-IAP fitting (lsq), active learning (al), and calculator, including scripts for installation in LAMMPS
104	GitHub - rk-lindsey/chimes_lsq: Tools to develop ChIMES parameter sets
105	GitHub - rk-lindsey/al_driver: AI-driven framework for automated ChIMES model development
106	GitHub - rk-lindsey/chimes_calculator: Tools to interface ChIMES with various external codes.
107
108	Alexandre Tkatchenko University of Luxembourg Navigating Chemical Compound Space with Machine Learning	Institute for Pure & Applied Mathematics - IPAM (ucla.edu)
109	QM7-X, a comprehensive dataset of quantum-mechanical properties spanning the chemical space of small organic molecules \| Scientific Data
110	“Freedom of design” in chemical compound space: towards rational in silico design of molecules with targeted quantum-mechanical properties
111	[2309.00506] Enabling Inverse Design in Chemical Compound Space: Mapping Quantum Properties to Structures for Small Organic Molecules
112
113	Nong Artrith Debye Institute for Nanomaterials Science Accelerated Sampling Strategies and Machine Learning Potentials for Modeling Non-Crystalline Energy Materials	atomisticnet/aenet-PyTorch: ænet-PyTorch: a GPU-supported implementation for machine learning atomic potentials training (github.com)
114	GitHub - atomisticnet/aenet-lammps: Interface aenet with the LAMMPS molecular dynamics software (https://lammps.sandia.gov)
115
116
117
118	Karel Berka Palacky University Olomouc MolMeDB - free database of molecules on membranes	Database of molecules on membrabes
119	MolMeDB \| MolMeDB (upol.cz)
120
121
122
123	interactive session Jason Hattrick-Simpers University of Toronto Tutorial on the use of LLMs in Science	LLM-Prop and LLM finetuning for materials prediction
124	https://github.com/vertaix/LLM-Prop
125	https://cohereai.notion.site/MRS-Fall-2023-DS04-Tutorial-Runbook-7ed8ca8fb2bf4e18937e6a119b63e3e5
126	https://colab.research.google.com/drive/1Al-mweiY6fnxjHTZJGia_z7RPoKPvZfM?usp=sharing#scrollTo=gZnFnjGdyGbV
127	https://vertaix.princeton.edu/
128	[2310.14029] LLM-Prop: Predicting Physical And Electronic Properties Of Crystalline Solids From Their Text Descriptions
129	Roberto Car Princeton University Deep potential models for equilibrium and near equilibrium processes	A deep potential model with long-range electrostatic interactions \| The Journal of Chemical Physics \| AIP Publishing
130	3.14. Deep potential long-range (DPLR) — DeePMD-kit documentation https://github.com/deepmodeling/deepmd-kit
131
132
133
134	Alan Aspuru-Guzik University of Toronto Self-driving Labs	Self-driving labs, robotics for synthesis and lab chatbots
135	On scientific understanding with artificial intelligence \| Nature Reviews Physics
136	The Matter Simulation (R)evolution \| ACS Central Science
137	Organa: A Robotic Assistant for Automated Chemistry Experimentation and Characterization
138	[2402.05015] A Sober Look at LLMs for Material Discovery: Are They Actually Good for Bayesian Optimization Over Molecules?
139	Pavlo Dral Xiamen University From fast potentials for dynamics to learning dynamics TALK SLIDES	MLatom – AI-enhanced computational chemistry http://mlatom.com/	Postdoc in method development for materials or molecular simulations
140	https://xacs.xmu.edu.cn/docs/mlatom/
141	Aditya Nandy UCLA \| University of Chicago Leveraging Community Knowledge to Forge a Path Forward for Transition Metal Complex and Metal-Organic Framework Design	Generation of MOFs and metallorganic complexes
142	hjkgrp/molSimplify: molSimplify code (github.com)
143
144
145
146	Richard Messerly LANL Nanoreactor active learning: Discovering chemistry with a general reactive machine learning potential	Reactive ANI-1xnr potential: code/applications/data
147	GitHub - atomistic-ml/ani-1xnr: Machine learning interatomic potential for condensed-phase reactive chemistry
148	https://springernature.figshare.com/ndownloader/files/43190004
149	Exploring the frontiers of condensed-phase chemistry with a general reactive machine learning potential
150
151	Bakhtiyor Rasulev North Dakota State University Application of Mixture-type Descriptors in Machine Learning Modeling of Materials"	Using nano-QSAR to predict the cytotoxicity of metal oxide nanoparticles \| Nature Nanotechnology
152	Development of QSAR Models on the Fouling-Release Performance of Silicone Oil-modified Siloxane Polyurethane Coatings \| Polymer Science \| ChemRxiv \| Cambridge Open Engage
153
154
155
156	Nina Andrejevic Argonne National Laboratory Advancing materials characterization through physics-guided machine learning	Welcome to e3nn! {#welcome} \| e3nn
157
158
159
160
161	Markus Reiher ETH Zurich Lifelong Machine Learning Potentials	Software for Chemical Interaction Networks - SCINE (ethz.ch)
162	Chemoton 2.0: Autonomous Exploration of Chemical Reaction Networks \| Journal of Chemical Theory and Computation (acs.org)
163	Lifelong Machine Learning Potentials \| Journal of Chemical Theory and Computation (acs.org)
164	ReiherGroup/CoRe_optimizer: Continual Resilient (CoRe) Optimizer for PyTorch (github.com)
165	Code/Data for Lifelong Machine Learning Potentials (zenodo.org)
166	Roman Zubatyuk Carnegie Mellon University AIMNet2: Robust neural network potential for organic, element-organic molecules and chemical reactions	isayevlab/AIMNet2 (github.com)
167	AIMNet2: A Neural Network Potential to Meet your Neutral, Charged, Organic, and Elemental-Organic Needs \| Theoretical and Computational Chemistry \| ChemRxiv \| Cambridge Open Engage
168
169
170
171	Johannes Margraf University of Bayreuth Materials Discovery with Foundation Models	Foundation models — mace 0.1.0 documentation (mace-docs.readthedocs.io)
172	TUM-DAML/gemnet_tf: GemNet model in TensorFlow, as proposed in "GemNet: Universal Directional Graph Neural Networks for Molecules" (NeurIPS 2021) (github.com)
173
174
175
176	Matthias Rupp Luxembourg Institute of Science and Technology (LIST) Thermal transport via machine-learning potentials	Phys. Rev. B 108, L100302 (2023) - Heat flux for semilocal machine-learning potentials (aps.org)
177	sirmarcel/gknet-archive: data and code for "heat flux for semi-local machine-learning potentials" (github.com)
178	sirmarcel/glp: tools for graph-based machine-learning potentials in jax (github.com)
179	uf3 - Ultra-Fast Force Fields · GitHub
180
181	Tom Penfold Newcastle Univeristy Deep Neural Networks for X-ray Spectroscopy: Hero or Zero?	Codes and Datasets (penfoldgroup.co.uk)
182	NNs for X-ray spectroscopy / xanesnet · GitLab
183	Accurate, Affordable, and Generalisable Machine Learning Simulations of Transition Metal X-ray Absorption Spectra using the XANESNET Deep Neural Network \| Theoretical and Computational Chemistry \| ChemRxiv \| Cambridge Open Engage
184
185
186	Connor Coley MIT Molecular design and the intersection with synthesis	Closing the Execution Gap in Generative AI for Chemicals and Materials: Freeways or Safeguards · From Novel Chemicals to Opera (pubpub.org)
187
188
189
190
191	Marivi Fernandez-Serra Stony Brook University Learning the exchange and correlation functional in DFT	semodi/neuralxc: Implementation of a machine learned density functional (github.com)
192	semodi/libnxc: A library for using machine-learned exchange-correlation functionals for density-functional theory (github.com)
193	David Yaron Carnegie Mellon University Quantum chemical Hamiltonians as flexible and interpretable model forms for machine learning	djyaron/DFTBML: Fitting DFTB Hamiltonians to data. (github.com)
194	[2210.11682] Semiempirical Hamiltonians learned from data can have accuracy comparable to Density Functional Theory (arxiv.org)
195	Treating Semiempirical Hamiltonians as Flexible Machine Learning Models Yields Accurate and Interpretable Results \| Journal of Chemical Theory and Computation (acs.org)
196	Jenna Pope Pacific Northwest National Laboratory Accelerating Atomic-scale Simulations of Molecules and Materials with Neural Network Potentials	[2312.07511] A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems (arxiv.org)
197	exalearn/hydronet: HydroNet: Benchmark Tasks for Preserving Long-range Interactions and Structural Motifs in Predictive and Generative Models for Molecular Data, at the 34th Conference on Neural Information Processing Systems (NuerIPS), Workshop on Machine Learning and the Physical Sciences [https://arxiv.org/abs/2012.00131] (github.com)
198	pnnl/Active-Sampling-for-Atomistic-Potentials: Active sampling for neural network potentials: Accelerated simulations of shear-induced deformation in Cu–Ni multilayers (github.com)
199	pnnl/downstream_mol_gnn: Reducing Down(stream)time: Pretraining Molecular GNNs using Heterogeneous AI Accelerators (github.com)
200
201	Jing Huang Westlake University DP/MM: a hybrid force field model for zinc-protein dynamics	JingHuangLab/openmm_deepmd_plugin (github.com)
202	Marina Meila University of Washington Coordinates with physical meaning	sjkoelle/montlake: Montlake contains tools for geometric data analysis. It includes vector field group lasso and basis pursuit methods for parametric manifold learning. It also contains differentiable shape featurizations including interpoint distances, planar angles, and torsions from data positions for shape space analysis. (github.com)
203	yuchaz/homology_emb (github.com)
204	yuchaz/independent_coordinate_search: Repository for "Selecting the independent coordinates of manifolds with large aspect ratios" at NeurIPS'19 (github.com)
205	megaman: Manifold Learning for Millions of Points — megaman 0.2 documentation (mmp2.github.io)
206	Manifold Learning: What, How, and Why \| Annual Reviews
207	Manifold Learning 2.0: Explanations and Eigenflows - The Fields Institute Workshop on Manifold and Graph-based learning (washington.edu)
208	Manifold Coordinates with Physical Meaning (jmlr.org)
209	A tutorial on Manifold Learning for real data \| Fields Institute for Research in Mathematical Sciences (utoronto.ca)
210	Focus Program on Data Science, Approximation Theory, and Harmonic Analysis \| Fields Institute for Research in Mathematical Sciences (utoronto.ca)
211

1

Speaker

Codes | Papers | Intresting Links

Job Openings

2

David Balcells
University of Oslo

Generative Machine Learning for Transition Metal Chemistry

tmQM and tmQM-L dataset and PL-MOGA genetic algorithm

3

tmQM Dataset—Quantum Geometries and Properties of 86k Transition Metal Complexes | Journal of Chemical Information and Modeling

4

Directional Multiobjective Optimization of Metal Complexes at the Billion-Scale with the tmQMg-L Dataset and PL-MOGA Algorithm | Theoretical and Computational Chemistry | ChemRxiv | Cambridge Open Engage

5

Repository for the tmQMg-L dataset files.

6

GitHub - hkneiding/PL-MOGA: Pareto Lighthouse Multiobjective Genetic Algorithm for the de novo design of transition metal complexes.

7

Volker Deringer
University of Oxford

Data-driven interatomic potentials for inorganic materials chemistry

How to validate machine-learned interatomic potentials | The Journal of Chemical Physics | AIP Publishing

8

GitHub - MorrowChem/how-to-validate-potentials: Some tutorial-style examples for validating machine-learned interatomic potentials

9

10

11

12

Eva Zurek
University at Buffalo, SUNY

Machine Learned Interatomic Potentials for Binary Carbides Trained on the AFLOW Database

XtalOpt - Zurek Lab

13

RandSpg: Generate Random Crystals with Specific Spacegroups

14

15

16

17

Matthew Carbone
Brookhaven National Laboratory

Flexible formulation of value for experiment interpretation and design

GitHub - matthewcarbone/ScientificValueAgent: The Scientific Value Agent

18

19

20

21

22

Richard Hennig
University of Florida

Accelerating Materials Discovery Through Deep Learning and Ultra-Fast Potentials

Ultra-fast force field

23

UF3: a python library for generating ultra-fast interatomic potentials

24

25

26

27

Fang Liu
Emory University

Machine learning aided chemical discovery in the solution phase

GitHub - Liu-group/AutoSolvate: Automated workflow for generating quantum chemistry calculation of explicitly solvated molecules

28

29

30

31

32

Chenru Duan
Microsoft

Diffusion models on sampling rare events in chemistry

GitHub - chenruduan/OAReactDiff: An object-aware diffusion model for generating chemical reactions

33

34

35

36

37

Rose Cersonsky
University of Wisconsin-Madison

Data-driven approaches to chemical and materials science:the impact of data selection, representation, and interpretability

GitHub - scikit-learn-contrib/scikit-matter: A collection of scikit-learn compatible utilities that implement methods born out of the materials science and chemistry communities

38

39

40

41

42

Will Bricker
University of New Mexico

Machine-learned electron densities of nucleic acids

Predicting accurate ab initio DNA electron densities with equivariant neural networks - ScienceDirect

43

44

45

46

47

Kieron Burke
University of California, Irvine

Machine Learning Density Functionals

Machine learning and density functional theory | Nature Reviews Physics

48

https://pubs.rsc.org/en/content/articlelanding/2024/cp/d4cp00878b

49

50

51

52

Gabor Csanyi
University of Cambridge

Foundation models for materials chemistry

53

54

55

56

57

Mario Barbatti
Aix Marseille University, CNRS, ICR

Machine Learning Nonadiabatic Dynamics

Newton-X package for non-adibatic dynamics + ML

58

The Newton-X platform for surface hopping and nuclear ensembles

59

Newton-X

60

61

62

Puck van Gerwen
EPFL

EquiReact: Equivariant Neural Networks for Chemical Reactions

EquiReact model and datasets

63

GitHub - lcmd-epfl/EquiReact

64

Comprehensive exploration of graphically defined reaction spaces | Scientific Data

65

High accuracy barrier heights, enthalpies, and rate coefficients for chemical reactions | Scientific Data

66

Reaction profiles for quantum chemistry-computed [3 + 2] cycloaddition reactions | Scientific Data

67

Reaction-based machine learning representations for predicting the enantioselectivity of organocatalysts

68

Y Z
University of Michigan

Unusual Dynamics of Tetrahedral Liquids Caused by the Competition between Dynamic Heterogeneity and Structural Heterogeneity

LiquidLib by Z Lab (z-laboratory.github.io)

69

70

71

72

73

Julien Lam
CNRS

Exploiting constrained linear models for machine-learning interaction potentials

Physical LassoLars Interactions Potential

74

GitHub - LAM-GROUP/PLIP: Linear Machine learning Interatomic Potential for atomistic simulations

75

76

77

78

Dmitry Zubarev
IBM Research

Foundational Models for Chemical Discovery

MolFormer

79

Large-scale chemical language representations capture molecular structure and properties | Nature Machine Intelligence

80

GitHub - IBM/molformer

81

82

83

Joshua Rackers
Genentech / Prescient Design

Building Physics into AI for Drug Discovery

Equivariant NNs

84

e3nn

85

19. Equivariant Neural Network for Predicting Trajectories

86

Structure-based drug design by denoising voxel grids

87

88

Sakib Matin
LANL

Machine Learning Potentials with long-range Coulomb interaction: Atomistic simulations of water

HIP-NN potential with tensor sensitivity

89

GitHub - lanl/hippynn: python library for atomistic machine learning

90

Lightweight and effective tensor sensitivity for atomistic neural networks | The Journal of Chemical Physics | AIP Publishing

91

92

93

Chiho Kim
GATech / Matmerize, Inc.

Machine Learning-Aided Design of Biodegradable Polymers

Bioplastic design using multitask deep neural networks | Communications Materials

94

PolymRize TM

95

96

97

98

Daniel Schwalbe-Koda
UCLA

Unifying Views on the Extrapolation Power of Machine Learning Potentials and Materials Thermodynamics

dskoda/quests: Quick Uncertainty and Entropy via STructural Similarity (github.com)

99

100

101

102

103

Rebecca Lindsey
University of Michigan, Ann Arbor

Explaining Performance of Physics-Informed Machine-Learned Interatomic Models

ChIMES ML-IAP fitting (lsq), active learning (al), and calculator, including scripts for installation in LAMMPS

104

GitHub - rk-lindsey/chimes_lsq: Tools to develop ChIMES parameter sets

105

GitHub - rk-lindsey/al_driver: AI-driven framework for automated ChIMES model development

106

GitHub - rk-lindsey/chimes_calculator: Tools to interface ChIMES with various external codes.

107

108

Alexandre Tkatchenko
University of Luxembourg

Navigating Chemical Compound Space with Machine Learning

Institute for Pure & Applied Mathematics - IPAM (ucla.edu)

109

QM7-X, a comprehensive dataset of quantum-mechanical properties spanning the chemical space of small organic molecules | Scientific Data

110

“Freedom of design” in chemical compound space: towards rational in silico design of molecules with targeted quantum-mechanical properties

111

[2309.00506] Enabling Inverse Design in Chemical Compound Space: Mapping Quantum Properties to Structures for Small Organic Molecules

112

113

Nong Artrith
Debye Institute for Nanomaterials Science

Accelerated Sampling Strategies and Machine Learning Potentials for Modeling Non-Crystalline Energy Materials

atomisticnet/aenet-PyTorch: ænet-PyTorch: a GPU-supported implementation for machine learning atomic potentials training (github.com)

114

GitHub - atomisticnet/aenet-lammps: Interface aenet with the LAMMPS molecular dynamics software (https://lammps.sandia.gov)

115

116

117

118

Karel Berka
Palacky University Olomouc

MolMeDB - free database of molecules on membranes

Database of molecules on membrabes

119

MolMeDB | MolMeDB (upol.cz)

120

121

122

123

interactive session
Jason Hattrick-Simpers
University of Toronto

Tutorial on the use of LLMs in Science

LLM-Prop and LLM finetuning for materials prediction

124

https://github.com/vertaix/LLM-Prop

125

https://cohereai.notion.site/MRS-Fall-2023-DS04-Tutorial-Runbook-7ed8ca8fb2bf4e18937e6a119b63e3e5

126

https://colab.research.google.com/drive/1Al-mweiY6fnxjHTZJGia_z7RPoKPvZfM?usp=sharing#scrollTo=gZnFnjGdyGbV

127

https://vertaix.princeton.edu/

128

[2310.14029] LLM-Prop: Predicting Physical And Electronic Properties Of Crystalline Solids From Their Text Descriptions

129

Roberto Car
Princeton University

Deep potential models for equilibrium and near equilibrium processes

A deep potential model with long-range electrostatic interactions | The Journal of Chemical Physics | AIP Publishing

130

3.14. Deep potential long-range (DPLR) — DeePMD-kit documentation
https://github.com/deepmodeling/deepmd-kit

131

132

133

134

Alan Aspuru-Guzik
University of Toronto

Self-driving Labs

Self-driving labs, robotics for synthesis and lab chatbots

135

On scientific understanding with artificial intelligence | Nature Reviews Physics

136

The Matter Simulation (R)evolution | ACS Central Science

137

Organa: A Robotic Assistant for Automated Chemistry Experimentation and Characterization

138

[2402.05015] A Sober Look at LLMs for Material Discovery: Are They Actually Good for Bayesian Optimization Over Molecules?

139

Pavlo Dral
Xiamen University

From fast potentials for dynamics to learning dynamics

TALK SLIDES

MLatom – AI-enhanced computational chemistry
http://mlatom.com/

Postdoc in method development for materials or molecular simulations

140

https://xacs.xmu.edu.cn/docs/mlatom/

141

Aditya Nandy
UCLA | University of Chicago

Leveraging Community Knowledge to Forge a Path Forward for Transition Metal Complex and Metal-Organic Framework Design

Generation of MOFs and metallorganic complexes

142

hjkgrp/molSimplify: molSimplify code (github.com)

143

144

145

146

Richard Messerly
LANL

Nanoreactor active learning: Discovering chemistry with a general reactive machine learning potential

Reactive ANI-1xnr potential: code/applications/data

147

GitHub - atomistic-ml/ani-1xnr: Machine learning interatomic potential for condensed-phase reactive chemistry

148